arXiv subjects

Johannes Nauta

Publications and source records attributed to Johannes Nauta.

6 recordsLinked to original sources

arXiv2026

Scaling laws in complex component systems as consequences of heterogeneous sampling

Complex component systems are collections of discrete units such as species, words, genes, whose observed realizations are naturally summarized by component counts. Many empirical laws have been observed in those systems, such as Taylor's law, Zipf's law, and Heaps' law, and domain-specific mechanisms are often employed to explain their emergence but, despite their ubiquity, a unifying framework remains elusive. In this work, we propose a null model showing that, under heterogeneous latent rates and finite sampling, several commonly observed scaling relations can arise without invoking domain-specific mechanisms. Taylor's law, for instance, reflects a crossover between sampling noise and genuine system heterogeneity and it is largely insensitive to the detailed latent distribution, while Zipf's and Heaps' laws arise from the convergence of order statistics and distinct component counts under heavy-tailed but otherwise generic priors. Our work thus suggests that these ubiquitous patterns are better interpreted as a transient sign of statistical convergence instead of fundamental principles that require tailored generative explanations.

physics.soc-ph↗

arXiv2025

Heterogeneity drives plasmid maintenance in large microbial communities

Microbiomes are complex systems comprised of many interacting species. Species can survive harsh or changing conditions by rapid adaptation, a process accelerated by the exchange of genetic material between different species through horizontal gene transfer. Conjugative plasmids are ubiquitous mobile genetic elements that mediate such exchanges both within and between species. Therefore, predicting whether a plasmid can invade and be maintained by a microbial community is critical, for example when assessing the risks of antimicrobial resistance gene spread in commensal or environmental microbiomes. However, existing theory developed to assist such predictions has generally focused on the balance among plasmid costs, benefits, and infection rates, overlooking other relevant factors such as the inherent dynamics and diversity of microbiomes. Here, we hypothesize that plasmid persistence in the absence of positive selection can arise purely from the heterogeneity present in large and diverse microbial communities. We introduce a generic model that integrates population-level dynamics with plasmid conjugation. Using this model, we show that we can predict plasmid maintenance, and that the probability for a plasmid to be maintained depends on traits of the plasmid, most importantly the conjugation rate, and the species abundance distribution of the community. Then, using both empirical abundance data and extensive numerical simulations, we demonstrate that the inherent randomness of ecological interactions and conjugation rates enables plasmid persistence -- even in the absence of positive selection. Our findings thus suggest that natural microbial communities are likely to maintain plasmids indefinitely, offering a new perspective on the spread, maintenance, and ubiquity of plasmids.

q-bio.PE↗

arXiv2025

Effective one-dimension reduction of multi-compartment complex systems dynamics

A broad class of systems, including ecological, epidemiological, and sociological ones, are characterized by populations of individuals assigned to specific categories, e.g., a chemical species, an opinion or an epidemic state, that are modeled as compartments. Due to interactions and intrinsic dynamics, individuals are allowed to change category, leading to concentrations varying over time with complex behavior, typical of reaction-diffusion systems. While compartmental modeling provides a powerful framework for studying the dynamics of such populations and describe the spatiotemporal evolution of a system, it mostly relies on deterministic mean-field descriptions to deal with systems with many degrees of freedom. Here, we propose a method to alleviate some of the limitations of compartmental models by capitalizing on tools originating from quantum physics to systematically reduce multi-dimensional systems to an effective one-dimensional representation. Using this reduced system, we are able to not only investigate the mean-field dynamics and their critical behavior, but we can additionally study stochastic representations that capture fundamental features of the system. We demonstrate the validity of our formalism by studying the critical behavior of models widely adopted to study epidemic, ecological and economic systems.

cond-mat.stat-mech↗

arXiv2024

Topological conditions drive stability in meta-ecosystems

On a global level, ecological communities are being perturbed at an unprecedented rate by human activities and environmental instabilities. Yet, we understand little about what factors facilitate or impede long-term persistence of these communities. While observational studies indicate that increased biodiversity must, somehow, be driving stability, theoretical studies have argued the exact opposite viewpoint instead. This encouraged many researchers to participate in the ongoing diversity-stability debate. Within this context, however, there has been a severe lack of studies that consider spatial features explicitly, even though nearly all habitats are spatially embedded. To this end, we study here the linear stability of meta-ecosystems on networks that describe how discrete patches are connected by dispersal between them. By combining results from random-matrix theory and network theory, we are able to show that there are three distinct features that underlie stability: edge density, tendency to triadic closure, and isolation or fragmentation. Our results appear to further indicate that network sparsity does not necessarily reduce stability, and that connections between patches are just as, if not more, important to consider when studying the stability of large ecological systems.

q-bio.PE↗

arXiv2020

Learning Perception and Planning with Deep Active Inference

Active inference is a process theory of the brain that states that all living organisms infer actions in order to minimize their (expected) free energy. However, current experiments are limited to predefined, often discrete, state spaces. In this paper we use recent advances in deep learning to learn the state space and approximate the necessary probability distributions to engage in active inference.

cs.LG↗

arXiv2019

Bayesian policy selection using active inference

Learning to take actions based on observations is a core requirement for artificial agents to be able to be successful and robust at their task. Reinforcement Learning (RL) is a well-known technique for learning such policies. However, current RL algorithms often have to deal with reward shaping, have difficulties generalizing to other environments and are most often sample inefficient. In this paper, we explore active inference and the free energy principle, a normative theory from neuroscience that explains how self-organizing biological systems operate by maintaining a model of the world and casting action selection as an inference problem. We apply this concept to a typical problem known to the RL community, the mountain car problem, and show how active inference encompasses both RL and learning from demonstrations.

cs.LG↗