Seeded Bayesian Networks: Constructing Genetic Networks From Microarray Data

1. Seeded Bayesian Networks: Constructing Genetic Networks From Microarray Data By: Amira Djebbari and John Quackenbush BMC Systems Biology 2008, 2: 57 Presented by: Garron Wright April 20, 2009 CSCE 582

2. Background Bioinformatics Gene Network Modeling Techniques Microarrays Seeded Bayesian Networks/a priori knowledge biasing

3. Bioinformatics Confluence of biology, computer science, and information technology Ultimate goal: enable the discovery of new biological insights as well as creating global perspective that elucidates unifying principles of biology Genetic information is then used to create a comprehensive picture of cellular function in normal state to then compare to diseased and other altered states Interpretation of nucleotide sequence data, amino acid sequence data, protein domains, and protein structures Source: http://www.ncbi.nlm.nih.gov

4. Microarrays Source: http://www.bio.davidson.edu/Courses/genomics/chip/chip.html

5. Microarrays

6. Gene Network Modeling Techniques Weighted matrices Boolean Networks Differential Equations Bayesian Networks

9. Cellular Complexity: An NP-Complete Problem All potential network topologies must be assessed Possible Solution: Heuristic Search Algorithms Example: Greedy Hill Climbing Problem: Often find local maxima but not global

10. Domain-Specific Knowledge in Constructing Bayesian Neworks Leads to near optimization in exploring the state space (relative gene expression state in this instance) Network seed biases the search for the best topology, however it does not limit novel gene interactions from being identified How do we seed/set a priori knowledge? Pathway/interaction databases Networks deduced from published literature in PubMed High-throughput interaction screens (PPI)

11. Procedure Deducing Prior Knowledge From Published Literature Co-occurrence method Limited papers/literature to just 2 genes thus exhibits scale-free behavior Deducing Prior Network Structure From High-Throughput Screens Interactome data PPI Represent unbiased screens for interactions and have shown new unreported interactions Modified Depth-First Search to imply directionality to the network structure and then bootstrapping 100 times for the following cases: no priors, literature derived priors, PPI derived priors, and a combination of the two Select features with bootstrap confidence of 0.7 or greater

12. Procedure (cont.) Bayesian Network analysis of gene expression on a leukemia study Comparison of Acute Lymphoblastic Lymphoma (ALL) to Acute Myeloid Leukemia (AML) Top 40 genes that distinguish between the two cancers Microarray does not probe entire genome, only a subset Bayesian analysis of gene expression on a second leukemia study Nearly the entire genome probed Evaluated network reconstruction on the KEGG cell cycle pathway Created a Receiver-Operator (ROC) Curve (True positive rate vs. False Positive rate)

13. Results

17. Discussion Advent of microarray and other high throughput technology led to the expectation that the pathways/networks that would link genotype to phenotype would be discovered Using BN analysis as described here can recover some of that promise The two gene expression data sets are typical of microarray studies Domain specific knowledge of prior network seeds improves the ability of BN learning the interactions between genes These interactions can be used to reconstruct predictive networks at any confidence level Automated way to derive network graphs from a gene list, refining the graph with the expression data, and learning conditional probabilities that can be used to predict gene response to various insults Wide range of uses: mechanistic studies to drug target prioritization