Biology subjects

Satagopam, V.

Publications and source records attributed to Satagopam, V..

8 recordsLinked to original sources

bioRxiv2026

User-driven development and evaluation of an agentic framework for analysis of large pathway diagrams

As biomedical knowledge keeps growing, resources storing available information multiply and grow in size and complexity. Such resources can be in the format of molecular interaction maps, which represent cellular and molecular processes under normal or pathological conditions. However, these maps can be complex and hard to navigate, especially to novice users. Large Language Models (LLMs), particularly in the form of agentic frameworks, have emerged as a promising technology to support this exploration. In this article, we describe a user-driven process of prototyping, development, and user testing of Llemy, an LLM-based system for exploring these molecular interaction maps. By involving domain experts from the very first prototyping in the form of a hackathon and collecting both fine-grained and general feedback on more refined versions, we were able to evaluate the perceived utility and quality of the developed system, in particular for summarising maps and pathways, as well as prioritise the development of future features. We recommend continued user-driven development and benchmarking to keep the community engaged. This will also facilitate the transition towards open-weight LLMs to support the needs of the open research environment in an ever-changing technology landscape.

bioinformatics↗

bioRxiv2025

Association of ulcerative colitis with atopic dermatitis: identification of shared and unique mechanisms by construction and computational analysis of disease maps

Background and AimsUlcerative colitis (UC) and atopic dermatitis (AD) are immune-mediated inflammatory diseases with limited treatment options. They are known to be related which may explain higher risk of development of UC in patients with AD. The goal of this work is to review and analyse molecular mechanisms of UC in comparison to AD towards insights into UC complexity, potential comorbidities and novel therapies. MethodsWe developed graphical computational models of UC and AD molecular mechanisms (disease maps) by integrating information from over 800 manually curated articles. The maps are available online at https://imi-immuniverse.elixir-luxembourg.org. Disease-specific risk variants and gene expression profiles are visualised to identify signatures specific to UC, and shared with AD. Computational analysis shows key proteins, their interactions and pathways shared between UC and AD. ResultsUC and AD maps include more than 2000 molecular interactions. Systematic computational comparison shows that both disorders exhibit epithelial barrier dysfunction, immune dysregulation involving abnormal Th2, Th1 and ILC response, common inflammatory pathways and biomarkers such as IL-13, IL-4R, IFNG, and IL-18. Visualisation and analysis of omics data demonstrates UC map usability. ConclusionsWe developed the first computational graphic model of UC molecular mechanisms. Its content focuses on mechanisms of epithelial barrier disruption and downstream immune dysregulation through cytokines and other mediators. The comparison of UC to AD mechanisms demonstrates common signalling pathways and biomarkers, and supports potential drug repurposing and treatment options. The workflow can be reused and new findings can be dynamically integrated into the maps. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/650149v1_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@1c4c80eorg.highwire.dtl.DTLVardef@2a1e66org.highwire.dtl.DTLVardef@131ece3org.highwire.dtl.DTLVardef@b74971_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGRAPHICAL ABSTRACTC_FLOATNO C_FIG

bioinformatics↗

bioRxiv2025

The inflammatory skin disease map (ISD map): an interactive computational resource focused on psoriasis and atopic dermatitis molecular mechanisms

BackgroundInflammatory skin diseases (ISD), including atopic dermatitis (AD) and psoriasis (PsO), emerge from a complex network of inter- and intracellular biochemical interactions under the influence of genetic and environmental factors. The complexity of ISD mechanisms hinders translation of research findings into effective treatments and may explain the low remission rates despite the availability of modern targeted therapies. ObjectiveTo model AD- and PsO-associated mechanisms as networks of context-specific molecular interactions, the so-called ISD map, and to check the usefulness of this map as a graphically guided review of AD and PsO mechanisms and as a mechanistic hypothesis-generating platform. MethodsThe ISD map was built by assembling mechanistically resolved causal interactions obtained from relevant biomedical literature via manual curation. ResultsWe demonstrate that the ISD map (https://imi-biomap.elixir-luxembourg.org/) serves as an interactive, graphical review of AD and PsO molecular mechanisms and as a mechanistic hypothesis-generating platform. By analysing the map structure itself or the map integrated with genetics and functional genomics data, we could generate the following mechanistic hypotheses: (i) AD poor response to dupilumab is associated with a potential upregulation of IFNG, IL22, TSLP, IL-17A and IL25 signalling pathways in keratinocytes and/or single nucleotide polymorphisms (SNPs) in genes encoding regulators of IFNG expression in Th1 cells and (ii) PsO resistance to cytokine-induced apoptosis is associated with SNPs in IFNG signalling genes regulating SOCS1 in keratinocytes. Finally, the IL4/IL13 pathway in the AD submap of the ISD map was converted into a probabilistic Boolean model to simulate the effects of IFNG in sensory perception of itching after treatment with dupilumab. Our findings suggest that inhibiting both IFNG and IL4R may improve the therapeutic management of itching. ConclusionThe ISD map provides a significant interactive, computationally accessible resource of molecular knowledge on AD and PsO that can be used to graphically review known AD and PsO mechanisms and generate mechanistic hypotheses.

bioinformatics↗

bioRxiv2024

FAIR assessment of MINERVA as an opportunity to foster open science and scientific crowdsourcing in systems biomedicine

1The Disease Maps Project (https://disease-maps.org) focuses on the development of diseasespecific comprehensive structured knowledge repositories supporting translational medicine research. These disease maps require continuous interdisciplinary collaboration and should be reusable and interoperable. Adhering to the Findable, Accessible, Interoperable and Reusable (FAIR) principles enhances the utility of such digital assets. We used the RDA FAIR Data Maturity Model and assessed the FAIRness of the Molecular Interaction NEtwoRk VisuAlization (MINERVA) Platform. MINERVA is a standalone webserver that allows users to manage, explore and analyse disease maps and their related data manually or programmatically. We exemplify the FAIR assessment on the Parkinsons Disease Map (PD map) and the COVID-19 Disease Map, which are large-scale projects under the umbrella of the Disease Maps Project, aiming to investigate molecular mechanisms of the Parkinsons disease and SARS-CoV-2 infection, respectively. We discuss the FAIR features supported by the MINERVA Platform and we outline steps to further improve the MINERVA FAIRness and to better connect this resource to other ongoing scientific initiatives supporting FAIR in computational systems biomedicine.

systems biology↗

bioRxiv2023

Using interactive platforms to encode, manage and explore immune-related adverse outcome pathways

We address the need for modelling and predicting adverse outcomes in immunotoxicology to improve non-clinical assessments of immunomodulatory therapy safety and efficacy. The integrated approach includes, first, the adverse outcome pathway concept established in the toxicology field, and, second, the systems medicine disease map approach for describing molecular mechanisms involved in a particular pathology. The proposed systems immunotoxicology workflow is demonstrated with CAR T cell treatment as a use case. To this end, the linear adverse outcome pathway (AOP) is expanded into a molecular interaction model in standard systems biology formats. Then it is shown how knowledge related to immunotoxic events can be integrated, encoded, managed and explored to benefit the research community. The map is accessible online via the MINERVA Platform for browsing, commenting and data visualisation (https://minerva.pages.uni.lu). Our work transforms a graphical illustration of an AOP into a digitally structured and standardised form, featuring precise and controlled vocabulary and supporting reproducible computational analyses. Because of annotations to source literature and databases, the map can be further expanded to match the evolving knowledge and research questions.

systems biology↗

bioRxiv2022

Altered infective competence of the human gut microbiome in COVID-19

ObjectivesInfections with SARS-CoV-2 have a pronounced impact on the gastrointestinal tract and its resident microbiome. Clear differences between severe cases of infection and healthy individuals have been reported, including the loss of commensal taxa. We aimed to understand if microbiome alterations including functional shifts are unique to severe cases or a common effect of COVID-19. DesignWe used high-resolution systematic multi-omic analyses to profile the gut microbiome in asymptomatic-to-moderate COVID-19 individuals compared to a control group. ResultsWe found a striking increase in the overall abundance and expression of both virulence factors and antimicrobial resistance genes in COVID-19. Importantly, these genes are encoded and expressed by commensal taxa from families such as Acidaminococcaceae and Erysipelatoclostridiaceae, which we found to be enriched in COVID-19 positive individuals. We also found an enrichment in the expression of a betaherpesvirus and rotavirus C genes in COVID-19 positive individuals compared to healthy controls. ConclusionOur analyses identified an altered and increased infective competence of the gut microbiome in COVID-19 patients.

microbiology↗

bioRxiv2021

PredictProtein - Predicting Protein Structure and Function for 29 Years

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProteins infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold; user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings. AvailabilityFreely accessible webserver PredictProtein.org; Source and docker images: github.com/rostlab

bioinformatics↗

bioRxiv2020

BioKC: a collaborative platform for systems biology model curation and annotation

Curation of biomedical knowledge into standardised and inter-operable systems biology models is essential for studying complex biological processes. However, systems-level curation is a laborious manual process, especially when facing ever increasing growth of domain literature. Currently, these systems-level curation efforts concentrate around dedicated pathway databases, with a limited input from the research community. The demand for systems biology knowledge increases with new findings demonstrating elaborate relationships between multiple molecules, pathways and cells. This new challenge calls for novel collaborative tools and platforms allowing to improve the quality and the output of the curation process. In particular, in the current systems biology environment, curation tools lack reviewing features and are not well suited for an open, community-based curation workflows. An important concern is the complexity of the curation process and the limitations of the tools supporting it. Currently, systems-level curation combines model-building with diagram layout design. However, diagram editing tools offer limited annotation features. On the other hand, text-oriented tools have insufficient capabilities representing and annotating relationships between biological entities. Separating model curation and annotation from diagram editing enables iterative and distributed building of annotated models. Here, we present BioKC (Biological Knowledge Curation), a web-based collaborative platform for the curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML).

systems biology↗