系統生物學簡介 Introduction to Systems Biology

系統生物學簡介Introduction to Systems Biology

授課教師 : 黃俊燕中華大學生物資訊系E-MAIL:jyhuang@chu.edu.twTel: (03)5186760

What is Systems Biology ?

• Systems biology is an emergent field that aims at system-level understanding of biological systems.

• A quantitative and systematic approach to study biology.

Systems Biology: the 21st Century Science

• Systems biology is the study of an organism, viewed as an integrated and interacting network of genes, proteins and biochemical reactions which give rise to life. Instead of analyzing individual components or aspects of the organism, such as sugar metabolism or a cell nucleus, systems biologists focus on all the components and the interactions among them, all as part of one system. These interactions are ultimately responsible for an organism´s form and functions. For example, the immune system is not the result of a single mechanism or gene. Rather the interactions of numerous genes, proteins, mechanisms and the organism´s external environment, produce immune responses to fight infections and diseases.

What is Systems Biology ?

• Systems biology is biology

• System biology is modeling

• Systems biology is data integration

• Systems biology is cross disciplinary

Systems Biology is hot !!!PubMed search by “systems biology”

Is Systems Biology new ?• Systems (holistic) thinking in antiquity• • Alfred Lotka, Elements of Physical Biology (1924)

• Ludwig von Bertalanffy, The Organism Considered as a Physical System (1940)

• • Erwin Schrodinger, What is life? with mind and matter and

autobiographical sketches.

• Warren Weaver, Science and Complexity (1948)• • Norbert Wiener, Cybernetics (1948)

• Rosen, Yates, Savageau, … (1960’s, 1970’s)

Organism is open, not closed (chemical or physical) system Dynamic (quasi-) steady state, as opposed to static equilibrium Fine-tuned coordination of all process rates for steady state Processes nonlinear Perturbations and stability Apparent teleology (vital force) consequence of dynamics “A quantitative theory of phenomena of life should be possible” “…even after a full explanation of all individual processes are we as far away from a total understanding of metabolism as the sky is wide.” (cited from Hartmann, 1927; translated)

What is new for Systems Biology ?

Demand caused by high-throughput data (microarrays, proteomics, metabolic profiles)

Data of higher accuracy

Better theory and modeling techniques

Better computers

“Democratization” of computers

Wider recognition that reductionism is not sufficient

High throughput Proteomics experiment

Science, Vol 291, Issue 5507, 1221-1224 , 16 February 2001

Systems biology is complex

• Large number of components

• Diversity of components

• Many time scales involved

• Large number of process

• Nonlinear and feedback of process

1. Understanding of system structure

2. Understanding behaviors of the system

3. Understanding how to control the system

4. Understanding how to design the system

-- by Hiroaki Kitano 2002

What should systems biology cover?

protein-gene interactions

protein-protein interactions

PROTEOME

GENOME

Citrate Cycle

METABOLISM

Bio-chemical reactions

Characteristic time scale in biological process

Types of biological network

• Genetic regulatory network

• Protein-protein interaction network

• Metabolic network

• Signal transduction network

Systems Biology≈

Network Biology≈

Molecular Network Biology + Network of Molecular Network

Biology

Emergence of Systems Biology

Science, vol.295, March 1, (2002).

Two approaches of systems biology

• Top-down approach: data integration—Leroy Hood

Institute for Systems Biology

• Bottom-up approach: Computer simulation—Hiroaki Kitano

Sony Computer Sciences Laboratories. Inc.

Institute for Systems Biology

Leroy Hood, Seattle, 2000

http://www.systemsbiology.org/

Symbiotic systems project

http://www.symbio.jst.go.jp/symbio2/index.html

Hiroaki Kitano

Feature of biological model

• Complex: diverse constituents, large system size, selective interaction

• Nonlinear: feedback, oscillatory, steady state(fixed point)

• Modularity: modular structures system

• Robustness

Feature of biological model

• Qualitative model: discrete model, Boolean network model, evolution of all initial states

• Quantitative model: continuous model, systems of rate equations, stochastic simulation

Science, vol. 310, 449, 2005

Different level of biological model

Systems Biology as a dynamical system

Input signal Output signal

feedback

Model

Control the system

An example of modeling

Cell cycle related genes

800 Genes involved in Yeast Cell Cycle

Spellman, et al. (1998)

Sneppen’s modelProtein-protein Interaction Network in Yeast Nucleus

There are 198 interactions between 123 proteins.The interactions can be divided into two kinds : Positive - Green,Negative - Red.

Sneppen’s model

Coarse Yeast Cell Cycle Model

Discrete network dynamics modelThe states of proteins are defined as: 0 - inactive, 1 - active.

The interaction rules:The protein states in the next logic time step are determined by the protein states in the present step.

)1(tSi

),(

,0

,1

tSi

jjij

jjij

jjij

tSa

tSa

tSa

0)(

0)(

0)(

for positive interactions (green arrows); for negative interactions (red arrows)

1ija 1ija

Fixed points

Flow diagram

1. Pink arrows: <64; Orange arrows: 64~128; Red arrows: >128; Blue arrows: Biological Pathway2. Big blue node: Biological ground G1 state.

Synthetic Biology

What will we learn in this course ?

• Network biology

• Nonlinear dynamics

• Mathematical modeling of biological network

• Feedback control theory

• Biological case study

• Software for systems biology

Impact of systems biology

• Preemptive molecular medicine

• Drug design

• New method of organ cloning

References• H. Kitano, Foundations of Systems Biology

• J.M. Bower, and H. Bolouri, Computational modeling of genetic and biochemical networks

• E. Klipp, R. Herwig, A. Kowald, C. Wierling, and H. Lehrach, Systems Biology in Practice: Concepts, Implementation and Application.

• E.O. Voit, Computational analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists.

• Julio Collado-Vides and Ralf Hofestädt, Gene Regulation and Metabolism-Postgenomic Computational Approaches

References• D. Kaplan, and L. Glass, Understanding nonlin

ear dynamics

• R.C. Hilborn, Chaos and nonlinear dynamics

• Alberts et al., James Watson, Molecular biology of the cell

• G.F. Franklin, J.D. Powell, Feedback control of dynamic systems