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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.tw Tel: (03)5186760. What is Systems Biology ?. Systems biology is an emergent field that aims at system-level understanding of biological systems.

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系統生物學簡介 Introduction to Systems Biology

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  1. 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.tw Tel: (03)5186760

  2. 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.

  3. 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.

  4. What is Systems Biology ? • Systems biology is biology • System biology is modeling • Systems biology is data integration • Systems biology is cross disciplinary

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

  6. 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)

  7. 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)

  8. 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

  9. High throughput Proteomics experiment Science, Vol 291, Issue 5507, 1221-1224 , 16 February 2001

  10. Systems biology is complex • Large number of components • Diversity of components • Many time scales involved • Large number of process • Nonlinear and feedback of process

  11. What should systems biology cover? • Understanding of system structure • Understanding behaviors of the system • Understanding how to control the system • Understanding how to design the system -- by Hiroaki Kitano 2002

  12. GENOME protein-gene interactions PROTEOME protein-protein interactions METABOLISM Bio-chemical reactions Citrate Cycle

  13. Characteristic time scale in biological process

  14. Types of biological network • Genetic regulatory network • Protein-protein interaction network • Metabolic network • Signal transduction network

  15. Systems Biology≈Network Biology≈Molecular Network Biology + Network of Molecular Network Biology

  16. Emergence of Systems Biology Science, vol.295, March 1, (2002).

  17. 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.

  18. Institute for Systems Biology Leroy Hood, Seattle, 2000 http://www.systemsbiology.org/

  19. Symbiotic systems project Hiroaki Kitano http://www.symbio.jst.go.jp/symbio2/index.html

  20. Feature of biological model • Complex: diverse constituents, large system size, selective interaction • Nonlinear: feedback, oscillatory, steady state(fixed point) • Modularity: modular structures system • Robustness

  21. 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

  22. Different level of biological model Science, vol. 310, 449, 2005

  23. Systems Biology as a dynamical system feedback Model Input signal Output signal Control the system

  24. An example of modeling

  25. Cell cycle related genes 800 Genes involved in Yeast Cell Cycle Spellman, et al. (1998)

  26. Sneppen’s model Protein-protein Interaction Network in Yeast Nucleus • There are 198interactions between 123proteins. • The interactions can be divided into two kinds : Positive - Green, Negative - Red. Sneppen’s model

  27. Coarse Yeast Cell Cycle Model

  28. Discrete network dynamics model The 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. for positive interactions (green arrows); for negative interactions (red arrows)

  29. Fixed points

  30. Flow diagram • 1. Pink arrows: <64; Orange arrows: 64~128; Red arrows: >128; Blue arrows: Biological Pathway • 2. Big blue node: Biological ground G1 state.

  31. Synthetic Biology

  32. 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

  33. Impact of systems biology • Preemptive molecular medicine • Drug design • New method of organ cloning

  34. 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

  35. References • D. Kaplan, and L. Glass, Understanding nonlinear 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

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