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Bürtin F, Mullins CS, Linnebacher M. Mouse models of colorectal cancer: Past, present and future perspectives. World J Gastroenterol 2020; 26:1394-1426. [PMID: 32308343 PMCID: PMC7152519 DOI: 10.3748/wjg.v26.i13.1394] [Citation(s) in RCA: 103] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 03/05/2020] [Accepted: 03/10/2020] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common diagnosed malignancy among both sexes in the United States as well as in the European Union. While the incidence and mortality rates in western, high developed countries are declining, reflecting the success of screening programs and improved treatment regimen, a rise of the overall global CRC burden can be observed due to lifestyle changes paralleling an increasing human development index. Despite a growing insight into the biology of CRC and many therapeutic improvements in the recent decades, preclinical in vivo models are still indispensable for the development of new treatment approaches. Since the development of carcinogen-induced rodent models for CRC more than 80 years ago, a plethora of animal models has been established to study colon cancer biology. Despite tenuous invasiveness and metastatic behavior, these models are useful for chemoprevention studies and to evaluate colitis-related carcinogenesis. Genetically engineered mouse models (GEMM) mirror the pathogenesis of sporadic as well as inherited CRC depending on the specific molecular pathways activated or inhibited. Although the vast majority of CRC GEMM lack invasiveness, metastasis and tumor heterogeneity, they still have proven useful for examination of the tumor microenvironment as well as systemic immune responses; thus, supporting development of new therapeutic avenues. Induction of metastatic disease by orthotopic injection of CRC cell lines is possible, but the so generated models lack genetic diversity and the number of suited cell lines is very limited. Patient-derived xenografts, in contrast, maintain the pathological and molecular characteristics of the individual patient's CRC after subcutaneous implantation into immunodeficient mice and are therefore most reliable for preclinical drug development - even in comparison to GEMM or cell line-based analyses. However, subcutaneous patient-derived xenograft models are less suitable for studying most aspects of the tumor microenvironment and anti-tumoral immune responses. The authors review the distinct mouse models of CRC with an emphasis on their clinical relevance and shed light on the latest developments in the field of preclinical CRC models.
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Affiliation(s)
- Florian Bürtin
- Department of General, Visceral, Vascular and Transplantation Surgery, University Medical Center Rostock, University of Rostock, Rostock 18057, Germany
| | - Christina S Mullins
- Department of Thoracic Surgery, University Medical Center Rostock, University of Rostock, Rostock 18057, Germany
| | - Michael Linnebacher
- Molecular Oncology and Immunotherapy, Department of General, Visceral, Vascular and Transplantation Surgery, University Medical Center Rostock, Rostock 18057, Germany
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Bag AK, Mandloi S, Jarmalavicius S, Mondal S, Kumar K, Mandal C, Walden P, Chakrabarti S, Mandal C. Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma. PLoS Comput Biol 2019; 15:e1007090. [PMID: 31386654 PMCID: PMC6684045 DOI: 10.1371/journal.pcbi.1007090] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Accepted: 05/13/2019] [Indexed: 12/21/2022] Open
Abstract
As malignant transformation requires synchronization of growth-driving signaling (S) and metabolic (M) pathways, defining cancer-specific S-M interconnected networks (SMINs) could lead to better understanding of oncogenic processes. In a systems-biology approach, we developed a mathematical model for SMINs in mutated EGF receptor (EGFRvIII) compared to wild-type EGF receptor (EGFRwt) expressing glioblastoma multiforme (GBM). Starting with experimentally validated human protein-protein interactome data for S-M pathways, and incorporating proteomic data for EGFRvIII and EGFRwt GBM cells and patient transcriptomic data, we designed a dynamic model for EGFR-driven GBM-specific information flow. Key nodes and paths identified by in silico perturbation were validated experimentally when inhibition of signaling pathway proteins altered expression of metabolic proteins as predicted by the model. This demonstrated capacity of the model to identify unknown connections between signaling and metabolic pathways, explain the robustness of oncogenic SMINs, predict drug escape, and assist identification of drug targets and the development of combination therapies. Complex and highly dynamic interconnected networks allow cancer to take different routes and circumvent chemotherapy. Therefore, understanding these context-specific networks and their dynamics of molecular interactions driven by different oncogenic signaling and metabolic pathways is very much needed to predict drug targets and the effect of therapeutics. We incorporated high-throughput transcriptome and proteome data into mathematical models to deduce properties of cancer cells through systems biology approach. Here we report the development, testing and validation of an integrated systems biology model of information flow between signaling and metabolic pathways to understand the regulation of the interconnection between them in cancer. Our model efficiently identified unique connections and key nodes important in signaling-metabolic information flow. We predicted some potential novel targets before performing actual drug tests. We have successfully applied this model to identify the interconnections altered in the constitutive signaling of the mutated EGFR by comparing EGF-dependent and wild-type EGFR signaling in glioblastoma multiforme.
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Affiliation(s)
- Arup K. Bag
- Cancer Biology and Inflammatory Disorder Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Sapan Mandloi
- Structural Biology and Bioinformatics Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Saulius Jarmalavicius
- Department of Dermatology, Venerology and Allergology, Charité– Universitätsmedizin Berlin corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Susmita Mondal
- Cancer Biology and Inflammatory Disorder Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Krishna Kumar
- Structural Biology and Bioinformatics Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Chhabinath Mandal
- National Institute of Pharmaceutical Education and Research, Kolkata, India
| | - Peter Walden
- Department of Dermatology, Venerology and Allergology, Charité– Universitätsmedizin Berlin corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- * E-mail: (PW); , (SC); , (CM)
| | - Saikat Chakrabarti
- Structural Biology and Bioinformatics Division, Indian Institute of Chemical Biology, Kolkata, India
- * E-mail: (PW); , (SC); , (CM)
| | - Chitra Mandal
- Cancer Biology and Inflammatory Disorder Division, Indian Institute of Chemical Biology, Kolkata, India
- * E-mail: (PW); , (SC); , (CM)
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Silverbush D, Sharan R. A systematic approach to orient the human protein-protein interaction network. Nat Commun 2019; 10:3015. [PMID: 31289271 PMCID: PMC6617457 DOI: 10.1038/s41467-019-10887-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2018] [Accepted: 06/06/2019] [Indexed: 11/16/2022] Open
Abstract
The protein-protein interaction (PPI) network of an organism serves as a skeleton for its signaling circuitry, which mediates cellular response to environmental and genetic cues. Understanding this circuitry could improve the prediction of gene function and cellular behavior in response to diverse signals. To realize this potential, one has to comprehensively map PPIs and their directions of signal flow. While the quality and the volume of identified human PPIs improved dramatically over the last decade, the directions of these interactions are still mostly unknown, thus precluding subsequent prediction and modeling efforts. Here we present a systematic approach to orient the human PPI network using drug response and cancer genomic data. We provide a diffusion-based method for the orientation task that significantly outperforms existing methods. The oriented network leads to improved prioritization of cancer driver genes and drug targets compared to the state-of-the-art unoriented network.
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Affiliation(s)
- Dana Silverbush
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Roded Sharan
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, 69978, Israel.
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Jean-Quartier C, Jeanquartier F, Jurisica I, Holzinger A. In silico cancer research towards 3R. BMC Cancer 2018; 18:408. [PMID: 29649981 PMCID: PMC5897933 DOI: 10.1186/s12885-018-4302-0] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Accepted: 03/26/2018] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Improving our understanding of cancer and other complex diseases requires integrating diverse data sets and algorithms. Intertwining in vivo and in vitro data and in silico models are paramount to overcome intrinsic difficulties given by data complexity. Importantly, this approach also helps to uncover underlying molecular mechanisms. Over the years, research has introduced multiple biochemical and computational methods to study the disease, many of which require animal experiments. However, modeling systems and the comparison of cellular processes in both eukaryotes and prokaryotes help to understand specific aspects of uncontrolled cell growth, eventually leading to improved planning of future experiments. According to the principles for humane techniques milestones in alternative animal testing involve in vitro methods such as cell-based models and microfluidic chips, as well as clinical tests of microdosing and imaging. Up-to-date, the range of alternative methods has expanded towards computational approaches, based on the use of information from past in vitro and in vivo experiments. In fact, in silico techniques are often underrated but can be vital to understanding fundamental processes in cancer. They can rival accuracy of biological assays, and they can provide essential focus and direction to reduce experimental cost. MAIN BODY We give an overview on in vivo, in vitro and in silico methods used in cancer research. Common models as cell-lines, xenografts, or genetically modified rodents reflect relevant pathological processes to a different degree, but can not replicate the full spectrum of human disease. There is an increasing importance of computational biology, advancing from the task of assisting biological analysis with network biology approaches as the basis for understanding a cell's functional organization up to model building for predictive systems. CONCLUSION Underlining and extending the in silico approach with respect to the 3Rs for replacement, reduction and refinement will lead cancer research towards efficient and effective precision medicine. Therefore, we suggest refined translational models and testing methods based on integrative analyses and the incorporation of computational biology within cancer research.
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Affiliation(s)
- Claire Jean-Quartier
- Holzinger Group, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria
| | - Fleur Jeanquartier
- Holzinger Group, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria
- Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria
| | - Igor Jurisica
- Krembil Research Institute, University Health Network; Depts. of Medical Bioph. and Comp. Sci., University of Toronto; Institute of Neuroimmunology, Slovak Academy of Sciences, Toronto, Canada
| | - Andreas Holzinger
- Holzinger Group, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria
- Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria
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Cell signaling heterogeneity is modulated by both cell-intrinsic and -extrinsic mechanisms: An integrated approach to understanding targeted therapy. PLoS Biol 2018. [PMID: 29522507 PMCID: PMC5844524 DOI: 10.1371/journal.pbio.2002930] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
During the last decade, our understanding of cancer cell signaling networks has significantly improved, leading to the development of various targeted therapies that have elicited profound but, unfortunately, short-lived responses. This is, in part, due to the fact that these targeted therapies ignore context and average out heterogeneity. Here, we present a mathematical framework that addresses the impact of signaling heterogeneity on targeted therapy outcomes. We employ a simplified oncogenic rat sarcoma (RAS)-driven mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase-protein kinase B (PI3K-AKT) signaling pathway in lung cancer as an experimental model system and develop a network model of the pathway. We measure how inhibition of the pathway modulates protein phosphorylation as well as cell viability under different microenvironmental conditions. Training the model on this data using Monte Carlo simulation results in a suite of in silico cells whose relative protein activities and cell viability match experimental observation. The calibrated model predicts distributional responses to kinase inhibitors and suggests drug resistance mechanisms that can be exploited in drug combination strategies. The suggested combination strategies are validated using in vitro experimental data. The validated in silico cells are further interrogated through an unsupervised clustering analysis and then integrated into a mathematical model of tumor growth in a homogeneous and resource-limited microenvironment. We assess posttreatment heterogeneity and predict vast differences across treatments with similar efficacy, further emphasizing that heterogeneity should modulate treatment strategies. The signaling model is also integrated into a hybrid cellular automata (HCA) model of tumor growth in a spatially heterogeneous microenvironment. As a proof of concept, we simulate tumor responses to targeted therapies in a spatially segregated tissue structure containing tumor and stroma (derived from patient tissue) and predict complex cell signaling responses that suggest a novel combination treatment strategy. A signaling pathway is a network of molecules in a cell that is typically initiated by stimuli (e.g., microenvironmental cues) acting on receptors and internal signaling molecules to determine cell fate. Signaling pathways in cancer cells are different from those in normal cells, and this difference helps cancer cells to grow and thrive indefinitely. Drugs that target the aberrant signaling pathways in cancer cells (often referred to as targeted therapy) are promising for improving treatment outcomes of many different cancers in patients. However, most patients eventually develop resistance to these drugs. Resistance may already be present in the tumor or may emerge via mutation or via microenvironmental mediation. Tumor heterogeneity, which is characterized by subtle or dramatic differences among tumor cells, plays a key role in the development of drug resistance. Some tumor cells respond well to therapy, while others may adapt to the stress induced by the drug within the microenvironment. Moreover, removal of drug-sensitive cells may result in the competitive release of drug-resistant cells. Here, we present mathematical models to assess the impact of heterogeneity in signaling pathways within tumor cells on the outcomes of targeted therapy. We consider a simplified version of two well-known signaling pathways that modulate the growth of lung cancer cells. By using different targeted therapies, we quantify the effect of pathway inhibition on protein activity and cell viability and developed a mathematical model of the network, which is trained to reproduce these data and to develop a panel of heterogeneous in silico cells. The model predicts potential mechanisms of drug resistance and proposes combination therapies that are effective across the panel. We validate these combination therapies experimentally using the lung cancer cells and integrated the in silico cells into a computational lung tissue model that explicitly captures the microenvironment of lung cancer. Our results suggest that heterogeneity in both the tumor and microenvironment impacts treatment response in different ways and suggest a novel combination therapy for a better response.
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Nava-Sedeño JM, Hatzikirou H, Klages R, Deutsch A. Cellular automaton models for time-correlated random walks: derivation and analysis. Sci Rep 2017; 7:16952. [PMID: 29209065 PMCID: PMC5717221 DOI: 10.1038/s41598-017-17317-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 11/22/2017] [Indexed: 11/09/2022] Open
Abstract
Many diffusion processes in nature and society were found to be anomalous, in the sense of being fundamentally different from conventional Brownian motion. An important example is the migration of biological cells, which exhibits non-trivial temporal decay of velocity autocorrelation functions. This means that the corresponding dynamics is characterized by memory effects that slowly decay in time. Motivated by this we construct non-Markovian lattice-gas cellular automata models for moving agents with memory. For this purpose the reorientation probabilities are derived from velocity autocorrelation functions that are given a priori; in that respect our approach is “data-driven”. Particular examples we consider are velocity correlations that decay exponentially or as power laws, where the latter functions generate anomalous diffusion. The computational efficiency of cellular automata combined with our analytical results paves the way to explore the relevance of memory and anomalous diffusion for the dynamics of interacting cell populations, like confluent cell monolayers and cell clustering.
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Affiliation(s)
- J M Nava-Sedeño
- Technische Universität Dresden, Center for Information Services and High Performance Computing, Nöthnitzer Straße 46, 01062, Dresden, Germany.
| | - H Hatzikirou
- Technische Universität Dresden, Center for Information Services and High Performance Computing, Nöthnitzer Straße 46, 01062, Dresden, Germany.,Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Center for Infection Research, Inhoffenstraße 7, 38124, Braunschweig, Germany
| | - R Klages
- School of Mathematical Sciences, Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom
| | - A Deutsch
- Technische Universität Dresden, Center for Information Services and High Performance Computing, Nöthnitzer Straße 46, 01062, Dresden, Germany
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7
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Zhou L, Wu F, Jin W, Yan B, Chen X, He Y, Yang W, Du W, Zhang Q, Guo Y, Yuan Q, Dong X, Yu W, Zhang J, Xiao L, Tong P, Shan L, Efferth T. Theabrownin Inhibits Cell Cycle Progression and Tumor Growth of Lung Carcinoma through c-myc-Related Mechanism. Front Pharmacol 2017; 8:75. [PMID: 28289384 PMCID: PMC5326752 DOI: 10.3389/fphar.2017.00075] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2016] [Accepted: 02/06/2017] [Indexed: 12/12/2022] Open
Abstract
Green tea, the fresh leaves of Camellia sinensis, is not only a health-promoting beverage but also a traditional Chinese medicine used for prevention or treatment of cancer, such as lung cancer. Theabrownin (TB) is the main fraction responsible for the medicinal effects of green tea, but whether it possesses anti-cancer effect is unknown yet. This study aimed to determine the in vitro and in vivo anti-lung cancer effect of TB and explore the underlying molecular mechanism, by using A549 cell line and Lewis lung carcinoma-bearing mice. In cellular experiment, MTT assay was performed to evaluate the inhibitory effect and IC50 values of TB, and flow cytometry was conducted to analyze the cell cycle progression affected by TB. In animal experiment, mice body mass, tumor incidence, tumor size and tumor weight were measured, and histopathological analysis on tumor was performed with Transferase dUTP nick-end labeling staining. Real time PCR and western blot assays were adopted to detect the expression of C-MYC associated genes and proteins for mechanism clarification. TB was found to inhibit A549 cell viability in a dose- and time-dependent manner and block A549 cell cycle at G0/G1 phase. Down-regulation of c-myc, cyclin A, cyclin D, cdk2, cdk4, proliferation of cell nuclear antigen and up-regulation of p21, p27, and phosphate and tension homolog in both gene and protein levels were observed with TB treatment. A c-myc-related mechanism was thereby proposed, since c-myc could transcriptionally regulate all other genes in its downstream region for G1/S transitions of cell cycle and proliferation of cancer cells. This is the first report regarding the anti-NSCLC effect and the underlying mechanism of TB on cell cycle progression and proliferation of A549 cells. The in vivo data verified the in vitro result that TB could significantly inhibit the lung cancer growth in mice and induce apoptosis on tumors in a dose-dependent manner. It provides a promising candidate of natural products for lung cancer therapy and new development of anti-cancer agent.
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Affiliation(s)
- Li Zhou
- Institute of Orthopaedics and Traumatology, Zhejiang Chinese Medical UniversityHangzhou, China
| | - Feifei Wu
- Institute for Cell-Based Drug Development of Zhejiang Province, S-Evans Biosciences Inc.Hangzhou, China
| | - Wangdong Jin
- Institute of Orthopaedics and Traumatology, Zhejiang Chinese Medical UniversityHangzhou, China
| | - Bo Yan
- Institute of Orthopaedics and Traumatology, Zhejiang Chinese Medical UniversityHangzhou, China
| | - Xin Chen
- Institute of Orthopaedics and Traumatology, Zhejiang Chinese Medical UniversityHangzhou, China
| | - Yingfei He
- Institute of Orthopaedics and Traumatology, Zhejiang Chinese Medical UniversityHangzhou, China
| | - Weiji Yang
- Institute of Orthopaedics and Traumatology, Zhejiang Chinese Medical UniversityHangzhou, China
| | - Wenlin Du
- Institute for Cell-Based Drug Development of Zhejiang Province, S-Evans Biosciences Inc.Hangzhou, China
| | - Qiang Zhang
- Institute for Cell-Based Drug Development of Zhejiang Province, S-Evans Biosciences Inc.Hangzhou, China
| | - Yonghua Guo
- Institute for Cell-Based Drug Development of Zhejiang Province, S-Evans Biosciences Inc.Hangzhou, China
| | - Qiang Yuan
- The Second Clinical Medical College, Zhejiang Chinese Medical UniversityHangzhou, China
| | | | - Wenhua Yu
- Hangzhou First People’s HospitalHangzhou, China
| | | | - Luwei Xiao
- Institute of Orthopaedics and Traumatology, Zhejiang Chinese Medical UniversityHangzhou, China
| | - Peijian Tong
- Institute of Orthopaedics and Traumatology, Zhejiang Chinese Medical UniversityHangzhou, China
| | - Letian Shan
- Institute of Orthopaedics and Traumatology, Zhejiang Chinese Medical UniversityHangzhou, China
- Institute for Cell-Based Drug Development of Zhejiang Province, S-Evans Biosciences Inc.Hangzhou, China
| | - Thomas Efferth
- Department of Pharmaceutical Biology, Institute of Pharmacy and Biochemistry, Johannes Gutenberg University of MainzMainz, Germany
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Soliman M, Nasraoui O, Cooper NGF. Building a glaucoma interaction network using a text mining approach. BioData Min 2016; 9:17. [PMID: 27152122 PMCID: PMC4857381 DOI: 10.1186/s13040-016-0096-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Accepted: 04/23/2016] [Indexed: 11/21/2022] Open
Abstract
Background The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease. Results A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx. Conclusions This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of relations that could not be found in existing interaction databases and that were found to be new, in addition to a smaller subnetwork consisting of interconnected clusters of seven glaucoma genes. Future improvements can be applied towards obtaining a better version of this network. Electronic supplementary material The online version of this article (doi:10.1186/s13040-016-0096-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Maha Soliman
- Department of Anatomical Sciences and Neurobiology, University of Louisville, School of Medicine, Louisville, KY USA
| | - Olfa Nasraoui
- Knowledge Discovery & Web Mining Lab, Department of Computer Engineering & Computer Science, University of Louisville, J.B Speed School of Engineering, Louisville, KY USA
| | - Nigel G F Cooper
- Department of Anatomical Sciences and Neurobiology, University of Louisville, School of Medicine, Louisville, KY USA
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Pastrello C, Pasini E, Kotlyar M, Otasek D, Wong S, Sangrar W, Rahmati S, Jurisica I. Integration, visualization and analysis of human interactome. Biochem Biophys Res Commun 2014; 445:757-73. [DOI: 10.1016/j.bbrc.2014.01.151] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2013] [Accepted: 01/24/2014] [Indexed: 02/06/2023]
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Kuczenski RS, Aggarwal K, Lee KH. Improved understanding of gene expression regulation using systems biology. Expert Rev Proteomics 2014; 2:915-24. [PMID: 16307520 DOI: 10.1586/14789450.2.6.915] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This article reviews the current state of systems biology approaches, including the experimental tools used to generate 'omic' data and computational frameworks to interpret this data. Through illustrative examples, systems biology approaches to understand gene expression and gene expression regulation are discussed. Some of the challenges facing this field and the future opportunities in the systems biology era are highlighted.
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Affiliation(s)
- Robert S Kuczenski
- Cornell University, School of Chemical & Biomolecular Engineering, 120 Olin Hall, Ithaca, NY 14853, USA.
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Molinelli EJ, Korkut A, Wang W, Miller ML, Gauthier NP, Jing X, Kaushik P, He Q, Mills G, Solit DB, Pratilas CA, Weigt M, Braunstein A, Pagnani A, Zecchina R, Sander C. Perturbation biology: inferring signaling networks in cellular systems. PLoS Comput Biol 2013; 9:e1003290. [PMID: 24367245 PMCID: PMC3868523 DOI: 10.1371/journal.pcbi.1003290] [Citation(s) in RCA: 91] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2012] [Accepted: 08/26/2013] [Indexed: 12/16/2022] Open
Abstract
We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. Drugs that target specific effects of signaling proteins are promising agents for treating cancer. One of the many obstacles facing optimal drug design is inadequate quantitative understanding of the coordinated interactions between signaling proteins. De novo model inference of network or pathway models refers to the algorithmic construction of mathematical predictive models from experimental data without dependence on prior knowledge. De novo inference is difficult because of the prohibitively large number of possible sets of interactions that may or may not be consistent with observations. Our new method overcomes this difficulty by adapting a method from statistical physics, called Belief Propagation, which first calculates probabilistically the most likely interactions in the vast space of all possible solutions, then derives a set of individual, highly probable solutions in the form of executable models. In this paper, we test this method on artificial data and then apply it to model signaling pathways in a BRAF-mutant melanoma cancer cell line based on a large set of rich output measurements from a systematic set of perturbation experiments using drug combinations. Our results are in agreement with established biological knowledge, predict novel interactions, and predict efficacious drug targets that are specific to the experimental cell line and potentially to related tumors. The method has the potential, with sufficient systematic perturbation data, to model, de novo and quantitatively, the effects of hundreds of proteins on cellular responses, on a scale that is currently unreachable in diverse areas of cell biology. In a disease context, the method is applicable to the computational design of novel combination drug treatments.
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Affiliation(s)
- Evan J. Molinelli
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- Tri-Institutional Program for Computational Biology and Medicine, Weill Cornell Medical College, New York, New York, United States of America
| | - Anil Korkut
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Weiqing Wang
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Martin L. Miller
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Nicholas P. Gauthier
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Xiaohong Jing
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Poorvi Kaushik
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- Tri-Institutional Program for Computational Biology and Medicine, Weill Cornell Medical College, New York, New York, United States of America
| | - Qin He
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Gordon Mills
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - David B. Solit
- Program in Molecular Pharmacology, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Christine A. Pratilas
- Program in Molecular Pharmacology, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- Department of Pediatrics, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Martin Weigt
- Laboratoire de Génomique des Microorganismes, Université Pierre et Marie Curie, Paris, France
| | - Alfredo Braunstein
- Politecnico di Torino and Human Genetics Foundation, HuGeF, Torino, Italy
| | - Andrea Pagnani
- Politecnico di Torino and Human Genetics Foundation, HuGeF, Torino, Italy
| | - Riccardo Zecchina
- Politecnico di Torino and Human Genetics Foundation, HuGeF, Torino, Italy
| | - Chris Sander
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
- * E-mail:
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12
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Wang CZ, Calway TD, Wen XD, Smith J, Yu C, Wang Y, Mehendale SR, Yuan CS. Hydrophobic flavonoids from Scutellaria baicalensis induce colorectal cancer cell apoptosis through a mitochondrial-mediated pathway. Int J Oncol 2013; 42:1018-26. [PMID: 23337959 PMCID: PMC3576930 DOI: 10.3892/ijo.2013.1777] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2012] [Accepted: 11/22/2012] [Indexed: 01/30/2023] Open
Abstract
Scutellaria baicalensis extract (SbE) has been shown to exert chemopreventive effects on several types of cancer. Baicalin, a hydrophilic flavonoid found in SbE, may have opposing effects that decrease the antitumor potential of SbE against colorectal cancer. In this study, after removing baicalin, we prepared an aglycone-rich fraction (ARF) of SbE and evaluated its anti-proliferative activity and mechanisms of action. The flavonoids found in ARF, baicalin fraction (BF) and SbE were determined by high-performance liquid chromatography (HPLC). The effects of ARF, BF, SbE and representative flavonoids on the proliferation of HCT-116 and HT-29 human colorectal cancer cells were determined by an MTS assay. The cell cycle, the expression of cyclins A and B1 and cell apoptosis were assayed using flow cytometry. Apoptosis-related gene expression was visualized by quantitative real-time polymerase chain reaction (PCR), and mitochondrial membrane potential was estimated following staining with JC-1. HPLC analysis showed that ARF contained two hydrophobic flavonoids, baicalein and wogonin, and that BF contained only baicalin. SbE had little anti-proliferative effect on the colorectal cancer cells; cancer cell growth was even observed at certain concentrations. ARF exerted potent anti-proliferative effects on the cancer cells. By contrast, BF increased cancer cell growth. ARF arrested cells in the S and G2/M phases, increased the expression of cyclins A and B1, and significantly induced cell apoptosis. Multiple genes in the mitochondrial pathway are involved in ARF-induced apoptosis, and subsequent cellular functional analysis validated the involvement of this pathway. These results suggest that removing baicalin from SbE produces an ARF that significantly inhibits the growth of colorectal cancer cells, and that the mitochondrial apoptotic pathway plays a role in hydrophobic flavonoid-induced apoptosis.
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Affiliation(s)
- Chong-Zhi Wang
- Tang Center for Herbal Medicine Research, and Department of Anesthesia and Critical Care, University of Chicago, Chicago, IL 60637, USA.
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13
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Martin KR, Barua D, Kauffman AL, Westrate LM, Posner RG, Hlavacek WS, MacKeigan JP. Computational model for autophagic vesicle dynamics in single cells. Autophagy 2013; 9:74-92. [PMID: 23196898 PMCID: PMC3542220 DOI: 10.4161/auto.22532] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Macroautophagy (autophagy) is a cellular recycling program essential for homeostasis and survival during cytotoxic stress. This process, which has an emerging role in disease etiology and treatment, is executed in four stages through the coordinated action of more than 30 proteins. An effective strategy for studying complicated cellular processes, such as autophagy, involves the construction and analysis of mathematical or computational models. When developed and refined from experimental knowledge, these models can be used to interrogate signaling pathways, formulate novel hypotheses about systems, and make predictions about cell signaling changes induced by specific interventions. Here, we present the development of a computational model describing autophagic vesicle dynamics in a mammalian system. We used time-resolved, live-cell microscopy to measure the synthesis and turnover of autophagic vesicles in single cells. The stochastically simulated model was consistent with data acquired during conditions of both basal and chemically-induced autophagy. The model was tested by genetic modulation of autophagic machinery and found to accurately predict vesicle dynamics observed experimentally. Furthermore, the model generated an unforeseen prediction about vesicle size that is consistent with both published findings and our experimental observations. Taken together, this model is accurate and useful and can serve as the foundation for future efforts aimed at quantitative characterization of autophagy.
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Affiliation(s)
- Katie R. Martin
- Laboratory of Systems Biology; Van Andel Research Institute; Grand Rapids, MI USA
| | - Dipak Barua
- Center for Nonlinear Studies and Theoretical Biology and Biophysics Group; Theoretical Division; Los Alamos National Laboratory; Los Alamos, NM USA
| | - Audra L. Kauffman
- Laboratory of Systems Biology; Van Andel Research Institute; Grand Rapids, MI USA
| | - Laura M. Westrate
- Laboratory of Systems Biology; Van Andel Research Institute; Grand Rapids, MI USA
- Van Andel Institute Graduate School; Grand Rapids, MI USA
| | - Richard G. Posner
- Clinical Translational Research Division; Translational Genomics Research Institute; Scottsdale, AZ USA
| | - William S. Hlavacek
- Center for Nonlinear Studies and Theoretical Biology and Biophysics Group; Theoretical Division; Los Alamos National Laboratory; Los Alamos, NM USA
- Clinical Translational Research Division; Translational Genomics Research Institute; Scottsdale, AZ USA
| | - Jeffrey P. MacKeigan
- Laboratory of Systems Biology; Van Andel Research Institute; Grand Rapids, MI USA
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14
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Majumder D, Mukherjee A. A passage through systems biology to systems medicine: adoption of middle-out rational approaches towards the understanding of therapeutic outcomes in cancer. Analyst 2011; 136:663-78. [DOI: 10.1039/c0an00746c] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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15
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Using a Systems Biology Approach to Explore Hypotheses Underlying Clinical Diversity of the Renin Angiotensin System and the Response to Antihypertensive Therapies. ACTA ACUST UNITED AC 2010. [DOI: 10.1007/978-1-4419-7415-0_20] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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16
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Zhang R, Monsma F. Binding kinetics and mechanism of action: toward the discovery and development of better and best in class drugs. Expert Opin Drug Discov 2010; 5:1023-9. [PMID: 22827742 DOI: 10.1517/17460441.2010.520700] [Citation(s) in RCA: 75] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Binding kinetics (BK), an often overlooked key aspect of the broader concept of drug mechanism of action (MOA), is increasingly recognized as a springboard from pharmacokinetics (PK) to pharmacodynamics, and as a critical differentiator and predictor for drug efficacy and safety. Just as greater attention to PK issues has helped reduce the attrition of drugs tested in clinical trials, the emerging paradigm shift from primarily affinity/potency-emphasized to a more holistic BK-perceptive and MOA-informed approach is expected to further enhance the success of drug discovery and development. This perspective attempts to envision what this new approach looks like when proper emphasis is placed on BK and MOA in designing better and best in class drugs.
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Affiliation(s)
- Rumin Zhang
- Merck Research Laboratories, In Vitro Pharmacology, 2015 Galloping Hill Road, Kenilworth, NJ 07033, USA.
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17
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Edelman LB, Eddy JA, Price ND. In silico models of cancer. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2010; 2:438-459. [PMID: 20836040 PMCID: PMC3157287 DOI: 10.1002/wsbm.75] [Citation(s) in RCA: 82] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Cancer is a complex disease that involves multiple types of biological interactions across diverse physical, temporal, and biological scales. This complexity presents substantial challenges for the characterization of cancer biology, and motivates the study of cancer in the context of molecular, cellular, and physiological systems. Computational models of cancer are being developed to aid both biological discovery and clinical medicine. The development of these in silico models is facilitated by rapidly advancing experimental and analytical tools that generate information-rich, high-throughput biological data. Statistical models of cancer at the genomic, transcriptomic, and pathway levels have proven effective in developing diagnostic and prognostic molecular signatures, as well as in identifying perturbed pathways. Statistically inferred network models can prove useful in settings where data overfitting can be avoided, and provide an important means for biological discovery. Mechanistically based signaling and metabolic models that apply a priori knowledge of biochemical processes derived from experiments can also be reconstructed where data are available, and can provide insight and predictive ability regarding the behavior of these systems. At longer length scales, continuum and agent-based models of the tumor microenvironment and other tissue-level interactions enable modeling of cancer cell populations and tumor progression. Even though cancer has been among the most-studied human diseases using systems approaches, significant challenges remain before the enormous potential of in silico cancer biology can be fully realized.
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Affiliation(s)
- Lucas B. Edelman
- Institute for Genomic Biology, Department of Bioengineering, University of Illinois, Urbana-Champaign
| | - James A. Eddy
- Institute for Genomic Biology, Department of Bioengineering, University of Illinois, Urbana-Champaign
| | - Nathan D. Price
- Department of Chemical and Biomolecular Engineering, Institute for Genomic Biology, Center for Biophysics and Computational Biology, University of Illinois, Urbana-Champaign
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18
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Vital-Lopez FG, Armaou A, Hutnik M, Maranas CD. Modeling the effect of chemotaxis on glioblastoma tumor progression. AIChE J 2010. [DOI: 10.1002/aic.12296] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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19
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Gökmen-Polar Y, Badve S. Promise of computational systems biology for cancer clinical trials: the voyage to be realized? Per Med 2010; 7:129-131. [PMID: 29783315 DOI: 10.2217/pme.09.71] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Affiliation(s)
- Yesim Gökmen-Polar
- Department of Medicine/Hematology/Oncology, Indiana University School of Medicine, IN, USA
| | - Sunil Badve
- Signature Center for Systems Biology & Personalized Medicine, Departments of Pathology & Laboratory Medicine & Internal Medicine, Indiana University School of Medicine, Clarian Pathology Laboratory, CPL-4050, 350 West 11th Street, Indianapolis, IN 46202, USA.
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20
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Edelman LB, Eddy JA, Price ND. In silico models of cancer. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2010; 2. [PMID: 20836040 PMCID: PMC3157287 DOI: 10.1002/wsbm.75 10.1002/wsbm.75] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Cancer is a complex disease that involves multiple types of biological interactions across diverse physical, temporal, and biological scales. This complexity presents substantial challenges for the characterization of cancer biology, and motivates the study of cancer in the context of molecular, cellular, and physiological systems. Computational models of cancer are being developed to aid both biological discovery and clinical medicine. The development of these in silico models is facilitated by rapidly advancing experimental and analytical tools that generate information-rich, high-throughput biological data. Statistical models of cancer at the genomic, transcriptomic, and pathway levels have proven effective in developing diagnostic and prognostic molecular signatures, as well as in identifying perturbed pathways. Statistically inferred network models can prove useful in settings where data overfitting can be avoided, and provide an important means for biological discovery. Mechanistically based signaling and metabolic models that apply a priori knowledge of biochemical processes derived from experiments can also be reconstructed where data are available, and can provide insight and predictive ability regarding the behavior of these systems. At longer length scales, continuum and agent-based models of the tumor microenvironment and other tissue-level interactions enable modeling of cancer cell populations and tumor progression. Even though cancer has been among the most-studied human diseases using systems approaches, significant challenges remain before the enormous potential of in silico cancer biology can be fully realized.
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Affiliation(s)
- Lucas B. Edelman
- Institute for Genomic Biology, Department of Bioengineering, University of Illinois, Urbana-Champaign
| | - James A. Eddy
- Institute for Genomic Biology, Department of Bioengineering, University of Illinois, Urbana-Champaign
| | - Nathan D. Price
- Department of Chemical and Biomolecular Engineering, Institute for Genomic Biology, Center for Biophysics and Computational Biology, University of Illinois, Urbana-Champaign
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21
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Laubenbacher R, Hower V, Jarrah A, Torti SV, Shulaev V, Mendes P, Torti FM, Akman S. A systems biology view of cancer. BIOCHIMICA ET BIOPHYSICA ACTA 2009; 1796:129-39. [PMID: 19505535 PMCID: PMC2782452 DOI: 10.1016/j.bbcan.2009.06.001] [Citation(s) in RCA: 47] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/20/2008] [Revised: 05/20/2009] [Accepted: 06/01/2009] [Indexed: 12/11/2022]
Abstract
In order to understand how a cancer cell is functionally different from a normal cell it is necessary to assess the complex network of pathways involving gene regulation, signaling, and cell metabolism, and the alterations in its dynamics caused by the several different types of mutations leading to malignancy. Since the network is typically complex, with multiple connections between pathways and important feedback loops, it is crucial to represent it in the form of a computational model that can be used for a rigorous analysis. This is the approach of systems biology, made possible by new -omics data generation technologies. The goal of this review is to illustrate this approach and its utility for our understanding of cancer. After a discussion of recent progress using a network-centric approach, three case studies related to diagnostics, therapy, and drug development are presented in detail. They focus on breast cancer, B-cell lymphomas, and colorectal cancer. The discussion is centered on key mathematical and computational tools common to a systems biology approach.
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Affiliation(s)
- Reinhard Laubenbacher
- Virginia Bioinformatics Institute, Washington St. (0477), Blacksburg, VA 24061, USA.
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22
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Li XL, Wang CZ, Mehendale SR, Sun S, Wang Q, Yuan CS. Panaxadiol, a purified ginseng component, enhances the anti-cancer effects of 5-fluorouracil in human colorectal cancer cells. Cancer Chemother Pharmacol 2009; 64:1097-104. [DOI: 10.1007/s00280-009-0966-0] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2008] [Accepted: 02/20/2009] [Indexed: 12/27/2022]
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23
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Reumann M, Gurev V, Rice JJ. Computational modeling of cardiac disease: potential for personalized medicine. Per Med 2009; 6:45-66. [DOI: 10.2217/17410541.6.1.45] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Cardiovascular diseases are leading causes of death, reduce life quality and consume almost half a trillion dollars in healthcare expenses in the USA alone. Cardiac modeling and simulation technologies hold promise as important tools to improve cardiac care and are already in use to elucidate the fundamental mechanisms of cardiac physiology and pathophysiology. However, the emphasis has been on simulating average or exemplar cases. This report describes two classes of cardiac modeling efforts. First, electrophysiological models of channelopathies simulate the altered electrical activity that is thought to promote arrhythmias. Second, electromechanical models attempt to capture both the electrophysiological and mechanical aspects of heart function. One goal of the community is to develop models with sufficient patient customization to assist in personalized treatment planning. Some model aspects can be customized with existing data collection techniques to more closely represent individual patients while other model aspects will likely remain based on generic data. Despite important challenges, cardiac models hold promise to be important enablers of personalized medicine.
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Affiliation(s)
- Matthias Reumann
- Functional Genomics and Systems Biology, IBM T.J. Watson Research Center, PO Box 218, Yorktown Heights, NY 10598, USA
| | - Viatcheslav Gurev
- Department of Biomedical Engineering and Institute for Computational Medicine, The Johns Hopkins University, MD, USA
| | - John Jeremy Rice
- Functional Genomics and Systems Biology, IBM T.J. Watson Research Center, PO Box 218, Yorktown Heights, NY 10598, USA
- Department of Biomedical Engineering and Institute for Computational Medicine, The Johns Hopkins University, MD, USA
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24
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Implication of dynamics in signal transduction and targeted disruption analyses of signaling networks. Comput Chem Eng 2008. [DOI: 10.1016/j.compchemeng.2007.10.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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25
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Abstract
This article provides an overview of principles and barriers relevant to intracellular drug and gene transport, accumulation and retention (collectively called as drug delivery) by means of nanovehicles (NV). The aim is to deliver a cargo to a particular intracellular site, if possible, to exert a local action. Some of the principles discussed in this article apply to noncolloidal drugs that are not permeable to the plasma membrane or to the blood-brain barrier. NV are defined as a wide range of nanosized particles leading to colloidal objects which are capable of entering cells and tissues and delivering a cargo intracelullarly. Different localization and targeting means are discussed. Limited discussion on pharmacokinetics and pharmacodynamics is also presented. NVs are contrasted to micro-delivery and current nanotechnologies which are already in commercial use. Newer developments in NV technologies are outlined and future applications are stressed. We also briefly review the existing modeling tools and approaches to quantitatively describe the behavior of targeted NV within the vascular and tumor compartments, an area of particular importance. While we list "elementary" phenomena related to different level of complexity of delivery to cancer, we also stress importance of multi-scale modeling and bottom-up systems biology approach.
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Affiliation(s)
- Ales Prokop
- Department of Chemical Engineering, 24th Avenue & Garland Avenues, 107 Olin Hall, Vanderbilt University, Nashville, Tennessee 37235, USA.
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26
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A computational model of chemotaxis-based cell aggregation. Biosystems 2008; 93:226-39. [PMID: 18602744 DOI: 10.1016/j.biosystems.2008.05.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2007] [Revised: 04/17/2008] [Accepted: 05/14/2008] [Indexed: 11/21/2022]
Abstract
We present a computational model that successfully captures the cell behaviors that play important roles in 2-D cell aggregation. A virtual cell in our model is designed as an independent, discrete unit with a set of parameters and actions. Each cell is defined by its location, size, rates of chemoattractant emission and response, age, life cycle stage, proliferation rate and number of attached cells. All cells are capable of emitting and sensing a chemoattractant chemical, moving, attaching to other cells, dividing, aging and dying. We validated and fine-tuned our in silico model by comparing simulated 24-h aggregation experiments with data derived from in vitro experiments using PC12 pheochromocytoma cells. Quantitative comparisons of the aggregate size distributions from the two experiments are produced using the Earth Mover's Distance (EMD) metric. We compared the two size distributions produced after 24 h of in vitro cell aggregation and the corresponding computer simulated process. Iteratively modifying the model's parameter values and measuring the difference between the in silico and in vitro results allow us to determine the optimal values that produce simulated aggregation outcomes closely matched to the PC12 experiments. Simulation results demonstrate the ability of the model to recreate large-scale aggregation behaviors seen in live cell experiments.
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27
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Shen Y, Senzer NN, Nemunaitis JJ. Use of Proteomics Analysis for Molecular Precision Approaches in Cancer Therapy. Drug Target Insights 2008. [DOI: 10.4137/dti.s649] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Affiliation(s)
| | - Neil N. Senzer
- LEAD Therapeutics, Inc., San Bruno, CA
- Mary Crowley Cancer Research Centers, Dallas, TX
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28
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Faratian D, Moodie SL, Harrison DJ, Goryanin I. Dynamic computational modeling in the search for better breast cancer drug therapy. Pharmacogenomics 2007; 8:1757-61. [DOI: 10.2217/14622416.8.12.1757] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Breast cancer is an excellent disease paradigm for systems biology. At the time of writing, a simple PubMed search for ‘breast cancer’ returns nearly 99,000 hits, compared with 51,000 or 16,000 for lung and colon cancer respectively, even though in terms of mortality lung and colon cancers are responsible for four-times more deaths per annum in the UK. These figures reflect the effort and money invested in breast cancer research. It is because breast cancer research is data-rich, crowded and competitive (often perceived as a negative for clinical and basic scientific researchers) that it is such an appealing area of research for systems biologists. For systems biologists, data is currency, and they scavenge diverse and multilayered datasets, from biochemical through genomics and transcriptomics to proteomics, in order to populate computational models. We discuss how dynamic modeling can be used as a tool for predicting responses to new and existing drugs, and what needs to be done to make systems biology a useful tool in the clinic.
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Affiliation(s)
- Dana Faratian
- Edinburgh Breakthrough Research Unit and Pathology, Cancer Research Centre, Crewe Road South, Edinburgh, EH4 2XR, UK
| | - Stuart L Moodie
- University of Edinburgh, Computational Systems Biology Group, Centre for Systems Biology at Edinburgh, Darwin Building, The King’s Buildings, Edinburgh, EH9 3JZ, UK
| | - David J Harrison
- Edinburgh Breakthrough Research Unit and Pathology, Cancer Research Centre, Crewe Road South, Edinburgh, EH4 2XR, UK
| | - Igor Goryanin
- University of Edinburgh, Computational Systems Biology Group, Centre for Systems Biology at Edinburgh, Darwin Building, The King’s Buildings, Edinburgh, EH9 3JZ, UK
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29
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Abstract
The identification, purification and characterization of cancer stem cells (CSCs) holds tremendous promise for improving the treatment of cancer. Mounting evidence is demonstrating that only certain tumour cells (i.e. the CSCs) can give rise to tumours when injected and that these purified cell populations generate heterogeneous tumours. While the cell of origin is still not determined definitively, specific molecular markers for populations containing these CSCs have been found for leukaemia, brain cancer and breast cancer, among others. Systems approaches, particularly molecular profiling, have proven to be of great utility for cancer diagnosis and characterization. These approaches also hold significant promise for identifying distinctive properties of the CSCs, and progress is already being made.
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30
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Price ND, Shmulevich I. Biochemical and statistical network models for systems biology. Curr Opin Biotechnol 2007; 18:365-70. [PMID: 17681779 PMCID: PMC2034526 DOI: 10.1016/j.copbio.2007.07.009] [Citation(s) in RCA: 42] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2007] [Accepted: 07/12/2007] [Indexed: 11/19/2022]
Abstract
The normal and abnormal behavior of a living cell is governed by complex networks of interacting biomolecules. Models of these networks allow us to make predictions about cellular behavior under a variety of environmental cues. In this review, we focus on two broad classes of such models: biochemical network models and statistical inference models. In particular, we discuss a number of modeling approaches in the context of the assumptions that they entail, the types of data required for their inference, and the range of their applicability.
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Affiliation(s)
| | - Ilya Shmulevich
- Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103
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31
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Nemunaitis J, Senzer N, Khalil I, Shen Y, Kumar P, Tong A, Kuhn J, Lamont J, Nemunaitis M, Rao D, Zhang YA, Zhou Y, Vorhies J, Maples P, Hill C, Shanahan D. Proof concept for clinical justification of network mapping for personalized cancer therapeutics. Cancer Gene Ther 2007; 14:686-95. [PMID: 17541424 DOI: 10.1038/sj.cgt.7701057] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
To identify signature targets associated with patient-specific cancer lesions based on tumor versus normal tissue differential protein and mRNA coexpression patterns for the purpose of synthesizing cancer-specific customized RNA interference knockdown therapeutics. Analysis of biopsied tissue involved two-dimensional difference in-gel electrophoresis (2D-DIGE) analysis coupled with MALDI-TOF/TOF mass spectrometry for proteomic assessment. Standard microarray techniques were utilized for mRNA analysis. Priority was assigned to overexpressed protein targets with co-overexpressed genes with a high likelihood of functional nodal centrality in the cancer network as defined by the interactive databases BIND, HPRD and ResNet. HPLC-grade small interfering RNA (siRNA) duplexes were utilized to assess knockdown of target proteins in expressive cell lines as measured by western blot. Seven patients with metastatic cancer underwent biopsy. One patient (RW001) had biopsies from two disease sites 10 months apart. Seven priority proteins were identified, one for each patient (RACK 1, Ras related nuclear protein, heat-shock 27 kDa protein 1, superoxide dismutase, enolase1, stathmin1 and cofilin1). Prioritized proteins in RW001 from the two disease sites over time were the same. We demonstrated >80% siRNA inhibition of RACK 1 and stathmin1 of inexpressive malignant cell lines with correlated cell kill. Identification of functionally relevant target gene fingerprints, unique to an individual's cancer, is feasible 'at the bedside' and can be utilized to synthesize siRNA knockdown therapeutics. Further animal safety testing followed by clinical study is recommended.
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Affiliation(s)
- J Nemunaitis
- Mary Crowley Medical Research Center, Dallas, TX, USA.
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32
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Wang CZ, Luo X, Zhang B, Song WX, Ni M, Mehendale S, Xie JT, Aung HH, He TC, Yuan CS. Notoginseng enhances anti-cancer effect of 5-fluorouracil on human colorectal cancer cells. Cancer Chemother Pharmacol 2007; 60:69-79. [PMID: 17009031 PMCID: PMC2657471 DOI: 10.1007/s00280-006-0350-2] [Citation(s) in RCA: 70] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2006] [Accepted: 09/06/2006] [Indexed: 01/29/2023]
Abstract
PURPOSE Panax notoginseng is a commonly used Chinese herb. Although a few studies have found that notoginseng shows anti-tumor effects, the effect of this herb on colorectal cancer cells has not been investigated. 5-Fluorouracil (5-FU) is a chemotherapeutic agent for the treatment of colorectal cancer that interferes with the growth of cancer cells. However, this compound has serious side effects at high doses. In this study, using HCT-116 human colorectal cancer cell line, we investigated the possible synergistic anti-cancer effects between notoginseng flower extract (NGF) and 5-FU on colon cancer cells. METHODS The anti-proliferation activity of these modes of treatment was evaluated by MTS cell proliferation assay. Apoptotic effects were analyzed by using Hoechst 33258 staining and Annexin-V/PI staining assays. The anti-proliferation effects of four major single compounds from NGF, ginsenosides Rb1, Rb3, Rc and Rg3 were also analyzed. RESULTS Both 5-FU and NGF inhibited proliferation of HCT-116 cells. With increasing doses of 5-FU, the anti-proliferation effect was slowly increased. The combined usage of 5-FU 5 microM and NGF 0.25 mg/ml, significantly increased the anti-proliferation effect (59.4 +/- 3.3%) compared with using the two medicines separately (5-FU 5 microM, 31.1 +/- 0.4%; NGF 0.25 mg/ml, 25.3 +/- 3.6%). Apoptotic analysis showed that at this concentration, 5-FU did not exert an apoptotic effect, while apoptotic cells induced by NGF were observed, suggesting that the anti-proliferation target(s) of NGF may be different from that of 5-FU, which is known to inhibit thymidilate synthase. CONCLUSIONS This study demonstrates that NGF can enhance the anti-proliferation effect of 5-FU on HCT-116 human colorectal cancer cells and may decrease the dosage of 5-FU needed for colorectal cancer treatment.
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Affiliation(s)
- Chong-Zhi Wang
- Tang Center for Herbal Medicine Research, The University of Chicago, 5841 South Maryland Avenue, MC 4028, Chicago, IL 60637, USA
- Department of Anesthesia and Critical Care, The University of Chicago, Chicago, IL 60637, USA
| | - Xiaoji Luo
- Molecular Oncology Laboratory, Department of Surgery, The University of Chicago Medical Center, Chicago, IL 60637, USA
| | - Bin Zhang
- Committee on Immunology and Department of Pathology, The University of Chicago, Chicago, IL 60637, USA
| | - Wen-Xin Song
- Molecular Oncology Laboratory, Department of Surgery, The University of Chicago Medical Center, Chicago, IL 60637, USA
| | - Ming Ni
- Tang Center for Herbal Medicine Research, The University of Chicago, 5841 South Maryland Avenue, MC 4028, Chicago, IL 60637, USA
- Department of Anesthesia and Critical Care, The University of Chicago, Chicago, IL 60637, USA
| | - Sangeeta Mehendale
- Tang Center for Herbal Medicine Research, The University of Chicago, 5841 South Maryland Avenue, MC 4028, Chicago, IL 60637, USA
- Department of Anesthesia and Critical Care, The University of Chicago, Chicago, IL 60637, USA
| | - Jing-Tian Xie
- Tang Center for Herbal Medicine Research, The University of Chicago, 5841 South Maryland Avenue, MC 4028, Chicago, IL 60637, USA
- Department of Anesthesia and Critical Care, The University of Chicago, Chicago, IL 60637, USA
| | - Han H. Aung
- Tang Center for Herbal Medicine Research, The University of Chicago, 5841 South Maryland Avenue, MC 4028, Chicago, IL 60637, USA
- Department of Anesthesia and Critical Care, The University of Chicago, Chicago, IL 60637, USA
| | - Tong-Chuan He
- Molecular Oncology Laboratory, Department of Surgery, The University of Chicago Medical Center, Chicago, IL 60637, USA
| | - Chun-Su Yuan
- Tang Center for Herbal Medicine Research, The University of Chicago, 5841 South Maryland Avenue, MC 4028, Chicago, IL 60637, USA, e-mail:
- Department of Anesthesia and Critical Care, The University of Chicago, Chicago, IL 60637, USA
- Committee on Clinical Pharmacology and Pharmacogenomics, The University of Chicago, Chicago, IL 60637, USA
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Gintautas V, Hübler AW. Experimental evidence for mixed reality states in an interreality system. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2007; 75:057201. [PMID: 17677199 DOI: 10.1103/physreve.75.057201] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2006] [Indexed: 05/16/2023]
Abstract
We present experimental data on the limiting behavior of an interreality system comprising a virtual horizontally driven pendulum coupled to its real-world counterpart, where the interaction time scale is much shorter than the time scale of the dynamical system. We present experimental evidence that, if the physical parameters of the simplified virtual system match those of the real system within a certain tolerance, there is a transition from an uncorrelated dual reality state to a mixed reality state of the system in which the motion of the two pendula is highly correlated. The region in parameter space for stable solutions has an Arnold tongue structure for both the experimental data and a numerical simulation. As virtual systems better approximate real ones, even weak coupling in other interreality systems may produce sudden changes to mixed reality states.
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Affiliation(s)
- Vadas Gintautas
- Center for Complex Systems Research, Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.
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Assmus HE, Herwig R, Cho KH, Wolkenhauer O. Dynamics of biological systems: role of systems biology in medical research. Expert Rev Mol Diagn 2007; 6:891-902. [PMID: 17140376 DOI: 10.1586/14737159.6.6.891] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Cellular systems are networks of interacting components that change with time in response to external and internal events. Studying the dynamic behavior of these networks is the basis for an understanding of cellular functions and disease mechanisms. Quantitative time-series data leading to meaningful models can improve our knowledge of human physiology in health and disease, and aid the search for earlier diagnoses, better therapies and a healthier life. The advent of systems biology is about to take the leap into clinical research and medical applications. This review emphasizes the importance of a dynamic view and understanding of cell function. We discuss the potential for computer-aided mathematical modeling of biological systems in medical research with examples from some of the major therapeutic areas: cancer, cardiovascular, diabetic and neurodegenerative medicine.
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Affiliation(s)
- Heike E Assmus
- University of Rostock, Systems Biology and Bioinformatics Group, Department of Computer Science, 18051 Rostock, Germany.
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35
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Haberichter T, Mädge B, Christopher RA, Yoshioka N, Dhiman A, Miller R, Gendelman R, Aksenov SV, Khalil IG, Dowdy SF. A systems biology dynamical model of mammalian G1 cell cycle progression. Mol Syst Biol 2007; 3:84. [PMID: 17299420 PMCID: PMC1828753 DOI: 10.1038/msb4100126] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2006] [Accepted: 01/03/2007] [Indexed: 01/10/2023] Open
Abstract
The current dogma of G(1) cell-cycle progression relies on growth factor-induced increase of cyclin D:Cdk4/6 complex activity to partially inactivate pRb by phosphorylation and to sequester p27(Kip1)-triggering activation of cyclin E:Cdk2 complexes that further inactivate pRb. pRb oscillates between an active, hypophosphorylated form associated with E2F transcription factors in early G(1) phase and an inactive, hyperphosphorylated form in late G(1), S and G(2)/M phases. However, under constant growth factor stimulation, cells show constitutively active cyclin D:Cdk4/6 throughout the cell cycle and thereby exclude cyclin D:Cdk4/6 inactivation of pRb. To address this paradox, we developed a mathematical model of G(1) progression using physiological expression and activity profiles from synchronized cells exposed to constant growth factors and included a metabolically responsive, activating modifier of cyclin E:Cdk2. Our mathematical model accurately simulates G(1) progression, recapitulates observations from targeted gene deletion studies and serves as a foundation for development of therapeutics targeting G(1) cell-cycle progression.
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Affiliation(s)
- Thomas Haberichter
- Gene Network Sciences Inc., Cambridge, MA, USA
- These authors contributed equally to this work
- Present address: Definiens, München, Germany
| | - Britta Mädge
- Department of Cellular and Molecular Medicine, Howard Hughes Medical Institute, University of California San Diego School of Medicine, La Jolla, CA, USA
- These authors contributed equally to this work
- Present address: Cell Press, Cambridge, MA, USA
| | | | - Naohisa Yoshioka
- Department of Cellular and Molecular Medicine, Howard Hughes Medical Institute, University of California San Diego School of Medicine, La Jolla, CA, USA
| | | | | | | | | | - Iya G Khalil
- Gene Network Sciences Inc., Cambridge, MA, USA
- Gene Network Sciences Inc., Cambridge, MA 02141, USA. Tel.: +1 617 494 0492; Fax: +1 617 494 0114;
| | - Steven F Dowdy
- Department of Cellular and Molecular Medicine, Howard Hughes Medical Institute, University of California San Diego School of Medicine, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, Howard Hughes Medical Institute, University of California San Diego School of Medicine, La Jolla, CA 92037-0686, USA. Tel.: +1 858 534 7772; Fax: +1 858 534 7797;
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Kumar N, Hendriks BS, Janes KA, de Graaf D, Lauffenburger DA. Applying computational modeling to drug discovery and development. Drug Discov Today 2007; 11:806-11. [PMID: 16935748 DOI: 10.1016/j.drudis.2006.07.010] [Citation(s) in RCA: 79] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2006] [Revised: 05/25/2006] [Accepted: 07/19/2006] [Indexed: 11/26/2022]
Abstract
Computational models of cells, tissues and organisms are necessary for increased understanding of biological systems. In particular, modeling approaches will be crucial for moving biology from a descriptive to a predictive science. Pharmaceutical companies identify molecular interventions that they predict will lead to therapies at the organism level, suggesting that computational biology can play a key role in the pharmaceutical industry. We discuss pharmaceutically-relevant computational modeling approaches currently used as predictive tools. Specific examples demonstrate how companies can employ these computational models to improve the efficiency of transforming targets into therapies.
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Affiliation(s)
- Neil Kumar
- Department of Chemical Engineering, Pfizer Research Technology Center, and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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Hornberg JJ, Bruggeman FJ, Bakker BM, Westerhoff HV. Metabolic control analysis to identify optimal drug targets. PROGRESS IN DRUG RESEARCH. FORTSCHRITTE DER ARZNEIMITTELFORSCHUNG. PROGRES DES RECHERCHES PHARMACEUTIQUES 2007; 64:171, 173-89. [PMID: 17195475 DOI: 10.1007/978-3-7643-7567-6_7] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
This chapter describes the basic principles of Metabolic Control Analysis (MCA) which is a quantitative methodology to evaluate the importance and relative contribution of individual metabolic steps in the overall functioning of a particular system. The control on the flux through a metabolic pathway or subsystem can be quantified by the control coefficients of the individual enzymes or components which reflects the extent to which the component is rate-limiting. The perturbation of an individual step is measured by its elasticity coefficient. The effect of perturbation of a single step on the entire pathway or subsystem is, in turn, measured by the response coefficient. Differential control analysis can be used to compare flux through a single metabolic pathway in a pathogen with the same pathway in its host to identify uniquely vulnerable steps with the greatest potential for specifically inhibiting flux through the pathogen metabolic pathway. The utility of this methodology is illustrated with the glycolysis in Trypanosomes and with oncogenic signaling.
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Affiliation(s)
- Jorrit J Hornberg
- Department of Molecular Cell Physiology, Institute for Molecular Cell Biology, Faculty of Earth and Life Sciences, Vrije Universiteit, Amsterdam, The Netherlands.
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38
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Lieu CA, Elliston KO. Applying a causal framework to system modeling. ERNST SCHERING RESEARCH FOUNDATION WORKSHOP 2007:139-52. [PMID: 17249500 DOI: 10.1007/978-3-540-31339-7_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
The emerging field of systems biology represents a revolution in our ability to understand biology. Perhaps for the first time in history we have the capacity to pursue biological understanding using a computer-aided integrative approach in conjunction with classical reductionist approaches. Technology has given us not only the ability to identify and measure the individual molecules of life and the way they change, but also the power to study these molecules and their changes in the context of a big picture. It is through the creation of a computer-aided framework for human understanding that we can begin to comprehend how these collections of molecules act as integrated biological systems, and to utilize this knowledge to rationally engineer the future of science and medicine.
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Affiliation(s)
- C A Lieu
- Genstruct., Inc., One Alewife Center, Cambridge, MA 02140, USA.
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39
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Wang CZ, Zhang B, Song WX, Wang A, Ni M, Luo X, Aung HH, Xie JT, Tong R, He TC, Yuan CS. Steamed American ginseng berry: ginsenoside analyses and anticancer activities. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2006; 54:9936-42. [PMID: 17177524 DOI: 10.1021/jf062467k] [Citation(s) in RCA: 126] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
This study was designed to determine the changes in saponin content in American ginseng berries after treatment by heating and to assess the anticancer effects of the extracts. After steaming treatment (100-120 degrees C for 1 h, and 120 degrees C for 0.5-4 h), the content of seven ginsenosides, Rg1, Re, Rb1, Rc, Rb2, Rb3, and Rd, decreased; the content of five ginsenosides, Rh1, Rg2, 20R-Rg2, Rg3, and Rh2, increased. Rg3, a previously identified anticancer ginsenoside, increased significantly. Two hours of steaming at 120 degrees C increased the content of ginsenoside Rg3 to a greater degree than other tested ginsenosides. When human colorectal cancer cells were treated with 0.5 mg/mL steamed berry extract (120 degrees C 2 h), the antiproliferation effects were 97.8% for HCT-116 and 99.6% for SW-480 cells. At the same treatment concentration, the effects of unsteamed berry extract were 34.1% for HCT-116 and 4.9% for SW-480 cells. After staining with Hoechst 33258, apoptotic cells increased significantly by treatment with steamed berry extract compared with unheated extracts. Induction of apoptosis activity was confirmed by flow cytometry after staining with annexin V/PI. The steaming of American ginseng berries augments ginsenoside Rg3 content and increases the antiproliferative effects on two human colorectal cancer cell lines.
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Affiliation(s)
- Chong-Zhi Wang
- Tang Center for Herbal Medicine Research, The Pritzker School of Medicine, University of Chicago, 5841 South Maryland Avenue, MC 4028, Chicago, Illinois 60637, USA
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40
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Valet G. Cytomics as a new potential for drug discovery. Drug Discov Today 2006; 11:785-91. [PMID: 16935745 DOI: 10.1016/j.drudis.2006.07.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2005] [Revised: 05/24/2006] [Accepted: 07/06/2006] [Indexed: 11/15/2022]
Abstract
At the single-cell level in conjunction with data-pattern analysis, high-content screening by image analysis or flow cytometry of clinical cell- or tissue-section samples provides differential molecular profiles for the personalized prediction of therapy-dependent disease progression in patients. The molecular reverse-engineering of these molecular profiles, which is the exploration of molecular pathways, backwards, to the origin of the observed molecular differentials, by systems biology has the potential to detect new drug targets in knowledge spaces, typically inaccessible to traditional hypotheses. Furthermore, predictive medicine, by cytomics in stratified patient groups, opens a new way for personalized (or individualized) medicine, as well as for the early detection of adverse drug reactions in patients.
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Affiliation(s)
- Günter Valet
- Max-Planck-Institut für Biochemie, Am Klopferspitz 18, D-82152 Martinsried, Germany.
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41
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Fitzgerald JB, Schoeberl B, Nielsen UB, Sorger PK. Systems biology and combination therapy in the quest for clinical efficacy. Nat Chem Biol 2006; 2:458-66. [PMID: 16921358 DOI: 10.1038/nchembio817] [Citation(s) in RCA: 421] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Combinatorial control of biological processes, in which redundancy and multifunctionality are the norm, fundamentally limits the therapeutic index that can be achieved by even the most potent and highly selective drugs. Thus, it will almost certainly be necessary to use new 'targeted' pharmaceuticals in combinations. Multicomponent drugs are standard in cytotoxic chemotherapy, but their development has required arduous empirical testing. However, experimentally validated numerical models should greatly aid in the formulation of new combination therapies, particularly those tailored to the needs of specific patients. This perspective focuses on opportunities and challenges inherent in the application of mathematical modeling and systems approaches to pharmacology, specifically with respect to the idea of achieving combinatorial selectivity through use of multicomponent drugs.
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Affiliation(s)
- Jonathan B Fitzgerald
- Merrimack Pharmaceuticals, One Kendall Square, Building 700 2nd floor, Cambridge, Massachusetts 02139, USA
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42
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Tan HT, Zubaidah RM, Tan S, Hooi SC, Chung MCM. 2-D DIGE analysis of butyrate-treated HCT-116 cells after enrichment with heparin affinity chromatography. J Proteome Res 2006; 5:1098-106. [PMID: 16674099 DOI: 10.1021/pr050435r] [Citation(s) in RCA: 18] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Butyrate, a 4-carbon short chain fatty acid, is responsible for the protective effects of fiber in colorectal cancer prevention. To better understand the 'blueprint' of butyrate's chemopreventive role in this disease, we performed 2-dimensional difference gel electrophoresis (2-D DIGE) of butyrate-treated HCT-116 colorectal cancer cells after pre-fractionation using heparin affinity chromatography. A combination of this enrichment step with overlapping narrow range IPGs (pH 4-7 and pH 6-11) in 2-D DIGE resulted in the detection of 46 differentially expressed spots. Twenty-four of these were identified by MS analyses, and 5 spots were found to be heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1). Three isoforms of 38 kDa were down-regulated while two with Mr approximately 26 kDa were up-regulated. These represent phosphorylated isoforms of hnRNP A1 as verified by immunoblotting with anti-phosphotyrosine and anti-phosphoserine antibodies. Using 2-DE, subcellular fractionation and western blot analysis, we further showed that full-length hnRNP A1 underwent down-regulation, cleavage and cytoplasmic retention upon butyrate treatment. These indicate that modulations of hnRNP A1 may play a significant role in the mediation of growth arrest and apoptosis by butyrate.
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Affiliation(s)
- Hwee Tong Tan
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 10 Kent Ridge Crescent, Singapore 117597
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43
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Abramovitz M, Leyland-Jones B. A systems approach to clinical oncology: focus on breast cancer. Proteome Sci 2006; 4:5. [PMID: 16595007 PMCID: PMC1456950 DOI: 10.1186/1477-5956-4-5] [Citation(s) in RCA: 35] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2006] [Accepted: 04/04/2006] [Indexed: 12/04/2022] Open
Abstract
During the past decade, genomic microarrays have been applied with some success to the molecular profiling of breast tumours, which has resulted in a much more detailed classification scheme as well as in the identification of potential gene signature sets. These gene sets have been applied to both the prognosis and prediction of outcome to treatment and have performed better than the current clinical criteria. One of the main limitations of microarray analysis, however, is that frozen tumour samples are required for the assay. This imposes severe limitations on access to samples and precludes large scale validation studies from being conducted. Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), on the other hand, can be used with degraded RNAs derived from formalin-fixed paraffin-embedded (FFPE) tumour samples, the most important and abundant source of clinical material available. More recently, the novel DASL (cDNA-mediated Annealing, Selection, extension and Ligation) assay has been developed as a high throughput gene expression profiling system specifically designed for use with FFPE tumour tissue samples.However, we do not believe that genomics is adequate as a sole prognostic and predictive platform in breast cancer. The key proteins driving oncogenesis, for example, can undergo post-translational modifications; moreover, if we are ever to move individualization of therapy into the practical world of blood-based assays, serum proteomics becomes critical. Proteomic platforms, including tissue micro-arrays (TMA) and protein chip arrays, in conjunction with surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF/MS), have been the technologies most widely applied to the characterization of tumours and serum from breast cancer patients, with still limited but encouraging results. This review will focus on these genomic and proteomic platforms, with an emphasis placed on the utilization of FFPE tumour tissue samples and serum, as they have been applied to the study of breast cancer for the discovery of gene signatures and biomarkers for the early diagnosis, prognosis and prediction of treatment outcome. The ultimate goal is to be able to apply a systems biology approach to the information gleaned from the combination of these techniques in order to select the best treatment strategy, monitor its effectiveness and make changes as rapidly as possible where needed to achieve the optimal therapeutic results for the patient.
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Affiliation(s)
- Mark Abramovitz
- Center for Clinical Research in Oncology and Department of Oncology, McGill University, 546 Pine Avenue West, Montreal, Quebec, H2W 1S6, Canada
| | - Brian Leyland-Jones
- Center for Clinical Research in Oncology and Department of Oncology, McGill University, 546 Pine Avenue West, Montreal, Quebec, H2W 1S6, Canada
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Glocker MO, Guthke R, Kekow J, Thiesen HJ. Rheumatoid arthritis, a complex multifactorial disease: on the way toward individualized medicine. Med Res Rev 2006; 26:63-87. [PMID: 16283676 DOI: 10.1002/med.20045] [Citation(s) in RCA: 42] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
With the availability of the human genome sequence and those of related species like chimpanzee, mouse, and rat, data driven research for tackling the molecular grounds of rheumatoid arthritis (RA), a multifactorial polygenic disease, can be considered a realistic challenge to the scientific community. A comprehensive research strategy is presented enabling the integration of multiple research efforts on studying autoimmunity by so called systems biology approaches. An integrative scientific concept is discussed of how to unravel molecular mechanisms of complex diseases by making use of state-of-the-art methodologies in functional and comparative genomics. A continuous interchange of data-driven and hypothesis-driven research is adjoined to determine the nature of rheumatic diseases with autoimmune background. Instead of studying single genes and proteins, RNA and protein microarray profiles are currently obtained in numerous research projects producing read-outs termed gene signatures rather than DNA and/or protein markers. A comprehensive study of the RNA, protein, and metabolite regimes is undertaken that eventually will lead to a "holistic" view of how all respective molecules, pathways and cells themselves interact with each other. Some of the above mentioned research aspects have already been studied by the authors, hopefully leading to new diagnostics and therapeutics in the future.
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45
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Hornberg JJ, Bruggeman FJ, Westerhoff HV, Lankelma J. Cancer: a Systems Biology disease. Biosystems 2006; 83:81-90. [PMID: 16426740 DOI: 10.1016/j.biosystems.2005.05.014] [Citation(s) in RCA: 248] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2004] [Revised: 04/15/2005] [Accepted: 05/26/2005] [Indexed: 12/01/2022]
Abstract
Cancer research has focused on the identification of molecular differences between cancerous and healthy cells. The emerging picture is overwhelmingly complex. Molecules out of many parallel signal transduction pathways are involved. Their activities appear to be controlled by multiple factors. The action of regulatory circuits, cross-talk between pathways and the non-linear reaction kinetics of biochemical processes complicate the understanding and prediction of the outcome of intracellular signaling. In addition, interactions between tumor and other cell types give rise to a complex supra-cellular communication network. If cancer is such a complex system, how can one ever predict the effect of a mutation in a particular gene on a functionality of the entire system? And, how should one go about identifying drug targets? Here, we argue that one aspect is to recognize, where the essence resides, i.e. recognize cancer as a Systems Biology disease. Then, more cancer biologists could become systems biologists aiming to provide answers to some of the above systemic questions. To this aim, they should integrate the available knowledge stemming from quantitative experimental results through mathematical models. Models that have contributed to the understanding of complex biological systems are discussed. We show that the architecture of a signaling network is important for determining the site at which an oncologist should intervene. Finally, we discuss the possibility of applying network-based drug design to cancer treatment and how rationalized therapies, such as the application of kinase inhibitors, may benefit from Systems Biology.
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Affiliation(s)
- Jorrit J Hornberg
- Department of Molecular Cell Physiology, Institute for Molecular Cell Biology, BioCentrum Amsterdam, Faculty of Earth and Life Sciences, Vrije Universiteit, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.
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Babu C V S, Song EJ, Babar SME, Wi MH, Yoo YS. Capillary electrophoresis at the omics level: Towards systems biology. Electrophoresis 2006; 27:97-110. [PMID: 16421959 DOI: 10.1002/elps.200500511] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Emerging systems biology aims at integrating the enormous amount of existing omics data in order to better understand their functional relationships at a whole systems level. These huge datasets can be obtained through advances in high-throughput, sensitive, precise, and accurate analytical instrumentation and technological innovation. Separation sciences play an important role in revealing biological processes at various omic levels. From the perspective of systems biology, CE is a strong candidate for high-throughput, sensitive data generation which is capable of tackling the challenges in acquiring qualitative and quantitative knowledge through a system-level study. This review focuses on the applicability of CE to systems-based analytical data at the genomic, transcriptomic, proteomic, and metabolomic levels.
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Affiliation(s)
- Suresh Babu C V
- Bioanalysis and Biotransformation Research Center, Korea Institute of Science and Technology, Cheongryang, Seoul, Korea
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Abstract
The recent decline in drug approvals and the increase in late-stage failures indicate that the ability to generate and screen large numbers of molecules has not improved the drug pipeline. Perhaps the pharmaceutical industry should follow the example of the automotive industry and agree upon a shared modeling language with vendors and academics to enable integration of predictive computational tools across the industry. This will then enable the virtual 'crash-testing' of drugs before synthesis, biological testing and, most importantly, clinical trials. This represents an ambitiously progressive approach using the models for simulating every stage of the drug discovery and development process. Combining the relevant computational algorithms into a grand unified model would enable prioritization of the best ideas before pursuing a discovery program, selecting a target or synthesizing a molecule. The successful application of these virtual crash-testing principles by any of its current proponents could revitalize the pharmaceutical industry so that failure is avoided.
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Affiliation(s)
- Peter W Swaan
- Department of Pharmaceutical Sciences, University of Maryland, 20 Penn Street, Baltimore, MD 21201, USA.
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Rajasethupathy P, Vayttaden SJ, Bhalla US. Systems modeling: a pathway to drug discovery. Curr Opin Chem Biol 2005; 9:400-6. [PMID: 16006180 DOI: 10.1016/j.cbpa.2005.06.008] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2005] [Accepted: 06/22/2005] [Indexed: 12/19/2022]
Abstract
Systems modeling is emerging as a valuable tool in therapeutics. This is seen by the increasing use of clinically relevant computational models and a rise in systems biology companies working with the pharmaceutical industry. Systems models have helped understand the effects of pharmacological intervention at receptor, intracellular and intercellular communication stages of cell signaling. For instance, angiogenesis models at the ligand-receptor interaction level have suggested explanations for the failure of therapies for cardiovascular disease. Intracellular models of myeloma signaling have been used to explore alternative drug targets and treatment schedules. Finally, modeling has suggested novel approaches to treating disorders of intercellular communication, such as diabetes. Systems modeling can thus fill an important niche in therapeutics by making drug discovery a faster and more systematic process.
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Affiliation(s)
- Priyamvada Rajasethupathy
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Campus, Bangalore, India
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Qutub AA, Hunt CA. Glucose transport to the brain: a systems model. ACTA ACUST UNITED AC 2005; 49:595-617. [PMID: 16269321 DOI: 10.1016/j.brainresrev.2005.03.002] [Citation(s) in RCA: 68] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2004] [Revised: 03/02/2005] [Accepted: 03/09/2005] [Indexed: 02/07/2023]
Abstract
Glucose transport to the brain involves sophisticated interactions of solutes, transporters, enzymes, and cell signaling processes, within an intricate spatial architecture. The dynamics of the transport are influenced by the adaptive nature of the blood-brain barrier (BBB), the semi-impermeable membranes of brain capillaries. As both the gate and the gatekeeper between blood-borne nutrients and brain tissue, the BBB helps govern brain homeostasis. Glucose in the blood must cross the BBB's luminal and abluminal membranes to reach neural tissue. A robust representation of the glucose transport mechanism can highlight a target for brain therapeutic intervention, help characterize mechanisms behind several disease phenotypes, or suggest a new delivery route for drugs. The challenge for researchers is understanding the relationships between influential physiological variables in vivo, and using that knowledge to predict how alterations or interventions affect glucose transport. This paper reviews factors influencing glucose transport and approaches to representing blood-to-brain glucose transport including in vitro, in vivo, and kinetic models. Applications for different models are highlighted, while their limitations in answering arising questions about the human in vivo BBB lead to a discussion of an alternate approach. A developing complex systems simulation is introduced, initiating a single platform to represent the dynamics of glucose transport across the adapting human blood-brain barrier.
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Affiliation(s)
- Amina A Qutub
- Joint Graduate Group in Bioengineering, University of California, Berkeley and San Francisco, USA.
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50
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Aksenov SV, Church B, Dhiman A, Georgieva A, Sarangapani R, Helmlinger G, Khalil IG. An integrated approach for inference and mechanistic modeling for advancing drug development. FEBS Lett 2005; 579:1878-83. [PMID: 15763567 DOI: 10.1016/j.febslet.2005.02.012] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2005] [Revised: 02/04/2005] [Accepted: 02/08/2005] [Indexed: 01/30/2023]
Abstract
An important challenge facing researchers in drug development is how to translate multi-omic measurements into biological insights that will help advance drugs through the clinic. Computational biology strategies are a promising approach for systematically capturing the effect of a given drug on complex molecular networks and on human physiology. This article discusses a two-pronged strategy for inferring biological interactions from large-scale multi-omic measurements and accounting for known biology via mechanistic dynamical simulations of pathways, cells, and organ- and tissue level models. These approaches are already playing a role in driving drug development by providing a rational and systematic computational framework.
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Affiliation(s)
- Sergej V Aksenov
- Gene Network Sciences, Inc. 31 Dutch Mill Road, Ithaca, NY 14850, USA
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