Search arXivSearch

arXiv · 2503.22133

Describing the Persistence Landscape for Introducing Microbes into Complex Communities

Abstract

The introduction of non-native organisms into complex microbiome communities holds enormous potential to benefit society. However, microbiome engineering faces several challenges including successful establishment of the organism into the community, its persistence in the microbiome to serve a specified purpose, and constraint of the organism and its activity to the intended environment. A theoretical framework is needed to represent the complex interactions that drive these dynamics. Building on the concept of the community functional landscape, we define the persistence landscape as the metabolic, genetic, and broader functional composition and ecological context of the target microbiome that can be used to predict the environmental fitness of an introduced organism. Here, we discuss critical aspects of persistence landscapes that impact interactions between an introduced organism and the target microbiome, including the communitys genetic and metabolic complementation potential, cellular defense strategies, spatial and temporal dynamics, and the introduced organisms ability to compete for resources to survive. Finally, we highlight important knowledge gaps in the fields of microbial ecology and microbiome engineering that limit characterization and engineering of persistence landscapes. As a model for understanding microbiome structure and interaction in the context of microbiome engineering, the persistence landscape model should enable development of novel containment approaches while improving controlled colonization of a complex microbiome community to address pressing challenges in human health, agronomy, and biomanufacturing.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jason E. McDermott, William C. Nelson, Amy E. Zimmerman, Winston Anthony, Devin Coleman-Derr, Joshua Elmore, Tara Nitka, Ryan S. McClure, Pubudu P. Handakumbura, Adam Guss, Travis J. Wheeler, Robert G. Egbert. 2025-03-28. Describing the Persistence Landscape for Introducing Microbes into Complex Communities. https://arxiv.org/abs/2503.22133

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

KEEP EXPLORING

Related papers

From daylight to darkness: a nonlocal model of circadian activity cycles

An animal's ability to perceive its surroundings, as well as the ecological interactions it experiences, can be strongly influenced by the sleep patterns of both itself and surrounding species. Animals with limited sensory capabilities may struggle to navigate their habitat at night, while in a predator-prey system, it may be detrimental for prey to be inactive while predators are awake and hunting. Here, we develop a general mathematical framework to study these interactions using partial differential equations, incorporating activity cycles and daylight-dependent perception into existing models of animal movement. We show that both the dominant sensory mode and the length of daylight are key factors determining the strength of aggregation in social species. Extending this framework to a two-species predator-prey system, we use game-theoretic approaches to show that the equilibrium sleep strategy of each species is shaped not only by its own sensory capabilities, but also by those of its ecological opponent. Depending on the sensory capabilities of the two species, equilibrium strategies may consist of a single sleep pattern or a combination of multiple sleep patterns.

q-bio.PE

Evolution as fitness landscape navigation: concepts, measures, and emerging questions

Fitness landscapes are mappings between genotypes, phenotypes, and fitness that shape evolution. In recent years, empirical work and theoretical models have greatly advanced our understanding of how populations navigate rugged fitness landscapes. Here, we provide a timely review of the theoretical aspects of this field. Its rapidly growing literature employs a wide range of terms, which are sometimes used ambiguously or inconsistently. We therefore begin by defining the major concepts and the field's vocabulary, highlighting our own terminology choices wherever needed. We then review key results on the relationships between epistasis, ruggedness, accessibility, and navigability for genotype-fitness maps, highlighting several complex and sometimes counterintuitive connections that have emerged. Further, we review how the conserved structural properties of the underlying genotype-phenotype map, which can lead to the formation of large connected neutral networks of genotypes, influence dynamics on fitness landscapes. We then compare the two levels to study landscape navigation: the level of genotype-phenotype maps and the level of genotype-fitness maps. Our review leads us to propose a new measure of navigability, based on evolutionary outcomes, that is broadly applicable and overcomes limitations of existing measures. Finally, we highlight examples from the smaller body of work that relaxes the common assumption of fitness-monotonic paths on static landscapes, and discuss how this can fundamentally change the nature of fitness landscape navigation. Throughout the review, we identify directions for future work to fill existing gaps and to synthesize the disparate strands of research within the field.

q-bio.PE

Best Matches in Phylogenetic Networks

Best match graphs (BMGs) were introduced in mathematical phylogenetics to describe the concept of closest relatives for related genes (leaves of rooted tree) in different organisms (defining leaf colors). We generalize this concept here to leaf-colored rooted networks, where least common ancestors are in general neither unique nor comparable. We characterize BMGs of rooted networks as those vertex-colored digraphs that are properly colored and satisfy an easy-to-check condition that we call the sicor-in-hub property. BMGs can be recognized in linear time and an explaining network can be constructed in quadratic time. Analogous results are obtained for reciprocal best match graphs (RBMGs), where an edge $\{x,y\}$ corresponds to pairs of vertices with different color that are mutually closest relatives.

q-bio.PE