Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2025.10.24.684155

Biologically informed neural network models are robust to spurious interactions via self-pruning

Nordenstorm, O.·Baghdassarian, H.·Lauffenburger, D. A.·Nilsson, A.
View original source ↗Export metadata ↓

Abstract

Computational models of cellular networks hold promise to uncover disease mechanisms and guide therapeutic strategies. Biology-informed neural networks (BINNs) is an emerging approach to create such models by combining the predictive power of deep learning with prior knowledge, a vital aspect of biological research. The architectures of BINNs enforces a network structure from which mechanism can ideally be inferred. However, a key challenge is to evaluate the reliability of these models, as cells are inherently complex, involving intricate and sometimes unknown interactions. Currently, analysis mainly focuses on selected pathways rather than a more comprehensive perspective. In this work we demonstrate an alternative holistic approach: we measure to which extent purposefully introduced spurious interactions are down-weighted by a BINN during training (self-pruning). The metric suggested RRA (Relative Residual Area) allows for direct distribution comparison with perfect self-pruning achieved at zero and a failure to self-prune if above one. To enable rapid testing, we updated LEMBAS (Large-scale knowledge-EMBedded Artificial Signaling-networks), our recurrent neural network framework for intracellular signaling dynamics, with full GPU acceleration. Our implementation achieves a >7-fold speedup compared to the original while preserving predictive accuracy. We evaluated self-pruning in 3 different datasets and found that when spurious interactions are introduced at random, the model prunes these to a larger extent than those from the prior knowledge network (PKN), provided the model is regularized with a sufficiently large L2 norm. This suggests that BINNs can be robust to uncertainty in the PKN. Implementation and applicationOur implementation of LEMBAS is freely available under a MIT license at https://github.com/AvlantNilssonLab/LEMBAS_GPU. The models and results to generate the figures can be downloaded through https://zenodo.org/records/17425598.

Source connections

Explore related subjects

systems biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nordenstorm, O., Baghdassarian, H., Lauffenburger, D. A., Nilsson, A.. 2025-10-27. Biologically informed neural network models are robust to spurious interactions via self-pruning. https://doi.org/10.1101/2025.10.24.684155

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Parsimonious Creatures: Building Gene Regulatory Networks Robust to Somatic Evolution

For early multicellular life to have formed and maintained cellular organization, preventing loss of controlled cell replication due to somatic evolution was of critical importance, and remains important to this day for control of cancer. We study how gene regulatory networks (GRNs) can be designed to slow the divergence of somatic clonal lineages in the context of early multicellular life. We model the mutation of GRNs within simplified 12-celled organisms we term "parsimonious creatures", who must replicate from a single cell to 12 cells and then prevent replication past 12 cells to maximize fitness. We found two minimal motifs can achieve these goals, but both are prone to breakdown through somatic evolution: The feedforward motif, which is loosely regulating but relatively robust, and the simple step motif, which uses strong interactions and feedback to form memoryless ultrasensitivity, but is fragile. These simple motifs may be duplicated to form redundant networks via one of two structures: averaging redundancy, which averages multiple sources of information, and min redundancy, which adopts the most conservative source. Averaging redundancy uniformly slows somatic evolution, while min redundancy stalls somatic evolution until neutral mutations accumulate. Either may be superior dependent on life history. However, averaging redundancy slowly increases fitness as the network becomes larger, but large min redundancy networks eventually become "paralyzed", increasing the probability of senescence and reducing fitness. These GRN designs are idealized, yet provide imperfect solutions with fundamental tradeoffs, offering insight as to why somatic evolution remains an incompletely solved problem for multicellular life.

systems biology↗
bioRxiv2026

Homogeneous network inference for subtype-stratified master regulator analysis in cancer

Master-regulator analysis of 34 TCGA tumour cohorts (cancer x molecular subtype; n between 27 and 562 tumours) requires one regulatory network per cohort. Mutual-information (MI) networks inferred with ARACNe3 are the usual choice, but they are not available for the smallest cohorts; filling the gap with correlation networks would mix two methods inside the very comparison between subtypes that the analysis is meant to make. We therefore infer every network with a single procedure, fixed-size Spearman regulons (FSR): rank correlation between regulator and target, a fixed effect-size threshold rho >= 0.2, the per-target data-processing-inequality (DPI) pruning implemented in corto, bootstrap support, and truncation to the 100 best-supported targets per regulator. We quantify what this choice concedes and what it provides, labelling each claim as a standard result, a heuristic, or an empirical result. Rank correlation is more powerful than the MI estimators we simulated for n < 200 but cannot see non-monotone dependence, which in a synthetic worst case costs at most about 0.05-0.10 of the master regulators recovered; the per-target DPI is a heuristic with a measurable disagreement rate with the MI-based one; and pairwise dependence is not direct interaction, for MI and correlation alike. Where both methods are feasible (n >= 95) they overlap in 0.7 of the top-20 regulators, the same as two filterings of the same ARACNe3 network, although the rank correlation of the full score vector shows a measurable method effect in the tail. Subsampling large real cohorts indicates that networks inferred from fewer than 60 tumours do not reliably reproduce their own cohort's master regulators, that 60-100 is a grey zone, and that a cancer-wide network does not rescue small subtypes. We present FSR as an adequate and uniform choice for multi-cohort analysis, not as a statistically superior one.

systems biology↗
bioRxiv2026

Low-fidelity simulator selection for multi-fidelity inference of epithelial vertex models

Vertex models link cell division, neighbour exchange, and intercellular forces to epithelial tissue form through parameters such as junctional line tension and perimeter contractility. Bayesian inference of these parameters from data quantifies their uncertainty, but for stochastic models of proliferating tissues it requires many costly simulations. Multi-fidelity simulation-based inference reduces this cost by training on many runs of a cheaper, less accurate ('low-fidelity') simulator before refining with a few full ('high-fidelity') runs. Low-fidelity versions can be constructed in several ways, for example by stopping simulations early, coarsening the timestep, or averaging fewer replicates, but it is unclear which to choose before committing to costly training. To address this, we introduce PRISM (Paired Ranking Index for Simulator Matching), a screen that compares each candidate with the full simulator on paired runs, scoring agreement in cell-packing statistics against cost. For a vertex model of a proliferating tissue, PRISM flags stopping early as unreliable before training; once trained, this choice can produce confidently wrong parameter estimates. By contrast, a coarser timestep, within the range tested, is cheap and accurate; while fewer replicates are accurate but too costly. Using a screened low-fidelity simulator, multi-fidelity inference outperforms approximate Bayesian computation and neural-network inference trained on full simulations alone, at matched cost and with well-calibrated uncertainty. The same pattern recurs for a second vertex model fitted to published Drosophila pupal-wing imaging data, although there stopping halfway proves safe. Together, our findings suggest that which approximations are safe depends on the model, and that screening them before training makes multi-fidelity inference of tissue mechanics practical.

systems biology↗