Defining coarse-grainability in a model of structured microbial ecosystems

Moran, Jacob; Tikhonov, Mikhail

doi:10.1101/2021.07.17.452786

Cited by 3 publications

(8 citation statements)

References 72 publications

(174 reference statements)

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“…2A, B highlights that both forms of emergent simplicity shown in Fig. 1D, E are distinct from this more familiar scenario, widely reported both empirically [1][2][3][4][5][6][7][8] and in models [14,24]. In Fig.…”

Section: A Standard Ecological Models Do Not Generically Exhibit Eith...mentioning

confidence: 66%

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Emergent predictability in microbial ecosystems

Moran,

Tikhonov

2024

Preprint

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Microbial ecosystems carry out essential functions for global climate, human health, and industry. These complex communities exhibit a surprising amount of functionally relevant diversity at all levels of taxonomic resolution, presenting a significant challenge for most modeling frameworks. A long-standing hope of theoretical ecology is that some patterns might persist despite community complexity -- or perhaps even emerge because of it. A deeper understanding of such "emergent simplicity" could enable new approaches for predicting the behaviors of the complex ecosystems in nature. However, most examples described so far afford limited predictive power, as they focused on reproducibility rather than prediction. Here, we propose an information-theoretic framework for defining, nuancing and quantifying emergent simplicity in empirical data based on the ability of simple models to predict community-level functional properties. Applying this framework to two published datasets, we demonstrate that the majority of properties measured across both experiments exhibit robust evidence of emergent predictability: surprisingly, as community richness increases, simple compositional descriptions become more predictive. We show that this behavior is not typical within the standard modeling frameworks of theoretical ecology, and argue that improving our ability to predict and control natural microbial communities will require a shift of focus: away from complexity of _ecosystems_, and towards prediction complexity of _properties_ of ecosystems.

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Section: A Standard Ecological Models Do Not Generically Exhibit Eith...mentioning

confidence: 66%

“…As in ref. [24], we set the baseline cost c = 0.1 and the cost per resource consumed λ = 0.5. Using the same approach as in studying the GLV model, we draw parameters K α , ϵ i and σ iα randomly.…”

Section: Simulations Of Standard Ecological Modelsmentioning

confidence: 99%

Emergent predictability in microbial ecosystems

Moran,

Tikhonov

2024

Preprint

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“…The first is the EQO proposed by [25] which we modified to incorporate the Akaike Information Criterion (AIC) into the optimization process. In the second method, the coefficients of a multiple linear regression against all species are fed into K-Means clustering for grouping [19]. We term this method “K-Means,” for simplicity.…”

Section: Resultsmentioning

confidence: 99%

“…We use synthetic data from this model to compare the performance of three grouping algorithms: the single-group EQO of Ref. [25]; its multi-group generalization [19]; and a new algorithm we propose here, based on a Metropolis-like [18] search of the space of candidate groupings of species.…”

Section: Introductionmentioning

confidence: 99%

Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function

Zhao,

Cordero,

Tikhonov

2024

Preprint

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Microbial communities play key roles across diverse environments. Predicting their function and dynamics is a key goal of microbial ecology, but detailed microscopic descriptions of these systems can be prohibitively complex. One approach to deal with this complexity is to resort to coarser representations. Several approaches have sought to identify useful groupings of microbial species in a data-driven way. Of these, recent work has claimed some empirical success at de novo discovery of coarse representations predictive of a given function using methods as simple as a linear regression, against multiple groups of species or even a single such group (the EQO approach of Shan et al., 2023). This success seems puzzling, since modeling community function as a linear combination of contributions of individual species appears simplistic. However, the task of identifying a predictive coarsening of an ecosystem is distinct from the task of predicting the function well, and it is conceivable that the former could be accomplished by a simpler methodology than the latter. Here, we use the resource competition framework to design a model where the "correct" grouping to be discovered is well-defined, and use synthetic data to evaluate and compare three regression-based methods, namely, two proposed previously and one we introduce. We find that regression-based methods can recover the groupings even when the function is manifestly nonlinear; that multi-group methods offer an advantage over a single-group EQO; and crucially, that simpler (linear) methods can outperform more complex ones.

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“…Furthermore, when studies are scaled to the complexity of most natural microbiomes, it becomes infeasible to test the performance of each individual organism in well-controlled monocultures. As we attempt to make use of increasingly complex models of microbiomes, systematic curation strategies will need to be coupled with expert intuition to establish the appropriate level of detail that is coarse-grained enough to be robust to parameter uncertainty and stochastic community dynamics ( 51 – 53 ) yet detailed enough to capture important processes. Researchers should keep this in mind when selecting the appropriate simulation scope for the application of GEMs to microbial communities and should try to balance the opportunities and challenges of each approach.…”

Section: Challengesmentioning

confidence: 99%