Voytas.ppt

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Graduate Educationat the Interface of the

Computational and Biological Sciences

Dan VoytasIowa State University

Computational Biology

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The Interdepartmental Graduate Major in Bioinformatics and Computational Biology

at Iowa State University

•Established in the Fall of 1999•Students currently working toward PhD degree: 54

•Number of faculty participants: 81 19 departments 4 colleges


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Outline of today’s presentation:

•What is computational biology?•Challenges faced at Iowa State University in: Interdisciplinary coursework and training

Integrating research across the campus Overcoming institutional barriers Creating a diverse student body


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What is Computational Biology?

Wikipedia: •Computational biology involves the use of techniques from mathematics, informatics, statistics, and computer science (& engineering) to solve biological problems


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What is Computational Biology?

Gerstein: •Computational molecular biology is conceptualizing biology in terms of molecules & applying “informatics” techniques - derived from disciplines such as mathematics, computer science, and statistics - to organize and understand information associated with these molecules, on a large scale


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What is the Information?Biological Sequences, Structures, Processes

Central Dogma of Molecular Biology

• DNA sequence -> RNA -> Protein -> Phenotype

• Molecules Sequence, Structure, Function

• Processes Mechanism, Specificity, Regulation

Central Paradigm for Computational Biology

• Genomic (DNA) Sequence -> mRNAs & other RNA sequences -> Protein sequences -> RNA & Protein Structures -> RNA & Protein Functions -> Phenotype

• Large Amounts of Information Standardized Statistical

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Genome, Transcriptome, Proteome

• Genome - the complete

collection of DNA (genes and "non-

genes") of an organism

• Transcriptome - the complete

collection of RNAs (mRNAs &

others) expressed in an organism

• Proteome - the complete

collection of of proteins

expressed in an organism

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Molecular Biology Information:Macromolecular Structures

DNA/RNA/Protein Structures• How does a protein (or RNA) sequence fold into an active 3-dimensional structure?

• Can we predict structure from sequence?

• Can we predict function from structure (or perhaps, from sequence alone?)

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Molecular Biology Information:Biological Processes

Functional Genomics• How do patterns of gene expression determine phenotype?

• Which genes and proteins are required for differentiation during during development?

• How do proteins interact in biological networks?

• Which genes and pathways have been most highly conserved during evolution?

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Molecular Biology Information:Integrating Data

Understanding the function of genomes requires integration of many diverse and complex types of information:• Metabolic pathways• Regulatory networks• Whole organism physiology• Evolution, phylogeny• Environment, ecology• Literature (MEDLINE)

Modified from Mark Gerstein

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“Informatics” Techniquesin Computational Biology

• Databases Building, Querying Object-oriented DB

• String Comparison Text search Alignment Significance statistics

• Finding Patterns Machine Learning Data Mining Statistics Linguistics

• Geometry Robotics Graphics (Surfaces, Volumes)

Comparison & 3D Matching

• Simulation & Modeling Newtonian Mechanics Electrostatics Numerical Algorithms Simulation Network modeling

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Computational Biology is Born!Computational Biologists are

Needed!

(Internet picture adaptedfrom D Brutlag, Stanford)

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History of Graduate Education in Computational Biology at Iowa State

• 1997 Iowa Computational Biology Laboratory

• 1998 Formal coursework begins• 1998 Graduate programs (Genetics and Mathematics) offer Areas of Specialization in Computational Molecular Biology

• 1998 Hired three computational biologists


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1999 - Onset of formal education in Bioinformatics and Computational

Biology

• Established the Interdepartmental Graduate Program in Bioinformatics and Computational Biology

• Received an NSF-IGERT (Integrative Graduate Education and Research Traineeship) grant to fund our educational program


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Educational Challenge:Interdisciplinary Training

How do you provide interdisciplinary training to students from a variety of academic

backgrounds?


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Ramp-Up Courses

•BCB 495 Molecular Biology for Computational Scientists

•BCB 484 Computational Mathematics for Biologists•ComS 381 Introduction to Data Structures for Biologists


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•Summer before entry into program: encourage participation in the NSF-NIH Bioinformatics and Computational Biology Summer Institute

•Increasing prerequisites for admission to the BCB graduate program: accepting more students with breadth in background training


Additional Measures


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The challenge of interdisciplinary training revisited: curricular reform

Twenty new courses developed sinceonset of the program in 1999

Problems•Too much overlap between some courses•No coverage of many important topics•Lack of adequate training in computational and statistical methods

•Need for a rationally designed curriculum with core courses that provide sufficient breadth and depth of coverage of computational biology

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Challenges in Curriculum Revisions - 2006

•Define background that can be reasonably assumed of incoming students

•Cover core topics with sufficient rigor yet without assuming a long chain of prerequisites

•Package the coursework into a small number of courses that can be taken within the first two years of the graduate program

•Staff the courses with faculty with the relevant expertise


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Proposed Solution

•A rational (and realistic) set of prerequisites and background courses

•A required set of four core courses covering major topics in computational biology BCB I: Fundamentals of Genome Informatics BCB II: Advanced Genome Informatics BCB III: Structural Genome Informatics BCB IV: Computational Functional Genomics and Systems Biology


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Highlights of Revised Curriculum

Bioinformatics I

Bioinformatics II Bioinformatics III Bioinformatics IV

Com S 208Com S 330Stat 341 Biol 314

Stat 3XX Gen 411 Com S 363

Prerequisites Background Core

Bioinformatics I


Com S 208Com S 330Stat 341 Biol 314

Stat 3XX Gen 411 Com S 363Bioinformatics I


Com S 208Programming

Com S 330Algorithms

Stat 341Prob. & Stat.

Biol 314Mol. Cell Biol.

Stat 432Prob. Models

Gen 411Mol. Genetics

Com S 363Databases

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Feasibility - Sample Course Plans• Well-prepared students can complete the core courses in 2 years• Students missing biology prerequisites but with strong computer

science background can complete the core courses in 2 years• Students missing computer science prerequisites but with strong

biology background can complete the core courses in 5 semesters• Students missing both computer science and statistics

prerequisites but with strong biology background can complete the core courses in 3 years

• Students missing basic mathematics, statistics and computer science prerequisites? Forget it!


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Educational Challenge: Interdisciplinary Training

What does the future hold?

Undergraduate training in computational biology

•Several undergraduate-level bioinformatics courses already offered or under development

•Proposal for new interdepartmental B.S. degree in bioinformatics and computational Biology at ISU has been developed

•Currently (Fall 06) under review by ISU Colleges

•Planned start date for new major: July 1, 2007

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Non-Classroom Training Activities for Students in Computational Biology

•Joint mentoring – Ph.D. research is guided by one life scientist and one computational scientist

•Research exploration rotations – ‘wet’ and ‘dry’ lab research experiences during the first year to expose students to breadth of opportunities and to help identify major and co-major professors

•Annual attendance at major national scientific meetings

•International and industrial internships


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Challenge: Fostering a Student Community within an Interdepartmental Program

•First Thursdays – monthly evening meal promotes social (and collaborative) interactions

•Students meet with invited seminar speakers•Students participate in a weekly seminar series where fellow students make research presentations

•Annual Joint Symposium – Iowa State University, The University of Iowa and New Mexico State University hold annual meeting


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Bioinformatics and Computational Biology Laboratory

•A student-led ‘consulting’ initiative•Biologists in need of help on a particular project contact the BCB lab. Students with the requisite expertise are identified to provide help

•More recently, undergraduates are getting involved, thereby providing mentorship opportunities for the graduate students


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Faculty participation in the computational biology educational program

•81 faculty members from 19 departments and 4 colleges

•Three interdependent research areas: Genome informatics Macromolecular dynamics & interactions Metabolic and regulatory networks


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Challenge: Promoting Interdisciplinary Research among Faculty

•Study in a second discipline•Faculty take courses in different disciplines

•Faculty hires in computational biology change culture of departments


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Laurence H. Baker Center for Bioinformatics and Biological

Statistics

•Organizes faculty research•Provides computer infrastructure•Home to computer support staff•Organizes campus-wide seminars and workshops

•Provides fellowships for senior students


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Institutional Challenges in Promoting Interdisciplinary Research

• Cultural differences in graduate education Rotations Teaching vs. research Summer funding

• Contributing to interdepartmental education Securing TA funds for courses Faculty time for course development and teaching

• Faculty credit for interdisciplinary research• Institutionalization is not necessarily fun


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Assessment – A Vehicle for Change

•Annual interviews with students•Annual faculty meeting•Annual surveys•Database for tracking student progress

•External reviews


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The Iowa challenge in recruiting trainees from under-represented groups:Statewide minority population = 5%Iowa State minority student population = 7%

Challenge: Promoting Diversity


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•Enrolled two minority students as

MS candidates•Both are now nearing completion of

PhD degrees

Fostering development of MS minority students:

Promoting Diversity


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Fostering Diversity through Linkages

Premise:Partner with an minority institution offering MS degrees in bioinformatics and computational biologyGoal: Formalize educational linkages between the two institutions to enhance participation of members of under-represented groups


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Linkages with other institutions:

New Mexico State University (NMSU)•Research interests that are

complementary to ISU•Foundation for inter-institutional interactions •NSF-CREST Center for Research Excellence in Bioinformatics and Computational Biology•Launching an MS level graduate program in computational biology•Diverse student body


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Computational Molecular Biology Training Group

Iowa State UniversityNew Mexico State University

2005-2011

NSF-IGERT

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Mechanisms to Formalize Educational Linkages:

•Share courses and workshops through distance education programs•Annual symposia•Offer student internships and faculty sabbatical opportunities•NIH-NSF summer workshop in bioinformatics•Curriculum equivalency during first two years of training

NSF-IGERT

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NSF-IGERT

Graduate Education at the Interface of the

Computational and Biological SciencesChallenges faced at Iowa State

University in: Interdisciplinary coursework and training

Integrating research across the campus Overcoming institutional barriers Creating a diverse student body

Voytas.ppt

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