Ecological landscapes guide the assembly of optimal microbial communities

The Nonlinear Dynamics and Chaos Control of Pricing Games in Group Robot Systems

System stability control in resource allocation is a critical issue in group robot systems. Against this backdrop, this study investigates the nonlinear dynamics and chaotic phenomena that arise during pricing games among finitely rational group robots and proposes control strategies to mitigate chaotic behaviors. A system model and a business model for group robots are developed based on market mechanism mapping, and the dynamics of resource allocation are formulated as a second-order discrete nonlinear system using game theory. Numerical simulations reveal that small perturbations in system parameters, such as pricing adjustment speed, product demand coefficients, and resource substitution coefficients, can induce chaotic behaviors. To address these chaotic phenomena, a control method combining state feedback and parameter adjustment is proposed. This approach dynamically tunes the state feedback intensity of the system via a control parameter M, thereby delaying bifurcations and suppressing chaotic behaviors. It ensures that the distribution of system eigenvalues satisfies stability conditions, allowing control over unstable periodic orbits and period-doubling bifurcations. Simulation results demonstrate that the proposed control method effectively delays period-doubling bifurcations and stabilizes unstable periodic orbits in chaotic attractors. The stability of the system's Nash equilibrium is significantly improved, and the parameter range for equilibrium pricing is expanded. These findings provide essential theoretical foundations and practical guidance for the design and application of group robot systems.

The architecture of theory and data in microbiome design: towards an S-matrix for microbiomes

Designing microbiomes for applications in health, bioengineering, and sustainability is intrinsically linked to a fundamental theoretical understanding of the rules governing microbial community assembly. Microbial ecologists have used a range of mathematical models to understand, predict, and control microbiomes, ranging from mechanistic models, putting microbial populations and their interactions as the focus, to purely statistical approaches, searching for patterns in empirical and experimental data. We review the success and limitations of these modeling approaches when designing novel microbiomes, especially when guided by (inevitably) incomplete experimental data. Although successful at predicting generic patterns of community assembly, mechanistic and phenomenological models tend to fall short of the precision needed to design and implement specific functionality in a microbiome. We argue that to effectively design microbiomes with optimal functions in diverse environments, ecologists should combine data-driven techniques with mechanistic models - a middle, third way for using theory to inform design.

Deterministic succession patterns in the rumen and fecal microbiome associate with host metabolic shifts in peripartum dairy cattle

BACKGROUND

Metabolic disorders in peripartum ruminants affect health and productivity, with gut microbiota playing a key role in host metabolism. Therefore, our study aimed to characterize the gut microbiota of peripartum dairy cows to better understand the relationship between metabolic phenotypes and the rumen and fecal microbiomes during the peripartum period.

RESULTS

In a longitudinal study of 91 peripartum cows, we analyzed rumen and fecal microbiomes via 16S rRNA and metagenomic sequencing across six time points. By using enterotype classification, ecological model, and random forest analysis, we identified distinct deterministic succession patterns in the rumen and fecal microbiomes (rumen: rapid transition-transition-stable; hindgut: stable-transition-stable). Key microbes, such as Succiniclasticum and Bifidobacterium, were found to drive microbial succession by balancing stochastic and deterministic processes. Notably, we observed that changes in gut microbiota succession patterns significantly influenced metabolic phenotypes (e.g., serum non-esterified fatty acid, glucose, and insulin levels). Mediation analysis suggested that specific gut microbes (e.g., Prevotella sp900315525 in the rumen and Alistipes sp015059845 in the hindgut) and metabolic pathways (e.g., glucose-related pathway) were associated with host metabolic phenotypes.

CONCLUSIONS

Overall, utilizing a large gut microbiome dataset and enterotype- and ecological model-based microbiome analyses, we comprehensively elucidated the succession and assembly of the gut microbiota in peripartum dairy cows. We further confirmed that changes in gut microbiota succession patterns were significantly related to the metabolic phenotypes of peripartum dairy cows. These findings provide valuable insights for developing health management strategies for peripartum ruminants.

Global epistasis and the emergence of function in microbial consortia

The many functions of microbial communities emerge from a complex web of interactions between organisms and their environment. This poses a significant obstacle to engineering microbial consortia, hindering our ability to harness the potential of microorganisms for biotechnological applications. In this study, we demonstrate that the collective effect of ecological interactions between microbes in a community can be captured by simple statistical models that predict how adding a new species to a community will affect its function. These predictive models mirror the patterns of global epistasis reported in genetics, and they can be quantitatively interpreted in terms of pairwise interactions between community members. Our results illuminate an unexplored path to quantitatively predicting the function of microbial consortia from their composition, paving the way to optimizing desirable community properties and bringing the tasks of predicting biological function at the genetic, organismal, and ecological scales under the same quantitative formalism.

Sparsity of higher-order landscape interactions enables learning and prediction for microbiomes

Microbiome engineering offers the potential to leverage microbial communities to improve outcomes in human health, agriculture, and climate. To translate this potential into reality, it is crucial to reliably predict community composition and function. But a brute force approach to cataloging community function is hindered by the combinatorial explosion in the number of ways we can combine microbial species. An alternative is to parameterize microbial community outcomes using simplified, mechanistic models, and then extrapolate these models beyond where we have sampled. But these approaches remain data-hungry, as well as requiring an a priori specification of what kinds of mechanisms are included and which are omitted. Here, we resolve both issues by introducing a mechanism-agnostic approach to predicting microbial community compositions and functions using limited data. The critical step is the identification of a sparse representation of the community landscape. We then leverage this sparsity to predict community compositions and functions, drawing from techniques in compressive sensing. We validate this approach on in silico community data, generated from a theoretical model. By sampling just [Formula: see text]1% of all possible communities, we accurately predict community compositions out of sample. We then demonstrate the real-world application of our approach by applying it to four experimental datasets and showing that we can recover interpretable, accurate predictions on composition and community function from highly limited data.

Universal abundance fluctuations across microbial communities, tropical forests, and urban populations

The growth of complex populations, such as microbial communities, forests, and cities, occurs over vastly different spatial and temporal scales. Although research in different fields has developed detailed, system-specific models to understand each individual system, a unified analysis of different complex populations is lacking; such an analysis could deepen our understanding of each system and facilitate cross-pollination of tools and insights across fields. Here, we use a shared framework to analyze time-series data of the human gut microbiome, tropical forest, and urban employment. We demonstrate that a single, three-parameter model of stochastic population dynamics can reproduce the empirical distributions of population abundances and fluctuations in all three datasets. The three parameters characterizing a species measure its mean abundance, deterministic stability, and stochasticity. Our analysis reveals that, despite the vast differences in scale, all three systems occupy a similar region of parameter space when time is measured in generations. In other words, although the fluctuations observed in these systems may appear different, this difference is primarily due to the different physical timescales associated with each system. Further, we show that the distribution of temporal abundance fluctuations is described by just two parameters and derive a two-parameter functional form for abundance fluctuations to improve risk estimation and forecasting.

Functional convergence in slow-growing microbial communities arises from thermodynamic constraints

The dynamics of microbial communities is complex, determined by competition for metabolic substrates and cross-feeding of byproducts. Species in the community grow by harvesting energy from chemical reactions that transform substrates to products. In many anoxic environments, these reactions are close to thermodynamic equilibrium and growth is slow. To understand the community structure in these energy-limited environments, we developed a microbial community consumer-resource model incorporating energetic and thermodynamic constraints on an interconnected metabolic network. The central element of the model is product inhibition, meaning that microbial growth may be limited not only by depletion of metabolic substrates but also by accumulation of products. We demonstrate that these additional constraints on microbial growth cause a convergence in the structure and function of the community metabolic network-independent of species composition and biochemical details-providing a possible explanation for convergence of community function despite taxonomic variation observed in many natural and industrial environments. Furthermore, we discovered that the structure of community metabolic network is governed by the thermodynamic principle of maximum free energy dissipation. Our results predict the decrease of functional convergence in faster growing communities, which we validate by analyzing experimental data from anaerobic digesters. Overall, the work demonstrates how universal thermodynamic principles may constrain community metabolism and explain observed functional convergence in microbial communities.

Pulsed, continuous or somewhere in between? Resource dynamics matter in the optimisation of microbial communities

The optimisation of synthetic and natural microbial communities has vast potential for emerging applications in medicine, agriculture and industry. Realising this goal is contingent on a close correlation between theory, experiments, and the real world. Although the temporal pattern of resource supply can play a major role in microbial community assembly, resource dynamics are commonly treated inconsistently in theoretical and experimental research. Here we explore how the composition of communities varies under continuous resource supply, typical of theoretical approaches, versus pulsed resource supply, typical of experiments. Using simulations of classical resource competition models, we show that community composition diverges rapidly between the two regimes, with almost zero overlap in composition once the pulsing interval stretches beyond just four hours. The implication for the rapidly growing field of microbial community optimisation is that the resource supply regime must be tailored to the community being optimised. As such, we argue that resource supply dynamics should be considered both a constraint in the design of novel microbial communities and as a tuning mechanism for the optimisation of pre-existing communities like those found in the human gut.