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Seminar in Bioinformatics (236818)

Dive into advanced algorithms for computational systems biology, focusing on pathway modeling, genome rearrangements, and more. Learn about networks, static and dynamic properties, and various analysis methods used in bioinformatics. Join this seminar to unravel the complexities of biological systems!

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Seminar in Bioinformatics (236818)

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  1. Seminar in Bioinformatics (236818) Ron Y. Pinter Fall 2018/19

  2. Why? • Really … Advanced Algorithmsin Computational Systems Biology II • Can’t fit everything in one term(236522) • 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 modeling and Analysis (Ron Pinter) • Genome Rearrangements (Ron Pinter) • Protein and RNA Structure Prediction • Expression analysis (ZoharYakhini) • Linkage analysis (Dan Geiger) • Phylogenetic analysis (Shlomo Moran) • Network dynamics (Flux Balance Analysis) (Tomer Shlomi) • Computational metagenomics (last term!!!)

  4. Biological Networks • Networks are a powerful tool for describing and analyzing relations among entities • Used extensively in Computer Science, Operations Research, Electrical engineering, Physics, Sociology, Behavior, Economics, Neuroscience etc. • We use them to model and analyze the complexities of small and large biological systems

  5. Some Biological Networks protein-protein interactions Transcription 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 (topological) properties (clustering coefficient, betweenness, centrality, degree distribution) allow us to examine similarity and perform classification • Layered networks • Comprise (small) basic building blocks • what are they (motifs)? • how do they compose? • InferredFunctional properties

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

  10. Modeling and Analysis Methods • Labeled graphs • Networks – transition systems: 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 – transition systems • some specific properties – various calculi

  12. A Few Examples • Static • Integrative Analyses of Interaction Networks Underlying the Cellular Circuitry in Yeast (Yeger-Lotem et al.,PNAS 2004) • [Seeded] Alignment of Metabolic Pathways (Pinter et al.,Bioinformatics, 2005; Lozano et al., WABI 2007) • 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. When, where and who? • Time and Place • Wednesday, 16:30-18:30, Taub 701 • Staff • Prof. Ron Y. Pinter, pinter@cs.technion.ac.il, or: • Ron.pinter@gmail.com • x4955, Taub 705; Office hours: Wednesday, 15:00-16:30 • Site – TBD

  15. How? • Duties • attendance (10%) • presentation (40%) • bonus for early birds • term paper: critical review (50%) • Prereqs • course in algorithms (234246 or 234247) • background in bioinformatics (236522 or 236523)

  16. Resources • Uri Alon, An Introduction toSystems Biology: Design Principles of Biological Circuits • Bernhard Ø. Palsson: Systems Biology: Properties of Reconstructed Networks. Cambridge University Press, 2006: Intro on network types • Papers from conference proceedings, and journals (RECOMBISMB, and ECCB; • JCB, and Bioinformatics)

  17. Topics • Network types • Static properties and characteristics of networks and pathways • Network motifs: discovery and applications • Network alignment • Network and Pathway queries • Dynamic analysis of small networks and pathways • “Natural algorithms” • Web-based tools and repositories

  18. Network Types • Bernhard Ø. Palsson: Systems Biology: Properties of Reconstructed Networks. Cambridge University Press, 2006: Intro on network types [Chapters 1-3] (Ron)

  19. Network characteristics and properties • Uri Alon, An Introduction toSystems Biology: Design Principles of Biological Circuits. • Appendix C ( ) • Network motifs -Chapters 3-4( );5-6 ( ) • Network biology: Albert-LászlóBarabási & Zoltán N. Oltvai: understanding the cell's functional organization • Nissan Levtov et al. detecting non-uniform clusters in large-scale interaction graphs, journal of computational biology Volume 21, Number 2, 2014 • D.Amarand R.Shamir: Constructing module maps for integrated analysis of heterogeneous biological networks

  20. alignment • Chorand Tuller 2006, • Pržulj, et al. 2006/7 • MPH, Koyuturk et al, 2005/6 • Graemlin • Pairwise Global Alignment of Protein Interaction Networks by Matching Neighborhood Topology, RohitSingh ,JinboXu, Bonnie Berger

  21. Network and graph queries • Alignment-based:PathBLAST, MPH, Cgraph, GraphFind, NetGrep,), Sharan and Ideker: constructing cellular machinery through biological network comparison ‏Nature Biotechnology 2006 • Motus(topology free Motus and Torque • GraphGrep: A fast and universal method for querying graphs • IlanSmoly et al.: “Algorithms for Regular Tree Grammar Network Search and Their Application to Mining Human–viral Infection Patterns” • PINQ etc. • Giugno,R. et et al. ”GraphGrep a fast and universal method for querying graphs”, Proceedings. 16th International Conference on Pattern Recognition, 2002 (Volume:2)

  22. Dynamic analysis of small networks • Accurate information transmission through dynamic biochemical signaling networks. Selimkhanov J, Taylor B, Yao J, Pilko A, Albeck J, Hoffmann A, Tsimring L, Wollman R. Science. 2014 • Faithful Modeling of Transient Behavior in Developmental Pathways (Rubinstein et al.,PNAS 2007)

  23. And now for something completely different : • Slime Mold Grows Network Just Like Tokyo Rail System: • http://www.wired.com/wiredscience/2010/01/slime-mold-grows-network-just-like-tokyo-rail-system/ -The effect of individual variation on the structure and function of interaction networks in harvester ants - Ants take a cue from Facebook • Information Processing in Social Insect Networks:http://gizmodo.com/5937981/ants-have-been-using-internet-algorithms-for-millions-of-years: • http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0040337

  24. Systems • Protonet and Protomap • Ingenuity (IPA) • String • Cytoscape • Bind • Biogrid • Spyke • Shamir and Zeira: Module guided Ranking of candidate PatHway genes

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