Algorithm of spatial–temporal simulation for environment–strain interactions in strain–strain consortia based on resource competition mechanism
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PROVIDER: S-EPMC11296241 | biostudies-literature | 2024 Jun
REPOSITORIES: biostudies-literature
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Project description:Competition for resources often contributes strongly to defining an organism's ecological niche. Endogenous biological rhythms are important adaptations to the temporal dimension of niches, but how other organisms influence such temporal niches has not been much studied, and the role of competition in particular has been even less examined. We investigated how interspecific competition and intraspecific competition for resources shape an organism's activity rhythms.To do this, we simulated communities of one or two species in an agent-based model. Individuals in the simulation move according to a circadian activity rhythm and compete for limited resources. Probability of reproduction is proportional to an individual's success in obtaining resources. Offspring may have variance in rhythm parameters, which allow for the population to evolve over time.We demonstrate that when organisms are arrhythmic, one species will always be competitively excluded from the environment, but the existence of activity rhythms allows niche differentiation and indefinite coexistence of the two species. Two species which are initially active at the same phase will differentiate their phase angle of entrainment over time to avoid each other. When only one species is present in an environment, competition within the species strongly selects for niche expansion through arrhythmicity, but the addition of an interspecific competitor facilitates evolution of increased rhythmic amplitude when combined with additional adaptations for temporal specialization. Finally, if individuals preferentially mate with others who are active at similar times of day, then disruptive selection by intraspecific competition can split one population into two reproductively isolated groups separated in activity time.These simulations suggest that biological rhythms are an effective method to temporally differentiate ecological niches and that competition is an important ecological pressure promoting the evolution of rhythms and sleep. This is the first study to use ecological modeling to examine biological rhythms.
| S-EPMC7593148 | biostudies-literature
Project description:Natural resources are scarce in the Loess Plateau, and the ecological environment is fragile. Sustainable development requires special attention to resource and environmental carrying capacity (RECC). This study selected 24 representative cities in five natural areas of the Loess Plateau; used the entropy-weight-based TOPSIS method to evaluate and analyze the RECC of each city and region from 2013 to 2018; established a diagnosis model to identify the obstacle factors restricting the improvement of RECC; and constructed the theoretical framework of the RECC system mechanism. The results show that the RECC of the Loess Plateau is increasing in general but is relatively small. The environmental and social subsystems have the highest and lowest carrying capacities, respectively. There is an evident contradiction between economic development and the environment. Population density, investment in technological innovation, per capita sown area, and per capita water resources are the main obstacles affecting the improvement of RECC in the Loess Plateau. Such evaluations and diagnoses can support ecological civilization and sustainable development.
| S-EPMC8372938 | biostudies-literature
Project description:BackgroundRecently, the availability of high-resolution microscopy together with the advancements in the development of biomarkers as reporters of biomolecular interactions increased the importance of imaging methods in molecular cell biology. These techniques enable the investigation of cellular characteristics like volume, size and geometry as well as volume and geometry of intracellular compartments, and the amount of existing proteins in a spatially resolved manner. Such detailed investigations opened up many new areas of research in the study of spatial, complex and dynamic cellular systems. One of the crucial challenges for the study of such systems is the design of a well stuctured and optimized workflow to provide a systematic and efficient hypothesis verification. Computer Science can efficiently address this task by providing software that facilitates handling, analysis, and evaluation of biological data to the benefit of experimenters and modelers.ResultsThe Spatio-Temporal Simulation Environment (STSE) is a set of open-source tools provided to conduct spatio-temporal simulations in discrete structures based on microscopy images. The framework contains modules to digitize, represent, analyze, and mathematically model spatial distributions of biochemical species. Graphical user interface (GUI) tools provided with the software enable meshing of the simulation space based on the Voronoi concept. In addition, it supports to automatically acquire spatial information to the mesh from the images based on pixel luminosity (e.g. corresponding to molecular levels from microscopy images). STSE is freely available either as a stand-alone version or included in the linux live distribution Systems Biology Operational Software (SB.OS) and can be downloaded from http://www.stse-software.org/. The Python source code as well as a comprehensive user manual and video tutorials are also offered to the research community. We discuss main concepts of the STSE design and workflow. We demonstrate it's usefulness using the example of a signaling cascade leading to formation of a morphological gradient of Fus3 within the cytoplasm of the mating yeast cell Saccharomyces cerevisiae.ConclusionsSTSE is an efficient and powerful novel platform, designed for computational handling and evaluation of microscopic images. It allows for an uninterrupted workflow including digitization, representation, analysis, and mathematical modeling. By providing the means to relate the simulation to the image data it allows for systematic, image driven model validation or rejection. STSE can be scripted and extended using the Python language. STSE should be considered rather as an API together with workflow guidelines and a collection of GUI tools than a stand alone application. The priority of the project is to provide an easy and intuitive way of extending and customizing software using the Python language.
| S-EPMC3114743 | biostudies-literature
Project description:Continuous-space population models can yield significantly different results from their panmictic counterparts when assessing evolutionary, ecological, or population-genetic processes. However, the computational burden of spatial models is typically much greater than that of panmictic models due to the overhead of determining which individuals interact with one another and how strongly they interact. Though these calculations are necessary to model local competition that regulates the population density, they can lead to prohibitively long runtimes. Here, we present a novel modeling method in which the resources available to a population are abstractly represented as an additional layer of the simulation. Instead of interacting directly with one another, individuals interact indirectly via this resource layer. We find that this method closely matches other spatial models, yet can dramatically increase the speed of the model, allowing the simulation of much larger populations. Additionally, models structured in this manner exhibit other desirable characteristics, including more realistic spatial dynamics near the edge of the simulated area, and an efficient route for modeling more complex heterogeneous landscapes.
| S-EPMC10827080 | biostudies-literature
Project description:Over the last 50 years, cue competition phenomena have shaped theoretical developments in animal and human learning. However, recent failures to observe competition effects in standard conditioning procedures, as well as the lengthy and ongoing debate surrounding cue competition in the spatial learning literature, have cast doubts on the generality of these phenomena. In the present study, we manipulated temporal contiguity between simultaneously trained predictors and outcomes (Experiments 1-4), and spatial contiguity between landmarks and goals in spatial learning (Supplemental Experiments 1 and 2; Experiment 5). Across different parametric variations, we observed overshadowing when temporal and spatial contiguity were strong, but no overshadowing when contiguity was weak. Thus, across temporal and spatial domains, we observed that contiguity is necessary for competition to occur, and that competition between cues presented simultaneously during learning is absent when these cues were either spatially or temporally discontiguous from the outcome. Consequently, we advance a model in which the contiguity between events is accounted for and which explains these results and reconciles the previously contradictory findings observed in spatial learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
| S-EPMC8988872 | biostudies-literature
Project description:The spatial context of microbial interactions common in natural systems is largely absent in traditional pure culture-based microbiology. The understanding of how interdependent microbial communities assemble and coexist in limited spatial domains remains sketchy. A mechanistic model of cell-level interactions among multispecies microbial populations grown on hydrated rough surfaces facilitated systematic evaluation of how trophic dependencies shape spatial self-organization of microbial consortia in complex diffusion fields. The emerging patterns were persistent irrespective of initial conditions and resilient to spatial and temporal perturbations. Surprisingly, the hydration conditions conducive for self-assembly are extremely narrow and last only while microbial cells remain motile within thin aqueous films. The resulting self-organized microbial consortia patterns could represent optimal ecological templates for the architecture that underlie sessile microbial colonies on natural surfaces. Understanding microbial spatial self-organization offers new insights into mechanisms that sustain small-scale soil microbial diversity; and may guide the engineering of functional artificial microbial consortia.
| S-EPMC5381366 | biostudies-literature
Project description:Understanding spatio-temporal variability in the demography of harvested species is essential to improve sustainability, especially if there is large geographic variation in demography. Reproductive patterns commonly vary spatially, which is particularly important for management of "roe"-based fisheries, since profits depend on both the number and reproductive condition of individuals. The red sea urchin, Mesocentrotus franciscanus, is harvested in California for its roe (gonad), which is sold to domestic and international sushi markets. The primary driver of price within this multi-million-dollar industry is gonad quality. A relatively simple measure of the fraction of the body mass that is gonad, the gonadosomatic index (GSI), provides important insight into the ecological and environmental factors associated with variability in reproductive quality, and hence value within the industry. We identified the seasonality of the reproductive cycle and determined whether it varied within a heavily fished region. We found that fishermen were predictable both temporally and spatially in collecting urchins according to the reproductive dynamics of urchins. We demonstrated the use of red sea urchin GSI as a simple, quantitative tool to predict quality, effort, landings, price, and value of the fishery. We found that current management is not effectively realizing some objectives for the southern California fishery, since the reproductive cycle does not match the cycle in northern California, where these management guidelines were originally shaped. Although regulations may not be meeting initial management goals, the scheme may in fact provide conservation benefits by curtailing effort during part of the high-quality fishing season right before spawning.
| S-EPMC5991392 | biostudies-literature
Project description:Feedbacks between plants and soil biota are increasingly identified as key determinants of species abundance patterns within plant communities. However, our understanding of how plant-soil feedbacks (PSFs) may contribute to invasions is limited by our understanding of how feedbacks may shift in the light of other ecological processes. Here we assess how the strength of PSFs may shift as soil microbial communities change along a gradient of soil nitrogen (N) availability and how these dynamics may be further altered by the presence of a competitor. We conducted a greenhouse experiment where we grew native Stipa pulchra and exotic Avena fatua, alone and in competition, in soils inoculated with conspecific and heterospecific soil microbial communities conditioned in low, ambient and high N environments. Stipa pulchra decreased in heterospecific soil and in the presence of a competitor, while the performance of the exotic A. fatua shifted with soil microbial communities from altered N environments. Moreover, competition and soil microbial communities from the high N environment eliminated the positive PSFs of Stipa. Our results highlight the importance of examining how individual PSFs may interact in a broader community context and contribute to the establishment, spread and dominance of invaders.
| S-EPMC4287689 | biostudies-literature
Project description:Microbial consortia are commonly observed in natural and synthetic systems, and these consortia frequently result in higher biomass production relative to monocultures. The focus here is on the impact of initial spatial localization and substrate diffusivity on the growth of a model microbial consortium consisting of a producer strain that consumes glucose and produces acetate and a scavenger strain that consumes the acetate. The mathematical model is based on an individual cell model where growth is described by Monod kinetics, and substrate transport is described by a continuum-based, non-equilibrium reaction-diffusion model where convective transport is negligible (e.g., in a biofilm). The first set of results focus on a single producer cell at the center of the domain and surrounded by an initial population of scavenger cells. The impact of the initial population density and substrate diffusivity is examined. A transition is observed from the highest initial density resulting in the greatest cell growth to cell growth being independent of initial density. A high initial density minimizes diffusive transport time and is typically expected to result in the highest growth, but this expected behavior is not predicted in environments with lower diffusivity or larger length scales. When the producer cells are placed on the bottom of the domain with the scavenger cells above in a layered biofilm arrangement, a similar critical transition is observed. For the highest diffusivity values examined, a thin, dense initial scavenger layer is optimal for cell growth. However, for smaller diffusivity values, a thicker, less dense initial scavenger layer provides maximal growth. The overall conclusion is that high density clustering of members of a food chain is optimal under most common transport conditions, but under some slow transport conditions, high density clustering may not be optimal for microbial growth.
| S-EPMC5207726 | biostudies-other
Project description:Stochastic simulations are one of the cornerstones of the analysis of dynamical processes on complex networks, and are often the only accessible way to explore their behavior. The development of fast algorithms is paramount to allow large-scale simulations. The Gillespie algorithm can be used for fast simulation of stochastic processes, and variants of it have been applied to simulate dynamical processes on static networks. However, its adaptation to temporal networks remains non-trivial. We here present a temporal Gillespie algorithm that solves this problem. Our method is applicable to general Poisson (constant-rate) processes on temporal networks, stochastically exact, and up to multiple orders of magnitude faster than traditional simulation schemes based on rejection sampling. We also show how it can be extended to simulate non-Markovian processes. The algorithm is easily applicable in practice, and as an illustration we detail how to simulate both Poissonian and non-Markovian models of epidemic spreading. Namely, we provide pseudocode and its implementation in C++ for simulating the paradigmatic Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and a Susceptible-Infected-Recovered model with non-constant recovery rates. For empirical networks, the temporal Gillespie algorithm is here typically from 10 to 100 times faster than rejection sampling.
| S-EPMC4627738 | biostudies-literature