Biology subjects

Ostaszewski, M.

Publications and source records attributed to Ostaszewski, M..

14 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

Sex-specific microRNA regulators of Parkinson disease: insights from cohort-stratified simulations of compensatory pathway dynamics

Parkinsons disease (PD) exhibits sex differences in prevalence, symptom severity, and progression, suggesting distinct underlying molecular mechanisms. However, the pathophysiological mechanisms remain largely obscure, particularly in the context of the post-transcriptional regulations, where microRNAs (miRNAs) suppress the expression of multiple genes. Bulk transcriptomic data often blur these effects, especially when regulatory patterns vary by sex or cell type. miRNAs have emerged as key regulators of PD-related processes, but their complexity demands computational methods that enable capturing the functional impact at the pathway level. In this study, we investigated how sex may affect miRNA-regulated PD pathways using Boolean modeling across two large PD cohorts, the Parkinsons Progression Markers Initiative (PPMI) and the Luxembourg Parkinsons Study (LuxPark). First, differential expression analysis identified significant variations in miRNA expression between the sexes across both cohorts. These miRNAs were analysed to identify molecular pathways that are over-represented among the predicted targets of the dysregulated microRNAs (i.e. enrichment analysis). The enriched pathways were used to build Boolean models to simulate the effects of sex-specific miRNA dysregulation. These simulations showed consistent male-specific impairment in mitochondrial biogenesis, and respiratory chain activity. Mitophagy and oxidative stress response pathways were also disrupted, alongside dysregulation of autophagy-related protein-folding mechanisms. Our findings suggest that sex-specific miRNA dysregulation contributes to differences in molecular patterns in PD by influencing compensatory related pathways and responses. These results highlight the need for sex-stratified approaches in modeling, translational research, and precision medicine strategies for disease-modifying treatments.

systems biology↗

bioRxiv2025

Application of evolutionary algorithms to optimization of Boolean models in biomedicine

Biological processes in health and disease are regulated in great complexity, imposing significant challenges in understanding and modifying their behavior for healthcare applications. Boolean networks have become essential tools for modeling gene regulatory systems and understanding cellular decision-making processes, but their optimization for biological relevance and precision medicine remains challenging. This study presents a comprehensive benchmark comparison of four prominent Boolean network optimization methods involving genetic algorithms, integer linear programming, and answer set programming, evaluating their performance across structural robustness, method reliability, and biological relevance using mean squared error (MSE) as the primary optimization criterion. Through systematic analysis of network reconstruction under varying perturbation levels (10-90%), we demonstrate that each method exhibits distinct performance profiles: answer set programming (ASP) achieves optimal topological similarity with computational efficiency, integer linear programming (ILP) produces reasonable MSE minimization but with high variance, genetic algorithms (GA) shows superior functional reconstruction stability despite longer computational times. Our results reveal critical limitations in current evaluation approaches, particularly the insufficient discriminatory power of F1 scores and Hamming distance metrics, and highlight fundamental trade-offs between data fitting accuracy and topological preservation. The analysis demonstrates that no single optimization method dominates across all criteria, with all methods showing significant performance degradation at perturbation thresholds above 10-30%, suggesting that method selection should be application-specific and guided by requirements for computational efficiency, reconstruction accuracy, and robustness to uncertainty in prior knowledge.

systems biology↗

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↗

bioRxiv2024

Cohort-specific Boolean models highlight different regulatory modules during Parkinson's disease progression

1Parkinsons Disease (PD) is a multifaceted neurodegenerative disorder characterised by complex molecular dysregulations and diverse comorbidities. It is critical to decode the molecular pathophysiology of PD, which involves complex molecular interactions and their changes over time. Systems medicine approaches can help with this by a) encoding knowledge about the mechanisms into computational models b) simulating these models using patient-specific omics data. This study employs the PD map, a detailed repository of PD-related molecular interactions, as a comprehensive knowledge resource. We aim to dissect and understand the intricate molecular pathways implicated in PD by using logical modelling. This approach is essential for capturing the dynamic interplay of molecular components that contribute to the disease. We incorporate cohort-level and real-world patient data to ensure our models accurately reflect PDs subtype-specific pathway deregulations. This integration is crucial for addressing the heterogeneity observed in PD manifestations and responses to treatment. To combine logical modelling with empirical data, we rely on Probabilistic Boolean Networks (PBNs).These networks provide a robust framework, capturing the stochastic nature of molecular interactions and offering insights into the variable progression of the disease. By combining logical modelling with empirical data through PBNs, we achieve a more refined and realistic representation of PDs molecular landscape. The findings provide insights into the molecular mechanisms of PD. We identify key regulatory biomolecules and pathways that differ significantly across PD subtypes. These discoveries have substantial implications for the development of precise medical treatments. The study provides hypothesis for targeted therapeutic interventions by linking molecular dysregulation patterns to clinical phenotypes and advancing our understanding of PD progression and patient stratification.

systems biology↗

bioRxiv2023

Applications of Boolean modeling to study the dynamics of a complex disease and therapeutics responses

Computational modeling has emerged as a critical tool in investigating the complex molecular processes involved in biological systems and diseases. In this study, we apply Boolean modeling to uncover the molecular mechanisms underlying Parkinsons disease (PD), one of the most prevalent neurodegenerative disorders. Our approach is based on the PD-map, a comprehensive molecular interaction diagram that captures the key mechanisms involved in the initiation and progression of PD. Using Boolean modeling, we aim to gain a deeper understanding of the disease dynamics, identify potential drug targets, and simulate the response to treatments. Our analysis demonstrates the effectiveness of this approach in uncovering the intricacies of PD. Our results confirm existing knowledge about the disease and provide valuable insights into the underlying mechanisms, ultimately suggesting potential targets for therapeutic intervention. Moreover, our approach allows us to parametrize the models based on omics data for further disease stratification. Our study highlight the value of computational modeling in advancing our understanding of complex biological systems and diseases, emphasizing the importance of continued research in this field. Furthermore, our findings have potential implications for the development of novel therapies for PD, which is a pressing public health concern. Overall, this study represents a significant step forward in the application of computational modeling to the investigation of neurodegenerative diseases, and underscores the power of interdisciplinary approaches in tackling challenging biomedical problems.

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

A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Community-driven and highly interdisciplinary, the project is collaborative and supports community standards, open access, and the FAIR data principles. The coordination of community work allowed for an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework links key molecules highlighted from broad omics data analysis and computational modeling to dysregulated pathways in a cell-, tissue- or patient-specific manner. We also employ text mining and AI-assisted analysis to identify potential drugs and drug targets and use topological analysis to reveal interesting structural features of the map. The proposed framework is versatile and expandable, offering a significant upgrade in the arsenal used to understand virus-host interactions and other complex pathologies.

systems biology↗

bioRxiv2020

Reusability and composability in process description maps: RAS-RAF-MEK-ERK signalling

Detailed maps of the molecular basis of the disease are powerful tools for interpreting data and building predictive models. Modularity and composability are considered necessary network features for large-scale collaborative efforts to build comprehensive molecular descriptions of disease mechanisms. An effective way to create and manage large systems is to compose multiple subsystems. Composable network components could effectively harness the contributions of many individuals and enable teams to seamlessly assemble many individual components into comprehensive maps. We examine manually-built versions of the RAS-RAF-MEK-ERK cascade from the Atlas of Cancer Signalling Network, PANTHER and Reactome databases and review them in terms of their reusability and composability for assembling new disease models. We identify design principles for managing complex systems that could make it easier for investigators to share and reuse network components. We demonstrate the main challenges including incompatible levels of detail and ambiguous representation of complexes and highlight the need to address these challenges.

systems biology↗

bioRxiv2020

COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms

We describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in enrichment and validation of the curated mechanisms. We describe the contents of the Map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases and highlight new testable hypotheses.

systems biology↗

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↗

bioRxiv2020

Mechanistic modeling of the SARS-CoV-2 disease map

Here we present a web interface that implements a comprehensive mechanistic model of the SARS-CoV-2 disease map in which the detailed activity of the human signaling circuits related to the viral infection and the different antiviral responses, including immune and inflammatory activities, can be inferred from gene expression experiments. Moreover, given to the mechanistic properties of the model, the effect of potential interventions, such as knock-downs, over-expression or drug effects (currently the system models the effect of more than 8000 DrugBank drugs) can be studied in specific conditions. By providing a holistic, systems biology approach to the understanding of the complexities of the viral infection process, this tool will become an important asset in the search for efficient antiviral treatments. The tool is freely available at: http://hipathia.babelomics.org/covid19/

systems biology↗