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1. Accelerating flux balance calculations in genome-scale metabolic models by localizing the application of loopless constraints

   https://doi.org/10.1093/bioinformatics/bty446  

   Chan, Siu H. J. ; Wang, Lin ; Dash, Satyakam ; Maranas, Costas D. ; Wren, ed., Jonathan ( June 2018 , Bioinformatics)

   Genome-scale metabolic network models and constraint-based modeling techniques have become important tools for analyzing cellular metabolism. Thermodynamically infeasible cycles (TICs) causing unbounded metabolic flux ranges are often encountered. TICs satisfy the mass balance and directionality constraints but violate the second law of thermodynamics. Current practices involve implementing additional constraints to ensure not only optimal but also loopless flux distributions. However, the mixed integer linear programming problems required to solve become computationally intractable for genome-scale metabolic models.

   We aimed to identify the fewest needed constraints sufficient for optimality under the loopless requirement. We found that loopless constraints are required only for the reactions that share elementary flux modes representing TICs with reactions that are part of the objective function. We put forth the concept of localized loopless constraints (LLCs) to enforce this minimal required set of loopless constraints. By combining with a novel procedure for minimal null-space calculation, the computational time for loopless flux variability analysis (ll-FVA) is reduced by a factor of 10–150 compared to the original loopless constraints and by 4–20 times compared to the current fastest method Fast-SNP with the percent improvement increasing with model size. Importantly, LLCs offer a scalable strategy for loopless flux calculations for multi-compartment/multi-organism models of large sizes, for example, shortening the CPU time for ll-FVA from 35 h to less than 2 h for a model with more than104 reactions.

   Matlab functions are available in the Supplementary Material or at https://github.com/maranasgroup/lll-FVA

   Supplementary data are available at Bioinformatics online.

    

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2. Comparing Prochlorococcus temperature niches in the lab and across ocean basins

   https://doi.org/10.1002/lno.11777  

   Smith, Alaina N. ; Hennon, Gwenn M. M. ; Zinser, Erik R. ; Calfee, Benjamin C. ; Chandler, Jeremy W. ; Barton, Andrew D. ( May 2021 , Limnology and Oceanography)

   Niche theory suggests that the realized niche occupied by an organism in the field is a subset of the fundamental niche space of the organism, absent additional biotic and abiotic factors. Though often assumed, this discrepancy is rarely tested for specific organisms, and could act as a source of error in model predictions of biogeographical shifts resulting from temperature change which assume niche theory constraints. Here, we quantify the difference between fundamental and realized temperature niches for four dominant ecotypes ofProchlorococcus, including eMED4, eMIT9312, eMIT9313, and eNATL2A, and ask whether the realized temperature niches of each ecotype vary across ocean basins. The realized niches for the four ecotypes are, on average, 3.84°C ± 1.18°C colder (mean ± SD across all ocean basins and ecotypes) and 2.15°C ± 1.89°C wider than the lab‐measured fundamental niches. When divided into four ocean regions—North Atlantic, South Atlantic, North Pacific, and South Pacific—we find that the realized temperature niche optimum for a given ecotype compared to the fundamental temperature niche optimum differs across regions by as much as 7.93°C, while the niche width can differ by up to 9.48°C. Colder and wider realized niches may be a result of the metabolic risk associated with living in variable environments when the mean temperature is too close to the optimal temperature for growth or due to physical processes such as dispersal. The strong differences in temperature niches across ocean basins suggest that unresolved genetic diversity within ecotypes, local adaptation, and variable interactive ecological and environmental factors are likely to be important in shapingProchlorococcusrealized temperature niches.

    

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3. Breaking down shell strength: inferences from experimental compression and future directions enabled by 3D printing

   https://doi.org/10.1111/brv.12692  

   Johnson, Erynn H. ( February 2021 , Biological Reviews)

   Mollusc and brachiopod shells have served as biological armour for hundreds of millions of years. Studying shell strength in compression experiments can provide insights into macroevolution, predator–prey dynamics, and anthropogenic impacts on aquatic ecosystems. These studies have been conducted across fields including palaeontology, ecology, conservation biology and engineering using a range of techniques for a variety of purposes. Using this approach, studies have demonstrated that predators can cause changes in prey shell morphology in the laboratory over both short timescales and over longer evolutionary timescales. Similarly, environmental factors such as nutrient concentration and ocean acidification have been shown to influence shell strength. Experimental compression tests have been used to study the functional morphology of shell‐crushing predators and to test how the taphonomic state of shells (e.g. presence of drill holes, degree of shell degradation) may influence their likelihood of being preserved in the fossil record. This review covers the basic principles and experimental design of compression tests used to infer shell strength. Although many investigations have used this methodology, few provide a detailed explanation of how meaningfully to interpret data generated using compression experiments for those unfamiliar with this method. Furthermore, this review provides a compilation of the findings of studies that have employed these experimental methods to address specific themes: taphonomy, morphology, predation, environmental variables, and climate change. Many authors have used experimental compression tests, however, disparities among methodologies (e.g. in experimental design, taxa, specimen preservation, etc.) limit the applicability of findings from taxon‐specific studies to broader eco‐evolutionary questions. The review highlights confounding factors, such as shell thickness, size, damage, microstructure, and taphonomic state, and address how they can be mitigated using three‐dimensional (3D)‐printed model shells. 3D prints have been demonstrated as valuable proxies for understanding aspects of shell morphology that cannot otherwise be experimentally isolated. Using 3D printed models allows simplification of complex biological systems for idealized experimental studies. Such studies can isolate specific aspects of shell morphology to establish fundamental relationships between form and function. Establishing standardized methods of testing shell strength in this way will not only permit comparison across studies but also will enable investigators systematically to add complexity to their models.

    

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4. Gillespie eco‐evolutionary models ( GEM s) reveal the role of heritable trait variation in eco‐evolutionary dynamics

   https://doi.org/10.1002/ece3.1959  

   DeLong, John P. ; Gibert, Jean P. ( January 2016 , Ecology and Evolution)

   Heritable trait variation is a central and necessary ingredient of evolution. Trait variation also directly affects ecological processes, generating a clear link between evolutionary and ecological dynamics. Despite the changes in variation that occur through selection, drift, mutation, and recombination, current eco‐evolutionary models usually fail to track how variation changes through time. Moreover, eco‐evolutionary models assume fitness functions for each trait and each ecological context, which often do not have empirical validation. We introduce a new type of model, Gillespie eco‐evolutionary models (GEMs), that resolves these concerns by tracking distributions of traits through time as eco‐evolutionary dynamics progress. This is done by allowing change to be driven by the direct fitness consequences of model parameters within the context of the underlying ecological model, without having to assume a particular fitness function.GEMs work by adding a trait distribution component to the standard Gillespie algorithm – an approach that models stochastic systems in nature that are typically approximated through ordinary differential equations. We illustrateGEMs with the Rosenzweig–MacArthur consumer–resource model. We show not only how heritable trait variation fuels trait evolution and influences eco‐evolutionary dynamics, but also how the erosion of variation through time may hinder eco‐evolutionary dynamics in the long run.GEMs can be developed for any parameter in any ordinary differential equation model and, furthermore, can enable modeling of multiple interacting traits at the same time. We expectGEMs will open the door to a new direction in eco‐evolutionary and evolutionary modeling by removing long‐standing modeling barriers, simplifying the link between traits, fitness, and dynamics, and expanding eco‐evolutionary treatment of a greater diversity of ecological interactions. These factors makeGEMs much more than a modeling advance, but an important conceptual advance that bridges ecology and evolution through the central concept of heritable trait variation.

    

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5. Improving structured population models with more realistic representations of non‐normal growth

   https://doi.org/10.1111/2041-210X.13240  

   DeMarche, Megan L. ; Morris, William ; Linares, Cristina ; Doak, Daniel ; Altwegg, ed., Res ( July 2019 , Methods in Ecology and Evolution)

   Structured population models are among the most widely used tools in ecology and evolution. Integral projection models (IPMs) use continuous representations of how survival, reproduction and growth change as functions of state variables such as size, requiring fewer parameters to be estimated than projection matrix models (PPMs). Yet, almost all published IPMs make an important assumption that size‐dependent growth transitions are or can be transformed to be normally distributed. In fact, many organisms exhibit highly skewed size transitions. Small individuals can grow more than they can shrink, and large individuals may often shrink more dramatically than they can grow. Yet, the implications of such skew for inference from IPMs has not been explored, nor have general methods been developed to incorporate skewed size transitions into IPMs, or deal with other aspects of real growth rates, including bounds on possible growth or shrinkage.

   Here, we develop a flexible approach to modelling skewed growth data using a modified beta regression model. We propose that sizes first be converted to a (0,1) interval by estimating size‐dependent minimum and maximum sizes through quantile regression. Transformed data can then be modelled using beta regression with widely available statistical tools. We demonstrate the utility of this approach using demographic data for a long‐lived plant, gorgonians and an epiphytic lichen. Specifically, we compare inferences of population parameters from discrete PPMs to those from IPMs that either assume normality or incorporate skew using beta regression or, alternatively, a skewed normal model.

   The beta and skewed normal distributions accurately capture the mean, variance and skew of real growth distributions. Incorporating skewed growth into IPMs decreases population growth and estimated life span relative to IPMs that assume normally distributed growth, and more closely approximate the parameters of PPMs that do not assume a particular growth distribution. A bounded distribution, such as the beta, also avoids the eviction problem caused by predicting some growth outside the modelled size range.

   Incorporating biologically relevant skew in growth data has important consequences for inference from IPMs. The approaches we outline here are flexible and easy to implement with existing statistical tools.

    

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