Microbial dynamics drive the biotic machinery of early soil evolution. However, integrated knowledge of microbial community establishment, functional associations, and community assembly processes in incipient soil is lacking. This study presents a novel approach of combining microbial phylogenetic profiling, functional predictions, and community assembly processes to analyze drivers of microbial community establishment in an emerging soil system. Rigorous submeter sampling of a basalt‐soil lysimeter after 2 years of irrigation revealed that microbial community colonization patterns and associated soil parameters were depth dependent. Phylogenetic analysis of 16S rRNA gene sequences indicated the presence of diverse bacterial and archaeal phyla, with high relative abundance of Actinomyceles on the surface and a consistently high abundance of
Predicting microbial metabolic rates and emergent biogeochemical fluxes remains challenging due to the many unknown population dynamical, physiological and reaction‐kinetic parameters and uncertainties in species composition. Here, we show that the need for these parameters can be eliminated when population dynamics and reaction kinetics operate at much shorter time scales than physical mixing processes. Such scenarios are widespread in poorly mixed water columns and sediments. In this ‘fast‐reaction‐transport’ (FRT) limit, all that is required for predictions are chemical boundary conditions, the physical mixing processes and reaction stoichiometries, while no knowledge of species composition, physiology or population/reaction kinetic parameters is needed. Using time‐series data spanning years 2001–2014 and depths 180–900 m across the permanently anoxic Cariaco Basin, we demonstrate that the FRT approach can accurately predict the dynamics of major electron donors and acceptors (Pearson
- NSF-PAR ID:
- 10396435
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Environmental Microbiology
- Volume:
- 25
- Issue:
- 2
- ISSN:
- 1462-2912
- Page Range / eLocation ID:
- p. 268-282
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract Proteobacteria (Alpha ,Beta ,Gamma , andDelta ) at all depths. Despite depth‐dependent variation in community diversity, predicted functional gene analysis suggested that microbial metabolisms did not differ with depth, thereby suggesting redundancy in functional potential throughout the system. Null modeling revealed that microbial community assembly patterns were predominantly governed by variable selection. The relative influence of variable selection decreased with depth, indicating unique and relatively harsh environmental conditions near the surface and more benign conditions with depth. Additionally, community composition near the center of the domain was influenced by high levels of dispersal, suggesting that spatial processes interact with deterministic selection imposed by the environment. These results suggest that for oligotrophic systems, there are major differences in the length scales of variation between vertical and horizontal dimensions with the vertical dimension dominating variation in physical, chemical, and biological features. -
more » « less
Abstract Understanding the complex growth and metabolic dynamics in microorganisms requires advanced kinetic models containing both metabolic reactions and enzymatic regulation to predict phenotypic behaviors under different conditions and perturbations. Most current kinetic models lack gene expression dynamics and are separately calibrated to distinct media, which consequently makes them unable to account for genetic perturbations or multiple substrates. This challenge limits our ability to gain a comprehensive understanding of microbial processes towards advanced metabolic optimizations that are desired for many biotechnology applications. Here, we present an integrated computational and experimental approach for the development and optimization of mechanistic kinetic models for microbial growth and metabolic and enzymatic dynamics. Our approach integrates growth dynamics, gene expression, protein secretion, and gene‐deletion phenotypes. We applied this methodology to build a dynamic model of the growth kinetics in batch culture of the bacterium
Cellvibrio japonicus grown using either cellobiose or glucose media. The model parameters were inferred from an experimental data set using an evolutionary computation method. The resulting model was able to explain the growth dynamics ofC. japonicus using either cellobiose or glucose media and was also able to accurately predict the metabolite concentrations in the wild‐type strain as well as in β‐glucosidase gene deletion mutant strains. We validated the model by correctly predicting the non‐diauxic growth and metabolite consumptions of the wild‐type strain in a mixed medium containing both cellobiose and glucose, made further predictions of mutant strains growth phenotypes when using cellobiose and glucose media, and demonstrated the utility of the model for designing industrially‐useful strains. Importantly, the model is able to explain the role of the different β‐glucosidases and their behavior under genetic perturbations. This integrated approach can be extended to other metabolic pathways to produce mechanistic models for the comprehensive understanding of enzymatic functions in multiple substrates. -
Stochastic reaction-diffusion models are employed to represent many complex physical, biological, societal, and ecological systems. The macroscopic reaction rates describing the large-scale, long-time kinetics in such systems are effective, scale-dependent renormalized parameters that need to be either measured experimentally or computed by means of a microscopic model. In a Monte Carlo simulation of stochastic reaction-diffusion systems, microscopic probabilities for specific events to happen serve as the input control parameters. To match the results of any computer simulation to observations or experiments carried out on the macroscale, a mapping is required between the microscopic probabilities that define the Monte Carlo algorithm and the macroscopic reaction rates that are experimentally measured. Finding the functional dependence of emergent macroscopic rates on the microscopic probabilities (subject to specific rules of interaction) is a very difficult problem, and there is currently no systematic, accurate analytical way to achieve this goal. Therefore, we introduce a straightforward numerical method of using lattice Monte Carlo simulations to evaluate the macroscopic reaction rates by directly obtaining the count statistics of how many events occur per simulation time step. Our technique is first tested on well-understood fundamental examples, namely, restricted birth processes, diffusion-limited two-particle coagulation, and two-species pair annihilation kinetics. Next we utilize the thus gained experience to investigate how the microscopic algorithmic probabilities become coarse-grained into effective macroscopic rates in more complex model systems such as the Lotka-Volterra model for predator-prey competition and coexistence, as well as the rock-paper-scissors or cyclic Lotka-Volterra model and its May-Leonard variant that capture population dynamics with cyclic dominance motifs. Thereby we achieve a more thorough and deeper understanding of coarse graining in spatially extended stochastic reaction-diffusion systems and the nontrivial relationships between the associated microscopic and macroscopic model parameters, with a focus on ecological systems. The proposed technique should generally provide a useful means to better fit Monte Carlo simulation results to experimental or observational data.more » « less
-
more » « less
Abstract Two long-standing challenges in theoretical population genetics and evolution are predicting the distribution of phenotype diversity generated by mutation and available for selection, and determining the interaction of mutation, selection and drift to characterize evolutionary equilibria and dynamics. More fundamental for enabling such predictions is the current inability to causally link genotype to phenotype. There are three major mechanistic mappings required for such a linking – genetic sequence to kinetic parameters of the molecular processes, kinetic parameters to biochemical system phenotypes, and biochemical phenotypes to organismal phenotypes. This article introduces a theoretical framework, the Phenotype Design Space (PDS) framework, for addressing these challenges by focusing on the mapping of kinetic parameters to biochemical system phenotypes. It provides a quantitative theory whose key features include (1) a mathematically rigorous definition of phenotype based on biochemical kinetics, (2) enumeration of the full phenotypic repertoire, and (3) functional characterization of each phenotype independent of its context-dependent selection or fitness contributions. This framework is built on Design Space methods that relate system phenotypes to genetically determined parameters and environmentally determined variables. It also has the potential to automate prediction of phenotype-specific mutation rate constants and equilibrium distributions of phenotype diversity in microbial populations undergoing steady-state exponential growth, which provides an ideal reference to which more realistic cases can be compared. Although the framework is quite general and flexible, the details will undoubtedly differ for different functions, organisms and contexts. Here a hypothetical case study involving a small molecular system, a primordial circadian clock, is used to introduce this framework and to illustrate its use in a particular case. The framework is built on fundamental biochemical kinetics. Thus, the foundation is based on linear algebra and reasonable physical assumptions, which provide numerous opportunities for experimental testing and further elaboration to deal with complex multicellular organisms that are currently beyond its scope. The discussion provides a comparison of results from the PDS framework with those from other approaches in theoretical population genetics.
-
more » « less
Background: Quadriceps weakness is common after anterior cruciate ligament (ACL) reconstruction and can alter gait mechanics. Functional resistance training (FRT) is a novel approach to retraining strength after injury, but it is unclear how it alters gait mechanics. Therefore, we tested how 3 different types of FRT devices: a knee brace resisting extension (unidirectional brace), a knee brace resisting extension and flexion (bidirectional brace), and an elastic band pulling backwards on the ankle (elastic band)–acutely alter gait kinetics in this population.
Hypothesis: The type of FRT device will affect ground-reaction forces (GRFs) during and after the training. Specifically, the uni- and bidirectional braces will increase GRFs when compared with the elastic band.
Study Design: Crossover study.
Level of Evidence: Level 2.
Methods: A total of 15 individuals with ACL reconstruction received FRT with each device over 3 separate randomized sessions. During training, participants walked on a treadmill while performing a tracking task with visual feedback. Sessions contained 5 training trials (180 seconds each) with rest between. Vertical and anterior-posterior GRFs were assessed on the ACL-reconstructed leg before, during, and after training. Changes in GRFs were compared across devices using 1-dimensional statistical parametric mapping.
Results: Resistance applied via bidirectional brace acutely increased gait kinetics during terminal stance/pre-swing (ie, push-off), while resistance applied via elastic band acutely increased gait kinetics during initial contact/loading (ie, braking). Both braces behaved similarly, but the unidirectional brace was less effective for increasing push-off GRFs.
Conclusion: FRT after ACL reconstruction can acutely alter gait kinetics during training. Devices can be applied to selectively alter gait kinetics. However, the long-term effects of FRT after ACL reconstruction with these devices are still unknown.
Clinical Relevance: FRT may be applied to alter gait kinetics of the involved limb after ACL reconstruction, depending on the device used.
