Synthetic lethality in large-scale integrated metabolic and regulatory network models of human cells

Abstract

Synthetic lethality (SL) is a promising concept in cancer research. A wide array of computational tools has been developed to predict and exploit synthetic lethality for the identification of tumour-specific vulnerabilities. Previously, we introduced the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to SL for genome-scale metabolic networks. The major challenge in our gMCS framework is to go beyond metabolic networks and extend existing algorithms to more complex protein-protein interactions. We present here a novel computation approach that adapts our previous gMCS formulation to incorporate linear regulatory pathways. Our novel approach is applied to calculate gMCSs in integrated metabolic and regulatory models of human cells. In particular, we integrate the most recent genome-scale metabolic network, Human1, with 3 different regulatory network databases: Omnipath, Dorothea and TRRUST. Based on the computed gMCSs and transcriptomic data, we detail new essential genes and their associated synthetic lethals for different cancer cell lines. The performance of the different integrated models is assessed with available large-scale in-vitro gene silencing data. Finally, we discuss the most relevant gene essentiality predictions based on published literature in cancer research.

gMCSpy: Efficient and accurate computation of Genetic Minimal Cut Sets in Python

“…This step is performed by the function buildGMatrix, which has been substantially improved in gMCSpy with respect to our previous work (Apaolaza et al, 2019). In particular, buildGMatrix requires the function parseGPRToModel, which transforms GPR rules into artificial reaction networks (GPR networks) (Apaolaza et al, 2019;Barrena et al, 2023). We have implemented an efficient recursive strategy in Python to reduce the computational expenditure in constructing GPR networks.…”

Review and meta-analysis of the genetic Minimal Cut Set approach for gene essentiality prediction in cancer metabolism

“…On a cutting-edge study, Barrena et al. [ 17 ] have integrated regulatory pathways on a metabolic network based on Boolean networks. Using well-known and different regulatory network databases, they assessed gene essentiality prediction in in vitro gene silencing data with promising results: an increase in the predicted number of essential genes in integrated metabolic and regulatory to the pure metabolic model, as well as the incorporation of key signalling genes to the study.…”