Topics in Computational Biology (COSI 230a)

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.

3. Background High-throughput methods … Transcriptional profiling cDNA arrays Oligonucleotide 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 High-throughput methods … High-Content Screening Transcriptional profiling 104 images in one experiment

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

6. Background High-throughput methods … … … Transcriptional profiling High-Content Screening 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.

8. Background Complex biological systems need novel computational methods … Stimuli S1 S2 S3 P1 K2 K1 P2 K3 Signal transduction networks P3 K5 K4 Gene group 3 Gene group 1 Transcriptional regulatory networks Gene group 4 Gene group 2 Cellular phenotypes

9. Background Complex biological systems need novel computational methods … Stimuli S1 S2 S3 P1 K2 K1 Spatial P2 K3 Signal transduction networks Temporal P3 K5 K4 Gene group 3 Gene group 1 Transcriptional regulatory networks Gene group 4 Gene group 2 Cellular phenotypes

10. Background Large scale data needs novel information systems Local Data Local Data SOAP APIs Functions Functions UBIC2 Unit A UBIC2 Unit B LocusLink MGI Remote biological databases HGNC … … RGD UCSC Ubiquitous bio-information computing (UBIC2) • 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. • Collect information while surfing the Internet. • Manage multimedia biological information (text, PDF, images, sequences, etc.) • Functional based literature search (about to release this year). GeneNotes

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

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

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

15. Background High demand for interdisciplinary scientists who are capable of speaking multiple “languages”. Goal: Customize cDNA arrays to measure the temporal transcriptional profiles of a set of genes Design experiments Carry out experiments Analyze data 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”. Goal: Use a 384 well plate to test the effects of various treatments on cells. Design experiments Carry out experiments Analyze data Duplicates? Treatment arrangement? Base line?

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

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

20. Main Themes • Presentation • Term Project

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

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

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

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

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

26. Evaluation • Grading will be based on class participation and on the project. • Teamwork is strongly encouraged !!! • Indicate the contribution of each individual.

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

28. Please fill the form and return it to me now. Thanks