TRIMER: Transcription Regulation Integrated with MEtabolic Regulation

Abstract

Advances in bioengineering have enabled numerous bio-based commodities. Yet most traditional approaches do not extend beyond a single metabolic pathway and do not attempt to modify gene regulatory networks in order to buffer metabolic perturbations. This is despite access to near universal technologies allowing genome-scale engineering. To help overcome this limitation, we have developed a pipeline enabling analysis of Transcription Regulation Integrated with MEtabolic Regulation (TRIMER). TRIMER utilizes a Bayesian network (BN) inferred from transcriptomic data to model the transcription factor regulatory network. TRIMER then infers the probabilities of gene states that are of relevance to the metabolism of interest, and predicts metabolic fluxes resulting from deletion of transcription factors at the genome scale. Additionally, we have developed a simulation framework to mimic the TF-regulated metabolic network, capable of generating both gene expression states and metabolic fluxes, thereby providing a fair evaluation platform for benchmarking models and predictions. Here, we present this computational pipeline. We demonstrate TRIMERs applicability to both simulated and experimental data and show that it outperforms current approaches on both data types.