BCB 570. Bioinformatics IV (Computational Functional Genomics and Systems Biology).
(Cross-listed with COM S, GDCB, STAT, CPR E.) (3-0) Cr. 3. S. Prereq: BCB 567, Biol 315,
Com S 311 and either 208 or 228, Gen 411, Stat 430. Algorithmic and statistical approaches in
computational functional genomics and systems biology. Elements of experiment design.
Analysis of high throughput gene expression, proteomics, and other datasets obtained using
system-wide measurements. Topological analysis, module discovery, and comparative analysis
of gene and protein networks. Modeling, analysis, simulation and inference of transcriptional
regulatory modules and networks, protein-protein interaction networks, metabolic networks, cells
and systems: Dynamic systems, Boolean, and probabilistic models. Multi-scale, multigranularity models. Ontology-driven, network based, and probabilistic approaches to information
integration.
Instructor Contact Information
Dr. Julie Dickerson: 3123 Coover Hall/2624 Howe Hall, julied@iastate.edu, 294-7705
Text: Systems Biology in Practice. Concepts, Implementation and Application., E. Klipp, R. Herwig, A.
Kowald, C. Wierling, H. Lehrach, Wiley, 2005.
Course Description
Algorithmic and statistical approaches in computational functional genomics and systems biology;
Biological Information Integration – Knowledge (ontology) driven and statistical approaches; Qualitative,
probabilistic, and dynamic network models; Modeling, analysis, simulation and inference of
transcriptional regulatory modules and networks, protein-protein interaction networks; metabolic
networks; cells and systems.
Syllabus
What is systems biology? From parts to interactions to wholes; Data integration, predictive model
construction, simulation and model-based prediction, model-driven experimentation, bridging levels of
abstraction.
What is a (mathematical or computational) model? What are models good for? How can we construct
models? How can we evaluate models?
Modeling metabolism: (JD)
 Metabolomics, metabolic flux BN
 Data and standards
 Differential, difference, and stochastic equations
 Enzyme Kinetics and thermodynamics
 Metabolic networks
 Metabolic control analysis
 Steady-state models
 Dynamic models
 Feedback control.

Modeling Signal Transduction:
 Intracellular communication
 Receptor-ligand interaction
 Structural components of signaling pathways
 Example pathways – MAP-Kinase, JAK-Stat
 Dynamic regulatory features
 Data and Standards
 Pathway Databases and Pathway Models
 Modeling and analysis.
Modeling Gene Expression and Gene expression data analysis:
 Gene expression data acquisition
 Data and Standards
 Transgenic animals, knockouts, and RNA-i
 Tests for differential expression, multiple testing
 Cluster analysis – hierarchical clustering, SOM, k-means, PCA, NNMF
 Modules of gene expression (network motifs)
 Classification based on gene expression
 Models of genetic networks:
 Differential equations
 Influence networks
 Boolean networks and temporal Boolean networks
 Bayesian networks, and temporal Bayesian Networks
 Stochastic equations
 Fuzzy models
Modeling and analysis of protein-protein (and possibly protein-DNA, and protein-RNA interaction
networks)
 Protein-protein interaction data acquisition
 Data and Standards
 Association networks, correlation networks, hypergraph models
 Analysis – module identification (spectral clustering), comparative analysis
 Integrating gene and protein networks
Integrative and multi-scale modelling
 What and why?
 Data integration
 Sources
 Model (ontology)-driven integration – ontologies, mappings, database federation
 Graph-theoretic methods
 Probabilistic methods
 Fuzzy methods
 Multi-scale modeling
Case studies from the literature
Grading: (Preliminary)
Homework Assignments
Projects in Modeling
Case Studies from literature
Modeling/Computational/Visualization Tools:
Matlab
R
Cytoscape
BCB Program/Orientation/2011/BCB570-Description.doc