Biology subjects

Lefevre, J. G.

Publications and source records attributed to Lefevre, J. G..

3 recordsLinked to original sources

bioRxiv2026

Clustering Compresses Attractors in Watts-Strogatz Threshold Boolean Networks

AbstractDoes higher clustering shorten attractor periods? We examine whether the global clustering coefficient C, a direct measure of triangle density, predicts attractor lengths in synchronous, signed-threshold Boolean networks on Watts-Strogatz (WS) graphs. We generate 330 directed, signed WS networks spanning sizes N = 10-100 and mean degrees [Formula], simulate dynamics from M = 100 random initial states per graph with exact cycle detection, and summarise each graph by the average log attractor period (equivalently, the geometric mean period). Our primary analysis relates this log-period summary to C while adjusting for N, [Formula], the mean directed shortest path (MSP), and including nonlinear size-degree and clustering-degree interactions. Main findingHigher clustering robustly shortens attractor periods: a 0.10 increase in C (C [isin] [0, 1]) corresponds to an {approx} 13-15% lower expected geometric mean period, and moving from C = 0.000 to C = 0.460 yields an {approx} 50% reduction, holding other properties fixed. The effect persists when the linear C term is replaced by a nonlinear function of C, and it replicates in held-out graph instances (graphs not used to fit the model). Shorter periods are not explained by an increase in fixed points under the strict comparator (>); rather, higher triangle density shifts mass from long cycles to medium-length cycles. SignificanceIn threshold-like logic, settling speed and oscillatory stability are central to computation and control. Our results provide a direct, quantitative link between triangle density and these long-run behaviours, showing that C acts as a structural lever on temporal complexity. All figures and tables are reproducible from the accompanying code, data, and analysis scripts.

bioinformatics↗

bioRxiv2022

Hypergraphs and centrality measures identifying keyfeatures in gene expression data

Multidisciplinary approaches can significantly advance our understanding of complex systems. For instance, gene co-expression networks align prior knowledge of biological systems with studies in graph theory, emphasising pairwise gene to gene interactions. In this paper, we extend these ideas, promoting hypergraphs as an investigative tool for studying multi-way interactions in gene expression data. Additional freedoms are achieved by representing individual genes with hyperedges, and simultaneous testing each gene against many features/vertices. Further gene/hyperedge interactions can be captured and explored using the line graph representations, a techniques that also reduces the complexity of dense hypergraphs. Such an approach provides access to graph centrality measures, which in turn identify salient features within a data set, for instance dominant or hub-like hyperedges leading to key knowledge on gene expression. The validity of this approach is established through the study of gene expression data for the plant species Senecio lautus and results will be interpreted within this biological setting.

bioinformatics↗

bioRxiv2020

LLAMA: a robust and scalable machine learning pipeline for analysis of cellsurface projections in large scale 4D microscopy data

We present LLAMA, a pipeline for systematic analysis of terabyte scale 4D microscopy datasets. Analysis of individual biological structures in imaging at this scale requires efficient and robust methods which do not require human micromanagement or editing of outputs. To meet this challenge, we use a machine learning method for semantic segmentation, followed by a robust and configurable object separation and tracking algorithm, and the generation of detailed object level statistics. Advanced visualisation is a key element of LLAMA: we provide a specialised software tool which supports quality control and optimisation as well as visualisation of outputs. LLAMA was used in a quantitative analysis of macrophage surface membrane projections (filopodia, ruffles, tent-pole ruffles) examining the differential effects of two interventions: lipopolysaccharide (LPS) and macrophage colony stimulating factor (CSF-1). Distinct patterns of increased activity were identified. In addition, a continuity of behaviour was found between tent pole ruffling and wave-like ruffling, further defining the role of filopodia in ruffling.

cell biology↗