Transcript Presentation

By: Amira Djebbari and John Quackenbush
BMC Systems Biology 2008, 2: 57
Presented by: Garron Wright
April 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