Nancy Griffeth Outline What will we be doing Why I think computational biology is fun Subject Matter How to model signaling pathways such as those that control cell proliferation Workshop Information ID: 1042302
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1. The Joy of Computational BiologyNancy Griffeth
2. OutlineWhat will we be doing Why I think computational biology is fun
3. Subject MatterHow to model signaling pathways, such as those that control cell proliferationWorkshop Information
4. ScheduleThis week: Introductory lecturesNext week: Initial modeling problemsThird week: ProjectsLast two daysTeam presentations Final visiting lectureWorkshop Information>Schedule
5. Pedagogical ApproachCollaborativeQuestions are always welcomeDiscussion is encouraged!Scribe to record unanswered questionsProblem-oriented: three problemsFrog cell cycleEGFR signaling pathwayFirst passage time distributionsWorkshop Information>Pedagogical Approach
6. Pedagogical ApproachInterdisciplinary team orientedAt least 1 chemistry/biology expertAt least 1 computer science expertAt least 1 math expertWorkshop Information>Pedagogical Approach
7. Speaking of Interdisciplinary…Workshop Information>Pedagogical Approachxkcd.com
8. General Learning ObjectivesBe aware of some hypotheses about how cancers developBe able to model cellular signaling pathwaysLearn to function on interdisciplinary teamsInvestigate some research problemsMeet new people and have some fun! Workshop Information>Pedagogical Approach
9. Specific Learning ObjectivesLearn how cells initiate cell division using signalingInvestigate the mechanisms that initiate cell division and ensure that cells are copied accuratelyBe able to model signaling pathways that initiate cell division, using wiring diagrams and reaction rulesCreate computer-processable models of signaling pathways Simulate their behaviorInvestigate their propertiesStudy distributions of first passage times, to support research on coarse-graining modelsWorkshop Information>Pedagogical Approach
10. Questions or Discussion?
11. What’s I like about comp bioBiology matters!Computational techniques help us understand biologyCells act a lot like computersCells make binary choicesCells have modular partsDifferent kinds of cells share the same mechanisms
12. Computational techniquesOrganize dataDescribe behaviors as a wholeBridge gaps where data is missingSuggest hypotheses
13. Example 1: Cell Cycle Regulation
14. Cell Cycle Regulation
15. Example 2:Protein ProductionTranscriptionFigure from wikipedia article on the central dogmaTranscriptionTranslation
16. Protein ProductionWiring diagramsNegative autoregulation: a protein represses its own productionProvides a quick increase to a robust level
17. Protein ProductioncAMP signals absence of glucosecAMP activates XX binds to the DNA promoter, but is not enough by itself to start arabinose metabolismX promotes transcription of Y; arabinose activates YX and Y together binding to the DNA promoter results in production of the enzymes that metabolize arabinoseFigure from Alon, “An Introduction to Systems Biology”
18. What triggers activation of transcription factors?An external signal (molecule) arrivesBinds to a receptorChanges in the receptor cause internal actions…Which cause cascading actions
19. Example 3: Regulating cell growth, proliferation, and differentiation
20. Example 4: Cell as Information Processorhttp://en.wikipedia.org/wiki/Cell_signaling
21. Example 5: Protein degradationUbiquitinTags proteins for destructionLike garbage collection!!
22. Rulebender Tool
23. Research ProblemsCuring cancer: How do signaling pathways relate to the development of cancer?Computational complexity: how can we simplify the models enough to make them tractable?
24. Relationship to cancerMitosis signals stay in on positionCells learn to send growth signals to themselvesCells send growth signals to other cells inappropriatelyCells send signals for creation of blood vessels
25. Simplifying modelsComputationally complexModular?Course-grainingNeed subsystems with behavior we can characterize
26. What you will learn…Regulation of the cell cycleA rule-based language to model cellular processesHigh-level view of how models can be “solved”Signaling pathwaysEGFR pathwayModel simplification by coarse-graining