topics in computational biology (cosi 230a)

Topics in Computational Topics in Computational Biology (COSI 230a)Biology (COSI 230a)

Pengyu HongPengyu Hong

09/02/200509/02/2005

BackgroundBackground

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

BackgroundBackgroundHigh-throughput methods …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• Personaliz

ed medicine

• … …



High-Content Screening

High-throughput methods …High-throughput methods …

104 images in one experiment





Statistical Machine Learning

Score histogram of wildtype images

Score histogram of phenotype images





Publications

PubMed: 15+ million bibliographic citations and abstracts

… …


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


S1 S2 S3

K

1

K2

K3

K4

P1

P2

P3 K5

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 Complex biological systems need novel computational methods …computational methods …


S1 S2 S3

K

1

K2

K3

K4

P1

P2

P3 K5

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 Complex biological systems need novel computational methods …computational methods …

Spatial

Temporal

BackgroundBackgroundLarge scale data needs novel information systemsLarge scale data needs novel information systems

Remote biological databases

LocusLink HGNC MGI

RGD UCSC … …

Local Data

Functions

SOAP APIs

UBIC2 Unit A

Local Data

FunctionsUBIC2 Unit B

Ubiquitous bio-information computing (UBIC2)

• Integrate heterogeneous data

BackgroundBackgroundNovel Human-computer interfaces (Novel Human-computer interfaces (e.g., visualization, e.g., visualization, multimodal interaction techniques, and context-aware learning multimodal interaction techniques, and context-aware learning functionsfunctions.) are needed to help biologists efficiently .) are needed to help biologists efficiently navigate through the complicated landscape of navigate through the complicated landscape of biomedical information and effectively manipulate biomedical information and effectively manipulate various computational tools.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).


There is high demand for scientists There is high demand for scientists who are capable of bridging these who are capable of bridging these disciplines. disciplines.

Shallow biology + Shallow computing

Shallow biology +

Deep computing

Deep biology+

shallow computing

Deep biology + Deep computing

or

Trend


High demand for interdisciplinary scientists who High demand for interdisciplinary scientists who are capable of speaking multiple “languages”. are capable of speaking multiple “languages”.

Design experime

nts

Carry out experime

nts

Analyze data

Generate biologically meaningful

computational results.

Generate informative

experimental data.



Design experime

nts

Carry out experimen

ts

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?



Design experime

nts

Carry out experimen

ts

Analyze data

Goal: Use a 384 well plate to test the effects of various treatments on cells.

Duplicates?Treatment arrangement?Base line?

GoalGoal

Create an environment Create an environment Transcends traditional departmental Transcends traditional departmental

boundaries boundaries Facilitates communications between Facilitates communications between

researchers from life sciences and researchers from life sciences and computational sciences.computational sciences.

GoalGoal

Learn knowledge (bio + comp) Learn knowledge (bio + comp) specific to a set of problems.specific to a set of problems.

• Regulatory motif finding

• Microarray data analysis

• Biomedical literature mining

• Signal transduction network modeling

• Cis-regulatory network discovery• … …

GoalGoal

Acquire skillsAcquire skills Initiate interdisciplinary collaborations Initiate interdisciplinary collaborations

(choose research partners)(choose research partners) Establish long-term win-win Establish long-term win-win

collaborations.collaborations.

Key: Seek first to understand, then to be understood. (Stephen R. Covey)

Main ThemesMain Themes

PresentationPresentation

Term ProjectTerm Project

Main ThemesMain Themes PresentationPresentation

Materials: Your own work or other Materials: Your own work or other people’s published resultspeople’s published results Your own work: This is a good Your own work: This is a good

opportunity for you to attract opportunity for you to attract collaborators.collaborators.

Published papers: Suggest to choose Published papers: Suggest to choose one and search for related ones.one and search for related ones.

60 Minutes followed by questions and 60 Minutes followed by questions and discussionsdiscussions

Written report after presentationWritten report after presentation


Materials: Your own work or other people’s Materials: Your own work or other people’s published resultspublished results

60 minutes presentation followed 60 minutes presentation followed by questions and discussionsby questions and discussions

Written report after presentationWritten report after presentation


Materials: Your own work or other people’s Materials: Your own work or other people’s published resultspublished results

60 minutes presentations followed by 60 minutes presentations followed by questions and discussionsquestions and discussions

Written report after presentationWritten report after presentation Background of the researchBackground of the research Motivation for the researchMotivation for the research ApproachApproach ResultsResults Criticisms and/or suggestions for Criticisms and/or suggestions for

improvement.improvement.

Main ThemesMain Themes Term projectTerm project

Decide by mid-termDecide by mid-term Due on 12/22 mid-night.Due on 12/22 mid-night.

EvaluationEvaluation

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

EvaluationEvaluation

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

Teamwork is strongly Teamwork is strongly encouraged encouraged !!!!!! Indicate the contribution of each Indicate the contribution of each

individual.individual.

Questions?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 135Office Volen 135 Email: Email: [email protected]@cs.brandeis.edu..

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

ThanksThanks

topics in computational biology (cosi 230a)

Documents

computational biology

computational sciences

biological data generation

biological problems

biological research

information science

genenotescollect information

interdisciplinary scientists