Executable models of signaling pathways enable transforming static pictures of signaling pathways into models that can be used for in-silico experimentation, thereby facilitating development of mechanistic models. Development of executable models for large-scale dataset is rare and require defining signal flow and signal integration. We develop Boolean Omics Network Invariant-Time Analysis (BONITA), for signal propagation, signal integration, and pathway analysis. Our signal propagation approach models heterogeneity in transcriptomic data as arising from intercellular heterogeneity rather than intracellular stochasticity, and propagates binary signals repeatedly across networks. Logic rules defining signal integration are inferred by genetic algorithm and are refined by local search. The rules determine the impact of each node in a pathway, which is used to score the probability of the pathway’s modulation by chance. We have comprehensively tested BONITA for application to transcriptomics data from translational studies and identified higher sensitivity at lower levels of pathway modulation compared to state-of-the-art pathway analysis methods.
We have applied BONITA and other network methods to investigate two key problems in HIV biology: B cell responses to vaccination and higher incidence of atherosclerosis in people living with HIV, using a systems biology approach. The first study looks at completed phase I and IIa trials of HIV vaccines to estimate durability of antibody response to protein or MVA-boosted vaccination and uncover underlying molecular differences in B cells. Our mixed effects models of antibody dynamics after HIV vaccination revealed half-lives of gp120-specific antibodies were longer but peak magnitudes were lower for Modified Vaccinia Ankara (MVA)-boosted regimens than protein-boosted regimens, leading to higher total area under the curve for protein regimens. To investigate molecular signatures of MVA and protein boosted trials we performed RNA sequencing of gp120-specific B cells from durable and transient vaccine responders in HVTN 094, 205 (MVA-boosted) and HVTN 105 (protein-boosted vaccines). BONITA reveals integration of key FCRL genes with BCR signaling pathway to influence difference between protein and MVA boosted regimens. Secondly, to elucidate factors that lead to development of atherosclerosis at higher levels in people living with HIV, we collected mRNA and miRNA expression in addition to cytokine levels in people living with HIV with and without atherosclerosis. These revealed a number of miRNAs that were clearly different between these groups. Analysis of an integrated network of all data types revealed increased importance of miRNAs in network regulation of HIV+ group in contrast with increased importance of cytokines in the HIV+AS+ group.
|Commitee:||Dewhurst, Stephen, Mathews, David, McCall, Matthew|
|School:||University of Rochester|
|Department:||School of Medicine and Dentistry|
|School Location:||United States -- New York|
|Source:||DAI-B 82/3(E), Dissertation Abstracts International|
|Subjects:||Bioinformatics, Biophysics, Immunology|
|Keywords:||Atherosclerosis, Boolean Networks, HIV, Pathway Analysis, Systems Immunology, Vaccines|
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