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Systems biology -the intro

Systems biology -the intro. 張晃猷 分子醫學研究所 hychang@life.nthu.edu.tw. What is Systems Biology????. To unravel the mysteries of human biology to identify strategies for predicting and preventing diseases such as cancer, diabetes and AIDS. http://www.systemsbiology.org/.

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Systems biology -the intro

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  1. Systems biology-the intro 張晃猷 分子醫學研究所 hychang@life.nthu.edu.tw

  2. What is Systems Biology????

  3. To unravel the mysteries of human biology to identify strategies for predicting and preventing diseases such as cancer, diabetes and AIDS. http://www.systemsbiology.org/

  4. A cross-disciplinary science

  5. The Human Genome Project Goals Impact • Discovery science vs hypothesis-driven • Biology is an Informational Science • Tools for high throughput quantitative measurement of biological information • The use of model organisms

  6. Completed prokaryotes eukaryotes Archae 19 eubacteria 167 ongoing ongoing Eukaryote 32

  7. Organizational and Descriptional Levels

  8. So what is Systems Biology?

  9. The definition • The types of biological information (DNA, RNA, protein, protein interactions, biomolecules, cells, tissues, etc.) also have their individual elements (e.g. specific genes or proteins) and the relationships of these with respect to one another and the elements of other types of biological information must be determined, all of this information integrated to obtain a view (model) of the system as a whole.

  10. Still vague?

  11. Systems Biology is a new field in biology that aims at system-level understanding of biological systems. Hiroaki Kitano (Director, ERATO Kitano Symbiotic Systems Project)

  12. What are biological systems? • Ranges from ecosystems (eg. Biosphere) to the system of reactions that form cellular biochemistry

  13. A Systems Approach to the Study of Biological SystemsSome examples

  14. Galactose utilization/galatosemia • How a defective control protein (red circle) alters the level of other proteins (circles in shades of gray) through interactions among proteins (blue lines) and interactions between proteins and DNA (yellow arrows).

  15. Reverse-engineer the computational principles underlying cellular processes; • Develop tools and techniques for modeling and analysis of experimental data at three levels: • individual genes; • network modules; • whole networks.

  16. Human immunity

  17. m CNS 10 cm Systems 1 cm Networks Neurons 100 um Synapses um Molecules nm

  18. 1014 connecting points

  19. Keynote Speakers: James J. CollinsCenter for BioDynamics, Boston UniversityJohn Doyle Control and Dynamical Systems, CaltechYoshihide HayasizakiGenome Exploration Group, RIKEN Genomic Sciences CenterStan LeiblerLaboratory of Living Matter, Rockefeller UniversityMark Ptashne Molecular Biology Program, Sloan-Kettering Institute

  20. System Biology The quantitative study of biological processes as integrated systems rather than as isolated parts. The aim is to understand the interactions between the myriad of sub-cellular components.

  21. The traditionally separated scientific disciplines, including physical chemistry, biochemistry, molecular biology, cell physiology and the behaviour of multicellular organisms, are unified by quantitative models. Advance techniques for global measurements of subcellular dynamics of gene expression, proteins, and metabolites will be applied. The progress will be crucial for a molecular understanding of many diseases and for development of novel biotechnological applications.

  22. Expression Experiments Time series: Multiple arrays at various temporal intervals Static: Snapshot of the activity in the cell

  23. Time Series Examples: Development Development of fruit flies[Arbeitman, Science 02]

  24. Time Series Examples (cont) Function Infectious diseases[Huang, Science 01; Nau, PNAS 02] Interactions Transcription factors knockout[Zhu, Nature 00; Pramilla, Genes Dev. 02]

  25. Systems Biology – from Bioscience to Medicine

  26. Metabolic Flux • Signal transduction • Microbial systems • Methods and softwares • Spatial models • Systems biology for medicine

  27. Metabolic flux From gene expression to metabolic fluxes Vertical genomics: From gene expression to function ... and back Dynamic metabolomics for systems biology Metabolic networks in motion: High-throughput analysis of molecular fluxes Prediction of regulatory pathways using mRNA expression and protein-protein interaction data: Application to prediction of galactose regulatory pathway Metabolic networks in plants: Statistical analysis and biological interpretation Minimal cut sets: Failure modes and target sets in metabolic networks

  28. Microbial systems biology Metabolome analysis and cell simulation Doing it their way: Metabolic differentiation in salmonella Receptor cooperativity and signal processing in bacterial chemotaxis Bacterial persistence: A phenotypic switch revealed by microfluidics An approach to generate testable hypothesis in microbiology The dynamic response of yeast cells to osmotic shock

  29. Methods and Software for Systems Biology Software and methods for modeling and simulating biochemical networks A hybrid approach for efficient and robust parameter estimation in biochemical pathways A modular approach to building the silicon yeast cell Model Orchestration: Addressing the challenges of model management and model composition in systems biology Dicovering Motifs in Biological Networks using Sub-Graph Isomorphism Principles of Systems Biology, illustrated with modeling of the heart

  30. Spatial Model Quantitative temporal and spatial analysis of cell division by 4D imaging Propagating chemical waves within and among cells Temporal and spatial control of signaling in the interferon-y/jak/Stat1 pathway Systems analysis of the quorum sensing phenomenon in a peculiar plant pathogen Agrobacterium tumefaciens Compensation effect of MAPK cascade on formation of phospho-protein gradient How to make a neurocrystal: Modelling the development patterning of the fruit fly´s retina

  31. Signal transduction • Dynamics and design of signalling networks: The Wnt-pathway • Synaptic signaling: Holding out against noise, diffusion, and turnover • Employing systems biology to quantify receptor tyrosine kinase signaling in time and space • Cellular decision making: Control of kinases and phosphatases on signaling kinetics • Modeling signal transduction systems without ignoring their combinatorial complexity • New quantitative approaches for modeling and simulation of large signal transduction networks reveal novel insights into programmed cell death

  32. Systems Biology and Medicine • Mathematical Modelling of metabolic diseases • Virus dynamics: Modeling of influenza A virus replication • Discovering activated regulatory networks in the DNA damage response pathway of yeast • Metabolic comparison of the in-silico phenotype-genotype relationship of Pseudomonas putida and Peudomonas aeruginosa • Systems biology approach to understand the stress response of P. aeruginosa to host innate immunity • Using a mammalian cell cycle simulation in anti-tumor pharmaceutical development to interpret differential kinase inhibition and biological knock-outs

  33. The position of Systems Biology

  34. What does it take to carry out Systems Biology? • A cross-disciplinary faculty who speak and understand the languages of different disciplines • Integrate new global technologies with the data acquisition, storage, integration, and analysis tools of computational biology and mathematics. • High-throughput facilities for genomics, proteomics etc… • An integration of effort with academia and industry. • Integration of discovery science with hypothesis-driven science for the integrated global analysis of systems.

  35. In another word……

  36. Why do we care about biological systems? • Ability to figure out what the effect will be of an intervention in one part of the system • What intervention one has to make in order to obtain some desired result = Which protein should be either activated or deactivated in order to stop a particular disease process while doing the least harm to the patient?

  37. Where do computers come in? • Systems modeling simulation, reasoning, discovery • Some properties to investigate Structure Dynamics Robustness Methods of control systems Methods to design and modify for desired properties

  38. The SYSTEOME Project • Systeome is an assembly of system profiles for all genetic variations and environmental stimuli responses. • Goal: to complete a detailed and comprehensive simulation model of the human cell at an estimated error margin of 20% by year 2020, and to finish identifying the system profile for all genetic variations, drug responses, and environmental stimuli by 2030. • Dr. Hiroaki Kitano

  39. Conclusion • System biology is a new and emerging field in biology • A long ways to go before understanding biological systems • “… systems biology will be the dominant paradigm in biology, and many medical applications as well as scientific discoveries are expected” – Hiroaki Kitano

  40. Further readings

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