1 / 27

Pathway Finding by Reverse Engineering Based on Simulation

Pathway Finding by Reverse Engineering Based on Simulation. C.Y. Tang Department of Computer Science NTHU. Reverse Engineering (Computer Aided Engineering). VLSI CAD Communication Protocol Bio-X ?. Protocol Implementation. Key Issues. Formal Specification Relationship Exploring.

kalani
Télécharger la présentation

Pathway Finding by Reverse Engineering Based on Simulation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Pathway Finding by Reverse Engineering Based on Simulation C.Y. Tang Department of Computer Science NTHU

  2. Reverse Engineering (Computer Aided Engineering) • VLSI CAD • Communication Protocol • Bio-X ?

  3. Protocol Implementation

  4. Key Issues • Formal Specification • Relationship Exploring

  5. Construction of cis-regulatory gene networks (update continuously athttp://www.its.caltech.edu/~mirsky/endomes.htm) Purpose: To understand how various genes are expressed under the regulation of cis-regulatory elements during the developmental period of sea urchin embryos Bolouri, H., and Davidson, E. H. (2002). Modeling DNA sequence-based cis-regulatory gene networks. Dev. Biol. 246, 2–13. Brown, C. T., Rust, A. G., Clarke, P. J. C., Pan, Z., Schilstra, M. J., De Buysscher, T., Griffin, G., Wold, B. J., Cameron, R. A., Davidson, E. H., and Bolouri, H. (2002). New computational approaches for analysis of cis-regulatory networks. Dev. Biol. 246, 86–102. Davidson, E. H., Rast, J. P., Oliveri, P., Ransick A., Calestani, C., Yuh, C.-H., Minokawa, T., Amore, G., Hinman, V., Arenas-Mena, C., Otim, O., Brown, C. T., Livi, C. B., Lee, P. Y., Revilla, R., Schilstra, M. J., Clarkes, P. J. C., Rust, A. G., Pan, Z., Arnone, M. I., Rowen, L., Cameron, R. A., McClay, D. R., Hood, L., and Bolouri, H. (2002). A provisional regulatory gene network for specification of endomesoderm in the sea urchin embryo. Dev. Biol. 246, 162–190. Yuh, C.-H., Brown, C. T., Livi, C. B., Rowen, L., Clarke, P. J. C., and Davidson, E. H. (2002). Patchy interspecific sequence similarities efficiently identify positive cis-regulatory elements in the sea urchin. Dev. Biol. 246, 148–161.

  6. and white sea urchin (Lytechinus variegatus) http://www.divebums.com/FishID/Pages/sea_urchin_purple.html   Purple sea urchin (Strongylocentrotus purpuratus) http://digimorph.org/specimens/Strongylocentrotus_purpuratus/

  7. Cells at different location have different fates in embryogenesis

  8. trans-Regulation cis-Regulation

  9. with some logical rules A network exists in the cis-regulatory elements

  10. Constructing Relationshipsof Networks fromExpression and Perturbation Data Developing a computational platform which can inference networks automatically by experiment data

  11. What is network ? • Network is commonly in organism. e.g. gene regulatory network, pathway, neuron network…etc. • Abstractly, network can defined as combinations of a group nodes and edges.

  12. Our approach • Mining the relationship between nodes from expression curve and perturbation matrix. • Inference networks by the relationships we found.

  13. Alignment Expression curve node A Scoring node B Score Relationship of A & B • Bioinformatics 2003 19: 905-912

  14. WT A B C D A 1 0 1 1 1 B 1 0 0 1 1 C 1 0 1 0 1 D 1 0 0 1 0 Perturbation matrix B D A B A C A A D B D C

  15. Integrated Genomic and Proteomic Analyses of a Systematically Perturbed Metabolic NetworkSCIENCE VOL 292:929-934, MAY 4, 2001Trey Ideker, Vesteinn Thorsson, Jeffrey A. Ranish, Rowan Christmas, Jeremy Buhler, Jimmy K. Eng, Roger Bumgarner, David R. Goodlett, Ruedi Aebersold, Leroy Hood

  16. Perturbation Matrix（mRNA Level） 數量變化

  17. 實際Microarray 輸出結果 Reverse Engineering Strategy Hypothesis 重新假設 Simulation Models 否 Match 是 Candidate Set 再作 Distinguishable 實驗 是 是否唯一吻合 Believe it or not 否

  18. A A A A A A A B B B B B B B C C C C C C C A A A A C B A C A B C A B q p C B C B C B C B g Possible Models a b c d e r f t v s u

  19. A (GAL4) Galactose B (GAL80) D (Galactose) C (GAL3) 調控機制核心

  20. A System of Cellular Pathway 化學連鎖反應 Metabolic Flow Structure Gene 化學物質 Regulation Gene 調控機制核心

  21. 化學連鎖反應（Metabolic Flow） Mutate structure genes and then we will find a Acyclic Directed Graph(ADG).

  22. ？ E A B C D A Pathway with Cycle(1/2) • 1 2 3 4 5 • On/Off Switch

  23. A Pathway with Cycle(2/2) Observed E A B C D • 1 2 3 4 5 • On/Off Switch

  24. Raw Data New Biological Experiments New Biological study Mining information Simulation Result Experiment results Biological Study Hypothesis Modeling N Match Error Report Experimental Simulator Y Verification by other biologists Error Report Confirm Revise hypothesis by biologists Significant Information N Y

More Related