Topics in Computational Biology (COSI 230a) Pengyu Hong 09/02/2005.
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Transcript of Topics in Computational Biology (COSI 230a) Pengyu Hong 09/02/2005.
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
• … …
BackgroundBackground
Transcriptional profiling
High-Content Screening
High-throughput methods …High-throughput methods …
104 images in one experiment
BackgroundBackground
Transcriptional profiling
High-Content Screening
High-throughput methods …High-throughput methods …
Statistical Machine Learning
Score histogram of wildtype images
Score histogram of phenotype images
BackgroundBackground
Transcriptional profiling
High-Content Screening
High-throughput methods …High-throughput methods …
Publications
PubMed: 15+ million bibliographic citations and abstracts
… …
BackgroundBackground
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.
BackgroundBackground
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 …
BackgroundBackground
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).
BackgroundBackground
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
BackgroundBackground
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.
BackgroundBackground
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.
BackgroundBackground
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 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?
BackgroundBackground
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 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
Main ThemesMain Themes PresentationPresentation
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
Main ThemesMain Themes PresentationPresentation
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