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 developed for genome-scale metabolic networks.

Prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods.

The relevance of phenolic compounds in the human diet has increased in recent years, particularly due to their role as natural antioxidants and chemopreventive agents in different diseases. In the human body, phenolic compounds are mainly metabolized by the gut microbiota; however, their metabolism is not well represented in public databases and existing reconstructions. In a previous work, using different sources of knowledge, bioinformatic and modelling tools, we developed AGREDA, an extended metabolic network more amenable to analyze the interaction of the human gut microbiota with diet.

The bone marrow niche regulates redox and energy balance in MLL::AF9 leukemia stem cells.

Eradicating leukemia requires a deep understanding of the interaction between leukemic cells and their protective microenvironment. The CXCL12/CXCR4 axis has been postulated as a critical pathway dictating leukemia stem cell (LSC) chemoresistance in AML due to its role in controlling cellular egress from the marrow. Nevertheless, the cellular source of CXCL12 in the acute myeloid leukemia (AML) microenvironment and the mechanism by which CXCL12 exerts its protective role in vivo remain unresolved.

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

Synthetic Lethality (SL) is currently defined as a type of genetic interaction in which the loss of function of either of two genes individually has limited effect in cell viability but inactivation of both genes simultaneously leads to cell death. Given the profound genomic aberrations acquired by tumor cells, which can be systematically identified with -omics data, SL is a promising concept in cancer research. In particular, SL has received much attention in the area of cancer metabolism, due to the fact that relevant functional alterations concentrate on key metabolic pathways that promote cellular proliferation.

An extended reconstruction of human gut microbiota metabolism of dietary compounds.

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 problem. However, when applied to nutritional questions, a major issue in existing reconstructions is the limited information about compounds in the diet that are metabolized by the gut microbiota.

Effect of Freezing on Gut Microbiota Composition and Functionality for In Vitro Fermentation Experiments.

The gut microbiota has a profound effect on human health and is modulated by food and bioactive compounds. To study such interaction, in vitro batch fermentations are performed with fecal material, and some experimental designs may require that such fermentations be performed with previously frozen stools. Although it is known that freezing fecal material does not alter the composition of the microbial community in 16S rRNA gene amplicon and metagenomic sequencing studies, it is not known whether the microbial community in frozen samples could still be used for in vitro fermentations.

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

Supplementary data are available at Bioinformatics online.

Adaptation of the Human Gut Microbiota Metabolic Network During the First Year After Birth.

Predicting the metabolic behavior of the human gut microbiota in different contexts is one of the most promising areas of constraint-based modeling. Recently, we presented a supra-organismal approach to build context-specific metabolic networks of bacterial communities using functional and taxonomic assignments of meta-omics data. In this work, this algorithm is applied to elucidate the metabolic changes induced over the first year after birth in the gut microbiota of a cohort of Spanish infants.

CANCERTOOL: A Visualization and Representation Interface to Exploit Cancer Datasets.

With the advent of OMICs technologies, both individual research groups and consortia have spear-headed the characterization of human samples of multiple pathophysiologic origins, resulting in thousands of archived genomes and transcriptomes. Although a variety of web tools are now available to extract information from OMICs data, their utility has been limited by the capacity of nonbioinformatician researchers to exploit the information. To address this problem, we have developed CANCERTOOL, a web-based interface that aims to overcome the major limitations of public transcriptomics dataset analysis for highly prevalent types of cancer (breast, prostate, lung, and colorectal).

gMCS: fast computation of genetic minimal cut sets in large networks.

Motivation: The identification of minimal gene knockout strategies to engineer metabolic systems constitutes one of the most relevant applications of the COnstraint-Based Reconstruction and Analysis (COBRA) framework. In the last years, the minimal cut sets (MCSs) approach has emerged as a promising tool to carry out this task. However, MCSs define reaction knockout strategies, which are not necessarily transformed into feasible strategies at the gene level.

COBRA methods and metabolic drug targets in cancer.

The identification of therapeutic strategies exploiting the metabolic alterations of malignant cells is a relevant area in cancer research. Here, we discuss a novel computational method, based on the COBRA (COnstraint-Based Reconstruction and Analysis) framework for metabolic networks, to perform this task. Current and future steps are presented.

In-silico gene essentiality analysis of polyamine biosynthesis reveals APRT as a potential target in cancer.

Constraint-based modeling for genome-scale metabolic networks has emerged in the last years as a promising approach to elucidate drug targets in cancer. Beyond the canonical biosynthetic routes to produce biomass, it is of key importance to focus on metabolic routes that sustain the proliferative capacity through the regulation of other biological means in order to improve in-silico gene essentiality analyses. Polyamines are polycations with central roles in cancer cell proliferation, through the regulation of transcription and translation among other things, but are typically neglected in in silico cancer metabolic models.