• Humans
• Genomics/ethics
• Biological Specimen Banks/ethics
• Precision Medicine/ethics
• Quality Control

• 3D cell culture
• HER2+ breast cancer
• Trastuzumab resistance
• mitochondria
• proteomics
• systems biology

• Humans
• Trastuzumab/pharmacology
• Trastuzumab/therapeutic use
• Breast Neoplasms/metabolism
• Breast Neoplasms/drug therapy
• Breast Neoplasms/pathology
• Female
• Drug Resistance, Neoplasm
• Electron Transport Complex I/metabolism
• Proteomics/methods
• Receptor, ErbB-2/metabolism
• Cell Line, Tumor
• Mitochondria/metabolism
• Mitochondria/drug effects
• Spheroids, Cellular/metabolism
• Spheroids, Cellular/drug effects
• Proteome/metabolism
• Antineoplastic Agents, Immunological/pharmacology
• Antineoplastic Agents, Immunological/therapeutic use

• UBACYT-20720170100019BA and UBACYT-20720220100018BA Universidad de Buenos Aires
• PIR01_00017 and IR0000010 ELIXIR
• RF2019-12370396 Ministero della Salute

• next-generation sequencing
• rna sequencing
• applied mathematics
• computational science
• scientific data
• statistics

• RNA-Seq
• Sequence Analysis, RNA/methods
• Pseudogenes
• High-Throughput Nucleotide Sequencing/methods
• Linear Models
• Gene Expression Profiling/methods

• Humans
• Precision Medicine/methods
• Medical Oncology

• Università degli Studi di Firenze

• biomarker
• chemotherapy effectiveness
• pancreatic cancer
• proteomics

• integrative systems pathology of Corona
• methodology of systemic modelling
• social ecology of pandemics
• systems science and thinking

• Humans
• COVID-19
• Pandemics/prevention & control
• SARS-CoV-2
• RNA, Viral
• Systems Analysis

• COVID-19
• Corona virus
• post-normal science
• systems medicine

• Automated system
• CLAPS
• Close loop
• Hemodynamics

• data science
• digital twin
• experimental design
• genomics
• machine learning
• personalized medicine

• Humans
• Research Design
• Machine Learning
• Big Data

• BNCT
• exosome
• mass spectrometry
• proteomics
• systems biology

• disease
• disease boundaries
• emergent probability
• function
• risk factors
• schemes of recurrence

• cancer
• cancer cell line encyclopedia
• dimension reduction
• feature selection
• matrix factorization
• systems pharmacology

• Algorithms
• Humans
• Neoplasms/drug therapy
• Neoplasms/genetics
• Network Pharmacology

• R25 GM067110 NIGMS NIH HHS
• Iran National Science Foundation
• National Institutes of Health

Recent studies have demonstrated a relevant role of the host genetics in the coronavirus disease 2019 (COVID-19) prognosis. Most of the 7000 rare diseases described to date have a genetic component, typically highly penetrant. However, this vast spectrum of genetic variability remains yet unexplored with respect to possible interactions with COVID-19. Here, a mathematical mechanistic model of the COVID-19 molecular disease mechanism has been used to detect potential interactions between rare disease genes and the COVID-19 infection process and downstream consequences. Out of the 2518 disease genes analyzed, causative of 3854 rare diseases, a total of 254 genes have a direct effect on the COVID-19 molecular disease mechanism and 207 have an indirect effect revealed by a significant strong correlation. This remarkable potential of interaction occurs for >300 rare diseases. Mechanistic modeling of COVID-19 disease map has allowed a holistic systematic analysis of the potential interactions between the loss of function in known rare disease genes and the pathological consequences of COVID-19 infection. The results identify links between disease genes and COVID-19 hallmarks and demonstrate the usefulness of the proposed approach for future preventive measures in some rare diseases.

• Citation network
• Information science
• Knowledge diffusion
• Knowledge management
• Scientometrics

• systems biology
• diseases
• cellular signalling networks
• mechanisms of disease
• chloride channels
• molecular medicine

• Biomarkers
• Cystic Fibrosis/etiology
• Cystic Fibrosis/metabolism
• Cystic Fibrosis Transmembrane Conductance Regulator/genetics
• Cystic Fibrosis Transmembrane Conductance Regulator/metabolism
• Databases, Genetic
• Disease Susceptibility
• Humans
• Signal Transduction
• Software
• Web Browser

• SFRH/PD/BD/131405/2017 Fundação para a Ciência e a Tecnologia
• UIDB/04046/2020 Fundação para a Ciência e a Tecnologia
• no. IMI 115446 (eTRIKS) Innovative Medicines Initiative
• no. IMI 115446 (eTRIKS) Innovative Medicines Initiative
• FP7/2007-2013 Seventh Framework Programme
• FP7/2007-2013 Seventh Framework Programme

Advances in (bio)medicine and technological innovations make it possible to combine high-dimensional, heterogeneous health data to better understand causes of diseases and make them usable for predictive, preventive, and precision medicine. This study aimed to determine views on and expectations of “systems medicine” from the perspective of citizens and patients in six focus group interviews, all transcribed verbatim and content analyzed. A future vision of the use of systems medicine in healthcare served as a stimulus for the discussion. The results show that although certain aspects of systems medicine were seen positive (e.g., use of smart technology, digitalization, and networking in healthcare), the perceived risks dominated. The high degree of technification was perceived as emotionally burdensome (e.g., reduction of people to their data, loss of control, dehumanization). The risk-benefit balance for the use of risk-prediction models for disease events and trajectories was rated as rather negative. There were normative and ethical concerns about unwanted data use, discrimination, and restriction of fundamental rights. These concerns and needs of citizens and patients must be addressed in policy frameworks and health policy implementation strategies to reduce negative emotions and attitudes toward systems medicine and to take advantage of its opportunities.

• artificial intelligence
• big data
• digital health
• implementation
• personalized medicine
• precision medicine
• systems biology
• systems medicine

• differential co-expression
• differential expression
• differential variability
• dysregulated pathway
• enrichment analysis
• gene regulation

• data science
• databases
• education
• machine learning
• neuroscience

• Brain
• Cloud Computing
• Data Science
• Publishing

• R24 MH114796 NIMH NIH HHS
• U24 ES026465 NIEHS NIH HHS
• R44 NS081792 NINDS NIH HHS

Precision medicine and molecular systems medicine (MSM) are highly utilized and successful approaches to improve understanding, diagnosis, and treatment of many diseases from bench-to-bedside. Especially in the COVID-19 pandemic, molecular techniques and biotechnological innovation have proven to be of utmost importance for rapid developments in disease diagnostics and treatment, including DNA and RNA sequencing technology, treatment with drugs and natural products and vaccine development. The COVID-19 crisis, however, has also demonstrated the need for systemic thinking and transdisciplinarity and the limits of MSM: the neglect of the bio-psycho-social systemic nature of humans and their context as the object of individual therapeutic and population-oriented interventions. COVID-19 illustrates how a medical problem requires a transdisciplinary approach in epidemiology, pathology, internal medicine, public health, environmental medicine, and socio-economic modeling. Regarding the need for conceptual integration of these different kinds of knowledge we suggest the application of general system theory (GST). This approach endorses an organism-centered view on health and disease, which according to Ludwig von Bertalanffy who was the founder of GST, we call Organismal Systems Medicine (OSM). We argue that systems science offers wider applications in the field of pathology and can contribute to an integrative systems medicine by (i) integration of evidence across functional and structural differentially scaled subsystems, (ii) conceptualization of complex multilevel systems, and (iii) suggesting mechanisms and non-linear relationships underlying the observed phenomena. We underline these points with a proposal on multi-level systems pathology including neurophysiology, endocrinology, immune system, genetics, and general metabolism. An integration of these areas is necessary to understand excess mortality rates and polypharmacological treatments. In the pandemic era this multi-level systems pathology is most important to assess potential vaccines, their effectiveness, short-, and long-time adverse effects. We further argue that these conceptual frameworks are not only valid in the COVID-19 era but also important to be integrated in a medicinal curriculum.

• Organismal Systems Medicine
• dynamic equilibrium
• excess mortality rate
• multi organ disease
• multi-level view model
• polypharmacology
• systems theory

• Biomarker
• Ethics
• Health economics
• Liquid biopsies
• Molecular and digital pathology
• Molecular imaging
• Omics technologies
• Patient management
• Pharmacology
• Sample quality
• Theranostics
• Treatment

• Environmental factor
• Epigenetic regulations
• Lifestyle
• Premature ovarian insufficiency (POI)

• Drug repurposing
• Drug sensitivity
• High throughput drug screening
• Precision medicine

Neurodegenerative diseases (NDDs) comprise a broad range of progressive neurological disorders with multifactorial etiology contributing to disease pathophysiology. Evidence of the microbiome involvement in the gut-brain axis urges the interest in understanding metabolic interactions between the microbiota and host physiology in NDDs. Systems Biology offers a holistic integrative approach to study the interplay between the different biologic systems as part of a whole, and may elucidate the host–microbiome interactions in NDDs. We reviewed direct and indirect pathways through which the microbiota can modulate the bidirectional communication of the gut-brain axis, and explored the evidence of microbial dysbiosis in Alzheimer’s and Parkinson’s diseases. As the gut microbiota being strongly affected by diet, the potential approaches to targeting the human microbiota through diet for the stimulation of neuroprotective microbial-metabolites secretion were described. We explored the potential of Genome-scale metabolic models (GEMs) to infer microbe-microbe and host-microbe interactions and to identify the microbiome contribution to disease development or prevention. Finally, a systemic approach based on GEMs and ‘omics integration, that would allow the design of sustainable personalized anti-inflammatory diets in NDDs prevention, through the modulation of gut microbiota was described.

• Alzheimer’s disease
• Parkinson’s disease
• biologic network
• biomarker discovery
• dietary therapy
• microbiota-gut-brain axis
• neurodegenerative diseases
• systems biology

• big data
• data integration
• integrated health care
• omics
• systems medicine

Systems medicine holds many promises, but has so far provided only a limited number of proofs of principle. To address this road block, possible barriers and challenges of translating systems medicine into clinical practice need to be identified and addressed. The members of the European Cooperation in Science and Technology (COST) Action CA15120 Open Multiscale Systems Medicine (OpenMultiMed) wish to engage the scientific community of systems medicine and multiscale modelling, data science and computing, to provide their feedback in a structured manner. This will result in follow-up white papers and open access resources to accelerate the clinical translation of systems medicine.

• computing
• data science
• modelling
• systems medicine

Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and artificial intelligence, the paradigm of biomarker discovery is shifting from traditional experimental and small data-based identification toward big data-driven and systems-level screening. Complex interactions between genetic factors and environmental effects provide opportunities for systems modeling of PCa genesis and evolution. We hereby review the current research frontiers in informatics for PCa clinical translation. First, the heterogeneity and complexity in PCa development and clinical theranostics are introduced to raise the concern for PCa systems biology studies. Then biomarkers and risk factors ranging from molecular alternations to clinical phenotype and lifestyle changes are explicated for PCa personalized management. Methodologies and applications for multi-dimensional data integration and computational modeling are discussed. The future perspectives and challenges for PCa systems medicine and holistic healthcare are finally provided.

• Biomarker discovery
• Clinical application
• Prostate cancer
• Systems medicine
• Translational informatics

• Alzheimer’s disease
• dementia
• interdisciplinary
• research methods
• treatment

• computational biology and bioinformatics
• machine learning
• computational science
• statistics

• Artificial Intelligence
• Biostatistics/methods
• Chronic Pain/diagnosis
• Computational Biology
• Deep Learning
• Electronic Health Records/standards
• Humans
• Machine Learning
• Neural Networks, Computer
• Phenotype

• Austrian Science Funds (project P30031)

• biostatistics
• computational biology
• data science
• genomics
• oncology
• precision medicine
• reproducible research

• Epistemological gaps
• Molecular systems medicine
• Organismal systems medicine
• Overestimated options
• Theoretical medicine

• Computational Biology
• Health Services Needs and Demand
• Humans
• Interdisciplinary Communication
• Knowledge
• Models, Theoretical
• Molecular Medicine
• Research
• Systems Analysis

GlycA is a nuclear magnetic resonance (NMR) spectroscopy biomarker that predicts risk of disease from myriad causes. It is heterogeneous; arising from five circulating glycoproteins with dynamic concentrations: alpha-1 antitrypsin (AAT), alpha-1-acid glycoprotein (AGP), haptoglobin (HP), transferrin (TF), and alpha-1-antichymotrypsin (AACT). The contributions of each glycoprotein to the disease and mortality risks predicted by GlycA remain unknown.

We trained imputation models for AAT, AGP, HP, and TF from NMR metabolite measurements in 626 adults from a population cohort with matched NMR and immunoassay data. Levels of AAT, AGP, and HP were estimated in 11,861 adults from two population cohorts with eight years of follow-up, then each biomarker was tested for association with all common endpoints. Whole blood gene expression data was used to identify cellular processes associated with elevated AAT.

Accurate imputation models were obtained for AAT, AGP, and HP but not for TF. While AGP had the strongest correlation with GlycA, our analysis revealed variation in imputed AAT levels was the most predictive of morbidity and mortality for the widest range of diseases over the eight year follow-up period, including heart failure (meta-analysis hazard ratio = 1.60 per standard deviation increase of AAT, P-value = 1×10⁻¹⁰), influenza and pneumonia (HR = 1.37, P = 6×10⁻¹⁰), and liver diseases (HR = 1.81, P = 1×10⁻⁶). Transcriptional analyses revealed association of elevated AAT with diverse inflammatory immune pathways.

This study clarifies the molecular underpinnings of the GlycA biomarker’s associated disease risk, and indicates a previously unrecognised association between elevated AAT and severe disease onset and mortality.

• angiography
• disease
• epidemiology
• inflammation
• mortality

• agent-based modeling
• computational medicine
• vocal folds

• R03 DC012112 NIDCD NIH HHS

• cancer
• cardiology
• computational biology
• drug discovery
• drug targets
• protein–protein interaction

• Genomics
• History, 21st Century
• Human Genetics
• Humans
• Periodicals as Topic/history

• MC_PC_13030 Medical Research Council
• MR/P01836X/1 Medical Research Council
• MC PC 13030 Medical Research Council
• UM1 HG006542 NHGRI NIH HHS
• R35 NS105078 NINDS NIH HHS
• MC UP A90 1006 Medical Research Council
• MR/P011705/1 Medical Research Council
• R01 GM106373 NIGMS NIH HHS
• Wellcome Trust
• MR/P024416/1 Medical Research Council
• MC_UP_A090_1006 Medical Research Council

• Foramen Ovale, Patent
• Heart Septal Defects, Atrial
• Humans
• Thromboembolism

• HeLa pLuc/705 cells
• antisense oligonucleotides
• in vitro splice correction
• lipid-polymer hybrid nanoparticles (LPNs)
• lipidoids
• quality-by-design
• statistical optimisation

• data sharing
• ethics
• mobile phone
• patient participation
• patient-generated health data
• questionnaire
• survey

• Bayesian network
• Data assimilation
• Machine learning
• Multisource evidence
• Pervasive sensing
• Systems biology

• Decision Making
• Internet
• Systems Biology
• Wearable Electronic Devices

Systems biology is a discipline that studies biological systems from a holistic and interdisciplinary perspective. It brings together biologists, mathematicians, computer scientists, physicists, and engineers, so it has both biology-oriented components and systems-oriented components. We applied several computational tools to analyze the bibliographic information of published articles in systems biology to answer the question: Did the research topics of systems biology become more biology-oriented or more systems-oriented from 1992 to 2013? We analyzed the metadata of 9923 articles on systems biology from the Web of Science database. We identified the most highly cited 330 references using computational tools and through close reading we divided them into nine categories of research types in systems biology. Interestingly, we found that articles in one category, namely, systems biology’s applications in medical research, increased tremendously. This finding was corroborated by computational analysis of the abstracts, which also suggested that the percentages of topics on vaccines, diseases, drugs and cancers increased over time. In addition, we analyzed the institutional backgrounds of the corresponding authors of those 9923 articles and identified the most highly cited 330 authors over time. We found that before the mid-1990s, systems-oriented scientists had made the most referenced contributions. However, in recent years, researchers from biology-oriented institutions not only represented a huge percentage of the total number of researchers, but also had made the most referenced contributions. Notably, interdisciplinary institutions only produced a small percentage of researchers, but had made disproportionate contributions to this field.

• Biomedical Research/statistics & numerical data
• Data Analysis
• Internet
• Metadata
• Publications/statistics & numerical data
• Systems Biology

• Division of Social and Economic Sciences
• China Scholarship Council

• Ethnopharmacology
• Metabolomics
• System biology
• Taxonomic validity
• Traditional medicine

• Animals
• Biological Products/isolation & purification
• Biological Products/pharmacology
• Drug Discovery/methods
• Ethnopharmacology
• Humans
• Medicine, Traditional/methods
• Paeonia/chemistry
• Plant Extracts/pharmacology
• Research Design

Whole exome and gene panel sequencing are increasingly used for oncological diagnostics. To investigate the accuracy of SCNA detection algorithms on simulated and clinical tumor samples, the precision and sensitivity of four SCNA callers were measured using 50 simulated whole exome and 50 simulated targeted gene panel datasets, and using 119 TCGA tumor samples for which SNP array data were available.

On synthetic exome and panel data, VarScan2 mostly called false positives, whereas Control-FREEC was precise (>90% correct calls) at the cost of low sensitivity (<40% detected). ONCOCNV was slightly less precise on gene panel data, with similarly low sensitivity. This could be explained by low sensitivity for amplifications and high precision for deletions. Surprisingly, these results were not strongly affected by moderate tumor impurities; only contaminations with more than 60% non-cancerous cells resulted in strongly declining precision and sensitivity. On the 119 clinical samples, both Control-FREEC and CNVkit called 71.8% and 94%, respectively, of the SCNAs found by the SNP arrays, but with a considerable amount of false positives (precision 29% and 4.9%).

Whole exome and targeted gene panel methods by design limit the precision of SCNA callers, making them prone to false positives. SCNA calls cannot easily be integrated in clinical pipelines that use data from targeted capture-based sequencing. If used at all, they need to be cross-validated using orthogonal methods.

Scripts are provided as supplementary information.

Supplementary data are available at Bioinformatics online.