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Systems Biology Haixu Tang School of Informatics Two ways of looking a problem • Top down or bottom up – Either look at the whole organism and abstract large portions of it – Or try to understand each small piece and then after understanding every small piece assemble into the whole – Both are used, valid and complement each other Bottom up (or reductionism) is traditional approach – You would study a biological process in details not worrying about how that process might interact with other elements in the cell. – You would strive to understand a gene or process in great detail, eventually you might extend this knowledge to other organisms and compare. Systems Biology: a top-down approach? • Challenge: put the pieces back together • Attempts to create predictive models of cells, organs, biochemical processes and complete organisms – Data combined with computational, mathematical and engineering disciplines – Model <-> simulations <-> experiment Definitions (Leroy Hood) • Systems biology – As global a view as possible – Fundamentally quantitative – Different scales integrated Common “themes” • Cross disciplinary: – Systems biology = biology + CS/informatics + engineering +… • Massive data/information/knowledge • Concepts of networks for abstract portrayal of many interaction types. • Model development – Predictive models – Models to drive experimentation – Models to understand processes What is a pathway? • A series of interconnected steps linked by the production of intermediates that are used in the next step • A series of consecutive reactions that lead to the ultimate goal. Requires a higher level of understanding • Heterogeneous information “feed” into this understanding – – – – – Microarrays Homology tools (BLAST, alignments COGS) Biochemical literature Genomic sequence Specialized databases A complex problem: human cells – 35,000 genes either on or off (huge simplification!) would have 2^35,000 solutions – Things can be simplified by grouping and finding key genes which regulate many other genes and genes which may only interact with one other gene – In reality there are lots of subtle interactions and non-binary states. Some real numbers from E. coli • 630 transcription units controlled by 97 transcription factors. • 100 enzymes that catalyse more than one biochemical reaction . • 68 cases where the same reaction is catalysed by more than one enzyme. • 99 cases where one reaction participates in multiple pathways. • The regulatory network is at most 3 nodes deep. • 50 of 85 studied transcription factors do not regulate other transcription factors, lots of negative auto-regulation An example – Bacterial Chemotaxis • Bacteria are able to sense temporal gradients of chemical ligands in their vicinity. • Their movement is composed of: – Smooth runs – Tumbling – in which a new direction is chosen randomly. • By modifying tumbling frequency a bacterium is able to direct its motion towards/away from attractants/repellents. E. coli and its flagella Mechanism 1. Chemotactic ligands bind to specialized receptors (MCP), a complex of proteins CheA and CheW; 2. CheA is a kinase that phosphorylates the response regulator, CheY, whose phosphorylated form (CheYp) binds to the flagellar motor and generates tumbling. Changing the kinase activity of CheA modifies the tumbling frequency. 3. The receptor can also be reversibly methylated. Methylation enhances the kinases activity and mediates adaptation to changes in ligand concentration. CheR methylates the receptor, CheB demethylates it. A feedback mechanism is achieved through the CheA-mediated phosphorylation of CheB, which enhances its demethylation activity. Adaptation Property • The steady state tumbling frequency in a homogenous ligand environment is insensitive to the ligand concentration. • Allows bacteria to maintain sensitivity to chemical gradient over a wide range of ligand concentrations. A Simple Two-State Model • Receptor complex (E) has two states: Active, inactive. • Active receptor has kinase activity, which induces tumbling. • Receptor activity is probabilistic, depending on – methylation level, m (feedback from E) – ligand occupancy, U or O – Activity probabilities um for Eu m, om for Eo m A Simple Two-State Model • Time contained network • Network input: Ligand concentration (L). • Network output: Predicted average complex activity. Model Simulations • Obtain biochemical parameters by experiments • Solve differential equations using numerical methods (~ 1min) • Variations in these biochemical parameters would occur naturally in a population —polymorphisms. • Model robustness comes from the feedback mechanism. Importance of robustness • Genetic polymorphism may change network biochemical parameters. • Enzyme concentrations are low and therefore subject to considerable variations. • Robust mechanism allows bacteria to tolerate both. Networks: the “system” of systems biology • Building network structure • Learning parameters • Simulation predicting behaviors • Dynamics