Systems biology; models; transcriptomics; proteomics; data mining; bioinformatics; genomics; therapeutic; taget; neurodegeneration
Abstract :
[en] Systems biology has emerged as a major trend in biological research during the past decade. As living organisms are described in more and more detail, it aims at filling the gap between understanding basic molecular processes and complex biological systems in which new properties often emerge from the combination of these elementary processes. This approach culminates in the development of computer-based mathematical models of physiological and pathophysiological processes. We review the state of the art in dynamic modelling, with emphasis on two complementary approaches: the modelling of small systems that is mostly developed by academic teams and aims at understanding generic biological properties, and the modelling of large systems that is mostly implemented by industrial companies and aims at the generation of new therapeutic strategies. We also provide an example of such large-scale modelling applied to the identification of drug targets for neurodegeneration.
Research center :
CART - Centre Interfacultaire d'Analyse des Résidus en Traces - ULiège
Disciplines :
Chemistry Pharmacy, pharmacology & toxicology Engineering, computing & technology: Multidisciplinary, general & others Biochemistry, biophysics & molecular biology Human health sciences: Multidisciplinary, general & others Genetics & genetic processes
Author, co-author :
Vujasinovic, Todor; Helios BioSciences
Zampera, André Sinisa; Helios BioSciences
Jackers, Pascale ; Université de Liège - ULiège > Center for Analytical Research and Technology (CART)
Sanoudou, Despina; Biomedical Research Foundation of the Academy of Athens > Molecular Biology Division
Depaulis, Antoine; Université Joseph Fourier - Grenoble 1 - UJF > Institut des Neurosciences
Language :
English
Title :
In Silico Dynamic Molecular Interaction Networks for the Discovery of New Therapeutic Targets
Validated "Predictive Dynamic Models of Complex Intracellular Pathways Related to Cell Death and Survival"
Funders :
Research funds from the European Union 6th Framework Program for Research and Technological Development, “Life sciences, genomics and biotechnology for health”, VALAPODYN, contract #LSHG-CT-2006-037277
Das RR, Seshadri S, Beiser AS, et al. Prevalence and correlates of silent cerebral infarcts in the Framingham offspring study. Stroke 2008; 39: 2929-35.
Seshadri S, Beiser A, Kelly-Hayes M, et al. The lifetime risk of stroke: estimates from the Framingham Study. Stroke 2006; 37: 345-50.
Sible JC and Tyson JJ. Mathematical modeling as a tool for investigating cell cycle control networks. Methods 2007; 41: 238-47.
Tyson JJ and Novak B. Regulation of the eukaryotic cell cycle: molecular antagonism, hysteresis, and irreversible transitions. J Theor Biol 2001; 210: 249-63.
Klipp E, Nordlander B, Kruger R, et al. Integrative model of the response of yeast to osmotic shock. Nat Biotechnol 2005; 23: 975- 82.
Sasagawa S, Ozaki Y, Fujita K, et al. Prediction and validation of the distinct dynamics of transient and sustained ERK activation. Nat Cell Biol 2005; 7: 365-73.
Reijenga KA, Westerhoff HV, Kholodenko BN, et al. Control analysis for autonomously oscillating biochemical networks. Biophys J 2002; 82: 99-108.
Ruoff P, Christensen MK, Wolf J, et al. Temperature dependency and temperature compensation in a model of yeast glycolytic oscillations. Biophys Chem 2003; 106: 179-92.
Schoeberl B, Eichler-Jonsson C, Gilles ED, et al. Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nat Biotechnol 2002; 20: 370-5.
Vilar JM, Jansen R and Sander C. Signal processing in the TGFbeta superfamily ligand-receptor network. PLoS Comput Biol 2006; 2: e3.
Dingemanse J and Appel-Dingemanse S. Integrated pharmacokinetics and pharmacodynamics in drug development. Clin Pharmacokinet 2007; 46: 713-37.
Elowitz MB and Leibler S. A synthetic oscillatory network of transcriptional regulators. Nature 2000; 403: 335-8.
Gardner TS, Cantor CR and Collins JJ. Construction of a genetic toggle switch in Escherichia coli. Nature 2000; 403: 339-42.
Nevozhay D, Adams RM, Murphy KF, et al. Negative autoregulation linearizes the dose-response and suppresses the heterogeneity of gene expression. Proc Natl Acad Sci U S A 2009; 106: 5123-8.
Hayer A and Bhalla US. Molecular switches at the synapse emerge from receptor and kinase traffic. PLoS Comput Biol 2005; 1: 137- 54.
Maass W, Joshi P and Sontag E. Computational aspects of feedback in neural circuits. PLoS Comput Biol. 2006; 3: e165.
Bhalla US and Iyengar R. Emergent properties of networks of biological signaling pathways. Science 1999; 283: 381-7.
de Almeida AC, Rodrigues AM, Scorza FA, et al. Mechanistic hypotheses for nonsynaptic epileptiform activity induction and its transition from the interictal to ictal state--computational simulation. Epilepsia 2008; 49: 1908-24.
Roberts JA and Robinson PA. Modeling absence seizure dynamics: implications for basic mechanisms and measurement of thalamocortical and corticothalamic latencies. J Theor Biol 2008; 253: 189- 201.
Somjen GG, Kager H and Wadman WJ. Calcium sensitive nonselective cation current promotes seizure-like discharges and spreading depression in a model neuron. J Comput Neurosci 2009; 26: 139-47.
Wu XE and Mel BW. Capacity-enhancing synaptic learning rules in a medial temporal lobe online learning model. Neuron 2009; 62: 31-41.
Chapuisat G, Dronne MA, Grenier E, et al. A global phenomenological model of ischemic stroke with stress on spreading depressions. Prog Biophys Mol Biol 2008; 97: 4-27.
Cakir T, Alsan S, Saybasili H, et al. Reconstruction and flux analysis of coupling between metabolic pathways of astrocytes and neurons: application to cerebral hypoxia. Theor Biol Med Model 2007; 4: 48.
Qi Z, Miller GW and Voit EO. A mathematical model of presynaptic dopamine homeostasis: implications for schizophrenia. Pharmacopsychiatry 2008; 41 Suppl 1: S89-98.
Qi Z, Miller GW and Voit EO. Computational systems analysis of dopamine metabolism. PLoS One 2008; 3: e2444.
Rangamani P and Sirovich L. Survival and apoptotic pathways initiated by TNF-alpha: modeling and predictions. Biotechnol Bioeng 2007; 97: 1216-29.
Dhingra S, Freedenberg M, Quo CF, et al. Computational modeling of a metabolic pathway in ceramide de novo synthesis. Conf Proc IEEE Eng Med Biol Soc 2007; 2007: 1405-8.
Fisher WG, Yang PC, Medikonduri RK, et al. NFAT and NFkappaB activation in T lymphocytes: a model of differential activation of gene expression. Ann Biomed Eng 2006; 34: 1712-28.
Le Novere N, Bornstein B, Broicher A, et al. BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucleic Acids Res 2006; 34: D689-91.
http://www.ebi.ac.uk/biomodels/, 2009.
http://www.nrcam.uchc.edu/index.html, 2009.
Mendes P. GEPASI: a software package for modelling the dynamics, steady states and control of biochemical and other systems. Comput Appl Biosci 1993; 9: 563-71.
Moraru, II, Schaff JC, Slepchenko BM, et al. Virtual Cell modelling and simulation software environment. IET Syst Biol 2008; 2: 352-62.
ten Tusscher KH, Noble D, Noble PJ, et al. A model for human ventricular tissue. Am J Physiol Heart Circ Physiol 2004; 286: H1573-89.
Hunter PJ and Borg TK. Integration from proteins to organs: the Physiome Project. Nat Rev Mol Cell Biol 2003; 4: 237-43.
http://www.entelos.com/index.php, 2009.
Li S, Assmann SM and Albert R. Predicting essential components of signal transduction networks: a dynamic model of guard cell abscisic acid signaling. PLoS Biol 2006; 4: e312.
Schadt EE, Friend SH and Shaywitz DA. A network view of disease and compound screening. Nat Rev Drug Discov 2009; 8: 286- 95.
Aksenov SV, Church B, Dhiman A, et al. An integrated approach for inference and mechanistic modeling for advancing drug development. FEBS Lett 2005; 579: 1878-83.
Keller MP, Choi Y, Wang P, et al. A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility. Genome Res 2008; 18: 706-16.
http://cabig.cancer.gov/
http://icgc.org/home
Gat-Viks I, Tanay A, Raijman D, et al.Lecture Notes in Bioinformatics 3500. Berlin, Springer. 2005, pp. 31-47.
Gat-Viks I, Tanay A and Shamir R. Modeling and analysis of heterogeneous regulation in biological networks. J Comput Biol 2004; 11: 1034-49.
Jong Hd. Modelling and Simulation of genetic regulatory systems: a literature review, rapport de recherche No. 4032, September 2000, INRIA. Jong Hd; INRIA, 2000.
Gillespie DT. Stochastic simulation of chemical kinetics. Annu Rev Phys Chem 2007; 58: 35-55.
Vujasinovic T and Zampera A. In: Anton Yuryev Ed, Pathway Analysis for Drug Discovery: Computational Infrastructure and applications. New Jersey, John Wiley & Sons, Inc. 2008, pp. 263- 284.
Vujasinovic T and Zampera A. Definir des cibles therapeutiques avec des modeles dynamiques. Biofutur 2007; 26: 40-43.
Goldberg D. Genetic Algorithms in Search, Optimization and Machine Learning Addison-Wesley Ed. 1989.
Pearlmutter BA. Gradient calculations for dynamic recurrent neural networks: a survey. IEEE Trans Neural Netw 1995; 6: 1212-28.
Han DK, Eng J, Zhou H, et al. Quantitative profiling of differentiation- induced microsomal proteins using isotope-coded affinity tags and mass spectrometry. Nat Biotechnol 2001; 19: 946-51.
Bayes A and Grant SG. Neuroproteomics: understanding the molecular organization and complexity of the brain. Nat Rev Neurosci 2009; 10: 635-46.
Ben-Ari Y and Cossart R. Kainate, a double agent that generates seizures: two decades of progress. Trends Neurosci 2000; 23: 580-7.
Suzuki F, Junier MP, Guilhem D, et al. Morphogenetic effect of kainate on adult hippocampal neurons associated with a prolonged expression of brain-derived neurotrophic factor. Neuroscience 1995; 64: 665-74.
Riban V, Bouilleret V, Pham-Le BT, et al. Evolution of hippocampal epileptic activity during the development of hippocampal sclerosis in a mouse model of temporal lobe epilepsy. Neuroscience 2002; 112: 101-11.
Wiebe S, Blume WT, Girvin JP, et al. A randomized, controlled trial of surgery for temporal-lobe epilepsy. N Engl J Med 2001; 345: 311-8.
Monteoliva L and Albar JP. Differential proteomics: an overview of gel and non-gel based approaches. Brief Funct Genomic Proteomic 2004; 3: 220-39.
Waters KM, Pounds JG and Thrall BD. Data merging for integrated microarray and proteomic analysis. Brief Funct Genomic Proteomic 2006; 5: 261-72.
Marcus F. Bioinformatics and Systems Biology: collaborative research and resources Springer. 2008.
Siena S, Sartore-Bianchi A, Di Nicolantonio F, et al. Biomarkers predicting clinical outcome of epidermal growth factor receptortargeted therapy in metastatic colorectal cancer. J Natl Cancer Inst 2009; 101: 1308-24.
Bharatam P V, Patel DS,Adane L, et al. Modeling and informatics in designing anti-diabetic agents. Curr Pharm Des 2007; 13(34): 3518-30.
Costantino G. In silico approaches towards the understanding of the structure-function relationships in metabotropic glutamate receptors (mGluRs) and other family C GPRCs. Curr Pharm Des 2006; 12(17): 2159-73.
Goutsias J, Lee NH. Computational and experimental approaches for modeling gene regulatory networks. Curr Pharm Des 2007; 13(14): 1415-36.
