METABOLIC BEHAVIOR PREDICTION UNDER GENOME-SCALE TRANSCRIPTION PERTURBATIONS

Abstract

Advances in bioengineering have enabled numerous bio-based commodities. Yet most traditional approaches do not extend beyond a single metabolic pathway or 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. BN-based modeling of transcription regulation can faithfully capture global dependencies in the network and allow more flexible transcriptional changes, thereby enabling one to predict condition-dependent metabolic behaviors for more general genetic engineering strategies. 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 TRIMER’s applicability to both simulated and experimental data and show that it outperforms current approaches on both data types.