PSoup: an R package for predicting the outcomes of biological networks without kinetic parameters

Mechanistic models built for prediction are often treated as a parameter estimation problem. Using Bayesian inference on a well-studied shoot branching ODE model, we show that multiple incompatible parameter calibrations can fit the same experimental data yet can make contradictory predictions under unseen treatment conditions. Given this behaviour, we decided to explore how well the causal connections of a network topology alone can be used to express biological outcomes. We developed PSoup, an R package that converts knowledge graphs directly into qualitative models free of kinetic parameters. Users draw their network diagram using standard SBGN-AF notation in the Newt Editor, capturing interactions across genes, hormones, and environmental interventions. PSoup then uses this diagram to automatically build a model using algebraic update rules anchored to a biologically interpretable baseline, enabling predictions of relative change without kinetic information. We validated PSoup against the shoot branching network of Bertheloot et al. (2019), spanning 78 experimental conditions across two plant species, two laboratories, and diverse treatments including hormone application, sucrose, decapitation, and mutant genotypes. PSoup correctly predicted 89.5\% of unique, biologically consistent comparisons, without any parameter fitting. In addition, PSoup is also useful to evaluate alternative network topologies, identifying which structural features drive observed behaviour and which experiments would best discriminate between competing hypotheses. 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.