1. Topics in Computational Biology (COSI 230a) Pengyu Hong 09/02/2005

2. Background As high-throughput methods for biological data generation become more prominent and the amount and complexity of the data increase, computational methods have become essential to biological research in this post-genome age. As high-throughput methods for biological data generation become more prominent and the amount and complexity of the data increase, computational methods have become essential to biological research in this post-genome age.

3. Background High-throughput methods … Transcriptional profiling cDNA arraysOligonucleotide arrays Simultaneously monitor the transcriptional activities of tens of thousands of genes. Functions of gene Relationships between gene-products … New drugs Personalized medicine …

4. Background Transcriptional profiling High-Content Screening High-throughput methods … 10 4 images in one experiment

5. Background Transcriptional profiling High-Content Screening High-throughput methods … Statistical Machine Learning Score histogram of wildtype images Score histogram of phenotype images

6. Background Transcriptional profiling High-Content Screening High-throughput methods … Publications PubMed: 15+ million bibliographic citations and abstracts …

7. Background In turn, biological problems are motivating innovations in computational sciences, such as computer science, information science, mathematics, and statistics. In turn, biological problems are motivating innovations in computational sciences, such as computer science, information science, mathematics, and statistics.

8. Background S1S1 S2S2 S3S3 K1K1 K2K2 K3K3 K4K4 P1P1 P2P2 P3P3 K5K5 Gene group 1 Gene group 2 Gene group 3 Gene group 4 Stimuli Signal transduction networks Transcriptional regulatory networks Cellular phenotypes Complex biological systems need novel computational methods …

9. Background S1S1 S2S2 S3S3 K1K1 K2K2 K3K3 K4K4 P1P1 P2P2 P3P3 K5K5 Gene group 1 Gene group 2 Gene group 3 Gene group 4 Stimuli Signal transduction networks Transcriptional regulatory networks Cellular phenotypes Complex biological systems need novel computational methods … Spatial Temporal

10. Background Large scale data needs novel information systems Remote biological databases LocusLink HGNC MGI RGD UCSC … Local Data Functions SOAP APIs UBIC 2 Unit A Local Data Functions UBIC 2 Unit B Ubiquitous bio-information computing (UBIC 2 ) Integrate heterogeneous data

11. Background Novel Human-computer interfaces ( e.g., visualization, multimodal interaction techniques, and context-aware learning functions.) are needed to help biologists efficiently navigate through the complicated landscape of biomedical information and effectively manipulate various computational tools. GeneNotes Collect information while surfing the Internet. Manage multimedia biological information (text, PDF, images, sequences, etc.) Functional based literature search (about to release this year).

12. Background There is high demand for scientists who are capable of bridging these disciplines. There is high demand for scientists who are capable of bridging these disciplines. Shallow biology + Shallow computing Shallow biology + Deep computing Deep biology + shallow computing Deep biology + Deep computing or Trend

13. Background High demand for interdisciplinary scientists who are capable of speaking multiple “languages”. Design experiment s Carry out experiment s Analyze data Generate biologically meaningful computational results. Generate informative experimental data.

14. Background High demand for interdisciplinary scientists who are capable of speaking multiple “languages”. Design experiment s Carry out experiment s Analyze data Generate biologically meaningful computational results. Generate informative experimental data.

15. Background High demand for interdisciplinary scientists who are capable of speaking multiple “languages”. Design experiment s Carry out experiments Analyze data Goal: Customize cDNA arrays to measure the temporal transcriptional profiles of a set of genes Genes besides those of interest? Computational tools? How to choose time point for sampling?

16. Background High demand for interdisciplinary scientists who are capable of speaking multiple “languages”. Design experiment s Carry out experiments Analyze data Goal: Use a 384 well plate to test the effects of various treatments on cells. Duplicates? Treatment arrangement? Base line?

17. Goal Create an environment Create an environment Transcends traditional departmental boundaries Transcends traditional departmental boundaries Facilitates communications between researchers from life sciences and computational sciences. Facilitates communications between researchers from life sciences and computational sciences.

18. Goal Learn knowledge (bio + comp) specific to a set of problems. Learn knowledge (bio + comp) specific to a set of problems. Regulatory motif finding Microarray data analysis Biomedical literature mining Signal transduction network modeling Cis-regulatory network discovery …

19. Goal Acquire skills Acquire skills Initiate interdisciplinary collaborations (choose research partners) Initiate interdisciplinary collaborations (choose research partners) Establish long-term win-win collaborations. Establish long-term win-win collaborations. Key: Seek first to understand, then to be understood. (Stephen R. Covey)

20. Main Themes Presentation Presentation Term Project Term Project

21. Main Themes Presentation Presentation Materials: Your own work or other people’s published results Materials: Your own work or other people’s published results Your own work: This is a good opportunity for you to attract collaborators. Your own work: This is a good opportunity for you to attract collaborators. Published papers: Suggest to choose one and search for related ones. Published papers: Suggest to choose one and search for related ones. 60 Minutes followed by questions and discussions 60 Minutes followed by questions and discussions Written report after presentation Written report after presentation

22. Main Themes Presentation Presentation Materials: Your own work or other people’s published results Materials: Your own work or other people’s published results 60 minutes presentation followed by questions and discussions 60 minutes presentation followed by questions and discussions Written report after presentation Written report after presentation

23. Main Themes Presentation Presentation Materials: Your own work or other people’s published results Materials: Your own work or other people’s published results 60 minutes presentations followed by questions and discussions 60 minutes presentations followed by questions and discussions Written report after presentation Written report after presentation Background of the research Background of the research Motivation for the research Motivation for the research Approach Approach Results Results Criticisms and/or suggestions for improvement. Criticisms and/or suggestions for improvement.

24. Main Themes Term project Term project Decide by mid-term Decide by mid-term Due on 12/22 mid-night. Due on 12/22 mid-night.

25. Evaluation Grading will be based on class participation and on the project. Grading will be based on class participation and on the project.

26. Evaluation Teamwork is strongly encouraged !!! Teamwork is strongly encouraged !!! Indicate the contribution of each individual. Indicate the contribution of each individual.

27. Questions? Prepare your presentation. Prepare your presentation. Choose a right project. Choose a right project. … … … … Me at: Me at: Office hour Tue & Fri 4:30-5:30pm. Office hour Tue & Fri 4:30-5:30pm. Office Volen 135 Office Volen 135 Email: hong@cs.brandeis.edu. Email: hong@cs.brandeis.edu.

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