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Seminar in Bioinformatics (236818)

Seminar in Bioinformatics (236818). Ron Y. Pinter Fall 2007/08. Why?. Really … Advanced Algorithms in Computational Biology II Can’t fit everything in one term Not just sequence alignment, HMMs, and Bayesian networks Ever evolving and changing needs and ideas

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Seminar in Bioinformatics (236818)

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  1. Seminar in Bioinformatics (236818) Ron Y. Pinter Fall 2007/08

  2. Why? • Really … Advanced Algorithmsin Computational Biology II • Can’t fit everything in one term • Not just sequence alignment, HMMs,and Bayesian networks • Ever evolving and changing needs and ideas • Still, trying to focus on some specific area

  3. What? • Pathway and Network Analysis • Genome Rearrangements • Protein and RNA Structure Prediction • Expression analysis (Zohar Yakhini) • Linkage analysis (Dan Geiger) • Phylogenetic analysis (Shlomo Moran)

  4. Biological Networks • Networks are a powerful method for describing and analyzing the relations among entities • Used in Computer Science, Operations Research, Electrical Engineering, etc. • We use them to model the complexitiesof large biological systems

  5. Some Networks protein-protein interactions gene regulation

  6. Pathways • Focus on a particular function • The “blue print” of cellular processes • Smaller scale than networks, but they contain a lot of detailed information

  7. Some Pathways a signaling pathway a mixed pathway

  8. Interesting Static Properties • Structural similarity • Functional similarity • Comprise (small) basic building blocks • what are they? • how do they compose? • …

  9. Quantitative kinetics concentrations expression levels … Qualitative robustness stability convergence oscillation transient expression fail-safety (sensitivity) … Interesting Dynamic Properties

  10. Modeling and Analysis Methods • Labeled graphs • Networks – Boolean, discrete, continuous • [Hybrid Functional] Petri Nets (HFPN) • ODEs/PDEs • Various calculi and process algebras • Flux Balance Analysis (FBA)

  11. When to Use What? • For static analysis • graph algorithms • algebraic methods • statistical tests • For dynamic analysis • detailed behavior – ODEs • qualitative behavior – networks • some specific properties – various calculi

  12. A Few Examples • Static • [Seeded] Alignment of Metabolic Pathways (Pinter et al.,Bioinformatics, 2005; Lozano et al., WABI 2007) • Integrative Analyses of Interaction Networks Underlying the Cellular Circuitry in Yeast (Yeger-Lotem et al.,PNAS 2004) • Elucidating Protein Function using Graphlet Degree Vectors in Prtoein-Protein Interactions Networks (Gordon et al., under review). • Dynamic • HFPN-based Simulation of the Reduced Folates Metabolic Pathway (Assaraf et al.,JTB 2006) • Faithful Modeling of Transient Behavior in Developmental Pathways (Rubinstein et al.,PNAS 2007) • Both • Flux Based vs. Topology Based Similarity of Metabolic Genes (Rokhlenko et al., WABI 2006; Bioinformatics 2007)

  13. 4-protein motifs as combinations of 3-Protein Motifs A B C D A B C D

  14. Topics • Static analysis of metabolic pathways • Network motifs: discovery and applications • Flux balance analysis of metabolic pathways • Pathway modeling and simulation • Dynamic analysis of small networks • Analysis of genomewide networks

  15. When, where and who? • Time and Place • Monday, 16:30am-18:30pm, Taub 4 • Staff • Prof. Ron Pinter, pinter@cs.technion.ac.il, x4955, Taub 705; Office hours: Tuesday, 10am-12noon • Itai Sharon, itaish@cs.technion.ac.il, x4946 • Site http://webcourse.cs.technion.ac.il/236818/

  16. How? • References • Uri Alon: An Introudction toSystems Biology: Design Principles of Biological Circuits. Chapman & Hall, 2006. • Bernhard Ø. Palsson: Systems Biology: Properties of Reconstructed Networks. Cambridge University Press, 2006 • Darren Wilkinson: Stochastic Modelling forSystems Biology. Chapman & Hall, 2006. • Duties • attendance (10%) • presentation (40%) • critical review (50%) • Prereqs • course in algorithms (234246 or 234247) • background in bioinformatics (236522 or 236523)

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