Transcript 基因调控网络： －数学模型与仿真

基因调控网络：
－数学模型与仿真
马宏宾
系统所
2003.10.30
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
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
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
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
Choice between alternative developmental
pathways controlled by network of genes,
proteins, and mutual regulatory interactions。
• 例子：
问题与背景
• 基因调控网络的复杂性
– Large networks
– Complex 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
interactions
– systematical derivation of behavioral predictions
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 network
Truth tables
State-transition diagram
模型：Generalized
logical network
模型：Nonlinear ODE
模型：Nonlinear ODE
• Negative feedback
– Gene encodes a protein inhibiting its own
expression
– important for homeostasis, maintenance
of system near a desired state
– Steady state analysis
– Transient behavior simulation
模型：Nonlinear ODE
• Positive feedback
– Gene encodes a protein activating its own
expression.
– important for differentiation, evolution
towards one of two alternative states of
system
– Steady states
– Transient behaviors
模型：Nonlinear ODE
Applications:
模型：Piecewise-linear ODE
模型：Qualitative Differential
Equation
• QDE:
– Abstraction of the form
–
–
–
–
Qualitative value x:
Qualitative function fi:
QSIM algorithmQualitative behaviors
Qualitative simulation
模型：Spatially Distributed
Model
o Configuration：
o Discrete model:
o Continuous model:
boundary conditions:
模型：Stochastic Model
模型：Stochastic Model
• Time evolution of p(X,t)：
master equation:
• ＝>Stochastic simulation: use r.v. τand ρ
模型：Stochastic Model
Simulations:
Applications:
模型：Rule-based formalism
• Knowledge 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
processes
• Variety of approaches available, representing genetic
regulatory systems on different levels of abstraction
• Choice of approach depends on aim of analysis and
on available information:
– knowledge on reaction mechanisms
– quantitative data on model parameters and gene
expression levels
• Serious 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. 27:199–
224.
Paul Smolen, Douglas A. Baxter And John H. Byrne, Modeling
Transcriptional Control in Gene Networks—Methods, Recent
Results, and Future Directions, Bulletin of Mathematical Biology
(2000) 62, 247–292.
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, 13:193–202.
Olivier Cinquin, Jacques Demongeot, Positive and negative
feedback: striking a balance between necessary antagonists,
Journal of Theoretical Biology, 216(2), pp229-241 (2002)