Biology subjects

Niarakis, A.

Publications and source records attributed to Niarakis, A..

10 recordsLinked to original sources

bioRxiv2026

TabularQual: A spreadsheet-based format for annotating and curating logical models in SBML-qual

Logical models are widely used to study regulatory and signaling systems, yet their reuse, annotation, and exchange across tools remain challenging. Although SBML Level 3 Qualitative Models (SBML-qual) provides a standard representation, its XML-based syntax is difficult to inspect and edit directly. Here we introduce TabularQual, a spreadsheet-based, community-driven representation for Boolean and multi-valued logical models, together with a bidirectional converter between spreadsheets and SBML-qual. The converter is accessible programmatically and via a web interface to support diverse user workflows. We further describe two integration strategies that enable existing modeling tools to operate with TabularQual, either through SBML-qual exchange or via direct support. Case studies using the Stress Knowledge Map and CaSQ demonstrate how this integration supports model construction, curation, and reuse. TabularQual provides a practical bridge between human-readable model representations and standardized executable formats, supporting reproducibility, interoperability, and community-driven model development.

systems biology↗

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

The SjD Map: An interactive pathway tour into Sjogren's disease signalling mechanisms

ObjectivesSjogrens disease (SjD) remains a major unmet medical challenge, marked by biological complexity, patient heterogeneity, and a lack of curative treatments. To advance the understanding of its pathogenesis and support therapeutic discovery, we developed a comprehensive knowledgebase in the form of a Molecular Interaction Map (MIM). MethodsDifferential expression analysis was performed on peripheral blood samples from SjD patients and healthy controls across three datasets: GSE51092 (190 SjD vs 32 controls), UKPSSR (151 SjD vs 29 controls) and PRECISESADS (304 SjD vs 341 controls). Pathway enrichment analyses provided guidance for MIM construction, which was further refined through literature mining to integrate data-driven results with curated knowledge. ResultsA total of 1,625 differentially expressed genes (DEGs) were identified: 725 from PRECISESADS, 1,162 from GSE51092, and 239 from UKPSSR, with 25 DEGs shared across all three datasets. Among these, nine common DEGs were associated with interferon signalling, reinforcing experimental evidence pointing to its pivotal role in SjD. Enrichment analyses revealed 146 pathways, 43 of which were successfully incorporated into the MIM. The resulting SjD Map freely accessible at https://sjdmap.elixir-luxembourg.org/, comprises 829 molecular entities connected by 598 interactions, with 45% of the information depicted supported by transcriptomic data and 47% derived from literature. The map also includes overlays of experimental data and clinical trial information. ConclusionThis first comprehensive Sjogrens Disease Map, developed through a hybrid data-driven and literature-based approach, offers an integrative view of SjD pathogenesis. It supports visualisation of mechanistic pathways, omics-based data overlays, enables incorporation of user data and drug queries. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/674876v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@1bdc517org.highwire.dtl.DTLVardef@1d68077org.highwire.dtl.DTLVardef@18c079forg.highwire.dtl.DTLVardef@487b64_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG Key messagesO_LIThis is the first molecular interaction map specific of Sjogrens Disease C_LIO_LIIt enables comprehensive visualisation of affected pathways through integrated transcriptomic and literature-based evidence, following systems biology graphical notation schemes C_LIO_LIThe map may support therapeutic discovery by linking molecular mechanisms to clinical data and drug targets C_LI

systems biology↗

bioRxiv2023

Large-scale computational modelling of the M1 and M2 synovial macrophages in Rheumatoid Arthritis

Macrophages play an essential role in rheumatoid arthritis (RA). Depending on their phenotype (M1 or M2), they can play a role in the initiation or resolution of inflammation. The M1/M2 ratio in RA is higher than in healthy controls. Despite this, no treatment targeting specifically macrophages is currently used in clinics. Thus, devising strategies to selectively deplete proinflammatory macrophages and promote anti-inflammatory macrophages could be a promising therapeutic approach in RA. State-of-the-art molecular interaction maps of M1 and M2 macrophages in rheumatoid arthritis are available and represent a dense source of knowledge; however, these maps remain limited by their static nature. Discrete dynamic modelling can be employed to study the emergent behaviours of these systems. Nevertheless, handling such large-scale models is challenging. Due to their massive size, it is computationally demanding to identify biologically relevant states in a cell- and disease-specific context. In this work, we developed an efficient computational framework that converts molecular interaction maps into Boolean models using the CaSQ tool. Next, we use a newly developed BMA tool version deployed to a high-performance computing cluster to identify the models steady states. The identified attractors are then validated using gene expression datasets and prior knowledge. We successfully applied our framework to generate and calibrate the first RA M1 and M2 macrophage Boolean models. Using single and double knockout simulations, we identified NFkB, JAK1/JAK2, and ERK1/Notch1 as potential targets that could selectively suppress proinflammatory macrophages, and GSK3B as a promising target that could promote anti-inflammatory macrophages in RA.

systems biology↗

bioRxiv2023

Hybrid computational modeling highlights reverse Warburg effect in breast cancer-associated fibroblasts

Cancer-associated fibroblasts (CAFs) are key players of the tumor microenvironment (TME) involved in cancer initiation, progression, and resistance to therapy. These cells exhibit aggressive phenotypes affecting, among others, extracellular matrix remodeling, angiogenesis, immune system modulation, tumor growth, and proliferation. CAFs phenotypic changes appear to be associated with metabolic alterations, notably a reverse Warburg effect that may drive fibroblasts transformation. However, its precise molecular mechanisms and regulatory drivers are still under investigation. Deciphering the reverse Warburg effect in breast CAFs may contribute to a better understanding of the interplay between TME and tumor cells, leading to new treatment strategies. In this regard, dynamic modeling approaches able to span multiple biological layers are essential to capture the emergent properties of various biological entities when complex and intertwined pathways are involved. This work presents the first hybrid large-scale computational model for breast CAFs covering major cellular signaling, gene regulation, and metabolic processes. It was generated by combining an asynchronous cell- and disease-specific regulatory Boolean model with a generic core metabolic network leveraging both data-driven and manual curation approaches. This model reproduces the experimentally observed reverse Warburg effect in breast CAFs and further identifies Hypoxia-Inducible Factor 1 (HIF-1) as its key molecular driver. Targeting HIF-1 as part of a TME-centered therapeutic strategy may prove beneficial in the treatment of breast cancer by addressing the reverse Warburg effect. Such findings in CAFs, considering our previously published results in rheumatoid arthritis synovial fibroblasts, point to a common HIF-1-driven metabolic reprogramming of fibroblasts in breast cancer and rheumatoid arthritis. All analyses are compiled and thoroughly annotated in Jupyter notebooks and R scripts available on a GitLab repository (https://gitlab.com/genhotel/breast-cafs-reverse-warburg-effect) and a Zenodo permanent archive [1].

systems biology↗

bioRxiv2023

A large-scale Boolean model of the Rheumatoid Arthritis Fibroblast-Like Synoviocytes predicts drug synergies in the arthritic joint

Rheumatoid arthritis (RA) is a complex autoimmune disease with an unknown aetiology. However, rheumatoid arthritis fibroblast-like synoviocytes (RA-FLS) play a significant role in initiating and perpetuating destructive joint inflammation by expressing immuno-modulating cytokines, adhesion molecules, and matrix remodelling enzymes. In addition, RA-FLS are primary drivers of inflammation, displaying high proliferative rates and an apoptosis-resistant phenotype. Thus, RA-FLS-directed therapies could become a complementary approach to immune-directed therapies by predicting the optimal conditions that would favour RA-FLS apoptosis, limit inflammation, slow the proliferation rate and minimise bone erosion and cartilage destruction. In this paper, we present a large-scale Boolean model for RA-FLS that consists of five submodels focusing on apoptosis, cell proliferation, matrix degradation, bone erosion and inflammation. The five phenotype-specific submodels can be simulated independently or as a global model. In-silico simulations and perturbations reproduced the expected biological behaviour of the system under defined initial conditions and input values. The model was then used to mimic the effect of mono or combined therapeutic treatments and predict novel targets and drug candidates through drug repurposing analysis.

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↗

bioRxiv2022

Metabolic Reprogramming in Rheumatoid Arthritis Synovial Fibroblasts: a Hybrid Modeling Approach

Rheumatoid Arthritis (RA) is an autoimmune disease characterized by a highly invasive pannus formation consisting mainly of synovial fibroblasts (RASFs). This pannus leads to cartilage, bone and soft tissue destruction in the affected joint. RASFs activation is associated with metabolic alterations resulting from dysregulation of extracellular signals transduction and gene regulation machinery. Deciphering the intricate mechanisms at the origin of this metabolic reprogramming may provide significant insight into RASFs involvement in RAs pathogenesis and offer new therapeutic strategies. Qualitative and quantitative dynamic modeling can address some of these features, but hybrid models represent a real asset in their ability to span multiple layers of biological machinery. This work presents the first hybrid RASF model: the combination of a cell-specific qualitative regulatory network with a global metabolic network. The automated framework for hybrid modeling exploits the regulatory networks trap-spaces as additional constraints on the metabolic networks. Subsequent flux balance analysis allows assessment of RASFs regulatory outcomes impact on their metabolic flux distribution. The hybrid RASF model simulates the experimentally observed metabolic reprogramming induced by signaling and gene regulation in RASFs. Simulations also enable further hypotheses on the potential reverse Warburg effect in RA. RASFs may undergo metabolic reprogramming to turn into "metabolic factories", producing high levels of energy-rich fuels and nutrients for neighboring demanding cells through the crucial role of HIF1. Author SummaryWe successfully built the first large-scale hybrid dynamical model for human Rheumatoid Arthritis Synovial Fibroblasts (RASFs) including signaling, gene regulation and metabolism. We used a state-of-the-art molecular map for upstream signaling and gene regulation, the tool CaSQ to infer a large-scale Boolean model, and a genome-scale metabolic model. Trap-spaces of the Boolean asynchronous model were used to infer additional metabolic constraints on the metabolic network for subsequent flux balance analysis. This method allowed us to study the impact of various regulatory initial conditions on RASFs metabolic fluxes distribution. Our model successfully reproduces the metabolic reprogramming of RASFs which shift their ATP production from oxidative pathways to glycolysis, highlighting the key role of HIF1 in this process. Our findings allow us to hypothesize a reverse Warburg relationship occurring between RASFs and other RA joint cells. Similarly to tumor microenvironments fibroblasts, RASFs would undergo a metabolic switch and reprogram their metabolism to adapt to their hypoxic environment and provide crucial metabolic intermediates to neighboring cells to sustain their inflammatory activity.

systems biology↗

bioRxiv2021

Inference of an integrative network for Rheumatoid Arthritis combining data-driven machine learning approaches and a state-of-the-art mechanistic disease map

Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental and epigenetic factors. Systems biology approaches provide the means to study complex diseases through the integration of different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first make use of publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. The integrative network is then used as a template to analyze patients data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. We use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations.

bioinformatics↗

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↗