基因调控网络数学模型与仿真实用教案

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1、纲要(gngyo) 必要的说明 问题与背景 模型(mxng)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(wi fn fn chn)“定性”微分方程(wi fn fn chn)偏微分方程(wi fn fn chn)随机模型基于规则的形式方法第1页/共34页第一页，共35页。必要(byo)的说明 我完全(wnqun)不懂生物学； 我为什么要讲这个？ 我讲的侧重点在哪？内容(nirng)完全基于：童维上传Modeling and Simulation of Genetic Regulatory Systems: A Literature Review第2

2、页/共34页第二页，共35页。问题(wnt)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(rn shi)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？第3页/共34页第三页，共35页。问题(wnt)与背景v 基因( jyn)和蛋白质v Genes code for proteins that are essential for development and functioning of organism: gene expression第4页/共34页第四页，共35页。问题(wnt)与背景v 基因

3、表达(biod)的调控：不同层次v Gene expression controlled by proteins produced by other genes: regulatory interactions第5页/共34页第五页，共35页。问题(wnt)与背景v基因调控(dio kn)网络：vGenetic regulatory network consists of set of genes, proteins, small molecules, and their mutual regulatory interactions。vDevelopment and functioning o

4、f organisms cell emerges from interactions in genetic regulatory networks。第6页/共34页第六页，共35页。问题(wnt)与背景 例子(l zi)：Choice between alternative developmental pathways controlled by network of genes, proteins, and mutual regulatory interactions。第7页/共34页第七页，共35页。 基因(jyn)调控网络的复杂性 Large networks Complex cells

5、 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

6、么需要数学建模与仿真？ precise and unambiguous description of network of interactions systematical derivation of behavioral predictions问题(wnt)与背景第8页/共34页第八页，共35页。问题(wnt)与背景 目标我们(w men)想知道： Which genes are expressed?When and where in the organisms?To which extent? Are there any universal laws? Can we predict th

7、e evolution of the network? How to predict the evolution of the network?第9页/共34页第九页，共35页。问题(wnt)与背景 途径实验(shyn)、建模、仿真：第10页/共34页第十页，共35页。模型(mxng)：有向图第11页/共34页第十一页，共35页。模型(mxng)：有向图第12页/共34页第十二页，共35页。模型(mxng)：Baysian network第13页/共34页第十三页，共35页。模型(mxng)：Baysian network第14页/共34页第十四页，共35页。模型(mxng)：Boolean

8、network第15页/共34页第十五页，共35页。模型(mxng)：Boolean network第16页/共34页第十六页，共35页。模型(mxng)：Boolean networkTruth tables State-transition diagram 第17页/共34页第十七页，共35页。模型(mxng)：Generalized logical network第18页/共34页第十八页，共35页。模型(mxng)：Nonlinear ODE第19页/共34页第十九页，共35页。 Negative feedback Gene encodes a protein inhibiting i

9、ts own expression important for homeostasis, maintenance of system near a desired state Steady state analysis Transient behavior simulation模型(mxng)：Nonlinear ODE第20页/共34页第二十页，共35页。模型(mxng)：Nonlinear ODE Positive feedback Gene encodes a protein activating its own expression. important for differentia

10、tion, evolution towards one of two alternative states of system Steady states Transient behaviors第21页/共34页第二十一页，共35页。模型(mxng)：Nonlinear ODEv Applications:第22页/共34页第二十二页，共35页。模型(mxng)：Piecewise-linear ODE第23页/共34页第二十三页，共35页。模型(mxng)：Qualitative Differential Equation QDE: Abstraction of the form Quali

11、tative value x: Qualitative function fi: QSIM algorithmQualitative behaviors Qualitative simulation第24页/共34页第二十四页，共35页。模型(mxng)：Spatially Distributed Modelo Configuration：o Discrete model:o Continuous model:boundary conditions:第25页/共34页第二十五页，共35页。模型(mxng)：Stochastic Model第26页/共34页第二十六页，共35页。模型(mxng)

12、：Stochastic Model Time evolution of p(X,t)：master equation: Stochastic simulation: use r.v. and 第27页/共34页第二十七页，共35页。模型(mxng)：Stochastic Modelv Simulations:v Applications:第28页/共34页第二十八页，共35页。模型(mxng)：Rule-based formalism Knowledge base Expert system? Facts： Rules：第29页/共34页第二十九页，共35页。总结(zngji)与展望第30页/

13、共34页第三十页，共35页。总结(zngji)与展望 Computer tools for modeling and simulation will be necessary to understand genetic regulatory processes Variety of approaches available, representing genetic regulatory systems on different levels of abstraction Choice of approach depends on aim of analysis and on availabl

14、e information: knowledge on reaction mechanisms quantitative data on model parameters and gene expression levels Serious applications are beginning to emerge第31页/共34页第三十一页，共35页。参考文献Hidde De Jong, Modeling and Simulation of Genetic Regulatory Systems: A Literature Review, Journal Of Computational Bio

15、logy, 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, Bullet

16、in 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

17、, Positive and negative feedback: striking a balance between necessary antagonists, Journal of Theoretical Biology, 216(2), pp229-241 (2002)第32页/共34页第三十二页，共35页。第33页/共34页第三十三页，共35页。感谢您的观看(gunkn)！第34页/共34页第三十四页，共35页。NoImage内容(nirng)总结纲要。模型(mxng)：Generalized logical network。Transient behavior simulation。Qualitative function fi:。模型(mxng)：Spatially Distributed Model。第33页/共34页。感谢您的观看第三十五页，共35页。