Computational modeling of metabolic dynamics in the intratumoral microenvironment.

Jorge Enrique Arellano Villavicencio

Currently, oncology research has been focused on investigating cancer metabolism due to its remarkable capacity to adapt to changes in its microenvironment, enabling it to efficiently respond to gradients of oxygen and nutrients. In 3D spheroid cultures of MCF-7 cells, three distinct cell subpopulations with varying metabolic characteristics have been identified, indicating that each subpopulation fulfills specific activities within the tumor, promoting its progression and survival.

This project proposes the utilization of genome-scale metabolic reconstructions (GEMS) to model the growth of each subpopulation. Additionally, by employing community modeling tools, it aims to simulate spheroid growth and characterize the dynamics of metabolites among the three communities. This research will pave the way for understanding the cooperativity between cells and their response to different types of stress, such as hypoxia and reduced carbon sources.

Machine Learning-Based Exploration of Gut Microbiota's Impact on Type 2 Diabetes

Juan José Oropeza Valdez

Employing machine learning algorithms to investigate the role of the gut microbiota in the development and management of Type 2 diabetes (T2DM). By analyzing microbiome data from individuals with multiple diabetes treatments, my aim is to identify specific microbial compositions associated with the disease and develop predictive models that assess an individual’s risk of developing T2DM based on their microbiota profile. Also using a systems biology approach (MICOM) using the gut microbiota data to identify the metabolic changes in the community associated with T2DM.

On Type 2 diabetes, and their relation with the gut microbiome metabolism.

Laura Elena Hernández Juárez

Type 2 diabetes mellitus (T2D) is a widespread disease worldwide, the etiology may be associated with gut microbiota influenced by different diet patterns. Metformin is a T2D treatment, and it is known that can alter the gut microbiota composition, but a few is known about the relation between this composition and the physio-pathological variables, therefore in this project the microbiota composition is analyzed, as well as the computational modeling of metabolism in microbiota to infer the growth rates of selected bacteria and the metabolic interactions into gut microbiota on patients with T2D under metformin and linagliptin treatment

Metabolic changes in macrophage polarization through in silico approaches

Perla Itzel Alvarado Luis

Macrophages, crucial components of the innate immune system, have the remarkable ability to polarize and adopt various phenotypes in response to fluctuations in their microenvironment. Considered as “double-edged swords”, these cells serve a wide array of physiological roles; however, their dysfunction can contribute to the development of various diseases, such as cancer, tuberculosis, and atherosclerosis. Furthermore, macrophage polarization is critically supported by metabolic shifts, and there is an exciting potential for regulating macrophage functions in different contexts by manipulating their metabolism.

The objective of this project is to use a systems biology approach to analyze metabolomic data from polarized macrophages in order to unravel the underlying mechanisms of metabolic reprogramming during macrophage polarization. Through this study, we aim to identify the specific metabolic factors that contribute to the transition between different phenotypes and ultimately, their potential use in inducing repolarization towards a desired phenotype.

Research on Children Leukemia

Brenda Loaiza

Leukemia is the most common cancer in children worldwide, highest incidences and worse prognostics are for low and middle-income countries where less than 30% are cured. In Mexico 4,000 to 6,000 new cases are registered each year. Epidemiological studies have shown the contribution of environmental factors to the development of Leukemia, but also clinical factors such as late and imprecise diagnosis of the disease, limited access, and /or adherence to treatment, and tolerance and toxicity of antineoplastic drugs. To confront this problem, a National Strategic Program (Pronace) was proposed for the integral study of Children Leukemia. I’m responsible for Collecting all kinds of data from most laboratories of Mexico, through the organization of a DataBase in SQL language to be publicly available, and for integral study including artificial intelligence analysis for creating models that identify risk variables, better prognosis of patients, and a deep understanding of the etiology of Acute Lymphoblastic Leukemia in mexican children.

Alteration of gut microbiota induced by metformin and linagliptin/metformin treatment prevents type 2 diabetes.

Estrella Martínez

Lifestyle modifications, metformin and dipeptidyl peptidase type 4 inhibitors (DPP4i) reduce the incidence of type 2 diabetes (T2D) in people with prediabetes. The efficacy of such interventions may be enhanced by the gut microbiota (GM), which plays a role in mediating glucose-lowering effects through the increased abundance of short-chain fatty acid (SCFA)-producing bacteria. We determined the effect of combined linagliptin+metformin vs metformin monotherapy on GM composition and its relationship to insulin sensitivity (IS) and pancreatic β-cell function (Pβf) in patients with prediabetes without a previous treatment and compared it between metformin monotherapy and the combination of linagliptin+metformin. A double-blind, randomized parallel clinical trial was conducted in 167 Mexican adults with prediabetes for 12 months. We analyzed the effects of the two treatments on GM using the machine learning algorithm (random forest) and mediation analysis with two structural equation models (SEM) to determine the relationship between body composition, IS, Pβf, and bacterial genera. These treatments modify GM composition, by increasing the abundance of SCFA-producing bacteria [Metformin (Fusicatenibacter and Blautia) and Linagliptin/metformin (Roseburia, Bifidobacterium and [Eubacterium] hallii group)].

Computational modeling of the gut microbiota metabolism in COVID-19 patients

David Girón Villalobos

I study the gut microbiota to find how microbes participate in the development of COVID-19. To do that, I use MICOM, a community metabolic computational model, that can predict metabolic interactions within the microbiota and the host.

Ecological study on gut microbiota

Crístian Mendoza Ortiz

We analyze the dynamics of the metabolism of the gut microbiota in longitudinal databases through a hybrid model between generalized Lotka-Volterra and flux balance analysis (FBA)

Gut microbiota and type 2 diabetes

Daniel Neri

A direct link between the gut microbiota (GM) and the progression of type 2 diabetes mellitus (T2D) in individuals has been described. We propose using supervised Machine Learning (ML) methods to identify predictive taxa for patients with prediabetes (pre-T2D) and T2D.

Manifold learning approaches for high dimensional biological data

Crístian Padrón

Modern high–throughput biological data yield detailed characterizations of the genomic, transcriptomic, and proteomic states of samples. This kind of data suffers from technical noise (reflected as excess of zeros in the count matrix) and the curse of dimensionality. This complicates downstream data analysis and compromises the scientific discovery reliability. Data sparsity makes it difficult to obtain a well-data structure and distorts the distribution of variables. Currently, there is a raised need to develop new algorithms with improved capacities to reduce noise and recover missing information. For that reason, we are developing new machine learning methods to better understand noisy and high-dimensional biological data. For example, microbiome and scRNA-seq data imputation. Also, we are developing multi-omic data integration methods to find key variables involved in complex biological systems which is in itself difficult to handle and the problem of the high-dimensionality is accentuated in multi-omics data.

In silico study of metabolic reprogramming during epithelial-mesenchymal transition

Meztli Matadamas

An epithelial-mesenchymal transition (EMT) is a biologic process that allows a polarized epithelial cell, which normally interacts with basement membrane via its basal surface, to undergo multiple biochemical changes that enable it to assume a mesenchymal cell phenotype, which includes enhanced migratory capacity, invasiveness, elevated resistance to apoptosis, and greatly increased production of ECM components. EMT induces invasive properties in epithelial tumors and promotes metastasis. Although EMT-mediated cellular and molecular changes are well understood, very little is known about EMT-induced metabolic changes.

The project combine high-throughput data to understand metabolic changes before and after EMT in lung cancer cell lines. In particular to find main fluxes used during EMT. To that extent we employ methods from bioinformatics and Systems Biology. Our goal is to found specific targets which could stop or reverse EMT in cancer cells.

Microbiome metabolism and diabetes

Jean Paul Sanchez

Alterations in the microbiome has been associated with diabetes progression.

Immunology and cancer: Boolean Modeling of regulatory networks

Ugo Avila Ponce de Leon

Macrophages are cells of the innate immune system endowed with the capacity to orchestrate the immune response in human tissues. Due to their plasticity biological property, they polarize to several subtypes based on the actions of the tumor microenvironment. These cells have plasticity, because once they are committed to a subtype fate, they can polarize to another subtype by simply modifying the microenvironment. We integrated experimental data for the construction of a network that will explain the plasticity and the importance of the microenvironment in shaping the polarization of macrophages. The mathematical model was used to describe the genetic control points of macrophage polarization and plasticity, and it can function as groundwork and guidance for an immunotherapeutic approach to modulate the proliferation of cancer cells.

Integrating transcriptomic and metabolomic to understand hepatocellular carcinoma in a rat model

Erika Hernandez

Hepatocellular carcinoma (HCC) is now the third leading cause of cancer deaths worldwide, with over 500,000 people affected. It occurs predominantly in patients with underlying chronic liver disease and cirrhosis. Despite this, knowledge about the metabolic states of this disease is limited. Using a rat model that recreates some of the most important characteristics of HCC, including cirrhosis, we aim to understand the metabolic state when compared to healthy liver. To this end we will integrate transcriptomic and metabolic data in a systems biology framework that point us changes in reactions. This data would not only helped us identify reactions important to maintain the cancerous state but also help us survey the regulatory mechanism this is achieved.

Metabolic heterogeneity in cancer and its applications in Personalized Medicine

Christian Diener

Cancer is a very heterogeneous disease and tumors can differ greatly across and within different cancer types. Consequently, cancer is not a single disease but thousands. One property shared by all cancers is the ability to sustain chronic uncontrolled proliferation which raises the question how different cancers alter their metabolism in order to achieve consistent proliferation.

In this project we combine large-scale genomic data from DNA and RNA sequencing as well as proteomics and metabolomics to understand the connection between variations in the genotype and cancer metabolism. In particular we are asking the question whether distinct genomic aberrations such as mutations or changes in transcription can be related to respective changes in cellular metabolism. To that extent we employ methods from Systems Biology as well as from Data Science and Machine Learning in order to connect genetic information to specific metabolic phenotypes.

Our aim is to use the knowledge we gain in the context of personalized medicine, particularly the use of genotyping for the prediction of the best course of treatment for a specific patient.

Systems biology and bioinformatics of single cell RNAseq data.

Thelma Escobedo

Research in personalized therapy has taken relevance because treatment failures due to intratumoral heterogenety which refers to celular diversity or subpopulations forming within the tumor. Currently, given complex molecular processes of cancer there has been greater use of omic technologies and computational analysis. With the purpuse to contribute in this line, we have opened a new line of research to describe the progress of expression profiles in tumor cell lines through bioinformatic analysis of single cell RNAseq data. Likewise, this work will contribute to infer the principles that guide the population heterogeneity mechanisms for the design of new optimized strategies for the treatment of cancer.

The impact of the microRNAs in the metabolic reprogramming of the MCF-7 cells during the spheroids development

Erick Muciño

Alterations in the metabolism are a common property in cancer cells, so that, many efforts have been directed to develop models to understand the mechanism by which cancer cells behave differently compared to normal tissues. In recent years, it has been reported that microRNAs (miRNAs) are involved in the regulation of all biological process, and there are evidences that shown its dysregulation play an important role in the development and progression of cancer. Hence, generate models that allow the integration of miRNAs regulation in cancer metabolism will allow us to analyze in a systematically and systemic manner the relations and potential mechanism underlying between miRNAs and central pathways of metabolism.

It is imperative to know the mechanism governing the pathogenesis and progression of cancer to design therapies with greater impact on diagnosis and disease progression. So, this project aims to suggest mechanism that trigger the metabolic change in a breast cancer cell line (MCF-7), integrating miRNAs network. To this end, we propose to develop a scheme of systems biology, which allow us to make an integrative analysis of regulatory networks of miRNAs and metabolism in MCF-7. This approach will allow us to develop models capable of identifying potential therapeutic targets with greater impact, biomarkers that allow early detection of cancer and penetrate in global mechanism in clinical cases.