Beyond synthetic lethality in large-scale metabolic and regulatory network models via genetic minimal intervention sets
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Motivation
The integration of genome-scale metabolic and regulatory networks has received significant interest in the field of Cancer Systems Biology. However, the identification of lethal genetic interventions in these integrated models remains a substantially challenge due to the combinatorial explosion of potential solutions. To address this issue, we previously developed the genetic Minimal Cut Set (gMCS) framework, which is capable of computing synthetic lethal interactions - minimal sets of gene knockouts that are lethal for cellular proliferation- in genome-scale metabolic networks with signed directed acyclic regulatory pathways. Here, we present a novel formulation to calculate genetic Minimal Intervention Sets, gMISs, which incorporate interventions with both gene knockouts and knock-ins.
Results
With our gMIS approach, we assessed the landscape of lethal genetic interactions in human cells and the impact of gene knock-in perturbations, which enable us to capture for the first-time genetic interventions beyond synthetic lethality, particularly synthetic dosage lethality and tumor suppressor gene complexes. We applied the concept of synthetic dosage lethality to predict essential genes in cancer and demonstrated a significant increase in sensitivity when compared to large-scale gene knockout screen data. Moreover, we conducted a tumor suppressor analysis in cancer cell lines and identified single gene knock-in strategies that block cellular proliferation. Finally, we illustrate the ability of our gMIS approach to elucidate potential targets for cancer research with several examples in hematological malignancies
Availability and implementation
The Python package gMCSpy has been updated to include the functionalities to calculate gMISs. It can be accessed here: https://github.com/PlanesLab/gMCSpy .
