Biology subjects

Barrena, N.

Publications and source records attributed to Barrena, N..

3 recordsLinked to original sources

bioRxiv2025

Beyond synthetic lethality in large-scale metabolic and regulatory network models via genetic minimal intervention sets

MotivationThe 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. ResultsWith 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 implementationThe Python package gMCSpy has been updated to include the functionalities to calculate gMISs. It can be accessed here: https://github.com/PlanesLab/gMCSpy.

systems biology↗

bioRxiv2024

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

MotivationThe identification of minimal genetic interventions that modulate metabolic processes constitutes one of the most relevant applications of genome-scale metabolic models (GEMs). The concept of Minimal Cut Sets (MCSs) and its extension at the gene level, genetic Minimal Cut Sets (gMCSs), have attracted increasing interest in the field of Systems Biology to address this task. Different computational tools have been developed to calculate MCSs and gMCSs using both commercial and open-source software. ResultsHere, we present gMCSpy, an efficient Python package to calculate gMCSs in GEMs using both commercial and non-commercial optimization solvers. We show that gMCSpy substantially overperforms our previous computational tool GMCS, which exclusively relied on commercial software. Moreover, we compared gMCSpy with recently published competing algorithms in the literature, finding significant improvements in both accuracy and computation time. All these advances make gMCSpy an attractive tool for researchers in the field of Systems Biology for different applications in health and biotechnology. Availability and ImplementationThe Python package gMCSpy can be accessed at: https://github.com/PlanesLab/gMCSpy Contactfplanes@tecnun.es Supplementary Information

systems biology↗

bioRxiv2023

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

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.

cancer biology↗