基因调控网络-数学模型与仿真.ppt

基因调控网络-数学模型与仿真 Still waters run deep.流静水深流静水深,人静心深人静心深 Where there is life,there is hope。有生命必有希望。有生命必有希望纲要必要的说明问题与背景模型与仿真总结与展望参考文献有向图Bayesian网络Boolean网络及推广常微分方程“定性”微分方程偏微分方程随机模型基于规则的形式方法必要的说明我完全不懂生物学；我为什么要讲这个？我讲的侧重点在哪？内容完全基于：童维上传Modeling and Simulation of Genetic Regulatory Systems:A Literature Review问题与背景什么是基因调控网络？细胞、DNA、蛋白质、基因、基因网络为什么要研究基因调控网络？从分子水平认识细胞组织的功能。基因调控网络与复杂性了解基因调控网络，对我们有什么启发？问题与背景v基因和蛋白质Genes code for proteins that are essential for development and functioning of organism:gene expression问题与背景v基因表达的调控：不同层次Gene expression controlled by proteins produced by other genes:regulatory interactions问题与背景v基因调控网络：Genetic regulatory network consists of set of genes,proteins,small molecules,and their mutual regulatory interactions。Development and functioning of organisms cell emerges from interactions in genetic regulatory networks。问题与背景例子：Choice between alternative developmental pathways controlled by network of genes,proteins,and mutual regulatory interactions。基因调控网络的复杂性Large networksComplex cells has many components that can interact in complex ways.Dynamics processes are hard to understand by intuitive approaches alone.Genetic regulatory networks have complicated interactions far beyond correlation of gene expression patterns.Clustering cannot reveal causal connections between genes.为什么需要数学建模与仿真？precise and unambiguous description of network of interactionssystematical derivation of behavioral predictions问题与背景问题与背景目标我们想知道：Which genes are expressed?When and where in the organisms?To which extent?Are there any universal laws?Can we predict the evolution of the network?How to predict the evolution of the network?问题与背景途径实验、建模、仿真：模型：有向图模型：有向图模型：Baysian network模型：Baysian network模型：Boolean network模型：Boolean network模型：Boolean networkTruth tables State-transition diagram 模型：模型：Generalized logical network模型：Nonlinear ODENegative feedbackGene encodes a protein inhibiting its own expressionimportant for homeostasis,maintenance of system near a desired stateSteady state analysisTransient behavior simulation模型：Nonlinear ODE模型：Nonlinear ODEPositive feedbackGene encodes a protein activating its own expression.important for differentiation,evolution towards one of two alternative states of systemSteady statesTransient behaviors模型：Nonlinear ODEvApplications:模型：Piecewise-linear ODE模型：Qualitative Differential EquationQDE:Abstraction of the formQualitative value x:Qualitative function fi:QSIM algorithmQualitative behaviorsQualitative simulation模型：Spatially Distributed ModeloConfiguration：oDiscrete model:oContinuous model:boundary conditions:模型：Stochastic Model模型：Stochastic ModelTime evolution of p(X,t)：master equation:Stochastic simulation:use r.v.and 模型：Stochastic ModelvSimulations:vApplications:模型：Rule-based formalismKnowledge base Expert system?Facts：Rules：总结与展望总结与展望Computer tools for modeling and simulation will be necessary to understand genetic regulatory processesVariety of approaches available,representing genetic regulatory systems on different levels of abstractionChoice of approach depends on aim of analysis and on available information:knowledge on reaction mechanismsquantitative data on model parameters and gene expression levelsSerious applications are beginning to emerge参考文献Hidde De Jong,Modeling and Simulation of Genetic Regulatory Systems:A Literature Review,Journal Of Computational Biology,9(1),2002.Harley H.McAdams,Adam Arkin,Simulation Of Prokaryotic Genetic Circuits,Annu.Rev.Biophys.Biomol.Struct.1998.27:199224.Paul Smolen,Douglas A.Baxter And John H.Byrne,Modeling Transcriptional Control in Gene NetworksMethods,Recent Results,and Future Directions,Bulletin of Mathematical Biology(2000)62,247292.Christophe Roos,Facing Biological Complexity From One Cell to a Multicellular Organism,Technology BIOINFORMATICS.Eric Alm and Adam P Arkin,Biological networks,Current Opinion in Structural Biology,2003,13:193202.Olivier Cinquin,Jacques Demongeot,Positive and negative feedback:striking a balance between necessary antagonists,Journal of Theoretical Biology,216(2),pp229-241(2002)