BCB 570. Bioinformatics IV (Computational Functional Genomics and Systems Biology).

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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 app
roaches 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, an
d 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, multi
-
granularity 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 approa
ches 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 inferenc
e 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, si
mulation 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


hier
archical clustering, SOM, k
-
means, PCA, NNMF



Modules of gene expression (network motifs)



Classification based on gene expression




Models of genetic networks:



Differential equations



I
nfluence networks



Boolean networks and temporal Boolean networks



Bayes
ian 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



Ass
ociation 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