Biology subjects

Mitsanis, C.

Publications and source records attributed to Mitsanis, C..

3 recordsLinked to original sources

bioRxiv2026

From Diverse Prior Knowledge to Mechanistic Causal Network Using PSoup: A Case Study in Shoot Branching

Mechanistic models of plant regulatory networks typically require extensive parameterization, limiting their generalisation and scalability. Here we present a parameter-free, topology-driven model of shoot branching that predicts phenotypic outcomes from network structure alone. We constructed a signed, directed causal network by distilling regulatory relationships from the published literature spanning many laboratories, species, years, data types, and methodological frameworks. This extracted the essential logic of the system, consistent with developmental-biological reasoning and anchored in empirical evidence. Using PSoup, which automatically translates network topology into algebraic equations, the model propagates information across the network and predicts the qualitative direction of change relative to a defined baseline, mirroring the comparative framework of biological experiments. The pipeline, from network construction through automated equation generation to prediction, is transparent and reproducible. Trained against branching phenotype data with 78 diverse perturbations spanning genetic mutations and hormone treatments, the model achieved 86% accuracy in predicting branching direction. On an independent test set of 84 perturbations measuring bud release and gene expression at nodes not used during training, accuracy reached 75%. The approach highlighted deficiencies in our understanding of the topology of the network around SMXL 6/7/8 and ABA nodes. Other errors came mainly from modelling choices, such as the threshold for scoring a node as changed relative to baseline. Beyond shoot branching, this work demonstrates a general strategy for synthesizing biological knowledge into validated predictive networks, providing a foundation for both applied breeding and the advancement of fundamental biology.

plant biology↗

bioRxiv2026

FLASH-P: Turning decades of biology into accurate causal networks with AI agents

Mechanistic networks that encode causal regulatory logic can predict the effects of genetic and environmental perturbations but constructing them is a bottleneck in systems biology because the relevant knowledge lies scattered across thousands of resources, untapped for both building and validating such networks. Here we present FLASH-P, a multi-agent framework that autonomously curates this literature into perturbable, signed-directed network models for any trait-species combination in under an hour without much computational power. Twelve FLASH-P networks across seven species predicted the directional outcome of 1,088 published perturbations with a mean accuracy of 90%. This accuracy was driven by the regulatory topology FLASH-P constructs, which is why it outperformed knowledge-graph derived networks. Its merging agent combined six networks into one that preserved single-trait accuracy and recovered pleiotropic effects, and consolidated independent runs of one trait into a comprehensive, high-accuracy network. FLASH-P networks enable applications that require trait models.

Plant Biology↗

bioRxiv2026

PSoup: an R package for simulating biological networks from a qualitative perspective

Mathematical modelling is essential for understanding how complex biological systems respond to genetic, physiological, and environmental changes. Existing approaches, however, often require trade-offs between mechanistic detail, model size, parameter uncertainty, and interpretability. Ordinary differential equation (ODE) models capture biochemical processes with quantitative precision but can demand extensive parameterisation. In contrast, large statistical and machine-learning models rely on substantial datasets and frequently lack mechanistic transparency. Qualitative approaches such as Boolean networks improve scalability but may oversimplify biological behaviour. To address some of these limitations, we present PSoup, an R package that automatically converts knowledge graphs into transparent, parameter-free, qualitative models. PSoup uses algebraic update rules designed around a fixed, biologically interpretable baseline, enabling predictions of relative change across diverse perturbations without requiring kinetic parameters. This design allows PSoup to integrate information across biological scales and from heterogeneous experimental sources. We evaluated PSoup using the well-studied shoot branching network of Bertheloot et al. (2019), which incorporates hormonal (auxin, strigolactone, cytokinin) and metabolic (sucrose) regulation. Across 78 experimental conditions, PSoup correctly predicted 88.5%of perturbation outcomes, including 89.5%accuracy for unique, biologically consistent comparisons. We further demonstrate how PSoup can distinguish among alternative plausible network topologies, revealing how structural differences influence emergent system behaviour. PSoup offers an intuitive, accessible, and mathematically transparent framework for exploring biological networks. Its capacity to integrate diverse knowledge and test alternative hypotheses positions it as a powerful tool for biological discovery and a valuable complement to existing modelling approaches.

plant biology↗