Dynamics of biological systems: role of systems biology in medical research

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.

[1]  F. Bruggeman,et al.  Cancer: a Systems Biology disease. , 2006, Bio Systems.

[2]  J Timmer,et al.  Quantitative data generation for systems biology: the impact of randomisation, calibrators and normalisers. , 2005, Systems biology.

[3]  Hamid Bolouri,et al.  A data integration methodology for systems biology. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[4]  L. Hood,et al.  A data integration methodology for systems biology: experimental verification. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[5]  S. L. Wong,et al.  Towards a proteome-scale map of the human protein–protein interaction network , 2005, Nature.

[6]  John D. Storey,et al.  A network-based analysis of systemic inflammation in humans , 2005, Nature.

[7]  H. Lehrach,et al.  A Human Protein-Protein Interaction Network: A Resource for Annotating the Proteome , 2005, Cell.

[8]  Mark C. Fishman,et al.  Pharmaceuticals: A new grammar for drug discovery , 2005, Nature.

[9]  R. Heinrich,et al.  Control of MAPK signalling: from complexity to what really matters , 2005, Oncogene.

[10]  U. Bhalla,et al.  Systems modeling: a pathway to drug discovery. , 2005, Current opinion in chemical biology.

[11]  Enrico Gratton,et al.  Measuring fast dynamics in solutions and cells with a laser scanning microscope. , 2005, Biophysical journal.

[12]  H. Lane,et al.  ERBB Receptors and Cancer: The Complexity of Targeted Inhibitors , 2005, Nature Reviews Cancer.

[13]  Martin A. Nowak,et al.  Dynamics of chronic myeloid leukaemia , 2005, Nature.

[14]  Jacky L Snoep,et al.  The Silicon Cell initiative: working towards a detailed kinetic description at the cellular level. , 2005, Current opinion in biotechnology.

[15]  H. Kitano,et al.  A comprehensive pathway map of epidermal growth factor receptor signaling , 2005, Molecular systems biology.

[16]  M. Mesarovic,et al.  Feedback dynamics and cell function: Why systems biology is called Systems Biology. , 2005, Molecular bioSystems.

[17]  R. Horobin,et al.  A predictive model for the selective accumulation of chemicals in tumor cells , 2005, European Biophysics Journal.

[18]  Axel Kowald,et al.  Systems Biology in Practice: Concepts, Implementation and Application , 2005 .

[19]  Y. Baba,et al.  A history of microarrays in biomedicine , 2005, Expert review of molecular diagnostics.

[20]  Kwang-Hyun Cho,et al.  The dynamic systems approach to control and regulation of intracellular networks , 2005, FEBS letters.

[21]  F J Doyle,et al.  Model identification of signal transduction networks from data using a state regulator problem. , 2005, Systems biology.

[22]  D. Lauffenburger,et al.  Integrating cell-level kinetic modeling into the design of engineered protein therapeutics , 2005, Nature Biotechnology.

[23]  N. Komarova Mathematical modeling of tumorigenesis: mission possible , 2005, Current opinion in oncology.

[24]  M. Newman,et al.  Network theory and SARS: predicting outbreak diversity , 2004, Journal of Theoretical Biology.

[25]  J. Doyle,et al.  Metabolic syndrome and robustness tradeoffs. , 2004, Diabetes.

[26]  James R. Johnson,et al.  Oscillations in NF-κB Signaling Control the Dynamics of Gene Expression , 2004, Science.

[27]  Elaine Holmes,et al.  The challenges of modeling mammalian biocomplexity , 2004, Nature Biotechnology.

[28]  Leroy Hood,et al.  The impact of systems approaches on biological problems in drug discovery , 2004, Nature Biotechnology.

[29]  B. Palsson,et al.  The evolution of molecular biology into systems biology , 2004, Nature Biotechnology.

[30]  E. Kunkel Systems biology in drug discovery , 2004, Nature Biotechnology.

[31]  Kwang-Hyun Cho,et al.  Modeling and simulation of intracellular dynamics: choosing an appropriate framework , 2004, IEEE Transactions on NanoBioscience.

[32]  A. Fernie,et al.  Metabolite profiling: from diagnostics to systems biology , 2004, Nature Reviews Molecular Cell Biology.

[33]  Vincent Lemaire,et al.  Modeling the interactions between osteoblast and osteoclast activities in bone remodeling. , 2004, Journal of theoretical biology.

[34]  K. Kinzler,et al.  Cancer genes and the pathways they control , 2004, Nature Medicine.

[35]  Z. Agur,et al.  Application of the Virtual Cancer Patient Engine (VCPE) for improving oncological treatment desig. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[36]  S. Gabriel,et al.  EGFR Mutations in Lung Cancer: Correlation with Clinical Response to Gefitinib Therapy , 2004, Science.

[37]  Catherine M Lloyd,et al.  CellML: its future, present and past. , 2004, Progress in biophysics and molecular biology.

[38]  Patricia L. Harris,et al.  Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. , 2004, The New England journal of medicine.

[39]  R. Christopher,et al.  Data‐Driven Computer Simulation of Human Cancer Cell , 2004, Annals of the New York Academy of Sciences.

[40]  D. Kell,et al.  Metabolomics by numbers: acquiring and understanding global metabolite data. , 2004, Trends in biotechnology.

[41]  T. Hubbard,et al.  A census of human cancer genes , 2004, Nature Reviews Cancer.

[42]  H. Kitano Cancer as a robust system: implications for anticancer therapy , 2004, Nature Reviews Cancer.

[43]  M. Nowak,et al.  Dynamics of cancer progression , 2004, Nature Reviews Cancer.

[44]  M. Nowak,et al.  Linear Model of Colon Cancer Initiation , 2004, Cell cycle.

[45]  C. Sander,et al.  The HUPO PSI's Molecular Interaction format—a community standard for the representation of protein interaction data , 2004, Nature Biotechnology.

[46]  S. Bonhoeffer,et al.  Recombination in HIV and the evolution of drug resistance: for better or for worse? , 2004, BioEssays : news and reviews in molecular, cellular and developmental biology.

[47]  E. Birney,et al.  EnsMart: a generic system for fast and flexible access to biological data. , 2003, Genome research.

[48]  Ursula Klingmüller,et al.  Simulation Methods for Optimal Experimental Design in Systems Biology , 2003, Simul..

[49]  Scott Banta,et al.  Metabolic engineering: advances in modeling and intervention in health and disease. , 2003, Annual review of biomedical engineering.

[50]  David M Eddy,et al.  Archimedes: a trial-validated model of diabetes. , 2003, Diabetes care.

[51]  Reinhart Heinrich,et al.  The Roles of APC and Axin Derived from Experimental and Theoretical Analysis of the Wnt Pathway , 2003, PLoS biology.

[52]  H. Wiley,et al.  An integrated model of epidermal growth factor receptor trafficking and signal transduction. , 2003, Biophysical journal.

[53]  E. T. Gawlinski,et al.  The glycolytic phenotype in carcinogenesis and tumor invasion: insights through mathematical models. , 2003, Cancer research.

[54]  R. Winslow,et al.  An integrated model of cardiac mitochondrial energy metabolism and calcium dynamics. , 2003, Biophysical journal.

[55]  Katherine C. Chen,et al.  Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. , 2003, Current opinion in cell biology.

[56]  Chris F. Taylor,et al.  A systematic approach to modeling, capturing, and disseminating proteomics experimental data , 2003, Nature Biotechnology.

[57]  J. Gray,et al.  The genetics and genomics of cancer , 2003, Nature Genetics.

[58]  Hiroaki Kitano,et al.  The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models , 2003, Bioinform..

[59]  J. Timmer,et al.  Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by databased modeling , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[60]  Giuseppe Tonini,et al.  Cancer chronotherapy: Principles, applications, and perspectives , 2003, Cancer.

[61]  Alan F. Scott,et al.  Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders , 2002, Nucleic Acids Res..

[62]  G. Nelson,et al.  Dynamic analysis of STAT6 signalling in living cells , 2002, FEBS letters.

[63]  C. Rao,et al.  Control, exploitation and tolerance of intracellular noise , 2002, Nature.

[64]  Neema Jamshidi,et al.  In silico model-driven assessment of the effects of single nucleotide polymorphisms (SNPs) on human red blood cell metabolism. , 2002, Genome research.

[65]  Eduardo Sontag,et al.  Untangling the wires: A strategy to trace functional interactions in signaling and gene networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[66]  Pascal Kahlem,et al.  A gene expression map of human chromosome 21 orthologues in the mouse , 2002, Nature.

[67]  Luca Scorrano,et al.  A novel mitochondriotoxic small molecule that selectively inhibits tumor cell growth. , 2002, Cancer cell.

[68]  D. Noble Modeling the Heart--from Genes to Cells to the Whole Organ , 2002, Science.

[69]  Peter Butler,et al.  Pulsatile insulin secretion: detection, regulation, and role in diabetes. , 2002, Diabetes.

[70]  J. Lindon,et al.  Metabonomics: a platform for studying drug toxicity and gene function , 2002, Nature Reviews Drug Discovery.

[71]  D. Schomburg,et al.  BRENDA: a resource for enzyme data and metabolic information. , 2002, Trends in biochemical sciences.

[72]  Hidde de Jong,et al.  Modeling and Simulation of Genetic Regulatory Systems: A Literature Review , 2002, J. Comput. Biol..

[73]  L. Greller,et al.  The dynamics of molecular networks: applications to therapeutic discovery. , 2001, Drug discovery today.

[74]  A. Arkin Synthetic cell biology. , 2001, Current opinion in biotechnology.

[75]  Jason E. Stewart,et al.  Minimum information about a microarray experiment (MIAME)—toward standards for microarray data , 2001, Nature Genetics.

[76]  W Xu,et al.  Digital in-line holography for biological applications , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[77]  M. Tomita Whole-cell simulation: a grand challenge of the 21st century. , 2001, Trends in biotechnology.

[78]  J. Tyson,et al.  Regulation of the eukaryotic cell cycle: molecular antagonism, hysteresis, and irreversible transitions. , 2001, Journal of theoretical biology.

[79]  Roger E Bumgarner,et al.  Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. , 2001, Science.

[80]  J. V. Moran,et al.  Initial sequencing and analysis of the human genome. , 2001, Nature.

[81]  M. Fussenegger,et al.  Streptogramin-based gene regulation systems for mammalian cells , 2000, Nature Biotechnology.

[82]  U Alon,et al.  Generation of oscillations by the p53-Mdm2 feedback loop: a theoretical and experimental study. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[83]  M. Fussenegger,et al.  A mathematical model of caspase function in apoptosis , 2000, Nature Biotechnology.

[84]  B. Kholodenko,et al.  Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. , 2000, European journal of biochemistry.

[85]  P. May,et al.  Twenty years of p53 research: structural and functional aspects of the p53 protein , 1999, Oncogene.

[86]  B. Kholodenko,et al.  Quantification of Short Term Signaling by the Epidermal Growth Factor Receptor* , 1999, The Journal of Biological Chemistry.

[87]  W. Webb,et al.  Molecular dynamics in living cells observed by fluorescence correlation spectroscopy with one- and two-photon excitation. , 1999, Biophysical journal.

[88]  B. Aguda,et al.  A quantitative analysis of the kinetics of the G(2) DNA damage checkpoint system. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[89]  L. Weiner An overview of monoclonal antibody therapy of cancer. , 1999, Seminars in oncology.

[90]  G. Church,et al.  Systematic determination of genetic network architecture , 1999, Nature Genetics.

[91]  D. Botstein,et al.  The transcriptional program in the response of human fibroblasts to serum. , 1999, Science.

[92]  L. Segel,et al.  Multiple attractors in immunology: theory and experiment. , 1998, Biophysical chemistry.

[93]  L. Segel,et al.  Modeling immunotherapy for allergy. , 1996, Bulletin of mathematical biology.

[94]  P. Argos,et al.  SRS: information retrieval system for molecular biology data banks. , 1996, Methods in enzymology.

[95]  L D Greller,et al.  Tumor heterogeneity and progression: conceptual foundations for modeling. , 1996, Invasion & metastasis.

[96]  J. Rashbass Online Mendelian Inheritance in Man. , 1995, Trends in genetics : TIG.

[97]  L. Segel,et al.  A quantitative model of autoimmune disease and T-cell vaccination: does more mean less? , 1995, Immunology today.

[98]  R. Anderson,et al.  Pulse mass measles vaccination across age cohorts. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[99]  J. Deniau,et al.  Disinhibition as a basic process in the expression of striatal functions , 1990, Trends in Neurosciences.

[100]  D. Scheinberg,et al.  Monoclonal antibody therapy of cancer. , 1990, Cancer chemotherapy and biological response modifiers.

[101]  H. Holzhütter,et al.  Mathematical modelling of metabolic pathways affected by an enzyme deficiency. Energy and redox metabolism of glucose-6-phosphate-dehydrogenase-deficient erythrocytes. , 1989, European journal of biochemistry.

[102]  D. Mccormick Sequence the Human Genome , 1986, Bio/Technology.

[103]  R. Torretti Mario Bunge: Scientific research. I. The search for system. II. The search for truth. Berlin Heidelberg. New York. Springer-Verlag. 1967 , 1967 .

[104]  Mario Bunge,et al.  Scientific Research I , 1967 .