Seminar in Bioinformatics (236818)

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