VAN IMPE, J. | Search bioRxiv

bioRxiv2026

Warming responses of microbial respiration emerge from spatially structured species interactions

Microbial communities are the main drivers of global carbon cycling. Yet, predicting their respiratory responses to warming is challenging because the underlying mechanisms remain elusive. Here, we developed a spatially explicit model, integrating single-cell metabolism with metabolic exchange and endogenous heat production, to disentangle the mechanisms driving community-level temperature responses. We reveal that mutualism amplifies respiratory temperature sensitivity compared to isolated taxa, as exchanged metabolites sustain a secondary growth phase with higher respiratory demand, and this phase creates disproportionately large CO2; emissions under warming (apparent activation energy Ea; increases from 0.65 to 0.77 eV). Spatial structure amplifies this further through threshold effects, whereby diffusion limitation shifts from constraining to enabling mutualistic benefits as metabolic rates increase with temperature (Ea; increases to 1.18 {+/-} 0.09 eV). Endogenous heat production drives additional amplification through positive feedback between local temperature rises and resource turnover rates (Ea; further increasing to 1.78 {+/-} 0.14 eV). Collectively, our results demonstrate that community-level temperature responses are not determined by enzyme kinetics alone but emerge from the interplay of ecological, physical, and metabolic interactions. Our model provides a general framework for understanding the combined effects of the underlying mechanisms on microbial temperature responses. Future climate carbon models should incorporate these considerations to improve predictions.

ecology↗

bioRxiv2026

Uncertainty Quantification in Stochastic Dynamical Gene Regulatory Networks

The dynamics of gene regulatory networks are governed by intrinsic noise, stemming from the random nature of biochemical reactions, and by extrinsic noise, arising from fluctuations in cellular components and environmental conditions. Together, these sources can compromise the reliability of predictive computational models if not properly accounted for, and capturing both effects within a single framework remains a non-trivial task in computational biology. In this work, we propose an uncertainty quantification framework that addresses these two contributions jointly: intrinsic stochasticity is described through a partial integro-differential equation (PIDE) for the protein probability density function, whereas extrinsic noise is represented as parametric uncertainty in the kinetic parameters. The propagation of the uncertainty is carried out via an intrusive polynomial chaos expansion (PCE), in which the PCE coefficients are obtained from a stochastic Galerkin projection of the PIDE, yielding a coupled deterministic system that is solved with standard numerical methods. We illustrate the approach on a positive autoregulatory gene network with one and two uncertain kinetic parameters. The proposed approach accurately reproduces the mean, variance, and full protein probability density function, including the bimodal distributions, at a substantially lower computational cost.

synthetic biology↗