arXiv subjects

Willem Bonnaffé

Publications and source records attributed to Willem Bonnaffé.

5 recordsLinked to original sources

arXiv2026

Linking individual bioenergetics to ecosystem dynamics with integral projection models

Linking individual-level processes, such as survival and reproduction, to ecosystem dynamics is challenging due to interactions among populations and species. These interactions depend on organism traits, such as age, body size, and mass, and are influenced by fluctuations in population structure. Integral projection models (IPMs) have advanced our understanding of dynamics arising from population structure in continuous traits. Yet, these models have only been applied to relatively simple systems, featuring one or two species and spanning a fraction of the trophic levels observed in natural food webs. We extend these models in four ways. First, we provide a general multi-species IPM where population structure can be included in primary producers and primary and secondary consumers. Second, we link individual-level processes to ecosystem functioning by mapping fluxes of biomass between species to fluxes within individuals, using bioenergetic principles for allocation to maintenance, reproduction, or growth. Third, we introduce a nutrient recycling loop through which decomposers break down organic matter into nutrients that enable plant growth. Finally, we provide an efficient fitting algorithm to calibrate the model with time series of population sizes. We showcase our approach by parameterising and fitting the model to counts of elk, bison, wolves, and cougars in northern Yellowstone National Park to study the impact of predator extirpation and recovery. Our model predicts that bison decrease following predator removal and recover following predator recovery, suggesting a positive contribution of wolves and cougars to the increase of bison in northern Yellowstone. It also reproduces indirect effects of predators on woody deciduous vegetation regeneration. Our approach is suitable for a broad range of ecosystems, with applications for population management and ecological forecasting.

q-bio.PE↗

arXiv2025

Histology-informed tiling of whole tissue sections improves the interpretability and predictability of cancer relapse and genetic alterations

Histopathologists establish cancer grade by assessing histological structures, such as glands in prostate cancer. Yet, digital pathology pipelines often rely on grid-based tiling that ignores tissue architecture. This introduces irrelevant information and limits interpretability. We introduce histology-informed tiling (HIT), which uses semantic segmentation to extract glands from whole slide images (WSIs) as biologically meaningful input patches for multiple-instance learning (MIL) and phenotyping. Trained on 137 samples from the ProMPT cohort, HIT achieved a gland-level Dice score of 0.83 +/- 0.17. By extracting 380,000 glands from 760 WSIs across ICGC-C and TCGA-PRAD cohorts, HIT improved MIL models AUCs by 10% for detecting copy number variation (CNVs) in genes related to epithelial-mesenchymal transitions (EMT) and MYC, and revealed 15 gland clusters, several of which were associated with cancer relapse, oncogenic mutations, and high Gleason. Therefore, HIT improved the accuracy and interpretability of MIL predictions, while streamlining computations by focussing on biologically meaningful structures during feature extraction.

cs.CV↗

arXiv2023

Beyond attention: deriving biologically interpretable insights from weakly-supervised multiple-instance learning models

Recent advances in attention-based multiple instance learning (MIL) have improved our insights into the tissue regions that models rely on to make predictions in digital pathology. However, the interpretability of these approaches is still limited. In particular, they do not report whether high-attention regions are positively or negatively associated with the class labels or how well these regions correspond to previously established clinical and biological knowledge. We address this by introducing a post-training methodology to analyse MIL models. Firstly, we introduce prediction-attention-weighted (PAW) maps by combining tile-level attention and prediction scores produced by a refined encoder, allowing us to quantify the predictive contribution of high-attention regions. Secondly, we introduce a biological feature instantiation technique by integrating PAW maps with nuclei segmentation masks. This further improves interpretability by providing biologically meaningful features related to the cellular organisation of the tissue and facilitates comparisons with known clinical features. We illustrate the utility of our approach by comparing PAW maps obtained for prostate cancer diagnosis (i.e. samples containing malignant tissue, 381/516 tissue samples) and prognosis (i.e. samples from patients with biochemical recurrence following surgery, 98/663 tissue samples) in a cohort of patients from the international cancer genome consortium (ICGC UK Prostate Group). Our approach reveals that regions that are predictive of adverse prognosis do not tend to co-locate with the tumour regions, indicating that non-cancer cells should also be studied when evaluating prognosis.

q-bio.QM↗

arXiv2022

Single chain differential evolution Monte-Carlo for self-tuning Bayesian inference

1. Bayesian inference is difficult because it often requires time consuming tuning of samplers. Differential evolution Monte-Carlo (DEMC) is a self-tuning multi-chain sampling approach which requires minimal input from the operator as samples are obtained by taking the difference of the current position of multiple randomly selected chains. However, this can also make DEMC more computationally intensive than single chain samplers. 2. We provide a single-chain adaptation of the DEMC algorithm by taking samples according to the difference in previous states of the chain, rather than the current state of multiple chains. This minimises computational costs by requiring only one posterior evaluation per step, while retaining the self-adaptive property of DEMC. We test the algorithm by sampling a bivariate normal distribution and by estimating the posterior distribution of parameters of an ODE model fitted to an artificial prey-predator time series. In both cases we compare the quality of DEMC generated samples to those obtained by a standard adaptive Markov chain Monte-Carlo sampler (AMC). 3. In both case studies, DEMC is as accurate as AMC in estimating posterior distributions, while being an order of magnitude faster due to simpler computations. DEMC also provides a higher effective samples size than AMC, and lower initial samples autocorrelations. 4. Its low computational cost and self-adaptive property make single chain DEMC particularly suitable for fitting models that are costly to evaluate, such as ODE models. The simplicity of the algorithm also makes it easy to implement in base R, hence offering a simple alternative to STAN.

stat.ME↗

arXiv2022

Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time series data

1. Inferring ecological interactions is hard because we often lack suitable parametric representations to portray them. Neural ordinary differential equations (NODEs) provide a way of estimating interactions nonparametrically from time series data. NODEs, however, are slow to fit, and inferred interactions have not been truthed. 2. We provide a fast NODE fitting method, Bayesian neural gradient matching (BNGM), which relies on interpolating time series with neural networks, and fitting NODEs to the interpolated dynamics with Bayesian regularisation. We test the accuracy of the approach by inferring ecological interactions in time series generated by an ODE model with known interactions. We also infer interactions in experimentally replicated time series of a microcosm featuring an algae, flagellate, and rotifer population, as well as in the hare and lynx system. 3. Our BNGM approach allows us to cut down the fitting time of NODE systems to only a few seconds. The method provides accurate estimates of ecological interactions in the artificial system, as linear and nonlinear true interactions are estimated with minimal error. In the real systems, dynamics are driven by a mixture of linear and nonlinear ecological interactions, of which only the strongest are consistent across replicates. 4. Overall, NODEs alleviate the need for a mechanistic understanding of interactions, and BNGM alleviates the heavy computational cost. This is a crucial step availing quick NODE fitting, cross-validation, and uncertainty quantification, as well as more objective estimation of interactions, and complex context dependence, than parametric models.

q-bio.PE↗