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Yang JM, Lee CK, Kim N, Cho KH. Attractor-Transition Control of Complex Biological Networks: A Constant Control Approach. IEEE TRANSACTIONS ON CYBERNETICS 2025; 55:24-37. [PMID: 39441678 DOI: 10.1109/tcyb.2024.3473945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2024]
Abstract
This article presents attractor-transition control of complex biological networks represented by Boolean networks (BNs) wherein the BN is steered from a prescribed initial attractor toward a desired one. The proposed approach leverages the similarity between attractors and Boolean algebraic properties embedded in the underlying state transition equations. To enhance the clarity of expression regarding stabilization toward the desired attractor, a simple coordinate transformation is performed on the considered BN. Based on the characteristics of transformed state equations, self-stabilizing state variables requiring no control efforts are derived in the first. Next, by applying the feedback vertex set (FVS) control scheme, control inputs stabilizing the remaining state variables are determined. The proposed control scheme exhibits versatility by accommodating both fixed-point and cyclic attractors. We validate the effectiveness of the proposed strategy through extensive numerical experiments conducted on random BNs as well as complex biological systems. In adherence to the reproducible research initiative, detailed results of numerical experiments and all the implementation codes are provided on the authors' website: https://github.com/choonlog/AttractorTransition.
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Gong J, Lee C, Kim H, Kim J, Jeon J, Park S, Cho K. Control of Cellular Differentiation Trajectories for Cancer Reversion. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2025; 12:e2402132. [PMID: 39661721 PMCID: PMC11744559 DOI: 10.1002/advs.202402132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 11/08/2024] [Indexed: 12/13/2024]
Abstract
Cellular differentiation is controlled by intricate layers of gene regulation, involving the modulation of gene expression by various transcriptional regulators. Due to the complexity of gene regulation, identifying master regulators across the differentiation trajectory has been a longstanding challenge. To tackle this problem, a computational framework, single-cell Boolean network inference and control (BENEIN), is presented. Applying BENEIN to human large intestinal single-cell transcriptome data, MYB, HDAC2, and FOXA2 are identified as the master regulators whose inhibition induces enterocyte differentiation. It is found that simultaneous knockdown of these master regulators can revert colorectal cancer cells into normal-like enterocytes by synergistically inducing differentiation and suppressing malignancy, which is validated by in vitro and in vivo experiments.
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Affiliation(s)
- Jeong‐Ryeol Gong
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and TechnologyDaejeon34141Republic of Korea
| | - Chun‐Kyung Lee
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and TechnologyDaejeon34141Republic of Korea
| | - Hoon‐Min Kim
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and TechnologyDaejeon34141Republic of Korea
| | - Juhee Kim
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and TechnologyDaejeon34141Republic of Korea
| | - Jaeog Jeon
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and TechnologyDaejeon34141Republic of Korea
| | - Sunmin Park
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and TechnologyDaejeon34141Republic of Korea
| | - Kwang‐Hyun Cho
- Department of Bio and Brain EngineeringKorea Advanced Institute of Science and TechnologyDaejeon34141Republic of Korea
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Li W, Li H. Edge Removal and Q-Learning for Stabilizability of Boolean Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:18212-18221. [PMID: 37713224 DOI: 10.1109/tnnls.2023.3312942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
This article develops a new edge removal mechanism for the global stabilizability of Boolean networks (BNs). In order to achieve the edge removal control, several control variables are properly placed into the dynamics of BNs based on the fundamental logical operators. On the basis of the new edge removal mechanism, several necessary and sufficient conditions are obtained for the global stabilizability and set stabilizability of BNs. Furthermore, a kind of stable edge removal control is proposed and achieved via the -learning algorithm to optimize the edge removal mechanism. As an application, the edge removal control is used to verify whether or not the mammalian cortical area development model can be made stabilizable to the expected stable states.
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Chen Z, Lu J, Zhao X, Yu H, Li C. Energy Landscape Reveals the Underlying Mechanism of Cancer-Adipose Conversion in Gene Network Models. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2404854. [PMID: 39258786 PMCID: PMC11538663 DOI: 10.1002/advs.202404854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Indexed: 09/12/2024]
Abstract
Cancer is a systemic heterogeneous disease involving complex molecular networks. Tumor formation involves an epithelial-mesenchymal transition (EMT), which promotes both metastasis and plasticity of cancer cells. Recent experiments have proposed that cancer cells can be transformed into adipocytes via a combination of drugs. However, the underlying mechanisms for how these drugs work, from a molecular network perspective, remain elusive. To reveal the mechanism of cancer-adipose conversion (CAC), this study adopts a systems biology approach by combing mathematical modeling and molecular experiments, based on underlying molecular regulatory networks. Four types of attractors are identified, corresponding to epithelial (E), mesenchymal (M), adipose (A) and partial/intermediate EMT (P) cell states on the CAC landscape. Landscape and transition path results illustrate that intermediate states play critical roles in the cancer to adipose transition. Through a landscape control approach, two new therapeutic strategies for drug combinations are identified, that promote CAC. These predictions are verified by molecular experiments in different cell lines. The combined computational and experimental approach provides a powerful tool to explore molecular mechanisms for cell fate transitions in cancer networks. The results reveal underlying mechanisms of intermediate cell states that govern the CAC, and identified new potential drug combinations to induce cancer adipogenesis.
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Affiliation(s)
- Zihao Chen
- Shanghai Center for Mathematical SciencesFudan UniversityShanghai200433China
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghai200433China
| | - Jia Lu
- State Key Laboratory of Component‐based Chinese MedicineTianjin University of Traditional Chinese MedicineTianjin301617China
| | - Xing‐Ming Zhao
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghai200433China
| | - Haiyang Yu
- State Key Laboratory of Component‐based Chinese MedicineTianjin University of Traditional Chinese MedicineTianjin301617China
- Haihe Laboratory of Traditional Chinese MedicineTianjin301617China
| | - Chunhe Li
- Shanghai Center for Mathematical SciencesFudan UniversityShanghai200433China
- Institute of Science and Technology for Brain‐Inspired IntelligenceFudan UniversityShanghai200433China
- School of Mathematical Sciences and MOE Frontiers Center for Brain ScienceFudan UniversityShanghai200433China
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Kim J, Lee W, Cho KH. Recursive Self-Composite Approach Toward Structural Understanding of Boolean Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:1775-1783. [PMID: 38885112 DOI: 10.1109/tcbb.2024.3415352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Boolean networks have been widely used in systems biology to study the dynamical characteristics of biological networks such as steady-states or cycles, yet there has been little attention to the dynamic properties of network structures. Here, we systematically reveal the core network structures using a recursive self-composite of the logic update rules. We find that all Boolean update rules exhibit repeated cyclic logic structures, where each converged logic leads to the same states, defined as kernel states. Consequently, the period of state cycles is upper bounded by the number of logics in the converged logic cycle. In order to uncover the underlying dynamical characteristics by exploiting the repeating structures, we propose leaping and filling algorithms. The algorithms provide a way to avoid large string explosions during the self-composition procedures. Finally, we present three examples-a simple network with a long feedback structure, a T-cell receptor network and a cancer network-to demonstrate the usefulness of the proposed algorithm.
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Kemkar S, Tao M, Ghosh A, Stamatakos G, Graf N, Poorey K, Balakrishnan U, Trask N, Radhakrishnan R. Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine. Front Physiol 2024; 15:1473125. [PMID: 39507514 PMCID: PMC11537925 DOI: 10.3389/fphys.2024.1473125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2024] [Accepted: 10/07/2024] [Indexed: 11/08/2024] Open
Abstract
Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.
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Affiliation(s)
- Sharvari Kemkar
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Mengdi Tao
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Alokendra Ghosh
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Georgios Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Zografos, Greece
| | - Norbert Graf
- Department of Pediatric Oncology and Hematology, Saarland University, Homburg, Germany
| | - Kunal Poorey
- Department of Systems Biology, Sandia National Laboratories, Livermore, CA, United States
| | - Uma Balakrishnan
- Department of Quant Modeling and SW Eng, Sandia National Laboratories, Livermore, CA, United States
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Nathaniel Trask
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, United States
| | - Ravi Radhakrishnan
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, United States
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
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Choi JH, Lee J, Kang U, Chang H, Cho KH. Network dynamics-based subtyping of Alzheimer's disease with microglial genetic risk factors. Alzheimers Res Ther 2024; 16:229. [PMID: 39415193 PMCID: PMC11481771 DOI: 10.1186/s13195-024-01583-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 09/29/2024] [Indexed: 10/18/2024]
Abstract
BACKGROUND The potential of microglia as a target for Alzheimer's disease (AD) treatment is promising, yet the clinical and pathological diversity within microglia, driven by genetic factors, poses a significant challenge. Subtyping AD is imperative to enable precise and effective treatment strategies. However, existing subtyping methods fail to comprehensively address the intricate complexities of AD pathogenesis, particularly concerning genetic risk factors. To address this gap, we have employed systems biology approaches for AD subtyping and identified potential therapeutic targets. METHODS We constructed patient-specific microglial molecular regulatory network models by utilizing existing literature and single-cell RNA sequencing data. The combination of large-scale computer simulations and dynamic network analysis enabled us to subtype AD patients according to their distinct molecular regulatory mechanisms. For each identified subtype, we suggested optimal targets for effective AD treatment. RESULTS To investigate heterogeneity in AD and identify potential therapeutic targets, we constructed a microglia molecular regulatory network model. The network model incorporated 20 known risk factors and crucial signaling pathways associated with microglial functionality, such as inflammation, anti-inflammation, phagocytosis, and autophagy. Probabilistic simulations with patient-specific genomic data and subsequent dynamics analysis revealed nine distinct AD subtypes characterized by core feedback mechanisms involving SPI1, CASS4, and MEF2C. Moreover, we identified PICALM, MEF2C, and LAT2 as common therapeutic targets among several subtypes. Furthermore, we clarified the reasons for the previous contradictory experimental results that suggested both the activation and inhibition of AKT or INPP5D could activate AD through dynamic analysis. This highlights the multifaceted nature of microglial network regulation. CONCLUSIONS These results offer a means to classify AD patients by their genetic risk factors, clarify inconsistent experimental findings, and advance the development of treatments tailored to individual genotypes for AD.
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Affiliation(s)
- Jae Hyuk Choi
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Jonghoon Lee
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Uiryong Kang
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hongjun Chang
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Kwang-Hyun Cho
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
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Venkatachalapathy H, Brzakala C, Batchelor E, Azarin SM, Sarkar CA. Inertial effect of cell state velocity on the quiescence-proliferation fate decision. NPJ Syst Biol Appl 2024; 10:111. [PMID: 39358384 PMCID: PMC11447052 DOI: 10.1038/s41540-024-00428-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 08/16/2024] [Indexed: 10/04/2024] Open
Abstract
Energy landscapes can provide intuitive depictions of population heterogeneity and dynamics. However, it is unclear whether individual cell behavior, hypothesized to be determined by initial position and noise, is faithfully recapitulated. Using the p21-/Cdk2-dependent quiescence-proliferation decision in breast cancer dormancy as a testbed, we examined single-cell dynamics on the landscape when perturbed by hypoxia, a dormancy-inducing stress. Combining trajectory-based energy landscape generation with single-cell time-lapse microscopy, we found that a combination of initial position and velocity on a p21/Cdk2 landscape, but not position alone, was required to explain the observed cell fate heterogeneity under hypoxia. This is likely due to additional cell state information such as epigenetic features and/or other species encoded in velocity but missing in instantaneous position determined by p21 and Cdk2 levels alone. Here, velocity dependence manifested as inertia: cells with higher cell cycle velocities prior to hypoxia continued progressing along the cell cycle under hypoxia, resisting the change in landscape towards cell cycle exit. Such inertial effects may markedly influence cell fate trajectories in tumors and other dynamically changing microenvironments where cell state transitions are governed by coordination across several biochemical species.
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Affiliation(s)
- Harish Venkatachalapathy
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN, USA
| | - Cole Brzakala
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN, USA
| | - Eric Batchelor
- Department of Integrative Biology and Physiology, University of Minnesota, Minneapolis, MN, USA
| | - Samira M Azarin
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN, USA.
| | - Casim A Sarkar
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA.
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9
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Kim N, Lee J, Kim J, Kim Y, Cho KH. Canalizing kernel for cell fate determination. Brief Bioinform 2024; 25:bbae406. [PMID: 39171985 PMCID: PMC11339868 DOI: 10.1093/bib/bbae406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 07/14/2024] [Accepted: 08/01/2024] [Indexed: 08/23/2024] Open
Abstract
The tendency for cell fate to be robust to most perturbations, yet sensitive to certain perturbations raises intriguing questions about the existence of a key path within the underlying molecular network that critically determines distinct cell fates. Reprogramming and trans-differentiation clearly show examples of cell fate change by regulating only a few or even a single molecular switch. However, it is still unknown how to identify such a switch, called a master regulator, and how cell fate is determined by its regulation. Here, we present CAESAR, a computational framework that can systematically identify master regulators and unravel the resulting canalizing kernel, a key substructure of interconnected feedbacks that is critical for cell fate determination. We demonstrate that CAESAR can successfully predict reprogramming factors for de-differentiation into mouse embryonic stem cells and trans-differentiation of hematopoietic stem cells, while unveiling the underlying essential mechanism through the canalizing kernel. CAESAR provides a system-level understanding of how complex molecular networks determine cell fates.
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Affiliation(s)
- Namhee Kim
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Jonghoon Lee
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Jongwan Kim
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Yunseong Kim
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Kwang-Hyun Cho
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
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10
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Plaugher D, Murrugarra D. Cancer mutationscape: revealing the link between modular restructuring and intervention efficacy among mutations. NPJ Syst Biol Appl 2024; 10:74. [PMID: 39003264 PMCID: PMC11246485 DOI: 10.1038/s41540-024-00398-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 06/24/2024] [Indexed: 07/15/2024] Open
Abstract
There is increasing evidence that biological systems are modular in both structure and function. Complex biological signaling networks such as gene regulatory networks (GRNs) are proving to be composed of subcategories that are interconnected and hierarchically ranked. These networks contain highly dynamic processes that ultimately dictate cellular function over time, as well as influence phenotypic fate transitions. In this work, we use a stochastic multicellular signaling network of pancreatic cancer (PC) to show that the variance in topological rankings of the most phenotypically influential modules implies a strong relationship between structure and function. We further show that induction of mutations alters the modular structure, which analogously influences the aggression and controllability of the disease in silico. We finally present evidence that the impact and location of mutations with respect to PC modular structure directly corresponds to the efficacy of single agent treatments in silico, because topologically deep mutations require deep targets for control.
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Affiliation(s)
- Daniel Plaugher
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY, USA.
| | - David Murrugarra
- Department of Mathematics, University of Kentucky, Lexington, KY, USA
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Plaugher D, Murrugarra D. Pancreatic cancer mutationscape: revealing the link between modular restructuring and intervention efficacy amidst common mutations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.27.577546. [PMID: 38352601 PMCID: PMC10862704 DOI: 10.1101/2024.01.27.577546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/04/2024]
Abstract
There is increasing evidence that biological systems are modular in both structure and function. Complex biological signaling networks such as gene regulatory networks (GRNs) are proving to be composed of subcategories that are interconnected and hierarchically ranked. These networks contain highly dynamic processes that ultimately dictate cellular function over time, as well as influence phenotypic fate transitions. In this work, we use a stochastic multicellular signaling network of pancreatic cancer (PC) to show that the variance in topological rankings of the most phenotypically influential modules implies a strong relationship between structure and function. We further show that induction of mutations alters the modular structure, which analogously influences the aggression and controllability of the disease in silico. We finally present evidence that the impact and location of mutations with respect to PC modular structure directly corresponds to the efficacy of single agent treatments in silico, because topologically deep mutations require deep targets for control.
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Affiliation(s)
- Daniel Plaugher
- Department of Toxicology and Cancer Biology, University of Kentucky
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Kim Y, Han Y, Hopper C, Lee J, Joo JI, Gong JR, Lee CK, Jang SH, Kang J, Kim T, Cho KH. A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations. CELL REPORTS METHODS 2024; 4:100773. [PMID: 38744288 PMCID: PMC11133856 DOI: 10.1016/j.crmeth.2024.100773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 03/19/2024] [Accepted: 04/19/2024] [Indexed: 05/16/2024]
Abstract
Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.
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Affiliation(s)
- Yunseong Kim
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Younghyun Han
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Corbin Hopper
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jonghoon Lee
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jae Il Joo
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Jeong-Ryeol Gong
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Chun-Kyung Lee
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Seong-Hoon Jang
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Junsoo Kang
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Taeyoung Kim
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Kwang-Hyun Cho
- Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea.
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13
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Kolokotroni E, Abler D, Ghosh A, Tzamali E, Grogan J, Georgiadi E, Büchler P, Radhakrishnan R, Byrne H, Sakkalis V, Nikiforaki K, Karatzanis I, McFarlane NJB, Kaba D, Dong F, Bohle RM, Meese E, Graf N, Stamatakos G. A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin. J Pers Med 2024; 14:475. [PMID: 38793058 PMCID: PMC11122096 DOI: 10.3390/jpm14050475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 04/11/2024] [Accepted: 04/17/2024] [Indexed: 05/26/2024] Open
Abstract
The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels, as well as their orchestration and links, are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof-of-concept study cases. Personalized simulations of the actual anatomy of a patient have been conducted. The hypermodel has also been applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin-based clinical decision support system and as the core of future in silico trial platforms, although additional retrospective adaptation and validation are necessary.
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Affiliation(s)
- Eleni Kolokotroni
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
| | - Daniel Abler
- Department of Oncology, Geneva University Hospitals and University of Geneva, 1205 Geneva, Switzerland;
- Department of Oncology, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
| | - Alokendra Ghosh
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; (A.G.); (R.R.)
| | - Eleftheria Tzamali
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - James Grogan
- Irish Centre for High End Computing, University of Galway, H91 TK33 Galway, Ireland;
| | - Eleni Georgiadi
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
- Biomedical Engineering Department, University of West Attica, 12243 Egaleo, Greece
| | | | - Ravi Radhakrishnan
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; (A.G.); (R.R.)
| | - Helen Byrne
- Mathematical Institute, University of Oxford, Oxford OX1 2JD, UK;
| | - Vangelis Sakkalis
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - Katerina Nikiforaki
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | - Ioannis Karatzanis
- Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, Greece; (E.T.); (V.S.); (K.N.); (I.K.)
| | | | - Djibril Kaba
- Department of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK;
| | - Feng Dong
- Department of Computer & Information Sciences, University of Strathclyde, Glasgow G1 1XH, UK;
| | - Rainer M. Bohle
- Department of Pathology, Saarland University, 66421 Homburg, Germany;
| | - Eckart Meese
- Department of Human Genetics, Saarland University, 66421 Homburg, Germany;
| | - Norbert Graf
- Department of Paediatric Oncology and Haematology, Saarland University, 66421 Homburg, Germany;
| | - Georgios Stamatakos
- In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Zografos, Greece;
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14
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Kim Y, Choi SR, Cho KH. Reducing State Conflicts between Network Motifs Synergistically Enhances Cancer Drug Effects and Overcomes Adaptive Resistance. Cancers (Basel) 2024; 16:1337. [PMID: 38611015 PMCID: PMC11010870 DOI: 10.3390/cancers16071337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/20/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024] Open
Abstract
Inducing apoptosis in cancer cells is a primary goal in anti-cancer therapy, but curing cancer with a single drug is unattainable due to drug resistance. The complex molecular network in cancer cells causes heterogeneous responses to single-target drugs, thereby inducing an adaptive drug response. Here, we showed that targeted drug perturbations can trigger state conflicts between multi-stable motifs within a molecular regulatory network, resulting in heterogeneous drug responses. However, we revealed that properly regulating an interconnecting molecule between these motifs can synergistically minimize the heterogeneous responses and overcome drug resistance. We extracted the essential cellular response dynamics of the Boolean network driven by the target node perturbation and developed an algorithm to identify a synergistic combinatorial target that can reduce heterogeneous drug responses. We validated the proposed approach using exemplary network models and a gastric cancer model from a previous study by showing that the targets identified with our algorithm can better drive the networks to desired states than those with other control theories. Of note, our approach suggests a new synergistic pair of control targets that can increase cancer drug efficacy to overcome adaptive drug resistance.
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Affiliation(s)
| | | | - Kwang-Hyun Cho
- Laboratory for Systems Biology and Bio-Inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; (Y.K.); (S.R.C.)
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15
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Kadelka C, Wheeler M, Veliz-Cuba A, Murrugarra D, Laubenbacher R. Modularity of biological systems: a link between structure and function. J R Soc Interface 2023; 20:20230505. [PMID: 37876275 PMCID: PMC10598444 DOI: 10.1098/rsif.2023.0505] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 10/05/2023] [Indexed: 10/26/2023] Open
Abstract
This paper addresses two topics in systems biology, the hypothesis that biological systems are modular and the problem of relating structure and function of biological systems. The focus here is on gene regulatory networks, represented by Boolean network models, a commonly used tool. Most of the research on gene regulatory network modularity has focused on network structure, typically represented through either directed or undirected graphs. But since gene regulation is a highly dynamic process as it determines the function of cells over time, it is natural to consider functional modularity as well. One of the main results is that the structural decomposition of a network into modules induces an analogous decomposition of the dynamic structure, exhibiting a strong relationship between network structure and function. An extensive simulation study provides evidence for the hypothesis that modularity might have evolved to increase phenotypic complexity while maintaining maximal dynamic robustness to external perturbations.
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Affiliation(s)
- Claus Kadelka
- Department of Mathematics, Iowa State University, Ames, IA, USA
| | - Matthew Wheeler
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Alan Veliz-Cuba
- Department of Mathematics, University of Dayton, Dayton, OH, USA
| | - David Murrugarra
- Department of Mathematics, University of Kentucky, Lexington, KY, USA
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16
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Kadelka C, Wheeler M, Veliz-Cuba A, Murrugarra D, Laubenbacher R. Modularity of biological systems: a link between structure and function. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.11.557227. [PMID: 37745485 PMCID: PMC10515856 DOI: 10.1101/2023.09.11.557227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
This paper addresses two topics in systems biology, the hypothesis that biological systems are modular and the problem of relating structure and function of biological systems. The focus here is on gene regulatory networks, represented by Boolean network models, a commonly used tool. Most of the research on gene regulatory network modularity has focused on network structure, typically represented through either directed or undirected graphs. But since gene regulation is a highly dynamic process as it determines the function of cells over time, it is natural to consider functional modularity as well. One of the main results is that the structural decomposition of a network into modules induces an analogous decomposition of the dynamic structure, exhibiting a strong relationship between network structure and function. An extensive simulation study provides evidence for the hypothesis that modularity might have evolved to increase phenotypic complexity while maintaining maximal dynamic robustness to external perturbations.
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Affiliation(s)
- Claus Kadelka
- Department of Mathematics, Iowa State University, Ames, IA 50011, United States
| | - Matthew Wheeler
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | - Alan Veliz-Cuba
- Department of Mathematics, University of Dayton, Dayton, OH, United States
| | - David Murrugarra
- Department of Mathematics, University of Kentucky, Lexington, KY, United States
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17
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Rumiantsau D, Lesne A, Hütt MT. Predicting attractors from spectral properties of stylized gene regulatory networks. Phys Rev E 2023; 108:014402. [PMID: 37583152 DOI: 10.1103/physreve.108.014402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 04/07/2023] [Indexed: 08/17/2023]
Abstract
How the architecture of gene regulatory networks shapes gene expression patterns is an open question, which has been approached from a multitude of angles. The dominant strategy has been to identify nonrandom features in these networks and then argue for the function of these features using mechanistic modeling. Here we establish the foundation of an alternative approach by studying the correlation of network eigenvectors with synthetic gene expression data simulated with a basic and popular model of gene expression dynamics: Boolean threshold dynamics in signed directed graphs. We show that eigenvectors of the network adjacency matrix can predict collective states (attractors). However, the overall predictive power depends on details of the network architecture, namely the fraction of positive 3-cycles, in a predictable fashion. Our results are a set of statistical observations, providing a systematic step towards a further theoretical understanding of the role of network eigenvectors in dynamics on graphs.
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Affiliation(s)
- Dzmitry Rumiantsau
- Department of Life Sciences and Chemistry, Constructor University, D-28759 Bremen, Germany
| | - Annick Lesne
- Sorbonne Université, CNRS, Laboratoire de Physique Théorique de la Matière Condensée, LPTMC, F-75252 Paris, France
- Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS, F-34293 Montpellier, France
| | - Marc-Thorsten Hütt
- Department of Life Sciences and Chemistry, Constructor University, D-28759 Bremen, Germany
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18
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Venkatachalapathy H, Brzakala C, Batchelor E, Azarin SM, Sarkar CA. Inertial effect of cell state velocity on the quiescence-proliferation fate decision in breast cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.22.541793. [PMID: 37292599 PMCID: PMC10245870 DOI: 10.1101/2023.05.22.541793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Energy landscapes can provide intuitive depictions of population heterogeneity and dynamics. However, it is unclear whether individual cell behavior, hypothesized to be determined by initial position and noise, is faithfully recapitulated. Using the p21-/Cdk2-dependent quiescence-proliferation decision in breast cancer dormancy as a testbed, we examined single-cell dynamics on the landscape when perturbed by hypoxia, a dormancy-inducing stress. Combining trajectory-based energy landscape generation with single-cell time-lapse microscopy, we found that initial position on a p21/Cdk2 landscape did not fully explain the observed cell-fate heterogeneity under hypoxia. Instead, cells with higher cell state velocities prior to hypoxia, influenced by epigenetic parameters, tended to remain proliferative under hypoxia. Thus, the fate decision on this landscape is significantly influenced by "inertia", a velocity-dependent ability to resist directional changes despite reshaping of the underlying landscape, superseding positional effects. Such inertial effects may markedly influence cell-fate trajectories in tumors and other dynamically changing microenvironments.
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Affiliation(s)
- Harish Venkatachalapathy
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455, USA
| | - Cole Brzakala
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455, USA
| | - Eric Batchelor
- Department of Integrative Biology and Physiology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Samira M. Azarin
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455, USA
| | - Casim A. Sarkar
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
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19
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Manicka S, Johnson K, Levin M, Murrugarra D. The nonlinearity of regulation in biological networks. NPJ Syst Biol Appl 2023; 9:10. [PMID: 37015937 PMCID: PMC10073134 DOI: 10.1038/s41540-023-00273-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 03/23/2023] [Indexed: 04/06/2023] Open
Abstract
The extent to which the components of a biological system are (non)linearly regulated determines how amenable they are to therapy and control. To better understand this property termed "regulatory nonlinearity", we analyzed a suite of 137 published Boolean network models, containing a variety of complex nonlinear regulatory interactions, using a probabilistic generalization of Boolean logic that George Boole himself had proposed. Leveraging the continuous-nature of this formulation, we used Taylor decomposition to approximate the models with various levels of regulatory nonlinearity. A comparison of the resulting series of approximations of the biological models with appropriate random ensembles revealed that biological regulation tends to be less nonlinear than expected, meaning that higher-order interactions among the regulatory inputs tend to be less pronounced. A further categorical analysis of the biological models revealed that the regulatory nonlinearity of cancer and disease networks could not only be sometimes higher than expected but also be relatively more variable. We show that this variation is caused by differences in the apportioning of information among the various orders of regulatory nonlinearity. Our results suggest that there may have been a weak but discernible selection pressure for biological systems to evolve linear regulation on average, but for certain systems such as cancer, on the other hand, to simultaneously evolve more nonlinear rules.
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Affiliation(s)
- Santosh Manicka
- Department of Biology, Tufts University, Medford, MA, 02155, USA
| | - Kathleen Johnson
- Department of Mathematics, University of Kentucky, Lexington, KY, 40506, USA
| | - Michael Levin
- Department of Biology, Tufts University, Medford, MA, 02155, USA
| | - David Murrugarra
- Department of Mathematics, University of Kentucky, Lexington, KY, 40506, USA.
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20
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Shin D, Cho KH. Critical transition and reversion of tumorigenesis. Exp Mol Med 2023; 55:692-705. [PMID: 37009794 PMCID: PMC10167317 DOI: 10.1038/s12276-023-00969-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 01/10/2023] [Accepted: 01/11/2023] [Indexed: 04/04/2023] Open
Abstract
Cancer is caused by the accumulation of genetic alterations and therefore has been historically considered to be irreversible. Intriguingly, several studies have reported that cancer cells can be reversed to be normal cells under certain circumstances. Despite these experimental observations, conceptual and theoretical frameworks that explain these phenomena and enable their exploration in a systematic way are lacking. In this review, we provide an overview of cancer reversion studies and describe recent advancements in systems biological approaches based on attractor landscape analysis. We suggest that the critical transition in tumorigenesis is an important clue for achieving cancer reversion. During tumorigenesis, a critical transition may occur at a tipping point, where cells undergo abrupt changes and reach a new equilibrium state that is determined by complex intracellular regulatory events. We introduce a conceptual framework based on attractor landscapes through which we can investigate the critical transition in tumorigenesis and induce its reversion by combining intracellular molecular perturbation and extracellular signaling controls. Finally, we present a cancer reversion therapy approach that may be a paradigm-changing alternative to current cancer cell-killing therapies.
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Affiliation(s)
- Dongkwan Shin
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
- Reasearch Institute, National Cancer Center, Goyang, 10408, Republic of Korea
| | - Kwang-Hyun Cho
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
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21
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An S, Jang SY, Park SM, Lee CK, Kim HM, Cho KH. Global stabilizing control of large-scale biomolecular regulatory networks. Bioinformatics 2023; 39:6998201. [PMID: 36688702 PMCID: PMC9891247 DOI: 10.1093/bioinformatics/btad045] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 01/13/2023] [Accepted: 01/20/2023] [Indexed: 01/24/2023] Open
Abstract
MOTIVATION Cellular behavior is determined by complex non-linear interactions between numerous intracellular molecules that are often represented by Boolean network models. To achieve a desired cellular behavior with minimal intervention, we need to identify optimal control targets that can drive heterogeneous cellular states to the desired phenotypic cellular state with minimal node intervention. Previous attempts to realize such global stabilization were based solely on either network structure information or simple linear dynamics. Other attempts based on non-linear dynamics are not scalable. RESULTS Here, we investigate the underlying relationship between structurally identified control targets and optimal global stabilizing control targets based on non-linear dynamics. We discovered that optimal global stabilizing control targets can be identified by analyzing the dynamics between structurally identified control targets. Utilizing these findings, we developed a scalable global stabilizing control framework using both structural and dynamic information. Our framework narrows down the search space based on strongly connected components and feedback vertex sets then identifies global stabilizing control targets based on the canalization of Boolean network dynamics. We find that the proposed global stabilizing control is superior with respect to the number of control target nodes, scalability, and computational complexity. AVAILABILITY AND IMPLEMENTATION We provide a GitHub repository that contains the DCGS framework written in Python as well as biological random Boolean network datasets (https://github.com/sugyun/DCGS). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | | | - Chun-Kyung Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Hoon-Min Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
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22
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Hérault L, Poplineau M, Remy E, Duprez E. Single Cell Transcriptomics to Understand HSC Heterogeneity and Its Evolution upon Aging. Cells 2022; 11:3125. [PMID: 36231086 PMCID: PMC9563410 DOI: 10.3390/cells11193125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/15/2022] [Accepted: 09/27/2022] [Indexed: 11/16/2022] Open
Abstract
Single-cell transcriptomic technologies enable the uncovering and characterization of cellular heterogeneity and pave the way for studies aiming at understanding the origin and consequences of it. The hematopoietic system is in essence a very well adapted model system to benefit from this technological advance because it is characterized by different cellular states. Each cellular state, and its interconnection, may be defined by a specific location in the global transcriptional landscape sustained by a complex regulatory network. This transcriptomic signature is not fixed and evolved over time to give rise to less efficient hematopoietic stem cells (HSC), leading to a well-documented hematopoietic aging. Here, we review the advance of single-cell transcriptomic approaches for the understanding of HSC heterogeneity to grasp HSC deregulations upon aging. We also discuss the new bioinformatics tools developed for the analysis of the resulting large and complex datasets. Finally, since hematopoiesis is driven by fine-tuned and complex networks that must be interconnected to each other, we highlight how mathematical modeling is beneficial for doing such interconnection between multilayered information and to predict how HSC behave while aging.
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Affiliation(s)
- Léonard Hérault
- I2M, CNRS, Aix Marseille University, 13009 Marseille, France
- Epigenetic Factors in Normal and Malignant Hematopoiesis Lab., CRCM, CNRS, INSERM, Institut Paoli Calmettes, Aix Marseille University, 13009 Marseille, France
| | - Mathilde Poplineau
- Epigenetic Factors in Normal and Malignant Hematopoiesis Lab., CRCM, CNRS, INSERM, Institut Paoli Calmettes, Aix Marseille University, 13009 Marseille, France
- Equipe Labellisée Ligue Nationale Contre le Cancer, 75013 Paris, France
| | - Elisabeth Remy
- I2M, CNRS, Aix Marseille University, 13009 Marseille, France
| | - Estelle Duprez
- Epigenetic Factors in Normal and Malignant Hematopoiesis Lab., CRCM, CNRS, INSERM, Institut Paoli Calmettes, Aix Marseille University, 13009 Marseille, France
- Equipe Labellisée Ligue Nationale Contre le Cancer, 75013 Paris, France
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23
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Choi M, Park SM, Cho KH. Evaluating a therapeutic window for precision medicine by integrating genomic profiles and p53 network dynamics. Commun Biol 2022; 5:924. [PMID: 36071176 PMCID: PMC9452682 DOI: 10.1038/s42003-022-03872-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 08/23/2022] [Indexed: 11/08/2022] Open
Abstract
The response variation to anti-cancer drugs originates from complex intracellular network dynamics of cancer. Such dynamic networks present challenges to determining optimal drug targets and stratifying cancer patients for precision medicine, although several cancer genome studies provided insights into the molecular characteristics of cancer. Here, we introduce a network dynamics-based approach based on attractor landscape analysis to evaluate the therapeutic window of a drug from cancer signaling networks combined with genomic profiles. This approach allows for effective screening of drug targets to explore potential target combinations for enhancing the therapeutic window of drug responses. We also effectively stratify patients into desired/undesired response groups using critical genomic determinants, which are network-specific origins of variability to drug response, and their dominance relationship. Our methods provide a viable and quantitative framework to connect genotype information to the phenotypes of drug response with regard to network dynamics determining the therapeutic window.
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Affiliation(s)
- Minsoo Choi
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Sang-Min Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
- College of Pharmacy, Chungnam National University, Daejeon, 34134, Korea
| | - Kwang-Hyun Cho
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
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24
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Plaugher D, Aguilar B, Murrugarra D. Uncovering potential interventions for pancreatic cancer patients via mathematical modeling. J Theor Biol 2022; 548:111197. [PMID: 35752283 DOI: 10.1016/j.jtbi.2022.111197] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 05/20/2022] [Accepted: 06/08/2022] [Indexed: 12/24/2022]
Abstract
Pancreatic Ductal Adenocarcinoma (PDAC) is widely known for its poor prognosis because it is often diagnosed when the cancer is in a later stage. We built a Boolean model to analyze the microenvironment of pancreatic cancer in order to better understand the interplay between pancreatic cancer, stellate cells, and their signaling cytokines. Specifically, we have used our model to study the impact of inducing four common mutations: KRAS, TP53, SMAD4, and CDKN2A. After implementing the various mutation combinations, we used our stochastic simulator to derive aggressiveness scores based on simulated attractor probabilities and long-term trajectory approximations. These aggression scores were then corroborated with clinical data. Moreover, we found sets of control targets that are effective among common mutations. These control sets contain nodes within both the pancreatic cancer cell and the pancreatic stellate cell, including PIP3, RAF, PIK3 and BAX in pancreatic cancer cell as well as ERK and PIK3 in the pancreatic stellate cell. Many of these nodes were found to be differentially expressed among pancreatic cancer patients in the TCGA database. Furthermore, literature suggests that many of these nodes can be targeted by drugs currently in circulation. The results herein help provide a proof of concept in the path towards personalized medicine through a means of mathematical systems biology. All data and code used for running simulations, statistical analysis, and plotting is available on a GitHub repository athttps://github.com/drplaugher/PCC_Mutations.
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Affiliation(s)
- Daniel Plaugher
- Department of Mathematics, University of Kentucky, Lexington, KY, USA.
| | | | - David Murrugarra
- Department of Mathematics, University of Kentucky, Lexington, KY, USA.
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25
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Stallaert W, Taylor SR, Kedziora KM, Taylor CD, Sobon HK, Young CL, Limas JC, Varblow Holloway J, Johnson MS, Cook JG, Purvis JE. The molecular architecture of cell cycle arrest. Mol Syst Biol 2022; 18:e11087. [PMID: 36161508 PMCID: PMC9511499 DOI: 10.15252/msb.202211087] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 09/07/2022] [Accepted: 09/09/2022] [Indexed: 11/09/2022] Open
Abstract
The cellular decision governing the transition between proliferative and arrested states is crucial to the development and function of every tissue. While the molecular mechanisms that regulate the proliferative cell cycle are well established, we know comparatively little about what happens to cells as they diverge into cell cycle arrest. We performed hyperplexed imaging of 47 cell cycle effectors to obtain a map of the molecular architecture that governs cell cycle exit and progression into reversible ("quiescent") and irreversible ("senescent") arrest states. Using this map, we found multiple points of divergence from the proliferative cell cycle; identified stress-specific states of arrest; and resolved the molecular mechanisms governing these fate decisions, which we validated by single-cell, time-lapse imaging. Notably, we found that cells can exit into senescence from either G1 or G2; however, both subpopulations converge onto a single senescent state with a G1-like molecular signature. Cells can escape from this "irreversible" arrest state through the upregulation of G1 cyclins. This map provides a more comprehensive understanding of the overall organization of cell proliferation and arrest.
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Affiliation(s)
- Wayne Stallaert
- Department of GeneticsUniversity of North Carolina at Chapel HillChapel HillNCUSA
- Computational Medicine ProgramUniversity of North Carolina at Chapel HillChapel HillNCUSA
- Present address:
Department of Computational and Systems BiologyUniversity of PittsburghPittsburghPAUSA
| | - Sovanny R Taylor
- Department of GeneticsUniversity of North Carolina at Chapel HillChapel HillNCUSA
- Computational Medicine ProgramUniversity of North Carolina at Chapel HillChapel HillNCUSA
| | - Katarzyna M Kedziora
- Department of GeneticsUniversity of North Carolina at Chapel HillChapel HillNCUSA
- Bioinformatics and Analytics Research Collaborative (BARC)University of North Carolina at Chapel HillChapel HillNCUSA
| | - Colin D Taylor
- Department of GeneticsUniversity of North Carolina at Chapel HillChapel HillNCUSA
- Computational Medicine ProgramUniversity of North Carolina at Chapel HillChapel HillNCUSA
| | - Holly K Sobon
- Department of GeneticsUniversity of North Carolina at Chapel HillChapel HillNCUSA
- Computational Medicine ProgramUniversity of North Carolina at Chapel HillChapel HillNCUSA
| | - Catherine L Young
- Department of GeneticsUniversity of North Carolina at Chapel HillChapel HillNCUSA
- Computational Medicine ProgramUniversity of North Carolina at Chapel HillChapel HillNCUSA
| | - Juanita C Limas
- Department of Biochemistry and BiophysicsUniversity of North Carolina at Chapel HillChapel HillNCUSA
| | - Jonah Varblow Holloway
- Department of GeneticsUniversity of North Carolina at Chapel HillChapel HillNCUSA
- Computational Medicine ProgramUniversity of North Carolina at Chapel HillChapel HillNCUSA
| | - Martha S Johnson
- Department of Biochemistry and BiophysicsUniversity of North Carolina at Chapel HillChapel HillNCUSA
| | - Jeanette Gowen Cook
- Department of Biochemistry and BiophysicsUniversity of North Carolina at Chapel HillChapel HillNCUSA
- Department of PharmacologyUniversity of North Carolina at Chapel HillChapel HillNCUSA
| | - Jeremy E Purvis
- Department of GeneticsUniversity of North Carolina at Chapel HillChapel HillNCUSA
- Computational Medicine ProgramUniversity of North Carolina at Chapel HillChapel HillNCUSA
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26
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Xie J, Zhang L, Liu B, Liang X, Shi J. Single-cell analysis of p53 transitional dynamics unravels stimulus- and cell type-dependent signaling output motifs. BMC Biol 2022; 20:85. [PMID: 35410287 PMCID: PMC9004066 DOI: 10.1186/s12915-022-01290-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 03/31/2022] [Indexed: 11/19/2022] Open
Abstract
Background To understand functional changes of complex biological networks, mathematical modeling of network topologies provides a quantitative measure of the way biological systems adapt to external stimuli. However, systemic network topology-based analysis often generates conflicting evidence depending on specific experimental conditions, leading to a limited mechanistic understanding of signaling networks and their differential dynamic outputs, an example of which is the regulation of p53 pathway responses to different stress stimuli and in variable mammalian cell types. Here, we employ a network motif approach to dissect key regulatory units of the p53 pathway and elucidate how network activities at the motif level generate context-specific dynamic responses. Results By combining single-cell imaging and mathematical modeling of dose-dependent p53 dynamics induced by three chemotherapeutics of distinct mechanism-of-actions, including Etoposide, Nutlin-3a and 5-fluorouracil, and in five cancer cell types, we uncovered novel and highly variable p53 dynamic responses, in particular p53 transitional dynamics induced at intermediate drug concentrations, and identified the functional roles of distinct positive and negative feedback motifs of the p53 pathway in modulating the central p53-Mdm2 negative feedback to generate stimulus- and cell type-specific signaling responses. The mechanistic understanding of p53 network dynamics also revealed previously unknown mediators of anticancer drug actions and phenotypic variations in cancer cells that impact drug sensitivity. Conclusions Our results demonstrate that transitional dynamics of signaling proteins such as p53, activated at intermediate stimulus levels, vary the most between the dynamic outputs of different generic network motifs and can be employed as novel quantitative readouts to uncover and elucidate the key building blocks of large signaling networks. Our findings also provide new insight on drug mediators and phenotypic heterogeneity that underlie differential drug responses. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-022-01290-7.
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Affiliation(s)
- Jun Xie
- Center for Quantitative Systems Biology, Department of Physics and Department of Biology, Hong Kong Baptist University, 224 Waterloo Road, Kowloon Tong, Kowloon, Hong Kong, China
| | - Lichun Zhang
- Center for Quantitative Systems Biology, Department of Physics and Department of Biology, Hong Kong Baptist University, 224 Waterloo Road, Kowloon Tong, Kowloon, Hong Kong, China
| | - Bodong Liu
- Center for Quantitative Systems Biology, Department of Physics and Department of Biology, Hong Kong Baptist University, 224 Waterloo Road, Kowloon Tong, Kowloon, Hong Kong, China
| | - Xiao Liang
- Center for Quantitative Systems Biology, Department of Physics and Department of Biology, Hong Kong Baptist University, 224 Waterloo Road, Kowloon Tong, Kowloon, Hong Kong, China
| | - Jue Shi
- Center for Quantitative Systems Biology, Department of Physics and Department of Biology, Hong Kong Baptist University, 224 Waterloo Road, Kowloon Tong, Kowloon, Hong Kong, China.
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Kim E, Ivanov I, Dougherty ER. Network Classification Based on Reducibility With Respect to the Stability of Canalizing Power of Genes in a Gene Regulatory Network - A Boolean Network Modeling Perspective. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:558-568. [PMID: 32750876 DOI: 10.1109/tcbb.2020.3005313] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
A key objective of studying biological systems is to design therapeutic intervention strategies for beneficially altering cell dynamics. Derivation of control policies is hindered by the high-dimensional state spaces associated with gene regulatory networks. Hence, it is critical to reduce the network complexity and the paper aims to address this issue by focusing on the distribution of the canalizing power (CP) of the genes in the model. Canalizing genes enforce broad corrective actions on cellular processes and play a crucial role in producing optimal reactions to external stimuli. Therefore, it is critical to reduce the network while preserving the canalizing power of genes. We reduce Boolean networks with perturbation by removing genes with the smallest canalizing power consecutively, and evaluate the stability of canalizing power. A systematic empirical study demonstrates that there are two classes of networks, reducible and irreducible with respect to the preservation of canalizing power of the genes. Based on these observations, we introduce the definition of reducible networks and proceed with the problem of selecting the relevant network features that allow for discriminating networks from the two different classes. We demonstrate the efficacy of the selected features on synthetic and real gene regulatory networks.
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Chamberlain V, Drew Y, Lunec J. Tipping Growth Inhibition into Apoptosis by Combining Treatment with MDM2 and WIP1 Inhibitors in p53 WT Uterine Leiomyosarcoma. Cancers (Basel) 2021; 14:cancers14010014. [PMID: 35008180 PMCID: PMC8750798 DOI: 10.3390/cancers14010014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/08/2021] [Accepted: 12/16/2021] [Indexed: 12/24/2022] Open
Abstract
As there is no optimal therapeutic strategy defined for women with advanced or recurrent uLMS, there is an urgent need for the discovery of novel, targeted approaches. One such area of interest is the pharmacological inhibition of the MDM2-p53 interaction with small-molecular-weight MDM2 inhibitors. Growth inhibition and cytotoxic assays were used to evaluate uLMS cell line responses to MDM2 inhibitors as single agents and in combination, qRT-PCR to assess transcriptional changes and Caspase-Glo 3/7 assay to detect apoptosis. RG7388 and HDM201 are potent, selective antagonists of the MDM2-p53 interaction that can effectively stabilise and activate p53 in a dose-dependent manner. GSK2830371, a potent and selective WIP1 phosphatase inhibitor, was shown to significantly potentiate the growth inhibitory effects of RG7388 and HDM201, and significantly increase the mRNA expression of p53 transcriptional target genes in a p53WT cell line at a concentration that has no growth inhibitory effects as a single agent. RG7388, HDM201 and GSK2830371 failed to induce apoptosis as single agents; however, a combination treatment tipped cells into apoptosis from senescence. These data present the possibility of MDM2 and WIP1 inhibitor combinations as a potential treatment option for p53WT uLMS patients that warrants further investigation.
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Affiliation(s)
- Victoria Chamberlain
- Newcastle University Centre for Cancer, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; (V.C.); (Y.D.)
| | - Yvette Drew
- Newcastle University Centre for Cancer, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; (V.C.); (Y.D.)
- BC Cancer Centre Vancouver and Faculty of Medicine, University of British Columbia, Vancouver, BC V5Z 4EH, Canada
| | - John Lunec
- Newcastle University Centre for Cancer, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; (V.C.); (Y.D.)
- Correspondence:
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29
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Gondal MN, Chaudhary SU. Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics. Front Oncol 2021; 11:712505. [PMID: 34900668 PMCID: PMC8652070 DOI: 10.3389/fonc.2021.712505] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/26/2021] [Indexed: 12/19/2022] Open
Abstract
Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.
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Affiliation(s)
- Mahnoor Naseer Gondal
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Safee Ullah Chaudhary
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
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30
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Anjum S, Hashim M, Malik SA, Khan M, Lorenzo JM, Abbasi BH, Hano C. Recent Advances in Zinc Oxide Nanoparticles (ZnO NPs) for Cancer Diagnosis, Target Drug Delivery, and Treatment. Cancers (Basel) 2021; 13:4570. [PMID: 34572797 PMCID: PMC8468934 DOI: 10.3390/cancers13184570] [Citation(s) in RCA: 157] [Impact Index Per Article: 39.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/09/2021] [Accepted: 09/10/2021] [Indexed: 12/24/2022] Open
Abstract
Cancer is regarded as one of the most deadly and mirthless diseases and it develops due to the uncontrolled proliferation of cells. To date, varieties of traditional medications and chemotherapies have been utilized to fight tumors. However, their immense drawbacks, such as reduced bioavailability, insufficient supply, and significant adverse effects, make their use limited. Nanotechnology has evolved rapidly in recent years and offers a wide spectrum of applications in the healthcare sectors. Nanoscale materials offer strong potential for curing cancer as they pose low risk and fewer complications. Several metal oxide NPs are being developed to diagnose or treat malignancies, but zinc oxide nanoparticles (ZnO NPs) have remarkably demonstrated their potential in the diagnosis and treatment of various types of cancers due to their biocompatibility, biodegradability, and unique physico-chemical attributes. ZnO NPs showed cancer cell specific toxicity via generation of reactive oxygen species and destruction of mitochondrial membrane potential, which leads to the activation of caspase cascades followed by apoptosis of cancerous cells. ZnO NPs have also been used as an effective carrier for targeted and sustained delivery of various plant bioactive and chemotherapeutic anticancerous drugs into tumor cells. In this review, at first we have discussed the role of ZnO NPs in diagnosis and bio-imaging of cancer cells. Secondly, we have extensively reviewed the capability of ZnO NPs as carriers of anticancerous drugs for targeted drug delivery into tumor cells, with a special focus on surface functionalization, drug-loading mechanism, and stimuli-responsive controlled release of drugs. Finally, we have critically discussed the anticancerous activity of ZnO NPs on different types of cancers along with their mode of actions. Furthermore, this review also highlights the limitations and future prospects of ZnO NPs in cancer theranostic.
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Affiliation(s)
- Sumaira Anjum
- Department of Biotechnology, Kinnaird College for Women, Jail Road, Lahore 54000, Pakistan; (M.H.); (S.A.M.); (M.K.)
| | - Mariam Hashim
- Department of Biotechnology, Kinnaird College for Women, Jail Road, Lahore 54000, Pakistan; (M.H.); (S.A.M.); (M.K.)
| | - Sara Asad Malik
- Department of Biotechnology, Kinnaird College for Women, Jail Road, Lahore 54000, Pakistan; (M.H.); (S.A.M.); (M.K.)
| | - Maha Khan
- Department of Biotechnology, Kinnaird College for Women, Jail Road, Lahore 54000, Pakistan; (M.H.); (S.A.M.); (M.K.)
| | - José M. Lorenzo
- Centro Tecnológico de la Carne de Galicia, Avenida de Galicia 4, Parque Tecnológico de Galicia, 32900 San Cibrao das Viñas, Ourense, Spain;
- Área de Tecnología de los Alimentos, Facultad de Ciencias de Ourense, Universidad de Vigo, 32004 Ourense, Spain
| | - Bilal Haider Abbasi
- Department of Biotechnology, Quaid-i-Azam University, Islamabad 15320, Pakistan;
| | - Christophe Hano
- Laboratoire de Biologie des Ligneux et des Grandes Cultures, INRAE USC1328, Eure & Loir Campus, University of Orleans, 28000 Chartres, France;
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31
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Ebata K, Yamashiro S, Iida K, Okada M. Building patient-specific models for receptor tyrosine kinase signaling networks. FEBS J 2021; 289:90-101. [PMID: 33755310 DOI: 10.1111/febs.15831] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 02/26/2021] [Accepted: 03/19/2021] [Indexed: 12/16/2022]
Abstract
Cancer progresses due to changes in the dynamic interactions of multidimensional factors associated with gene mutations. Cancer research has actively adopted computational methods, including data-driven and mathematical model-driven approaches, to identify causative factors and regulatory rules that can explain the complexity and diversity of cancers. A data-driven, statistics-based approach revealed correlations between gene alterations and clinical outcomes in many types of cancers. A model-driven mathematical approach has elucidated the dynamic features of cancer networks and identified the mechanisms of drug efficacy and resistance. More recently, machine learning methods have emerged that can be used for mining omics data and classifying patient. However, as the strengths and weaknesses of each method becoming apparent, new analytical tools are emerging to combine and improve the methodologies and maximize their predictive power for classifying cancer subtypes and prognosis. Here, we introduce recent advances in cancer systems biology aimed at personalized medicine, with focus on the receptor tyrosine kinase signaling network.
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Affiliation(s)
- Kyoichi Ebata
- Institute for Protein Research, Osaka University, Suita, Japan
| | - Sawa Yamashiro
- Institute for Protein Research, Osaka University, Suita, Japan
| | - Keita Iida
- Institute for Protein Research, Osaka University, Suita, Japan
| | - Mariko Okada
- Institute for Protein Research, Osaka University, Suita, Japan.,Center for Drug Design and Research, National Institutes of Biomedical Innovation, Health and Nutrition, Ibaraki, Japan.,Institute for Chemical Research, Kyoto University, Japan
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32
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Zhong J, Li B, Liu Y, Lu J, Gui W. Steady-State Design of Large-Dimensional Boolean Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:1149-1161. [PMID: 32287018 DOI: 10.1109/tnnls.2020.2980632] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Analysis and design of steady states representing cell types, such as cell death or unregulated growth, are of significant interest in modeling genetic regulatory networks. In this article, the steady-state design of large-dimensional Boolean networks (BNs) is studied via model reduction and pinning control. Compared with existing literature, the pinning control design in this article is based on the original node's connection, but not on the state-transition matrix of BNs. Hence, the computational complexity is dramatically reduced in this article from O(2n×2n) to O(2×2r) , where n is the number of nodes in the large-dimensional BN and is the largest number of in-neighbors of the reduced BN. Finally, the proposed method is well demonstrated by a T-LGL survival signaling network with 18 nodes and a model of survival signaling in large granular lymphocyte leukemia with 29 nodes. Just as shown in the simulations, the model reduction method reduces 99.98% redundant states for the network with 18 nodes, and 99.99% redundant states for the network with 29 nodes.
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Kim J, T. Jakobsen S, Natarajan KN, Won KJ. TENET: gene network reconstruction using transfer entropy reveals key regulatory factors from single cell transcriptomic data. Nucleic Acids Res 2021; 49:e1. [PMID: 33170214 PMCID: PMC7797076 DOI: 10.1093/nar/gkaa1014] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 10/05/2020] [Accepted: 10/14/2020] [Indexed: 12/22/2022] Open
Abstract
Accurate prediction of gene regulatory rules is important towards understanding of cellular processes. Existing computational algorithms devised for bulk transcriptomics typically require a large number of time points to infer gene regulatory networks (GRNs), are applicable for a small number of genes and fail to detect potential causal relationships effectively. Here, we propose a novel approach 'TENET' to reconstruct GRNs from single cell RNA sequencing (scRNAseq) datasets. Employing transfer entropy (TE) to measure the amount of causal relationships between genes, TENET predicts large-scale gene regulatory cascades/relationships from scRNAseq data. TENET showed better performance than other GRN reconstructors, in identifying key regulators from public datasets. Specifically from scRNAseq, TENET identified key transcriptional factors in embryonic stem cells (ESCs) and during direct cardiomyocytes reprogramming, where other predictors failed. We further demonstrate that known target genes have significantly higher TE values, and TENET predicted higher TE genes were more influenced by the perturbation of their regulator. Using TENET, we identified and validated that Nme2 is a culture condition specific stem cell factor. These results indicate that TENET is uniquely capable of identifying key regulators from scRNAseq data.
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Affiliation(s)
- Junil Kim
- Biotech Research and Innovation Centre (BRIC), University of Copenhagen, 2200 Copenhagen N, Denmark
- Novo Nordisk Foundation Center for Stem Cell Biology, DanStem, Faculty of Health and Medical Sciences, University of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen N, Denmark
| | - Simon T. Jakobsen
- Functional Genomics and Metabolism Unit, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Denmark
| | - Kedar N Natarajan
- Functional Genomics and Metabolism Unit, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Denmark
- Danish Institute of Advanced Study (D-IAS), University of Southern Denmark, Denmark
| | - Kyoung-Jae Won
- Biotech Research and Innovation Centre (BRIC), University of Copenhagen, 2200 Copenhagen N, Denmark
- Novo Nordisk Foundation Center for Stem Cell Biology, DanStem, Faculty of Health and Medical Sciences, University of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen N, Denmark
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Erenpreisa J, Salmina K, Anatskaya O, Cragg MS. Paradoxes of cancer: Survival at the brink. Semin Cancer Biol 2020; 81:119-131. [PMID: 33340646 DOI: 10.1016/j.semcancer.2020.12.009] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 12/07/2020] [Accepted: 12/09/2020] [Indexed: 12/17/2022]
Abstract
The fundamental understanding of how Cancer initiates, persists and then progresses is evolving. High-resolution technologies, including single-cell mutation and gene expression measurements, are now attainable, providing an ever-increasing insight into the molecular details. However, this higher resolution has shown that somatic mutation theory itself cannot explain the extraordinary resistance of cancer to extinction. There is a need for a more Systems-based framework of understanding cancer complexity, which in particular explains the regulation of gene expression during cell-fate decisions. Cancer displays a series of paradoxes. Here we attempt to approach them from the view-point of adaptive exploration of gene regulatory networks at the edge of order and chaos, where cell-fate is changed by oscillations between alternative regulators of cellular senescence and reprogramming operating through self-organisation. On this background, the role of polyploidy in accessing the phylogenetically pre-programmed "oncofetal attractor" state, related to unicellularity, and the de-selection of unsuitable variants at the brink of cell survival is highlighted. The concepts of the embryological and atavistic theory of cancer, cancer cell "life-cycle", and cancer aneuploidy paradox are dissected under this lense. Finally, we challenge researchers to consider that cancer "defects" are mostly the adaptation tools of survival programs that have arisen during evolution and are intrinsic of cancer. Recognition of these features should help in the development of more successful anti-cancer treatments.
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Affiliation(s)
| | - Kristine Salmina
- Latvian Biomedical Research and Study Centre, Riga, LV-1067, Latvia
| | | | - Mark S Cragg
- Centre for Cancer Immunology, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, UK
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35
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Yin Z, Guo B, Ma S, Sun Y, Mi Z, Zheng Z. DReSS: a method to quantitatively describe the influence of structural perturbations on state spaces of genetic regulatory networks. Brief Bioinform 2020; 22:6032613. [PMID: 33313791 DOI: 10.1093/bib/bbaa315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 09/23/2020] [Accepted: 10/16/2020] [Indexed: 11/14/2022] Open
Abstract
Structures of genetic regulatory networks are not fixed. These structural perturbations can cause changes to the reachability of systems' state spaces. As system structures are related to genotypes and state spaces are related to phenotypes, it is important to study the relationship between structures and state spaces. However, there is still no method can quantitively describe the reachability differences of two state spaces caused by structural perturbations. Therefore, Difference in Reachability between State Spaces (DReSS) is proposed. DReSS index family can quantitively describe differences of reachability, attractor sets between two state spaces and can help find the key structure in a system, which may influence system's state space significantly. First, basic properties of DReSS including non-negativity, symmetry and subadditivity are proved. Then, typical examples are shown to explain the meaning of DReSS and the differences between DReSS and traditional graph distance. Finally, differences of DReSS distribution between real biological regulatory networks and random networks are compared. Results show most structural perturbations in biological networks tend to affect reachability inside and between attractor basins rather than to affect attractor set itself when compared with random networks, which illustrates that most genotype differences tend to influence the proportion of different phenotypes and only a few ones can create new phenotypes. DReSS can provide researchers with a new insight to study the relation between genotypes and phenotypes.
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Affiliation(s)
- Ziqiao Yin
- Shenyuan Honors College and School of Mathematical Sciences, Beihang University, and Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Ministry of Education. He currently works as a visiting scholar at Yale University
| | - Binghui Guo
- Artificial Intelligence Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing, LMIB, NLSDE, School of Mathematical Sciences, Beihang University, and Peng Cheng Laboratory
| | - Shuangge Ma
- Department of Biostatistics, Yale University
| | - Yifan Sun
- School of Statistics, Renmin University of China
| | - Zhilong Mi
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Ministry of Education, and School of Mathematical Sciences from Beihang University
| | - Zhiming Zheng
- Artificial Intelligence Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing, LMIB, NLSDE, School of Mathematical Sciences, Beihang University, and Peng Cheng Laboratory
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36
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An S, Cho SY, Kang J, Lee S, Kim HS, Min DJ, Son E, Cho KH. Inhibition of 3-phosphoinositide-dependent protein kinase 1 (PDK1) can revert cellular senescence in human dermal fibroblasts. Proc Natl Acad Sci U S A 2020; 117:31535-31546. [PMID: 33229519 PMCID: PMC7733858 DOI: 10.1073/pnas.1920338117] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Cellular senescence is defined as a stable, persistent arrest of cell proliferation. Here, we examine whether senescent cells can lose senescence hallmarks and reenter a reversible state of cell-cycle arrest (quiescence). We constructed a molecular regulatory network of cellular senescence based on previous experimental evidence. To infer the regulatory logic of the network, we performed phosphoprotein array experiments with normal human dermal fibroblasts and used the data to optimize the regulatory relationships between molecules with an evolutionary algorithm. From ensemble analysis of network models, we identified 3-phosphoinositide-dependent protein kinase 1 (PDK1) as a promising target for inhibitors to convert the senescent state to the quiescent state. We showed that inhibition of PDK1 in senescent human dermal fibroblasts eradicates senescence hallmarks and restores entry into the cell cycle by suppressing both nuclear factor κB and mTOR signaling, resulting in restored skin regeneration capacity. Our findings provide insight into a potential therapeutic strategy to treat age-related diseases associated with the accumulation of senescent cells.
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Affiliation(s)
- Sugyun An
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Si-Young Cho
- R&D Unit, Amorepacific Corporation, 17074 Gyeonggi-do, Republic of Korea
| | - Junsoo Kang
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Soobeom Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Hyung-Su Kim
- R&D Unit, Amorepacific Corporation, 17074 Gyeonggi-do, Republic of Korea
| | - Dae-Jin Min
- R&D Unit, Amorepacific Corporation, 17074 Gyeonggi-do, Republic of Korea
| | - EuiDong Son
- R&D Unit, Amorepacific Corporation, 17074 Gyeonggi-do, Republic of Korea
| | - Kwang-Hyun Cho
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea;
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Joo JI, Choi M, Jang SH, Choi S, Park SM, Shin D, Cho KH. Realizing Cancer Precision Medicine by Integrating Systems Biology and Nanomaterial Engineering. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e1906783. [PMID: 32253807 DOI: 10.1002/adma.201906783] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 12/19/2019] [Indexed: 06/11/2023]
Abstract
Many clinical trials for cancer precision medicine have yielded unsatisfactory results due to challenges such as drug resistance and low efficacy. Drug resistance is often caused by the complex compensatory regulation within the biomolecular network in a cancer cell. Recently, systems biological studies have modeled and simulated such complex networks to unravel the hidden mechanisms of drug resistance and identify promising new drug targets or combinatorial or sequential treatments for overcoming resistance to anticancer drugs. However, many of the identified targets or treatments present major difficulties for drug development and clinical application. Nanocarriers represent a path forward for developing therapies with these "undruggable" targets or those that require precise combinatorial or sequential application, for which conventional drug delivery mechanisms are unsuitable. Conversely, a challenge in nanomedicine has been low efficacy due to heterogeneity of cancers in patients. This problem can also be resolved through systems biological approaches by identifying personalized targets for individual patients or promoting the drug responses. Therefore, integration of systems biology and nanomaterial engineering will enable the clinical application of cancer precision medicine to overcome both drug resistance of conventional treatments and low efficacy of nanomedicine due to patient heterogeneity.
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Affiliation(s)
- Jae Il Joo
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Minsoo Choi
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Seong-Hoon Jang
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Sea Choi
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Sang-Min Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Dongkwan Shin
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Kwang-Hyun Cho
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
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38
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Abstract
Network theory provides one of the most potent analysis tools for the study of complex systems. In this paper, we illustrate the network-based perspective in drug research and how it is coherent with the new paradigm of drug discovery. We first present data sources from which networks are built, then show some examples of how the networks can be used to investigate drug-related systems. A section is devoted to network-based inference applications, i.e., prediction methods based on interactomes, that can be used to identify putative drug-target interactions without resorting to 3D modeling. Finally, we present some aspects of Boolean networks dynamics, anticipating that it might become a very potent modeling framework to develop in silico screening protocols able to simulate phenotypic screening experiments. We conclude that network applications integrated with machine learning and 3D modeling methods will become an indispensable tool for computational drug discovery in the next years.
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Affiliation(s)
- Maurizio Recanatini
- Department of Pharmacy and
Biotechnology, Alma Mater Studiorum—University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
| | - Chiara Cabrelle
- Department of Pharmacy and
Biotechnology, Alma Mater Studiorum—University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy
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39
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Aguilar B, Gibbs DL, Reiss DJ, McConnell M, Danziger SA, Dervan A, Trotter M, Bassett D, Hershberg R, Ratushny AV, Shmulevich I. A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma. Gigascience 2020; 9:giaa075. [PMID: 32696951 PMCID: PMC7374045 DOI: 10.1093/gigascience/giaa075] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 02/14/2020] [Accepted: 06/21/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Mechanistic models, when combined with pertinent data, can improve our knowledge regarding important molecular and cellular mechanisms found in cancer. These models make the prediction of tissue-level response to drug treatment possible, which can lead to new therapies and improved patient outcomes. Here we present a data-driven multiscale modeling framework to study molecular interactions between cancer, stromal, and immune cells found in the tumor microenvironment. We also develop methods to use molecular data available in The Cancer Genome Atlas to generate sample-specific models of cancer. RESULTS By combining published models of different cells relevant to pancreatic ductal adenocarcinoma (PDAC), we built an agent-based model of the multicellular pancreatic tumor microenvironment, formally describing cell type-specific molecular interactions and cytokine-mediated cell-cell communications. We used an ensemble-based modeling approach to systematically explore how variations in the tumor microenvironment affect the viability of cancer cells. The results suggest that the autocrine loop involving EGF signaling is a key interaction modulator between pancreatic cancer and stellate cells. EGF is also found to be associated with previously described subtypes of PDAC. Moreover, the model allows a systematic exploration of the effect of possible therapeutic perturbations; our simulations suggest that reducing bFGF secretion by stellate cells will have, on average, a positive impact on cancer apoptosis. CONCLUSIONS The developed framework allows model-driven hypotheses to be generated regarding therapeutically relevant PDAC states with potential molecular and cellular drivers indicating specific intervention strategies.
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Affiliation(s)
- Boris Aguilar
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA
| | - David L Gibbs
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA
| | - David J Reiss
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Mark McConnell
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Samuel A Danziger
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Andrew Dervan
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Matthew Trotter
- BMS Center for Innovation and Translational Research Europe (CITRE), Pabellon de Italia, Calle Isaac Newton 4, Sevilla 41092, Spain
| | - Douglas Bassett
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Robert Hershberg
- Formerly Celgene Corporation, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Alexander V Ratushny
- Bristol-Myers Squibb, 400 Dexter Avenue North, Suite 1200, Seattle, WA 98109, USA
| | - Ilya Shmulevich
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA
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Konrath F, Mittermeier A, Cristiano E, Wolf J, Loewer A. A systematic approach to decipher crosstalk in the p53 signaling pathway using single cell dynamics. PLoS Comput Biol 2020; 16:e1007901. [PMID: 32589666 PMCID: PMC7319280 DOI: 10.1371/journal.pcbi.1007901] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 04/22/2020] [Indexed: 01/15/2023] Open
Abstract
The transcription factors NF-κB and p53 are key regulators in the genotoxic stress response and are critical for tumor development. Although there is ample evidence for interactions between both networks, a comprehensive understanding of the crosstalk is lacking. Here, we developed a systematic approach to identify potential interactions between the pathways. We perturbed NF-κB signaling by inhibiting IKK2, a critical regulator of NF-κB activity, and monitored the altered response of p53 to genotoxic stress using single cell time lapse microscopy. Fitting subpopulation-specific computational p53 models to this time-resolved single cell data allowed to reproduce in a quantitative manner signaling dynamics and cellular heterogeneity for the unperturbed and perturbed conditions. The approach enabled us to untangle the integrated effects of IKK/ NF-κB perturbation on p53 dynamics and thereby derive potential interactions between both networks. Intriguingly, we find that a simultaneous perturbation of multiple processes is necessary to explain the observed changes in the p53 response. Specifically, we show interference with the activation and degradation of p53 as well as the degradation of Mdm2. Our results highlight the importance of the crosstalk and its potential implications in p53-dependent cellular functions. Cells can respond to external and internal inputs by transducing information to the nucleus where transcription factors initiate corresponding cellular responses. Cellular signaling is mediated by several pathways; molecular networks that can interact with each other, which alters signal processing and modulates cellular responses. As deregulated signaling can lead to the development of tumors it is important to understand not only how signaling pathways function but also the contribution of their interaction on the signaling dynamics. Here, we analyzed the interplay of the IKK/ NF-κB and p53 pathway, which are both critical for the cellular response to DNA damage and have been implicated in tumor development. To systematically identify interaction points between both pathways, we perturbed IKK/ NF-κB signaling and tracked the changes in the response of p53 to DNA damage. Using computational methods, we show that several reactions in the p53 pathway are simultaneously affected by NF-κB signaling and that this combined action is necessary to explain altered behaviour of the p53 pathway. Hence, our results provide new insights into the interplay between the NF-κB and p53 pathway and help to gain a more comprehensive understanding of the crosstalk.
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Affiliation(s)
- Fabian Konrath
- Mathematical Modelling of Cellular Processes, Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Anna Mittermeier
- Systems Biology of the Stress Response, Department of Biology, Technische Universität Darmstadt, Darmstadt, Germany
| | - Elena Cristiano
- Signaling Dynamics in Single Cells, Berlin Institute for Medical Systems Biology, Max Delbrueck Center for Molecular Medicine, Berlin, Germany
| | - Jana Wolf
- Mathematical Modelling of Cellular Processes, Max Delbrueck Center for Molecular Medicine, Berlin, Germany
- * E-mail: (JW); (AL)
| | - Alexander Loewer
- Systems Biology of the Stress Response, Department of Biology, Technische Universität Darmstadt, Darmstadt, Germany
- Signaling Dynamics in Single Cells, Berlin Institute for Medical Systems Biology, Max Delbrueck Center for Molecular Medicine, Berlin, Germany
- * E-mail: (JW); (AL)
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41
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Rajapakse VN, Herrada S, Lavi O. Phenotype stability under dynamic brain-tumor environment stimuli maps glioblastoma progression in patients. SCIENCE ADVANCES 2020; 6:eaaz4125. [PMID: 32832595 PMCID: PMC7439317 DOI: 10.1126/sciadv.aaz4125] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 03/20/2020] [Indexed: 06/11/2023]
Abstract
Although tumor invasiveness is known to drive glioblastoma (GBM) recurrence, current approaches to treatment assume a fairly simple GBM phenotype transition map. We provide new analyses to estimate the likelihood of reaching or remaining in a phenotype under dynamic, physiologically likely perturbations of stimuli ("phenotype stability"). We show that higher stability values of the motile phenotype (Go) are associated with reduced patient survival. Moreover, induced motile states are capable of driving GBM recurrence. We found that the Dormancy and Go phenotypes are equally represented in advanced GBM samples, with natural transitioning between the two. Furthermore, Go and Grow phenotype transitions are mostly driven by tumor-brain stimuli. These are difficult to regulate directly, but could be modulated by reprogramming tumor-associated cell types. Our framework provides a foundation for designing targeted perturbations of the tumor-brain environment, by assessing their impact on GBM phenotypic plasticity, and is corroborated by analyses of patient data.
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Affiliation(s)
- Vinodh N. Rajapakse
- Integrative Cancer Dynamics Unit, Laboratory of Cell Biology, CCR, NCI, NIH, Bethesda, MD, USA
| | - Sylvia Herrada
- Integrative Cancer Dynamics Unit, Laboratory of Cell Biology, CCR, NCI, NIH, Bethesda, MD, USA
| | - Orit Lavi
- Integrative Cancer Dynamics Unit, Laboratory of Cell Biology, CCR, NCI, NIH, Bethesda, MD, USA
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42
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AEON: Attractor Bifurcation Analysis of Parametrised Boolean Networks. COMPUTER AIDED VERIFICATION 2020. [PMCID: PMC7363220 DOI: 10.1007/978-3-030-53288-8_28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Boolean networks (BNs) provide an effective modelling tool for various phenomena from science and engineering. Any long-term behaviour of a BN eventually converges to a so-called attractor. Depending on various logical parameters, the structure and quality of attractors can undergo a significant change, known as a bifurcation. We present a tool for analysing bifurcations in asynchronous parametrised Boolean networks. To fight the state-space and parameter-space explosion problem the tool uses a parallel semi-symbolic algorithm.
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43
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Sordo Vieira L, Laubenbacher RC, Murrugarra D. Control of Intracellular Molecular Networks Using Algebraic Methods. Bull Math Biol 2019; 82:2. [PMID: 31919596 DOI: 10.1007/s11538-019-00679-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 12/02/2019] [Indexed: 10/25/2022]
Abstract
Many problems in biology and medicine have a control component. Often, the goal might be to modify intracellular networks, such as gene regulatory networks or signaling networks, in order for cells to achieve a certain phenotype, what happens in cancer. If the network is represented by a mathematical model for which mathematical control approaches are available, such as systems of ordinary differential equations, then this problem might be solved systematically. Such approaches are available for some other model types, such as Boolean networks, where structure-based approaches have been developed, as well as stable motif techniques. However, increasingly many published discrete models are mixed-state or multistate, that is, some or all variables have more than two states, and thus the development of control strategies for multistate networks is needed. This paper presents a control approach broadly applicable to general multistate models based on encoding them as polynomial dynamical systems over a finite algebraic state set, and using computational algebra for finding appropriate intervention strategies. To demonstrate the feasibility and applicability of this method, we apply it to a recently developed multistate intracellular model of E2F-mediated bladder cancerous growth and to a model linking intracellular iron metabolism and oncogenic pathways. The control strategies identified for these published models are novel in some cases and represent new hypotheses, or are supported by the literature in others as potential drug targets. Our Macaulay2 scripts to find control strategies are publicly available through GitHub at https://github.com/luissv7/multistatepdscontrol.
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Affiliation(s)
- Luis Sordo Vieira
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, 06032, USA
| | - Reinhard C Laubenbacher
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, 06032, USA.,Center for Quantitative Medicine, UConn Health, Farmington, CT, 06032, USA
| | - David Murrugarra
- Department of Mathematics, University of Kentucky, Lexington, KY, 40506, USA.
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44
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Hou X, Li M, Jia C, Zhang X, Wang Y. Attractor - a new turning point in drug discovery. DRUG DESIGN DEVELOPMENT AND THERAPY 2019; 13:2957-2968. [PMID: 31686779 PMCID: PMC6709805 DOI: 10.2147/dddt.s216397] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 07/28/2019] [Indexed: 11/23/2022]
Abstract
Drug discovery for complex diseases can be viewed as a challenging problem in which the influence of compounds on dynamic features of disease system should be considered, especially the strategies escaping from the disease attractors. Moreover, escaping from the disease-related attractors has been proved to be a cue for the treatment of the complex diseases. The drug discovery methodology based on the attractor theory indicates new solutions for target identification, drug discovery and drug combination design. The methodology is based on the holism level of the organism and the features of system dynamics, so it has advantages for the classification of complex diseases and drug discovery. Currently, research results of this method have increased, which expand the insight scope for drug discovery. This article introduces the major drug discovery methods in the history of pharmacy development and their characteristics, so as to illustrate the reasons and inevitability of the appearance of attractor method, its position in the history of pharmacy development, and its advantages for drug discovery and design, thereby to prove that the attractor method can indeed become the next major drug development method. In addition, it provides a comprehensive description about the concept of attractor, the pipeline of attractor analysis, the common methods of each process and its research progress, so as to provide a macroscopic framework and optional methods and tools for the follow-up researchers.
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Affiliation(s)
- Xucan Hou
- Department of Traditional Chinese Medicine Information Fusion and Utilization, Beijing University of Chinese Medicine, Beijing, People's Republic of China
| | - Meng Li
- Department of Traditional Chinese Medicine Information Fusion and Utilization, Beijing University of Chinese Medicine, Beijing, People's Republic of China
| | - Congmin Jia
- Department of Traditional Chinese Medicine Information Fusion and Utilization, Beijing University of Chinese Medicine, Beijing, People's Republic of China
| | - Xianbao Zhang
- Department of Traditional Chinese Medicine Information Fusion and Utilization, Beijing University of Chinese Medicine, Beijing, People's Republic of China
| | - Yun Wang
- Department of Traditional Chinese Medicine Information Fusion and Utilization, Beijing University of Chinese Medicine, Beijing, People's Republic of China
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45
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Abstract
Complex disease such as cancer is often caused by genetic mutations that eventually alter the signal flow in the intra-cellular signaling network and result in different cell fate. Therefore, it is crucial to identify control targets that can most effectively block such unwanted signal flow. For this purpose, systems biological analysis provides a useful framework, but mathematical modeling of complicated signaling networks requires massive time-series measurements of signaling protein activity levels for accurate estimation of kinetic parameter values or regulatory logics. Here, we present a novel method, called SFC (Signal Flow Control), for identifying control targets without the information of kinetic parameter values or regulatory logics. Our method requires only the structural information of a signaling network and is based on the topological estimation of signal flow through the network. SFC will be particularly useful for a large-scale signaling network to which parameter estimation or inference of regulatory logics is no longer applicable in practice. The identified control targets have significant implication in drug development as they can be putative drug targets.
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46
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Choo SM, Park SM, Cho KH. Minimal intervening control of biomolecular networks leading to a desired cellular state. Sci Rep 2019; 9:13124. [PMID: 31511585 PMCID: PMC6739335 DOI: 10.1038/s41598-019-49571-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 08/27/2019] [Indexed: 02/07/2023] Open
Abstract
A cell phenotype can be represented by an attractor state of the underlying molecular regulatory network, to which other network states eventually converge. Here, the set of states converging to each attractor is called its basin of attraction. A central question is how to drive a particular cell state toward a desired attractor with minimal interventions on the network system. We develop a general control framework of complex Boolean networks to provide an answer to this question by identifying control targets on which one-time temporary perturbation can induce a state transition to the boundary of a desired attractor basin. Examples are shown to illustrate the proposed control framework which is also applicable to other types of complex Boolean networks.
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Affiliation(s)
- Sang-Mok Choo
- Department of Mathematics, University of Ulsan, Ulsan, 44610, Republic of Korea
| | - Sang-Min Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Kwang-Hyun Cho
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
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Abstract
Systems medicine is a holistic approach to deciphering the complexity of human physiology in health and disease. In essence, a living body is constituted of networks of dynamically interacting units (molecules, cells, organs, etc) that underlie its collective functions. Declining resilience because of aging and other chronic environmental exposures drives the system to transition from a health state to a disease state; these transitions, triggered by acute perturbations or chronic disturbance, manifest as qualitative shifts in the interactions and dynamics of the disease-perturbed networks. Understanding health-to-disease transitions poses a high-dimensional nonlinear reconstruction problem that requires deep understanding of biology and innovation in study design, technology, and data analysis. With a focus on the principles of systems medicine, this Review discusses approaches for deciphering this biological complexity from a novel perspective, namely, understanding how disease-perturbed networks function; their study provides insights into fundamental disease mechanisms. The immediate goals for systems medicine are to identify early transitions to cardiovascular (and other chronic) diseases and to accelerate the translation of new preventive, diagnostic, or therapeutic targets into clinical practice, a critical step in the development of personalized, predictive, preventive, and participatory (P4) medicine.
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Affiliation(s)
- Kalliopi Trachana
- From the Institute for Systems Biology, Seattle, WA (K.T., R.B., G.G., N.D.P., S.H., L.E.H.)
| | - Rhishikesh Bargaje
- From the Institute for Systems Biology, Seattle, WA (K.T., R.B., G.G., N.D.P., S.H., L.E.H.)
| | - Gustavo Glusman
- From the Institute for Systems Biology, Seattle, WA (K.T., R.B., G.G., N.D.P., S.H., L.E.H.)
| | - Nathan D Price
- From the Institute for Systems Biology, Seattle, WA (K.T., R.B., G.G., N.D.P., S.H., L.E.H.)
| | - Sui Huang
- From the Institute for Systems Biology, Seattle, WA (K.T., R.B., G.G., N.D.P., S.H., L.E.H.).,Department of Biological Sciences, University of Calgary, Alberta, Canada (S.H.)
| | - Leroy E Hood
- From the Institute for Systems Biology, Seattle, WA (K.T., R.B., G.G., N.D.P., S.H., L.E.H.)
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48
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Zhong J, Ho DWC, Lu J, Jiao Q. Pinning Controllers for Activation Output Tracking of Boolean Network Under One-Bit Perturbation. IEEE TRANSACTIONS ON CYBERNETICS 2019; 49:3398-3408. [PMID: 29994143 DOI: 10.1109/tcyb.2018.2842819] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
This paper studies pinning controllers for activation output tracking (AOT) of Boolean network under one-bit perturbation, based on the semitensor product of matrices. First, the definition of AOT with respect to an activation number is presented, where the activation number means the number of active outputs whose logical variables are 1 s. Then, several criteria are established for AOT issue. Further, the impact of one-bit perturbation on AOT is studied, where one-bit perturbation means that only one logical function has one-bit change of its truth table by flipping the value from 1 to 0 or 0 to 1. In addition, if a one-bit perturbation is a valid perturbation on AOT, an output feedback pinning control is designed to recover AOT. The obtained results are effectively illustrated by a D. melanogaster segmentation polarity gene network and a reduced signal transduction network.
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49
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Apostolopoulou I, Marculescu D. Tractable Learning and Inference for Large-Scale Probabilistic Boolean Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:2720-2734. [PMID: 30629517 DOI: 10.1109/tnnls.2018.2886207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Probabilistic Boolean networks (PBNs) have previously been proposed so as to gain insights into complex dynamical systems. However, identification of large networks and their underlying discrete Markov chain which describes their temporal evolution still remains a challenge. In this paper, we introduce an equivalent representation for PBNs, the stochastic conjunctive normal form network (SCNFN), which enables a scalable learning algorithm and helps predict long-run dynamic behavior of large-scale systems. State-of-the-art methods turn out to be 400 times slower for middle-sized networks (i.e., containing 100 nodes) and incapable of terminating for large networks (i.e., containing 1000 nodes) compared to the SCNFN-based learning, when attempting to achieve comparable accuracy. In addition, in contrast to the currently used methods which introduce strict restrictions on the structure of the learned PBNs, the hypothesis space of our training paradigm is the set of all possible PBNs. Moreover, SCNFNs enable efficient sampling so as to statistically infer multistep transition probabilities which can provide information on the activity levels of individual nodes in the long run. Extensive experimental results showcase the scalability of the proposed approach both in terms of sample and runtime complexity. In addition, we provide examples to study large and complex cell signaling networks to show the potential of our model. Finally, we suggest several directions for future research on model variations, theoretical analysis, and potential applications of SCNFNs.
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50
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Tan A, Huang H, Zhang P, Li S. Network-based cancer precision medicine: A new emerging paradigm. Cancer Lett 2019; 458:39-45. [DOI: 10.1016/j.canlet.2019.05.015] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 04/29/2019] [Accepted: 05/15/2019] [Indexed: 12/20/2022]
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