Biology subjects

Remy, E.

Publications and source records attributed to Remy, E..

7 recordsLinked to original sources

bioRxiv2026

Scalable and semi-automated Boolean network inference from multi-condition single-cell transcriptomes with scBOLT

Predictive logical models have made substantial contributions to biomedical research by facilitating the identification of regulatory mechanisms driving biological processes. Yet, their construction remains a major bottleneck, as it typically relies on extensive prior knowledge at the genome scale. The emergence of high-throughput omics technologies has opened new opportunities for data-driven logical model construction by providing high-resolution observations of biological states. In parallel, formal constraint-based approaches have emerged, reformulating executable model inference as a constraint-satisfaction problem. However, transforming omics observations into biologically meaningful Boolean state abstractions and dynamical constraints suitable for logical synthesis engines remains challenging, particularly for poorly understood biological systems and multi-condition experimental settings. To address these challenges, we introduce scBOLT, a semi-automated framework for Boolean constraint engineering and logical model inference from multi-condition single-cell transcriptomes. Beyond these methodological contributions, scBOLT substantially improves computational tractability, enabling the inference of Boolean networks comprising several hundred components within practical runtimes.

systems biology↗

bioRxiv2026

Towards a general Detector of terrestrial Arthropods in Natural backgrounds

Widespread arthropod declines pose risks to ecosystem functioning and agriculture. Assessing this decline or potential remediation implies the need for standardized and scalable population monitoring. Image-based methods, including camera traps and citizen science programs, are increasingly used, but the volume of data collected requires automated analysis. Robust arthropod detection is essential for individual counting or fine-grained classification, yet current datasets and algorithms do not address the vast morphological diversity across arthropod species and often overlook the variety of photographic contexts, such as differences in background, lighting, and image composition, in which arthropods are captured. To address this gap, we developed an arthropod detection dataset, covering all terrestrial families present in France with available validated images on the iNaturalist platform (749 families). To achieve this, we employed an iterative workflow in which a YOLOv11 model pre-annotated images -- using one representative species per family-- followed by manual correction and model retraining. Repeating this process progressively reduced annotation effort and improved model accuracy. The final outcome consists of a publicly available curated detection dataset and a robust arthropod detector for natural background scenes. The detector achieves an F1-score of 0.91, demonstrating strong performance despite substantial interspecific morphological variation and heterogeneity in photographic contexts. We further demonstrated the taxonomical universality of the model showing high F1-score and IoU averaged at the class (0.79, 0.85) and order level (0.82, 0.86) and also a good detection generalizability (F1-score>0.90, IoU>0.83) on species, genera and families never encountered by the model during training. Finally, we show how this model can be improved to generalize to new datasets using data augmentation, complementary training data or fine-tuning and increase detection of small objects. In particular, we report performance of the improved models on three use cases largely used in non lethal insect monitoring: (i) diurnal pollinator monitoring through citizen science or (ii) flower and nocturnal insects monitoring through smartphone time-lapse of a UV-illuminated white panel. These results mark an important step toward automated analysis of arthropod images in natural contexts, from both large-scale automated monitoring approaches or from citizen science monitoring programs.

ecology↗

bioRxiv2022

Modular analysis of a logical regulatory network reveals how Fe-S cluster biogenesis is controlled in the face of stress

Iron-sulfur (Fe-S) clusters are important cofactors conserved in all domains of life, yet their synthesis and stability is compromised in stressful conditions such as iron deprivation and oxidative stress. Two conserved machineries, Isc and Suf, assemble and transfer Fe-S clusters to client proteins. The model bacterium Escherichia coli, where Fe-S biogenesis has been extensively studied, possesses both Isc and Suf machineries and their utilisation is under the control of a complex regulatory network. To better understand the underlying regulatory mechanisms and the dynamics behind Fe-S biogenesis in function of environmental conditions, we built a logical model describing Fe-S biogenesis in E. coli. This logical model is centered on three modules : 1) the Fe-S biogenesis module containing the Fe-S cluster assembly machineries Isc and Suf and the transcription factor IscR, the main regulator of Fe-S homeostasis; 2) the iron homeostasis module containing the free intracellular iron regulated by the iron sensing regulator Fur and the non-coding regulatory RNA RyhB involved in iron sparing; 3) the oxidative stress module representing intracellular H2O2 accumulation, which activates OxyR, the regulator of catalases and peroxidases that decompose H2O2 and limit Fenton reaction. Inputs of the model represent extracellular iron and oxygen environmental conditions, whereas ErpA, NfuA (Fe-S carrier proteins), and Suf are output nodes of the model. Analysis of this comprehensive model reveals 5 different types of system behaviours depending on environmental conditions, and provides a better understanding on how oxidative stress and iron homeostasis combine and control Fe-S biogenesis. Using the model, we were able to predict that an iscR mutant would present growth defects in iron starvation, due to partial inability to build Fe-S clusters, and we validated this prediction experimentally. Author summaryIron sulfur (Fe-S) clusters appeared at the origins of life, when oxygen tension was low and iron plentiful, and have been used since as important cofactors for a wide variety of proteins involved in a plethora of reactions. However, synthesis and stability of Fe-S clusters is compromised in conditions where iron is low or in presence of reactive oxygen species. Living organisms have developed complex regulatory network to allow biogenesis of Fe-S clusters in function of environmental conditions. Thus, understanding this regulation is of primary importance as changes in Fe-S cluster biogenesis impact the physiology of the cell and is for instance involved in resistance of bacteria to certain antibiotics. To do this, we used a modeling approach to gain a global systemic understanding of the process. We thus developed a mathematical logical model which extensively describes the regulatory network that controls biogenesis of Fe-S clusters in the model bacterium Escherichia coli. Analysis of the model reveals how Fe-S biogenesis is organized in function of environmental conditions and reveals how oxidative stress and iron homeostasis combine and control Fe-S biogenesis.

systems biology↗

bioRxiv2020

Single-cell RNA-seq reveals a concomitant delay in differentiation and cell cycle of aged hematopoietic stem cell

Hematopoietic stem cells (HSCs) are the guarantor of the proper functioning of hematopoiesis due to their incredible diversity of potential. During aging the heterogeneity of mouse HSCs evolves, which contributes to the deterioration of the immune system. Here we address the transcriptional plasticity of HSC upon aging at the single-cell resolution. Through the analysis of 15,000 young and aged transcriptomes, we reveal 15 clusters of HSCs unveiling rare and specific HSC abilities that change with age. Pseudotime ordering complemented with regulon analysis showed that the consecutive differentiation states of HSC are delayed upon aging. By analysing cell cycle at the single cell level we highlight an imbalance of cell cycle regulators of very immature aged HSC that may contribute to their accumulation in an undifferentiated state. Our results therefore establish a reference map of young and old mouse HSC differentiation and reveal a potential mechanism that delay aged HSC differentiation.

genomics↗

bioRxiv2020

Benchmarking joint multi-omics dimensionality reduction approaches for cancer study

High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve this multi-omics data integration, Joint Dimensionality Reduction (jDR) methods are among the most efficient approaches. However, several jDR methods are available, urging the need for a comprehensive benchmark with practical guidelines. We performed a systematic evaluation of nine representative jDR methods using three complementary benchmarks. First, we evaluated their performances in retrieving ground-truth sample clustering from simulated multi-omics datasets. Second, we used TCGA cancer data to assess their strengths in predicting survival, clinical annotations and known pathways/biological processes. Finally, we assessed their classification of multi-omics single-cell data. From these in-depth comparisons, we observed that intNMF performs best in clustering, while MCIA offers a consistent and effective behavior across many contexts. The full code of this benchmark is implemented in a Jupyter notebook - multi-omics mix (momix) - to foster reproducibility, and support data producers, users and future developers.

bioinformatics↗

bioRxiv2019

Integrative proteomic and phosphoproteomic profiling of prostate cell lines

BackgroundProstate cancer is a major public health issue, mainly because patients relapse after androgen deprivation therapy. Proteomic strategies, aiming to reflect the functional activity of cells, are nowadays among the leading approaches to tackle the challenges not only of better diagnosis, but also of unraveling mechanistic details related to disease etiology and progression.\n\nMethodsWe conducted here a large SILAC-based Mass Spectrometry experiment to map the proteomes and phosphoproteomes of four widely used prostate cell lines, namely PNT1A, LNCaP, DU145 and PC3, representative of different cancerous and hormonal status.\n\nResultsWe identified more than 3000 proteins and phosphosites, from which we quantified more than 1000 proteins and 500 phosphosites after stringent filtering. Extensive exploration of this proteomics and phosphoproteomics dataset allowed characterizing housekeeping as well as cell-line specific proteins, phosphosites and functional features of each cell line. In addition, by comparing the sensitive and resistant cell lines, we identified protein and phosphosites differentially expressed in the resistance context. Further data integration in a molecular network highlighted the differentially expressed pathways, in particular migration and invasion, RNA splicing, DNA damage repair response and transcription regulation.\n\nConclusionsOverall, this study proposes a valuable resource toward the characterization of proteome and phosphoproteome of four widely used prostate cell lines and reveals candidates to be involved in prostate cancer progression for further experimental validation.

bioinformatics↗

bioRxiv2017

Random Walk With Restart On Multiplex And Heterogeneous Biological Networks

Recent years have witnessed an exponential growth in the number of identified interactions between biological molecules. These interactions are usually represented as large and complex networks, calling for the development of appropriated tools to exploit the functional information they contain. Random walk with restart is the state-of-the-art guilt-by-association approach. It explores the network vicinity of gene/protein seeds to study their functions, based on the premise that nodes related to similar functions tend to lie close to each others in the networks.\n\nIn the present study, we extended the random walk with restart algorithm to multiplex and heterogeneous networks. The walk can now explore different layers of physical and functional interactions between genes and proteins, such as protein-protein interactions and co-expression associations. In addition, the walk can also jump to a network containing different sets of edges and nodes, such as phenotype similarities between diseases.\n\nWe devised a leave-one-out cross-validation strategy to evaluate the algorithms abilities to predict disease-associated genes. We demonstrate the increased performances of the multiplex-heterogeneous random walk with restart as compared to several random walks on monoplex or heterogeneous networks. Overall, our framework is able to leverage the different interaction sources to outperform current approaches.\n\nFinally, we applied the algorithm to predict genes candidate for being involved in the Wiedemann-Rautenstrauch syndrome, and to explore the network vicinity of the SHORT syndrome.\n\nThe source code and the software are freely available at: https://github.com/alberto-valdeolivas/RWR-MH.

systems biology↗