Résumé
A cellular model is a mathematical model of aspects of a biological cell, for the purposes of in silico research. Developing such models has been a task of systems biology and mathematical biology. It involves developing efficient algorithms, data structures, visualization and communication tools to orchestrate the integration of large quantities of biological data with the goal of computer modeling. It involves the use of computer simulations of cellular subsystems, such as the networks of metabolites and enzymes which comprise metabolism, signal transduction pathways and gene regulatory networks. The eukaryotic cell cycle is very complex and is one of the most studied topics, since its misregulation leads to cancers. It is possibly a good example of a mathematical model as it deals with simple calculus but gives valid results. Two research groups have produced several models of the cell cycle simulating several organisms. They have recently produced a generic eukaryotic cell cycle model which can represent a particular eukaryote depending on the values of the parameters, demonstrating that the idiosyncrasies of the individual cell cycles are due to different protein concentrations and affinities, while the underlying mechanisms are conserved (Csikasz-Nagy et al., 2006). By means of a system of ordinary differential equations these models show the change in time (dynamical system) of the protein inside a single typical cell; this type of model is called a deterministic process (whereas a model describing a statistical distribution of protein concentrations in a population of cells is called a stochastic process). To obtain these equations an iterative series of steps must be done: first the several models and observations are combined to form a consensus diagram and the appropriate kinetic laws are chosen to write the differential equations, such as rate kinetics for stoichiometric reactions, Michaelis-Menten kinetics for enzyme substrate reactions and Goldbeter–Koshland kinetics for ultrasensitive transcription factors, afterwards the parameters of the equations (rate constants, enzyme efficiency coefficients and Michaelis constants) must be fitted to match observations; when they cannot be fitted the kinetic equation is revised and when that is not possible the wiring diagram is modified.
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Concepts associés (3)
Cellular model
A cellular model is a mathematical model of aspects of a biological cell, for the purposes of in silico research. Developing such models has been a task of systems biology and mathematical biology. It involves developing efficient algorithms, data structures, visualization and communication tools to orchestrate the integration of large quantities of biological data with the goal of computer modeling. It involves the use of computer simulations of cellular subsystems, such as the networks of metabolites and enzymes which comprise metabolism, signal transduction pathways and gene regulatory networks.
Biologie des systèmes
La biologie des systèmes (ou biologie intégrative) est un domaine récent de la biologie qui étudie les organismes vivants comme les systèmes qu'ils sont en réalité, par opposition aux approches historiques qui tendent à décomposer l'étude à tous les niveaux, en biologie, physiologie, biochimie... La biologie systémique cherche à intégrer différents niveaux d'informations pour comprendre comment fonctionne réellement un système biologique.
Bio-informatique
La bioinformatique (ou bio-informatique), est un champ de recherche multidisciplinaire de la biotechnologie où travaillent de concert biologistes, médecins, informaticiens, mathématiciens, physiciens et bioinformaticiens, dans le but de résoudre un problème scientifique posé par la biologie. Plus généralement, la bio-informatique est l'application de la statistique et de l'informatique à la science biologique. Le spécialiste qui travaille à mi-chemin entre ces sciences et l'informatique est appelé bioinformaticien ou bionaute.
Cours associés (2)
PHYS-302: Biophysics : physics of biological systems
Understand and use the results and methods of population genetics, population dynamics, network theory, and reaction network dynamics to analyze and predict the behavior of living systems
ChE-411: Principles and applications of systems biology
The course introduces and develops the key concepts from systems biology and systems engineering in the context of complex biological networks. The lectures elaborate on techniques and methods to mode