index.xml

<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Webpage of the Resendis Lab</title><link>https://resendislab.github.io/</link><description>Recent content on Webpage of the Resendis Lab</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Sun, 08 Sep 2024 00:00:00 +0000</lastBuildDate><atom:link href="https://resendislab.github.io/index.xml" rel="self" type="application/rss+xml"/><item><title>Exploring metabolic anomalies in COVID-19 and post-COVID-19: a machine learning approach with explainable artificial intelligence</title><link>https://resendislab.github.io/pubs/postcovid/</link><pubDate>Sun, 08 Sep 2024 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/postcovid/</guid><description>The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant challenges worldwide, including diverse clinical outcomes and prolonged post-recovery symptoms known as Long COVID or Post-COVID-19 syndrome. Emerging evidence suggests a crucial role of metabolic reprogramming in the infection’s long-term consequences. This study employs a novel approach utilizing machine learning (ML) and explainable artificial intelligence (XAI) to analyze metabolic alterations in COVID-19 and Post-COVID-19 patients. Samples were taken from a cohort of 142 COVID-19, 48 Post-COVID-19, and 38 control patients, comprising 111 identified metabolites.</description></item><item><title>Diffusion on PCA-UMAP Manifold: The Impact of Data Structure Preservation to Denoise High-Dimensional Single-Cell RNA Sequencing Data</title><link>https://resendislab.github.io/pubs/phenix/</link><pubDate>Tue, 09 Jul 2024 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/phenix/</guid><description>Single-cell transcriptomics (scRNA-seq) is revolutionizing biological research, yet it faces challenges such as inefficient transcript capture and noise. To address these challenges, methods like neighbor averaging or graph diffusion are used. These methods often rely on k-nearest neighbor graphs from low-dimensional manifolds. However, scRNA-seq data suffer from the ‘curse of dimensionality’, leading to the over-smoothing of data when using imputation methods. To overcome this, sc-PHENIX employs a PCA-UMAP diffusion method, which enhances the preservation of data structures and allows for a refined use of PCA dimensions and diffusion parameters (e.</description></item><item><title>Effect of metformin and metformin/linagliptin on gut microbiota in patients with prediabetes</title><link>https://resendislab.github.io/pubs/metformin/</link><pubDate>Tue, 23 Apr 2024 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/metformin/</guid><description>Lifestyle modifications, metformin, and linagliptin reduce the incidence of type 2 diabetes (T2D) in people with prediabetes. The gut microbiota (GM) may enhance such interventions' efficacy. We determined the effect of linagliptin/metformin (LM) vs metformin (M) on GM composition and its relationship to insulin sensitivity (IS) and pancreatic β-cell function (Pβf) in patients with prediabetes. A cross-sectional study was conducted at different times: basal, six, and twelve months in 167 Mexican adults with prediabetes.</description></item><item><title>mb-PHENIX: Diffusion and Supervised Uniform Manifold Approximation for denoising microbiota data</title><link>https://resendislab.github.io/pubs/mbphenix/</link><pubDate>Fri, 01 Dec 2023 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/mbphenix/</guid><description>Motivation Microbiota data encounters challenges arising from technical noise and the curse of dimensionality, which affect the reliability of scientific findings. Furthermore, abundance matrices exhibit a zero-inflated distribution due to biological and technical influences. Consequently, there is a growing demand for advanced algorithms that can effectively recover missing taxa while also considering the preservation of data structure. Results We present mb-PHENIX, an open-source algorithm developed in Python that recovers taxa abundances from the noisy and sparse microbiota data.</description></item><item><title>Computational modeling of metabolic dynamics in the intratumoral microenvironment.</title><link>https://resendislab.github.io/projects/gem_spheroids/</link><pubDate>Tue, 27 Jun 2023 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/projects/gem_spheroids/</guid><description>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.</description></item><item><title>Dysbiosis signatures of gut microbiota and the progression of type 2 diabetes: a machine learning approach in a Mexican cohort</title><link>https://resendislab.github.io/pubs/dysbiosis_diabetes/</link><pubDate>Tue, 27 Jun 2023 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/dysbiosis_diabetes/</guid><description>Introduction: The gut microbiota (GM) dysbiosis is one of the causal factors for the progression of different chronic metabolic diseases, including type 2 diabetes mellitus (T2D). Understanding the basis that laid this association may lead to developing new therapeutic strategies for preventing and treating T2D, such as probiotics, prebiotics, and fecal microbiota transplants. It may also help identify potential early detection biomarkers and develop personalized interventions based on an individual’s gut microbiota profile.</description></item><item><title>Machine Learning-Based Exploration of Gut Microbiota's Impact on Type 2 Diabetes</title><link>https://resendislab.github.io/projects/diabetes_micom/</link><pubDate>Tue, 27 Jun 2023 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/projects/diabetes_micom/</guid><description>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&amp;rsquo;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.</description></item><item><title>On Type 2 diabetes, and their relation with the gut microbiome metabolism.</title><link>https://resendislab.github.io/projects/diabetes_metformin/</link><pubDate>Tue, 27 Jun 2023 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/projects/diabetes_metformin/</guid><description>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</description></item><item><title>Uncoding the interdependency of tumor microenvironment and macrophage polarization: insights from a continuous network approach</title><link>https://resendislab.github.io/pubs/uncoding_macrophages/</link><pubDate>Mon, 22 May 2023 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/uncoding_macrophages/</guid><description>The balance between pro- and anti-inflammatory immune system responses is crucial to preventing complex diseases like cancer. Macrophages are essential immune cells that contribute to this balance constrained by the local signaling profile of the tumor microenvironment. To understand how pro- and anti-inflammatory unbalance emerges in cancer, we developed a theoretical analysis of macrophage differentiation that is derived from activated monocytes circulating in the blood. Once recruited to the site of inflammation, monocytes can be polarized based on the specific interleukins and chemokines in the microenvironment.</description></item><item><title>A network perspective on the ecology of gut microbiota and progression of type 2 diabetes: Linkages to keystone taxa in a Mexican cohort</title><link>https://resendislab.github.io/pubs/mycrobiota_diabetes/</link><pubDate>Wed, 12 Apr 2023 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/mycrobiota_diabetes/</guid><description>Introduction: The human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host.
Methods: Here, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment).</description></item><item><title>Spermiogenesis alterations in the absence of CTCF revealed by single cell RNA sequencing</title><link>https://resendislab.github.io/pubs/spermiogenesis/</link><pubDate>Thu, 30 Mar 2023 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/spermiogenesis/</guid><description>CTCF is an architectonic protein that organizes the genome inside the nucleus in almost all eukaryotic cells. There is evidence that CTCF plays a critical role during spermatogenesis as its depletion produces abnormal sperm and infertility. However, defects produced by its depletion throughout spermatogenesis have not been fully characterized. In this work, we performed single cell RNA sequencing in spermatogenic cells with and without CTCF. We uncovered defects in transcriptional programs that explain the severity of the damage in the produced sperm.</description></item><item><title>A New Approach to Personalized Nutrition: Postprandial Glycemic Response and its Relationship to Gut Microbiota</title><link>https://resendislab.github.io/pubs/postprandial_glycemic/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/postprandial_glycemic/</guid><description>A prolonged and elevated postprandial glucose response (PPGR) is now considered a main factor contributing for the development of metabolic syndrome and type 2 diabetes, which could be prevented by dietary interventions. However, dietary recommendations to prevent alterations in PPGR have not always been successful. New evidence has supported that PPGR is not only dependent of dietary factors like the content of carbohydrates, or the glycemic index of the foods, but is also dependent on genetics, body composition, gut microbiota, among others.</description></item><item><title>Chronic Comorbidities in Middle Aged Patients Contribute to Ineffective Emergency Hematopoiesis in Covid-19 Fatal Outcomes</title><link>https://resendislab.github.io/pubs/comorbidities_covid/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/comorbidities_covid/</guid><description>Background and Aims Mexico is among the countries with the highest estimated excess mortality rates due to the COVID–19 pandemic, with more than half of reported deaths occurring in adults younger than 65 years old. Although this behavior is presumably influenced by the young demographics and the high prevalence of metabolic diseases, the underlying mechanisms have not been determined. Methods The age–stratified case fatality rate (CFR) was estimated in a prospective cohort with 245 hospitalized COVID–19 cases, followed through time, for the period October 2020–September 2021.</description></item><item><title>Machine Learning and COVID-19: Lessons from SARS-CoV-2</title><link>https://resendislab.github.io/pubs/machine_covid/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/machine_covid/</guid><description>Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population.</description></item><item><title>Metabolic changes in macrophage polarization through in silico approaches</title><link>https://resendislab.github.io/projects/metabolic_macrophages/</link><pubDate>Sun, 01 Jan 2023 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/projects/metabolic_macrophages/</guid><description>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 &amp;ldquo;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.</description></item><item><title> Research on Children Leukemia</title><link>https://resendislab.github.io/projects/leukemia/</link><pubDate>Thu, 15 Dec 2022 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/projects/leukemia/</guid><description>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.</description></item><item><title>Alteration of gut microbiota induced by metformin and linagliptin/metformin treatment prevents type 2 diabetes.</title><link>https://resendislab.github.io/projects/t2d/</link><pubDate>Thu, 15 Dec 2022 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/projects/t2d/</guid><description>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.</description></item><item><title>Computational modeling of the gut microbiota metabolism in COVID-19 patients</title><link>https://resendislab.github.io/projects/covidmicom/</link><pubDate>Thu, 15 Dec 2022 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/projects/covidmicom/</guid><description>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.</description></item><item><title>Ecological study on gut microbiota</title><link>https://resendislab.github.io/projects/ecologicalgut/</link><pubDate>Thu, 15 Dec 2022 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/projects/ecologicalgut/</guid><description>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)</description></item><item><title>Gut microbiota and type 2 diabetes</title><link>https://resendislab.github.io/projects/t2dmicom/</link><pubDate>Thu, 15 Dec 2022 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/projects/t2dmicom/</guid><description>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.</description></item><item><title>Manifold learning approaches for high dimensional biological data</title><link>https://resendislab.github.io/projects/scphenix/</link><pubDate>Thu, 15 Dec 2022 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/projects/scphenix/</guid><description>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.</description></item><item><title>Macrophage Boolean networks in the time of SARS-CoV-2</title><link>https://resendislab.github.io/pubs/macrophage_boolean/</link><pubDate>Mon, 17 Oct 2022 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/macrophage_boolean/</guid><description>The post-pandemic period of the current coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has lasted longer than expected despite the huge impact of the world-wide vaccination campaign in the past years. Since the pandemic began, endless mathematical models have been published to describe the viral outbreak at a population level. However, the molecular mechanism that drives the pathogenesis of the virus in the human microenvironment has been scarce.</description></item><item><title>Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era</title><link>https://resendislab.github.io/pubs/type2_diabetes/</link><pubDate>Mon, 15 Aug 2022 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/type2_diabetes/</guid><description>The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions.</description></item><item><title>prePrint: A network perspective on the ecology of gut microbiota and progression of Type 2 Diabetes: linkages to keystone taxa in a Mexican cohort</title><link>https://resendislab.github.io/pubs/ecology_gut/</link><pubDate>Wed, 20 Jul 2022 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/ecology_gut/</guid><description>Background
The human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host. Results
Here, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment).</description></item><item><title>Comparative subcellular localization of NRF2 and KEAP1 during the hepatocellular carcinoma development in vivo</title><link>https://resendislab.github.io/pubs/comparative_subcellular/</link><pubDate>Sun, 01 May 2022 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/comparative_subcellular/</guid><description>The activation of Nuclear Factor, Erythroid 2 Like 2 – Kelch Like ECH Associated Protein 1 (NRF2-KEAP1) signaling pathway plays a critical dual role by either protecting or promoting the carcinogenesis process. However, its activation or nuclear translocation during hepatocellular carcinoma (HCC) progression has not been addressed yet. This study characterizes the subcellular localization of both NRF2 and KEAP1 during diethylnitrosamine-induced hepatocarcinogenesis in the rat. NRF2-KEAP1 pathway was continuously activated along with the increased expression of its target genes, namely Nqo1, Hmox1, Gclc, and Ptgr1.</description></item><item><title>Physiological Network Is Disrupted in Severe COVID-19</title><link>https://resendislab.github.io/pubs/physiological_networks/</link><pubDate>Thu, 10 Mar 2022 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/physiological_networks/</guid><description>The human body is a complex system maintained in homeostasis thanks to the interactions between multiple physiological regulation systems. When faced with physical or biological perturbations, this system must react by keeping a balance between adaptability and robustness. The SARS-COV-2 virus infection poses an immune system challenge that tests the organism’s homeostatic response. Notably, the elderly and men are particularly vulnerable to severe disease, poor outcomes, and death. Mexico seems to have more infected young men than anywhere else.</description></item><item><title>Stochastic Analysis of the RT-PCR Process in Single-Cell RNA-Seq</title><link>https://resendislab.github.io/pubs/mathematics_2021/</link><pubDate>Thu, 07 Oct 2021 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/mathematics_2021/</guid><description>The single-cell RNA-seq allows exploring the transcriptome for one cell at a time. By doing so, cellular regulation is pictured. One limitation is the dropout events phenomenon, where a gene is observed at a low or moderate expression level in one cell but not detected in another. Dropouts obscure legitimate biological heterogeneity leading to the description of a small fraction of the meaningful relations. We used a stochastic approach to model the Reverse Transcription Polymerase Chain Reaction (RT-PCR) kinetic, in which we contemplated the temperature profile, RT-PCR duration, and reaction rates.</description></item><item><title>On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markers</title><link>https://resendislab.github.io/pubs/frontiers_immunology_2021/</link><pubDate>Thu, 16 Sep 2021 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/frontiers_immunology_2021/</guid><description>COVID-19 is a disease with a spectrum of clinical responses ranging from moderate to critical. To study and control its effects, a large number of researchers are focused on two substantial aims. On the one hand, the discovery of diverse biomarkers to classify and potentially anticipate the disease severity of patients. These biomarkers could serve as a medical criterion to prioritize attention to those patients with higher prone to severe responses.</description></item><item><title>Transcriptional and Microenvironmental Landscape of Macrophage Transition in Cancer: A Boolean Analysis</title><link>https://resendislab.github.io/pubs/frontiers_immunology_2021_ugo/</link><pubDate>Thu, 10 Jun 2021 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/frontiers_immunology_2021_ugo/</guid><description>The balance between pro- and anti-inflammatory immune system responses is crucial to face and counteract complex diseases such as cancer. Macrophages are an essential population that contributes to this balance in collusion with the local tumor microenvironment. Cancer cells evade the attack of macrophages by liberating cytokines and enhancing the transition to the M2 phenotype with pro-tumoral functions. Despite this pernicious effect on immune systems, the M1 phenotype still exists in the environment and can eliminate tumor cells by liberating cytokines that recruit and activate the cytotoxic actions of TH1 effector cells.</description></item><item><title>MicroRNAs Regulate Metabolic Phenotypes During Multicellular Tumor Spheroids Progression</title><link>https://resendislab.github.io/pubs/fontiers_oncology_2020_erick/</link><pubDate>Fri, 04 Dec 2020 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/fontiers_oncology_2020_erick/</guid><description>During tumor progression, cancer cells ire their metabolism to face their bioenergetic demands. In recent years, microRNAs (miRNAs) have emerged as regulatory elements that inhibit the translation and stability of crucial mRNAs, some of them causing direct metabolic alterations in cancer. In this study, we investigated the relationship between miRNAs and their targets mRNAs that control metabolism, and how this fine-tuned regulation is diversified depending on the tumor stage. To do so, we implemented a paired analysis of RNA-seq and small RNA-seq in a breast cancer cell line (MCF7).</description></item><item><title>Analysis of Epithelial-Mesenchymal Transition Metabolism Identifies Possible Cancer Biomarkers Useful in Diverse Genetic Backgrounds</title><link>https://resendislab.github.io/pubs/frontiers_onlogy2020/</link><pubDate>Sun, 02 Aug 2020 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/frontiers_onlogy2020/</guid><description>Epithelial-to-mesenchymal transition (EMT) relates to many molecular and cellular alterations that occur when epithelial cells undergo a switch in differentiation generating mesenchymal-like cells with newly acquired migratory and invasive properties. In cancer cells, EMT leads to drug resistance and metastasis. Moreover, differences in genetic backgrounds, even between patients with the same type of cancer, also determine resistance to some treatments. Metabolic rewiring is essential to induce EMT, hence it is important to identify key metabolic elements for this process, which can be later used to treat cancer cells with different genetic backgrounds.</description></item><item><title>Unveiling functional heterogeneity in breast cancer multicellular tumor spheroids through single-cell RNA-seq</title><link>https://resendislab.github.io/pubs/scientificreports2020/</link><pubDate>Thu, 02 Jul 2020 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/scientificreports2020/</guid><description>Heterogeneity is an intrinsic characteristic of cancer. Even in isogenic tumors, cell populations exhibit differential cellular programs that overall supply malignancy and decrease treatment efficiency. In this study, we investigated the functional relationship among cell subtypes and how this interdependency can promote tumor development in a cancer cell line. To do so, we performed single-cell RNA-seq of MCF7 Multicellular Tumor Spheroids as a tumor model. Analysis of single-cell transcriptomes at two-time points of the spheroid growth, allowed us to dissect their functional relationship.</description></item><item><title>Memote: A community driven effort towards a standardized genome-scale metabolic model test suite</title><link>https://resendislab.github.io/pubs/memote/</link><pubDate>Mon, 02 Mar 2020 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/memote/</guid><description>Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed. Here, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation.</description></item><item><title>Micom: metagenome-scale modeling to infer metabolic interactions in the microbiota.</title><link>https://resendislab.github.io/pubs/micom/</link><pubDate>Tue, 04 Feb 2020 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/micom/</guid><description>Alterations in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn&amp;rsquo;s disease and diabetes. However, establishing the causality between the microbial composition and disease remains a challenge. We introduce a strategy based on metabolic models of complete microbial gut communities in order to derive the particular metabolic consequences of the microbial composition for the diabetic gut in a balanced cohort of 186 individuals. By using a heuristic optimization approach based on L2 regularization we were able to obtain a unique set of realistic growth rates that allows growth for the majority of observed taxa in a sample.</description></item><item><title>In silico study of metabolic reprogramming during epithelial-mesenchymal transition</title><link>https://resendislab.github.io/projects/emt/</link><pubDate>Fri, 06 Dec 2019 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/projects/emt/</guid><description>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.</description></item><item><title>Microbiome metabolism and diabetes</title><link>https://resendislab.github.io/projects/diabe_metabolism/</link><pubDate>Fri, 06 Dec 2019 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/projects/diabe_metabolism/</guid><description>Alterations in the microbiome has been associated with diabetes progression.</description></item><item><title>Biological Physics Mexico City 2019</title><link>https://resendislab.github.io/events/biophysmex2019/</link><pubDate>Fri, 06 Sep 2019 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/events/biophysmex2019/</guid><description>Frontier Science at the Intersection of Physics, Math and Biology The BioPhys Mexico City 2019 conference, the third in a biennial series, is intended as an international, multidisciplinary scientific forum to discuss the latest developments in biological physics, including experimental, theoretical and computational methods, from a single molecule perspective to complex multi-component environments.
The conference is expected to boost the new paradigm of interdisciplinary approaches converging into specific problems in biological physics.</description></item><item><title>3st International Summer Symposium on Systems Biology</title><link>https://resendislab.github.io/events/is3b_2019/</link><pubDate>Mon, 05 Aug 2019 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/events/is3b_2019/</guid><description>Those are the proceedings for the 3rd edition of the “International Summer Symposium on Systems Biology”. The meeting took place at the National Institute of Genomic Medicine (INMEGEN) in Mexico City from August 4-6, 2014. This event was the first of a series of meetings to encourage the development of the systems biology in Mexico and the development of this area to tackle basic and applied research in medical and biomedical fields.</description></item><item><title>Immunology and cancer: Boolean Modeling of regulatory networks</title><link>https://resendislab.github.io/projects/inmunology_cancer/</link><pubDate>Thu, 06 Dec 2018 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/projects/inmunology_cancer/</guid><description>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.</description></item><item><title>Integrating transcriptomic and metabolomic to understand hepatocellular carcinoma in a rat model</title><link>https://resendislab.github.io/projects/hepato/</link><pubDate>Thu, 06 Dec 2018 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/projects/hepato/</guid><description>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.</description></item><item><title>Metabolic heterogeneity in cancer and its applications in Personalized Medicine</title><link>https://resendislab.github.io/projects/prolif/</link><pubDate>Thu, 06 Dec 2018 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/projects/prolif/</guid><description>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.</description></item><item><title>Systems biology and bioinformatics of single cell RNAseq data.</title><link>https://resendislab.github.io/projects/singlecell_thelma/</link><pubDate>Thu, 06 Dec 2018 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/projects/singlecell_thelma/</guid><description>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.</description></item><item><title>The impact of the microRNAs in the metabolic reprogramming of the MCF-7 cells during the spheroids development</title><link>https://resendislab.github.io/projects/spheroids/</link><pubDate>Thu, 06 Dec 2018 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/projects/spheroids/</guid><description>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.</description></item><item><title>Cancer: a complex disease</title><link>https://resendislab.github.io/pubs/cancer_a_complex_disease/</link><pubDate>Sat, 01 Dec 2018 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/cancer_a_complex_disease/</guid><description>This is an EBook can be downloaded for free.
The study of complex systems and their related phenomena has become a major research venue in the recent years and it is commonly regarded as an important part of the scientific revolution developing through the 21st century. The science of complexity is concerned with the laws of operation and evolution of systems formed by many locally interacting elements that produce collective order at spatiotemporal scales larger than that of the single constitutive elements.</description></item><item><title>Distinct microbes, metabolites, and ecologies define the microbiome in deficient and proficient mismatch repair colorectal cancers.</title><link>https://resendislab.github.io/pubs/pm30376889/</link><pubDate>Wed, 31 Oct 2018 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm30376889/</guid><description>Links between colorectal cancer (CRC) and the gut microbiome have been established, but the specific microbial species and their role in carcinogenesis remain an active area of inquiry. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology. We collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes.</description></item><item><title>Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer.</title><link>https://resendislab.github.io/pubs/pm29704665/</link><pubDate>Sun, 29 Apr 2018 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm29704665/</guid><description>Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals.</description></item><item><title>Quantitative Models for Microscopic to Macroscopic Biological Macromolecules and Tissues</title><link>https://resendislab.github.io/pubs/quantitative_modeling/</link><pubDate>Thu, 01 Mar 2018 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/quantitative_modeling/</guid><description>This book presents cutting-edge research on the use of physical and mathematical formalisms to model and quantitatively analyze biological phenomena ranging from microscopic to macroscopic systems. The systems discussed in this compilation cover protein folding pathways, gene regulation in prostate cancer, quorum sensing in bacteria to mathematical and physical descriptions to analyze anomalous diffusion in patchy environments and the physical mechanisms that drive active motion in large sets of particles, both fundamental descriptions that can be applied to different phenomena in biology.</description></item><item><title>Systems Biology and the Challenge of Deciphering the Metabolic Mechanisms Underlying Cancer</title><link>https://resendislab.github.io/pubs/frontiers_ebook/</link><pubDate>Mon, 27 Nov 2017 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/frontiers_ebook/</guid><description>This is an EBook compendium of the respective Frontiers Research Topic and can be downloaded for free.
Since the discovery of the Warburg effect in the 1920s cancer has been tightly associated with the genetic and metabolic state of the cell. One of the hallmarks of cancer is the alteration of the cellular metabolism in order to promote proliferation and undermine cellular defense mechanisms such as apoptosis or detection by the immune system.</description></item><item><title>micom</title><link>https://resendislab.github.io/software/micom/</link><pubDate>Sun, 01 Oct 2017 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/software/micom/</guid><description>micom is a Python package for metabolic modeling of microbial communities developed in the Human Systems Biology Group of Prof. Osbaldo Resendis Antonio at the National Institute of Genomic Medicine Mexico.
micom allows you to construct a community model from a list on input COBRA models and manages exchange fluxes between individuals and individuals with the environment. It explicitly accounts for different abundances of individuals in the community and can thus incorporate data from 16S rRNA sequencing experiments.</description></item><item><title>Our microbiome pipeline</title><link>https://resendislab.github.io/software/microbiome/</link><pubDate>Tue, 01 Aug 2017 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/software/microbiome/</guid><description>This repository contains the standardized analysis pipeline for 16S and metagenome data. It serves as a testing ground for what will be required to analyze around 500 samples.</description></item><item><title>Editorial: Systems Biology and the Challenge of Deciphering the Metabolic Mechanisms Underlying Cancer</title><link>https://resendislab.github.io/pubs/editorial_frontiers/</link><pubDate>Fri, 28 Jul 2017 12:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/editorial_frontiers/</guid><description>This is the Editorial for our Frontiers Research Topic &amp;ldquo;Systems Biology and the challenge of deciphering the metabolic mechanisms underlying cancer&amp;rdquo;.
The corresponding E-Book will be available soon.</description></item><item><title>Graduate student positions</title><link>https://resendislab.github.io/positions/students/</link><pubDate>Thu, 29 Jun 2017 12:53:05 -0500</pubDate><guid>https://resendislab.github.io/positions/students/</guid><description>We extend an invitation to undergrads and grad students with interest to continue his/her academic education through a Master&amp;rsquo;s or Doctoral degree in one of these academic programs: biological (http://pcbiol.posgrado.unam.mx), biochemical (http://www.mdcbq.posgrado.unam.mx/) or Biomedical (http://www.pdcb.unam.mx/) Sciences at UNAM. We encourage candidates with an academic background in biology, biology physics, biophysics, genome sciences, applied mathematics and computational sciences. The students incorporated to one of these programs will be guided to develop a systems biology description in one of these areas:</description></item><item><title>Postdoctoral position</title><link>https://resendislab.github.io/positions/postdocs/</link><pubDate>Thu, 29 Jun 2017 12:52:56 -0500</pubDate><guid>https://resendislab.github.io/positions/postdocs/</guid><description>We always are looking for researchers with interest to contribute in Systems Biology to understand human diseases. If you are interested in any of the general areas of research described before and would like to carry out post-doctoral or research stays in Systems Biology of the Microbiome, or develop systems paradigms in precision medicine, send your curriculum vitae, a brief statement of your research interests, and the names of 2-3 references to [oresendis [at] inmegen.</description></item><item><title>"Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases.</title><link>https://resendislab.github.io/pubs/pm28536537/</link><pubDate>Fri, 26 May 2017 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm28536537/</guid><description>The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the &amp;ldquo;psychiatric phenotype&amp;rdquo; is to provide an improved vision of the shape of the phenotype as it is visualized by &amp;ldquo;Gestalt&amp;rdquo; psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part.</description></item><item><title>Biological Physics Mexico City 2017</title><link>https://resendislab.github.io/events/biological_physics/</link><pubDate>Wed, 17 May 2017 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/events/biological_physics/</guid><description>Frontiers at the interface of Physics, Math and Biology. This conference (the second in a series) is intended as an international, multidisciplinary scientific forum to discuss the latest developments in biological physics (including proteins, peptides and enzymes, among many other topics).
The conference is expected to boost a new paradigm of interdisciplinary approaches converging into specific problems in biological physics. Hence, the conference audience is broad: We aim to attract the attention of biologists as well as biochemists, organic chemists, engineers, computational scientists, physicists, and mathematicians.</description></item><item><title>CORDA for Python</title><link>https://resendislab.github.io/software/corda/</link><pubDate>Mon, 01 May 2017 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/software/corda/</guid><description>This is a Python implementation based on the papers of Schultz et. al. with some added optimizations. It is based on the publications of Schultz et. al. [1, 2].
CORDA, short for Cost Optimization Reaction Dependency Assessment is a method for the reconstruction of metabolic networks from a given reference model (a database of all known reactions) and a confidence mapping for reactions. It allows you to reconstruct metabolic models for tissues, patients or specific experimental conditions from a set of transcription or proteome measurements.</description></item><item><title>Natural selection drove metabolic specialization of the chromatophore in Paulinella chromatophora.</title><link>https://resendislab.github.io/pubs/pm28410570/</link><pubDate>Sun, 16 Apr 2017 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm28410570/</guid><description>Genome degradation of host-restricted mutualistic endosymbionts has been attributed to inactivating mutations and genetic drift while genes coding for host-relevant functions are conserved by purifying selection. Unlike their free-living relatives, the metabolism of mutualistic endosymbionts and endosymbiont-originated organelles is specialized in the production of metabolites which are released to the host. This specialization suggests that natural selection crafted these metabolic adaptations. In this work, we analyzed the evolution of the metabolism of the chromatophore of Paulinella chromatophora by in silico modeling.</description></item><item><title>Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies.</title><link>https://resendislab.github.io/pubs/pm28082911/</link><pubDate>Sat, 14 Jan 2017 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm28082911/</guid><description>Cancer is a heterogeneous disease and its genetic and metabolic mechanism may manifest differently in each patient. This creates a demand for studies that can characterize phenotypic traits of cancer on a per-sample basis. Combining two large data sets, the NCI60 cancer cell line panel, and The Cancer Genome Atlas, we used a linear interaction model to predict proliferation rates for more than 12,000 cancer samples across 33 different cancers from The Cancer Genome Atlas.</description></item><item><title>Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine.</title><link>https://resendislab.github.io/pubs/pm28018236/</link><pubDate>Tue, 27 Dec 2016 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm28018236/</guid><description>It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer.</description></item><item><title>Hola!!!</title><link>https://resendislab.github.io/posts/test/</link><pubDate>Tue, 06 Dec 2016 09:19:57 -0600</pubDate><guid>https://resendislab.github.io/posts/test/</guid><description>&lt;h2 id="this-is-an-example-post">This is an example post&lt;/h2>
&lt;p>Please substitute all text below &amp;ldquo;+++&amp;rdquo; with your own!&lt;/p>
&lt;p>This is my text now grrrr :)&lt;/p></description></item><item><title>Who we are</title><link>https://resendislab.github.io/about/we/</link><pubDate>Mon, 05 Dec 2016 14:48:16 -0600</pubDate><guid>https://resendislab.github.io/about/we/</guid><description>Welcome to the webpage of the Human Systems Biology group in the National Institute for Genomic Medicine at Mexico City, INMEGEN. Our group is interdisciplinary and have the objective to develop a systems biology framework to analyze mainly human diseases and metabolic phenotype in microorganisms through the use of computational models and high-throughput technologies.
Currently, our laboratory focuses on the analysis of metabolic alterations in cancer cells by the implementation of genome scale metabolic reconstructions and assess the predictions in terms of experimental data at different scales.</description></item><item><title>Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks.</title><link>https://resendislab.github.io/pubs/pm27616995/</link><pubDate>Tue, 13 Sep 2016 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm27616995/</guid><description/></item><item><title>2nd International Summer Symposium on Systems Biology</title><link>https://resendislab.github.io/events/is3b/</link><pubDate>Tue, 02 Aug 2016 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/events/is3b/</guid><description>With great pleasure we are hereby announcing the 2nd International Summer Symposium on Systems Biology (IS3B) taking place in Mexico City, Mexico from August 2nd - 4th 2016. The IS3B 2016 is organized by The Human Systems Biology Laboratory (HSBL), RAI-UNAM &amp;amp; INMEGEN.
The IS3B is currently the largest symposium on Systems Biology in Mexico and Latin America, and strives to unite leading researchers and students in an informal setting with the aim to present current research in Systems Biology and Systems Medicine.</description></item><item><title>The space of enzyme regulation in HeLa cells can be inferred from its intracellular metabolome.</title><link>https://resendislab.github.io/pubs/pm27335086/</link><pubDate>Fri, 24 Jun 2016 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm27335086/</guid><description>During the transition from a healthy state to a cancerous one, cells alter their metabolism to increase proliferation. The underlying metabolic alterations may be caused by a variety of different regulatory events on the transcriptional or post-transcriptional level whose identification contributes to the rational design of therapeutic targets. We present a mechanistic strategy capable of inferring enzymatic regulation from intracellular metabolome measurements that is independent of the actual mechanism of regulation.</description></item><item><title>dycone</title><link>https://resendislab.github.io/software/dycone/</link><pubDate>Wed, 01 Jun 2016 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/software/dycone/</guid><description>Dycone (&amp;ldquo;dynamic cone&amp;rdquo;) allows you infer enzymatic regulation from metabolome measurements. It employs formalisms based on flux and k-cone analysis to connect metabolome data to distinct regulations of enzyme activity. Most of the analysis methods can be applied to genome-scale data.</description></item><item><title>1st International Summer Symposium on Systems Biology</title><link>https://resendislab.github.io/events/is3b_2014/</link><pubDate>Mon, 04 Aug 2014 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/events/is3b_2014/</guid><description>Those are the proceedings for the 1st edition of the “International Summer Symposium on Systems Biology: From networks to phenotypes in human diseases”. The meeting took place at the National Institute of Genomic Medicine (INMEGEN) in Mexico City from August 4-6, 2014. This event was the first of a series of meetings to encourage the development of the systems biology in Mexico and the development of this area to tackle basic and applied research in medical and biomedical fields.</description></item><item><title>Modeling metabolism: a window toward a comprehensive interpretation of networks in cancer.</title><link>https://resendislab.github.io/pubs/pm24747697/</link><pubDate>Tue, 22 Apr 2014 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm24747697/</guid><description>Given the multi-factorial nature of cancer, uncovering its metabolic alterations and evaluating their implications is a major challenge in biomedical sciences that will help in the optimal design of personalized treatments. The advance of high-throughput technologies opens an invaluable opportunity to monitor the activity at diverse biological levels and elucidate how cancer originates, evolves and responds under drug treatments. To this end, researchers are confronted with two fundamental questions: how to interpret high-throughput data and how this information can contribute to the development of personalized treatment in patients.</description></item><item><title>Encyclopedia of Systems Biology</title><link>https://resendislab.github.io/pubs/encyclopedia/</link><pubDate>Sat, 01 Jun 2013 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/encyclopedia/</guid><description>The Encyclopedia of Systems Biology is conceived as a comprehensive reference work covering all aspects of systems biology, in particular the investigation of living matter involving a tight coupling of biological experimentation, mathematical modeling and computational analysis and simulation. The main goal of the Encyclopedia is to provide a complete reference of established knowledge in systems biology – a ‘one-stop shop’ for someone seeking information on key concepts of systems biology.</description></item><item><title>Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells.</title><link>https://resendislab.github.io/pubs/pm23316163/</link><pubDate>Tue, 15 Jan 2013 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm23316163/</guid><description>One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines.</description></item><item><title>Symbiotic Endophytes</title><link>https://resendislab.github.io/pubs/symbiotic_endophytes/</link><pubDate>Tue, 01 Jan 2013 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/symbiotic_endophytes/</guid><description>This Soil Biology volume examines our current understanding of the mechanisms involved in the beneficial effects transferred to plants by endophytes such as rhizobial, actinorhizal, arbuscular mycorrhizal symbionts and yeasts. Topics presented include how symbiosis starts on the molecular level; chemical signaling in mycorrhizal symbiosis; genomic and functional diversity of endophytes; nitrogen fixation; nutrient uptake and cycling; as well as plant protection against various stress conditions. Further, the use of beneficial microorganisms as biopesticides is discussed, particularly the application of Plant Growth Promoter Rhizobacteria (PGPR) in agriculture with the aim to increase yields.</description></item><item><title>Boolean modeling reveals that cyclic attractors in macrophage polarization serve as reservoirs of states to balance external perturbations from the tumor microenvironment</title><link>https://resendislab.github.io/pubs/boolean_modeling/</link><pubDate>Wed, 05 Dec 2012 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/boolean_modeling/</guid><description>Cyclic attractors generated from Boolean models may explain the adaptability of a cell in response to a dynamical complex tumor microenvironment. In contrast to this idea, we postulate that cyclic attractors in certain cases could be a systemic mechanism to face the perturbations coming from the environment. To justify our conjecture, we present a dynamic analysis of a highly curated transcriptional regulatory network of macrophages constrained into a cancer microenvironment. We observed that when M1-associated transcription factors (STAT1 or NF-κB) are perturbed and the microenvironment balances to a hyper-inflammation condition, cycle attractors activate genes whose signals counteract this effect implicated in tissue damage.</description></item><item><title>Functional modules, structural topology, and optimal activity in metabolic networks.</title><link>https://resendislab.github.io/pubs/pm23071431/</link><pubDate>Wed, 17 Oct 2012 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm23071431/</guid><description>Modular organization in biological networks has been suggested as a natural mechanism by which a cell coordinates its metabolic strategies for evolving and responding to environmental perturbations. To understand how this occurs, there is a need for developing computational schemes that contribute to integration of genomic-scale information and assist investigators in formulating biological hypotheses in a quantitative and systematic fashion. In this work, we combined metabolome data and constraint-based modeling to elucidate the relationships among structural modules, functional organization, and the optimal metabolic phenotype of Rhizobium etli, a bacterium that fixes nitrogen in symbiosis with Phaseolus vulgaris.</description></item><item><title>Systems biology of bacterial nitrogen fixation: high-throughput technology and its integrative description with constraint-based modeling.</title><link>https://resendislab.github.io/pubs/pm21801415/</link><pubDate>Tue, 02 Aug 2011 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm21801415/</guid><description>Bacterial nitrogen fixation is the biological process by which atmospheric nitrogen is uptaken by bacteroids located in plant root nodules and converted into ammonium through the enzymatic activity of nitrogenase. In practice, this biological process serves as a natural form of fertilization and its optimization has significant implications in sustainable agricultural programs. Currently, the advent of high-throughput technology supplies with valuable data that contribute to understanding the metabolic activity during bacterial nitrogen fixation.</description></item><item><title>Proteomic patterns of cervical cancer cell lines, a network perspective.</title><link>https://resendislab.github.io/pubs/pm21696634/</link><pubDate>Fri, 24 Jun 2011 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm21696634/</guid><description>Cervical cancer is a major mortality factor in the female population. This neoplastic is an excellent model for studying the mechanisms involved in cancer maintenance, because the Human Papilloma Virus (HPV) is the etiology factor in most cases. With the purpose of characterizing the effects of malignant transformation in cellular activity, proteomic studies constitute a reliable way to monitor the biological alterations induced by this disease. In this contextual scheme, a systemic description that enables the identification of the common events between cell lines of different origins, is required to distinguish the essence of carcinogenesis.</description></item><item><title>Modeling core metabolism in cancer cells: surveying the topology underlying the Warburg effect.</title><link>https://resendislab.github.io/pubs/pm20811631/</link><pubDate>Fri, 03 Sep 2010 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm20811631/</guid><description>Alterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over oxidative phosphorylation to generate ATP. Regulatory action over metabolic enzymes has opened a new window for designing more effective anti-cancer treatments. This enterprise is not trivial and the development of computational models that contribute to identifying potential enzymes for breaking the robustness of cancer cells is a priority.</description></item><item><title>Filling kinetic gaps: dynamic modeling of metabolism where detailed kinetic information is lacking.</title><link>https://resendislab.github.io/pubs/pm19305506/</link><pubDate>Tue, 24 Mar 2009 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm19305506/</guid><description>Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases. To overcome this constraint, we present and illustrate a new statistical approach that has two purposes: integrate high throughput data and survey the general dynamical mechanisms emerging for a slightly perturbed metabolic network.</description></item><item><title>Regulation by transcription factors in bacteria: beyond description.</title><link>https://resendislab.github.io/pubs/pm19076632/</link><pubDate>Wed, 17 Dec 2008 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm19076632/</guid><description>Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts.</description></item><item><title>Metabolic reconstruction and modeling of nitrogen fixation in Rhizobium etli.</title><link>https://resendislab.github.io/pubs/pm17922569/</link><pubDate>Wed, 10 Oct 2007 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm17922569/</guid><description>Rhizobiaceas are bacteria that fix nitrogen during symbiosis with plants. This symbiotic relationship is crucial for the nitrogen cycle, and understanding symbiotic mechanisms is a scientific challenge with direct applications in agronomy and plant development. Rhizobium etli is a bacteria which provides legumes with ammonia (among other chemical compounds), thereby stimulating plant growth. A genome-scale approach, integrating the biochemical information available for R. etli, constitutes an important step toward understanding the symbiotic relationship and its possible improvement.</description></item><item><title>Identification of regulatory network topological units coordinating the genome-wide transcriptional response to glucose in Escherichia coli.</title><link>https://resendislab.github.io/pubs/pm17559662/</link><pubDate>Fri, 15 Jun 2007 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm17559662/</guid><description>Glucose is the preferred carbon and energy source for Escherichia coli. A complex regulatory network coordinates gene expression, transport and enzyme activities in response to the presence of this sugar. To determine the extent of the cellular response to glucose, we applied an approach combining global transcriptome and regulatory network analyses.</description></item><item><title>Robustness and evolvability in genetic regulatory networks.</title><link>https://resendislab.github.io/pubs/pm17188715/</link><pubDate>Tue, 26 Dec 2006 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm17188715/</guid><description>Living organisms are robust to a great variety of genetic changes. Gene regulation networks and metabolic pathways self-organize and reaccommodate to make the organism perform with stability and reliability under many point mutations, gene duplications and gene deletions. At the same time, living organisms are evolvable, which means that these kind of genetic perturbations can eventually make the organism acquire new functions and adapt to new environments. It is still an open problem to determine how robustness and evolvability blend together at the genetic level to produce stable organisms that yet can change and evolve.</description></item><item><title>Modular analysis of the transcriptional regulatory network of E. coli.</title><link>https://resendislab.github.io/pubs/pm15680508/</link><pubDate>Tue, 01 Feb 2005 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/pubs/pm15680508/</guid><description>The transcriptional network of Escherichia coli is currently the best-understood regulatory network of a single cell. Motivated by statistical evidence, suggesting a hierarchical modular architecture in this network, we identified eight modules with well-defined physiological functions. These modules were identified by a clustering approach, using the shortest path to trace regulatory relationships across genes in the network. We report the type (feed forward and bifan) and distribution of motifs between and within modules.</description></item><item><title>Contact</title><link>https://resendislab.github.io/about/contact/</link><pubDate>Mon, 01 Jan 1900 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/about/contact/</guid><description>Directions Osbaldo Resendis-Antonio, PhD
Laboratory in Systems Biology and Human Diseases
Associated Professor
Instituto Nacional de Medicina Genomica – INMEGEN
Periferico Sur 4809, Arenal Tepepan, Tlalpan, 14610 Mexico City, CDMX
Phone: +52 55 5350 1900 - Ext.1198</description></item><item><title/><link>https://resendislab.github.io/members/dummy/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/members/dummy/</guid><description>This is a dummy page. It&amp;rsquo;s content will not be rendered.</description></item></channel></rss>