Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Computational Systems Biology
Vasant Honavar
Artificial Intelligence Research Laboratory
Department of Computer Science
Bioinformatics and Computational Biology Graduate Program
Center for Computational Intelligence, Learning, & Discovery
Iowa State University
honavar@cs.iastate.edu
www.cs.iastate.edu/~honavar/
www.cs.iastate.edu/~honavar/aigroup.html
www.cild.iastate.edu
www.bcb.iastate.edu
www.igert.iastate.edu
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Can a biologist fix a radio?
“We would eventually find how to open the radios and will find objects of
various shape, color, and size [...]. We would describe and classify them
into families according to their appearance. We would describe a family of
square metal objects, a family of round brightly colored objects with two
legs, round-shaped objects with three legs and so on. Because the objects
would vary in color, we will investigate whether changing the colors affects
the radio’s performance. Al-though changing the colors would have only
attenuating effects (the music is still playing but a trained ear of some
people can discern some distortion), this approach will produce many
publications and result in a lively debate”.
Y. Lazebnik. Can a biologist fix a radio? Or, what I learned while studying
apoptosis. Cancer Cell 2 179-182. 2002.
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Biology, circa 19th and 20th centuries
• “All science is either stamp collecting or physics”
• Biology has largely been about stamp collecting
– Collecting, cataloging, organizing, and describing:
• species
• cells, tissues, organs
• genomes, genes, regulatory elements and proteins
• protein structures and protein-protein, protein-DNA,
protein-RNA complexes
• gene expression profiles
Ernest Rutherford
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Biology: from stamp collecting to physics
• Biology has been largely a descriptive science
– akin to physics before Newton
• We have been limited by
– Our instruments of observation
– Our ability to construct predictive models
• Modern systems biology is about
– Approaching biology as fundamentally an information
science
– Transforming biology into a predictive science
– Integration, rather than reduction
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Biology: From stamp collecting to physics
• Computation : Biology :: Calculus : Physics
• Universality of computation: Any structure or process that
is describable can be described in the form of computer
programs
– Differential equations (stoichiometric models, kinetic
models)
– Undirected and directed graphs
– Boolean networks
– State transition systems e.g., Petri nets
– Probabilistic models
– Grammars
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Computational Systems Biology
• Building and testing predictive models in biology requires
system-wide observational and experimental data
• To construct a cellular network model, we minimally need
– a list of the molecular players
– a list of the ‘influences’ of one set of players on another
– ways to structure and query the data
– computational approaches to construct descriptive and
predictive models from the data
– ways to generate testable hypothesis from the model
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Computational Systems Biology: Data
• Connectivity without activity: yeast 2 hybrid
• Activity without connectivity: microarray
• Activity and connectivity: pathway inference
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Yeast Gene regulatory network
• 1276 regulatory
interactions among
682 proteins
• Analysis
–
–
–
–
Network motifs
Topological properties
Modules
Comparison across
multiple networks
Maslov, S., Brookhaven
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Computational Systems Biology: Finding Network motifs
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Computational Systems Biology: Protein networks
Modules and programming
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Computational Systems Biology: Modeling metabolic pathways
Jeong et al, Nature, 407, 651-654, 2000.
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Computational Systems Biology: Network Reconstruction
Direct analysis
Indirect analysis e.g., promoter
analysis
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Computational Systems Biology: Network reconstruction
Dana Pe’er, 2003
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Computational Systems Biology: Pathway inference
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Computational Systems Biology: Pathway inference
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Computational systems biology: Different data, Different models
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Computational systems biology
• Building and testing predictive models in biology requires
– Working with high dimensional, noisy, sparse,
heterogeneous data
– Integration across
• Disciplines
• Levels of abstraction
– DNA, mRNA, proteins
– Macromolecular Networks
– Cells
– Tissues
– Organs
– Organisms ..
• Spatial and temporal scales
– Computational thinking among biologists
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Computational Systems Biology: Challenges
How to
• Fully decipher the (digital) information content of the genome
• Do all-vs-all comparisons of 1000s of genomes
• Extract protein and gene regulatory networks from the above
• Reliably integrate disparate multi-scale data types
• Construct predictive models from large-scale, sparse, multidimensional data
• Convert static network maps into dynamic mathematical
models
• Identify cellular signatures for cellular states (e.g. healthy vs.
diseased)
• Build models across multiple scales of time & space
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Computational systems biology
• Building and testing predictive models in biology
– Requires advances in
• High level modeling languages
• Databases and knowledge bases
• Mathematical analysis techniques
• Data and knowledge integration tools
• Data mining algorithms
• Simulation tools
– Presents challenges in computer science, mathematics,
statistics, control theory, engineering
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Computational Systems Biology
Physics
BIOLOGY
Computational
Models
Computational
Systems Biology
COMPUTATION
TECHNOLOGY
StampCollecting
Collecting
Stamp
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007
Iowa State University
Department of Computer Science
Center for Computational Intelligence, Learning, and Discovery
Bioinformatics and Computational Biology Program
Computational Systems Biology at ISU
•
•
Can build on strengths
– Bioinformatics and Computational Biology Program
– Biological sciences
• GDCB, BBMB, EEOB
– Computational sciences
• Computer Science, Statistics, Mathematics
– Physical Sciences
– Engineering
• Chemical and Biological Engg., Electrical and Computer Engg.
– Interdisciplinary centers and initiatives
• LH Baker Center for Bioinformatics and Biological Statistics
• Center for Integrated Animal Genomics
• Center for Computational Intelligence, Learning, and Discovery
Complements university priorities in
– Biological sciences
– Information sciences
– E-science
Vasant Honavar, Computational Systems Biology, ISU, November 9, 2007