Executable Network Models of Integrated Multiomics Data

Abstract

Multiomics profiling provides a holistic picture of a condition being examined and captures the complexity of signaling events, beginning from the original cause (environmental or genetic), to downstream functional changes at multiple molecular layers. Pathway enrichment analysis has been used with multiomics data sets to characterize signaling mechanisms. However, technical and biological variability between these layered data limit an integrative computational analyses. We present a Boolean network-based method, multiomics Boolean Omics Network Invariant-Time Analysis (mBONITA), to integrate omics data sets that quantify multiple molecular layers. mBONITA utilizes prior knowledge networks to perform topologybased pathway analysis. In addition, mBONITA identifies genes that are consistently modulated across molecular measurements by combining observed fold-changes and variance, with a measure of node (i.e., gene or protein) influence over signaling, and a measure of the strength of evidence for that gene across data sets. We used mBONITA to integrate multiomics data sets from RAMOS B cells treated with the immunosuppressant drug cyclosporine A under varying O 2 tensions to identify pathways involved in hypoxia-mediated chemotaxis. We compare mBONITA's performance with 6 other pathway analysis methods designed for multiomics data and show that mBONITA identifies a set of pathways with evidence of modulation across all omics layers. mBONITA is freely available at https://github.com/Thakar-Lab/mBONITA.

Optimizing sequential gene expression modulation for cellular reprogramming - Coupled Boolean modeling and Reinforcement Learning based method

“…The proposed method relies on the Boolean network model for gaining the biological context. Thus, the method can be extended further towards: (1) incorporating Boolean models of genome-scale regulatory networks, thereby capturing system-wide biological context; and (2) using multi-omics data for developing such models, thereby capturing multilevel regulation of gene expression [35], [71], [72], as opposed to the current models that are derived only from transcriptomic data.…”

Second Special Issue on Methods for Omics Research: Proteome Research and Beyond

“…It is becoming more and more evident that a systems-wide understanding of a biological context cannot be achieved without the large-scale profiling and quantification of its small molecules in combination with the other traditional omics approaches. Palshikar et al have tackled the problem of comparing and integrating different omics data sets by developing a multiomics Boolean Omics Network Invariant-Time Analysis (mBONITA), available at . mBONITA uses prior knowledge networks to perform topology-based pathway analysis and identifies genes that are consistently modulated across transcriptomics, phosphoproteomics, and proteomics measurements.…”