Biology subjects

Planes, F. J.

Publications and source records attributed to Planes, F. J..

9 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

Extending PROXIMAL to predict degradation pathways of phenolic compounds in the human gut microbiota

Despite significant advances in reconstructing genome-scale metabolic networks, the understanding of cellular metabolism remains incomplete for many organisms. A promising approach for elucidating cellular metabolism is analysing the full scope of enzyme promiscuity, which exploits the capacity of enzymes to bind to non-annotated substrates and generate novel reactions. To guide time-consuming costly experimentation, different computational methods have been proposed for exploring enzyme promiscuity. One relevant algorithm is PROXIMAL, which strongly relies on KEGG to define generic reaction rules and link specific molecular substructures with associated chemical transformations. Here, we present a completely new pipeline, PROXIMAL2, which overcomes the dependency on KEGG data. In addition, PROXIMAL2 introduces two relevant improvements with respect to the former version: i) correct treatment of multi-step reactions and ii) tracking of electric charges in the transformations. We compare PROXIMAL and PROXIMAL2 in recovering annotated products from substrates in KEGG reactions, finding a highly significant improvement in the level of accuracy. We then applied PROXIMAL2 to predict degradation reactions of phenolic compounds in the human gut microbiota. The results were compared to RetroPath RL, a different and relevant enzyme promiscuity method. We found a significant overlap between these two methods but also complementary results, which open new research directions into this relevant question in nutrition.

biochemistry↗

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↗

bioRxiv2022

gMCStool: automated network-based tool to search for metabolic vulnerabilities in cancer

The development of computational tools for the systematic prediction of metabolic vulnerabilities of cancer cells constitutes a central question in systems biology. Here, we present gMCStool, a freely accessible and online tool that allows us to carry out this task in a simple, efficient and intuitive environment. gMCStool exploits the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to synthetic lethality based on genome-scale metabolic networks, including a unique database of thousands of synthetic lethals computed from Human1, the most recent metabolic reconstruction of human cells. Based on RNA-seq data, gMCStool extends and improves our previously developed algorithms to predict, visualize and analyze metabolic essential genes in cancer, demonstrating a superior performance than competing algorithms in both accuracy and computational performance. A detailed illustration of gMCStool is presented for multiple myeloma (MM), an incurable hematological malignancy. gMCStool could identify a synthetic lethal that explains the dependency on CTP Synthase 1 (CTPS1) in a sub-group of MM patients. We provide in vitro experimental evidence that supports this hypothesis, which opens a new research area to treat MM.

systems biology↗

bioRxiv2021

A network-based approach to integrate nutrient environment in the prediction of synthetic lethality in cancer metabolism

Synthetic Lethality (SL) is a promising concept in cancer research. A number of computational methods have been developed to predict SL in cancer metabolism, among which our network-based computational approach, based on genetic Minimal Cut Sets (gMCSs), can be found. A major challenge of these approaches to SL is to systematically consider tumor environment, which is particularly relevant in cancer metabolism. Here, we propose a novel definition of SL for cancer metabolism that integrates genetic interactions and nutrient availability in the environment. We extend our gMCSs approach to determine this new family of metabolic synthetic lethal interactions. A computational and experimental proof-of-concept is presented for predicting the lethality of dihydrofolate reductase inhibition in different environments. Finally, our novel approach is applied to identify extracellular nutrient dependences of tumor cells, elucidating cholesterol and myo-inositol depletion as potential vulnerabilities in different malignancies.

systems biology↗

bioRxiv2020

BOSO: a novel feature selection algorithm for linear regression with high-dimensional data

MotivationWith the frenetic growth of high-dimensional datasets in different biomedical domains, there is an urgent need to develop predictive methods able to deal with this complexity. Feature selection is a relevant strategy in machine learning to address this challenge. ResultsWe introduce a novel feature selection algorithm for linear regression called BOSO (Bilevel Optimization Selector Operator). We conducted a benchmark of BOSO with key algorithms in the literature, finding a superior performance in highdimensional datasets. Proof-of-concept of BOSO for predicting drug sensitivity in cancer is presented. A detailed analysis is carried out for methotrexate, a well-studied drug targeting cancer metabolism. AvailabilityA Matlab implementation of BOSO is available as a Supplementary Material. Contactfplanes@tecnun.es Supplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

An extended reconstruction of human gut microbiota metabolism for personalized nutrition

Understanding how diet and gut microbiota interact in the context of human health is a key question in personalized nutrition. Genome-scale metabolic networks and constraint-based modeling approaches are promising to systematically address this complex question. However, when applied to nutritional questions, a major issue in existing reconstructions is the lack of information about degradation pathways of relevant nutrients in the diet that are metabolized by the gut microbiota. Here, we present AGREDA, an extended reconstruction of the human gut microbiota metabolism for personalized nutrition. AGREDA includes the degradation pathways of 231 nutrients present in the human diet and allows us to more comprehensively simulate the interplay between food and gut microbiota. We show that AGREDA is more accurate than existing reconstructions in predicting output metabolites of the gut microbiota. Finally, using AGREDA, we established relevant metabolic differences among clinical subgroups of Spanish children: lean, obese, allergic to foods and celiac.

bioinformatics↗

bioRxiv2019

On the inconsistent treatment of gene-protein-reaction rules in context-specific metabolic models

With the publication of high-quality genome-scale metabolic models for several organisms, the Systems Biology community has developed a number of algorithms for their analysis making use of ever growing -omics data. In particular, the reconstruction of the first genome-scale human metabolic model, Recon1, promoted the development of Context-Specific Model (CS-Model) reconstruction methods. This family of algorithms aims to identify the set of metabolic reactions that are active in a cell in a given condition using omics data, such as gene expression levels. Different CS-Model reconstruction algorithms have their own strengths and weaknesses depending on the problem under study and omics data available. However, after careful inspection, we found that all of these algorithms share common issues in the way GPR rules and gene expression data are treated. The first issue is related with how gapfilling reactions are managed after the reconstruction is conducted. The second issue concerns the molecular context, which is used to build the CS-model but neglected for posterior analyses. To evaluate the effect of these issues, we reconstructed [~]400 CS-Models of cancer cell lines and conducted gene essentiality analysis, using CRISPR-Cas9 essentiality data for validation purposes. Altogether, our results illustrate the importance of correcting the errors introduced during the GPR translation in many of the published metabolic reconstructions.

bioinformatics↗