Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function DOI Creative Commons
Yuanchen Zhao, Otto X. Cordero, Mikhail Tikhonov

и другие.

PLoS Computational Biology, Год журнала: 2024, Номер 20(11), С. e1012590 - e1012590

Опубликована: Ноя. 13, 2024

Microbial communities play key roles across diverse environments. Predicting their function and dynamics is a goal of microbial ecology, but detailed microscopic descriptions these systems can be prohibitively complex. One approach to deal with this complexity resort coarser representations. Several approaches have sought identify useful groupings species in data-driven way. Of these, recent work has claimed some empirical success at de novo discovery coarse representations predictive given using methods as simple linear regression, against multiple groups or even single such group (the ensemble quotient optimization (EQO) approach). Modeling community combination individual species' contributions appears simplistic. However, the task identifying coarsening an ecosystem distinct from predicting well, it conceivable that former could accomplished by simpler methodology than latter. Here, we use resource competition framework design model where "correct" grouping discovered well-defined, synthetic data evaluate compare three regression-based methods, namely, two proposed previously one introduce. We find recover when manifestly nonlinear; multi-group offer advantage over single-group EQO; crucially, (linear) outperform more complex ones.

Язык: Английский

Assembly Graph as the Rosetta Stone of Ecological Assembly DOI
Chuliang Song

Environmental Microbiology, Год журнала: 2025, Номер 27(1)

Опубликована: Янв. 1, 2025

Ecological assembly-the process of ecological community formation through species introductions-has recently seen exciting theoretical advancements across dynamical, informational, and probabilistic approaches. However, these theories often remain inaccessible to non-theoreticians, they lack a unifying lens. Here, I introduce the assembly graph as an integrative tool connect emerging theories. The visually represents dynamics, where nodes symbolise combinations edges represent transitions driven by introductions. Through lens graphs, review how processes reduce uncertainty in random arrivals (informational approach), identify graphical properties that guarantee coexistence examine class dynamical models constrain topology graphs (dynamical quantify transition probabilities with incomplete information (probabilistic approach). To facilitate empirical testing, also methods decompose complex into smaller, measurable components, well computational tools for deriving graphs. In sum, this math-light progress aims catalyse research towards predictive understanding assembly.

Язык: Английский

Процитировано

0
Assembly Graph as the Rosetta Stone of Ecological Assembly DOI Creative Commons
Chuliang Song

Опубликована: Авг. 2, 2024

Ecological assembly---the process of ecological community formation through species introductions---has recently seen exciting theoretical advancements across dynamical, informational, and probabilistic approaches. However, these theories often remain inaccessible to non-theoreticians, they lack a unifying lens. Here, I introduce the assembly graph as an integrative tool connect emerging theories. The visually represents dynamics, where nodes symbolize combinations edges represent transitions driven by introductions. Through lens graphs, review how processes reduce uncertainty in random arrivals (informational approach), identify graphical properties that guarantee coexistence examine class dynamical models constrain topology graphs (dynamical quantify transition probabilities with incomplete information (probabilistic approach). To facilitate empirical testing, also methods decompose complex into smaller, measurable components, well computational tools for deriving graphs. In sum, this math-light progress aims catalyze research towards predictive understanding assembly.

Язык: Английский

Процитировано

2
Designing host-associated microbiomes using the consumer/resource model DOI Creative Commons
Germán Plata,

Karthik Srinivasan,

Madan Krishnamurthy

и другие.

mSystems, Год журнала: 2024, Номер unknown

Опубликована: Дек. 9, 2024

A key step toward rational microbiome engineering is in silico sampling of realistic microbial communities that correspond to desired host phenotypes, and vice versa. This remains challenging due a lack generative models simultaneously capture compositions host-associated microbiomes phenotypes. To end, we present model based on the mechanistic consumer/resource (C/R) framework. In model, variation ecosystem composition arises differences availability effective resources (inferred latent variables), while species' resource preferences remain conserved. Simultaneously, variables are used phenotypic states hosts. generated by our accurately reproduce universal dataset-specific statistics bacterial communities. The allows us address three salient questions ecologies: (i) which phenotypes maximally constrain microbiomes? (ii) how context-specific phenotype/microbiome associations, (iii) what plausible phenotypes? Our approach aids analysis design associated with interest. Generative extremely popular modern biology. They have been protein sequences, entire genomes, RNA sequencing profiles. Importantly, extrapolate interpolate unobserved regimes data biological systems properties. For example, there has boom machine-learning aiding proteins user-specified structures or functions. Host-associated play important roles animal health disease, as well productivity environmental footprint livestock species. However, no microbiomes. One chief reason off-the-shelf hungry, studies usually deal large variability small sample sizes. Moreover, heavily context dependent, characteristics abiotic environment leading distinct patterns host-microbiome associations. Consequently, modeling not successfully applied microbiomes.To these challenges, develop for derived from derivation fit readily available cross-sectional profile data. Using hosts, show this several features: identifies space represents determine growth and, therefore, relative abundances Probabilistic generate can assign probabilities microbiomes, thereby allowing discriminate between dissimilar ecosystems. predictively captures corresponding hosts' enabling characteristics.

Язык: Английский

Процитировано

2
Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function DOI Creative Commons
Yuanchen Zhao, Otto X. Cordero, Mikhail Tikhonov

и другие.

PLoS Computational Biology, Год журнала: 2024, Номер 20(11), С. e1012590 - e1012590

Опубликована: Ноя. 13, 2024

Microbial communities play key roles across diverse environments. Predicting their function and dynamics is a goal of microbial ecology, but detailed microscopic descriptions these systems can be prohibitively complex. One approach to deal with this complexity resort coarser representations. Several approaches have sought identify useful groupings species in data-driven way. Of these, recent work has claimed some empirical success at de novo discovery coarse representations predictive given using methods as simple linear regression, against multiple groups or even single such group (the ensemble quotient optimization (EQO) approach). Modeling community combination individual species' contributions appears simplistic. However, the task identifying coarsening an ecosystem distinct from predicting well, it conceivable that former could accomplished by simpler methodology than latter. Here, we use resource competition framework design model where "correct" grouping discovered well-defined, synthetic data evaluate compare three regression-based methods, namely, two proposed previously one introduce. We find recover when manifestly nonlinear; multi-group offer advantage over single-group EQO; crucially, (linear) outperform more complex ones.

Язык: Английский

Процитировано

1