By: Amira Djebbari and John QuackenbushBMC Systems Biology 2008, 2: 57

Presented by: Garron WrightApril 20, 2009

CSCE 582

•Bioinformatics•Gene Network Modeling Techniques•Microarrays•Seeded Bayesian Networks/a priori knowledge biasing

•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

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

•Weighted matrices•Boolean Networks•Differential Equations•Bayesian Networks

•All potential network topologies must be assessed•Possible Solution: Heuristic Search Algorithms

• Example: Greedy Hill Climbing• Problem: Often find local maxima but not

global

•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)

•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

•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)

•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