Dataset Information

Assumptions on decision making and environment can yield multiple steady states in microbial community models.

ABSTRACT:

Background

Microbial community simulations using genome scale metabolic networks (GSMs) are relevant for many application areas, such as the analysis of the human microbiome. Such simulations rely on assumptions about the culturing environment, affecting if the culture may reach a metabolically stationary state with constant microbial concentrations. They also require assumptions on decision making by the microbes: metabolic strategies can be in the interest of individual community members or of the whole community. However, the impact of such common assumptions on community simulation results has not been investigated systematically.

Results

Here, we investigate four combinations of assumptions, elucidate how they are applied in literature, provide novel mathematical formulations for their simulation, and show how the resulting predictions differ qualitatively. Our results stress that different assumption combinations give qualitatively different predictions on microbial coexistence by differential substrate utilization. This fundamental mechanism is critically under explored in the steady state GSM literature with its strong focus on coexistence states due to crossfeeding (division of labor). Furthermore, investigating a realistic synthetic community, where the two involved strains exhibit no growth in isolation, but grow as a community, we predict multiple modes of cooperation, even without an explicit cooperation mechanism.

Conclusions

Steady state GSM modelling of microbial communities relies both on assumed decision making principles and environmental assumptions. In principle, dynamic flux balance analysis addresses both. In practice, our methods that address the steady state directly may be preferable, especially if the community is expected to display multiple steady states.

SUBMITTER: Theorell A

PROVIDER: S-EPMC10288676 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Publications

Assumptions on decision making and environment can yield multiple steady states in microbial community models.

Theorell Axel A Stelling Jörg J

BMC bioinformatics 20230622 Suppl 1

<h4>Background</h4>Microbial community simulations using genome scale metabolic networks (GSMs) are relevant for many application areas, such as the analysis of the human microbiome. Such simulations rely on assumptions about the culturing environment, affecting if the culture may reach a metabolically stationary state with constant microbial concentrations. They also require assumptions on decision making by the microbes: metabolic strategies can be in the interest of individual community membe ...[more]

PMID: 37349675

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Project description:BackgroundDecision thresholds play important role in medical decision-making. Individual decision-making differences may be attributable to differences in subjective judgments or cognitive processes that are captured through the decision thresholds. This systematic scoping review sought to characterize the literature on non-expected utility decision thresholds in medical decision-making by identifying commonly used theoretical paradigms and contextual and subjective factors that inform decision thresholds.MethodsA structured search designed around three concepts-individual decision-maker, decision threshold, and medical decision-was conducted in MEDLINE (Ovid) and Scopus databases from inception to July 2023. ProQuest (Dissertations and Theses) database was searched to August 2023. The protocol, developed a priori, was registered on Open Science Framework and PRISMA-ScR guidelines were followed for reporting on this study. Titles and abstracts of 1,618 articles and the full texts for the 228 included articles were reviewed by two independent reviewers. 95 articles were included in the analysis. A single reviewer used a pilot-tested data collection tool to extract study and author characteristics, article type, objectives, theoretical paradigm, contextual or subjective factors, decision-maker, and type of medical decision.ResultsOf the 95 included articles, 68 identified a theoretical paradigm in their approach to decision thresholds. The most common paradigms included regret theory, hybrid theory, and dual processing theory. Contextual and subjective factors that influence decision thresholds were identified in 44 articles.ConclusionsOur scoping review is the first to systematically characterizes the available literature on decision thresholds within medical decision-making. This study offers an important characterization of the literature through the identification of the theoretical paradigms for non-expected utility decision thresholds. Moreover, this study provides insight into the various contextual and subjective factors that have been documented within the literature to influence decision thresholds, as well as these factors juxtapose theoretical paradigms.

Project description:ObjectivesTo (1) provide an up-to-date overview of shared decision making (SDM)-models, (2) give insight in the prominence of components present in SDM-models, (3) describe who is identified as responsible within the components (patient, healthcare professional, both, none), (4) show the occurrence of SDM-components over time, and (5) present an SDM-map to identify SDM-components seen as key, per healthcare setting.DesignSystematic review.Eligibility criteriaPeer-reviewed articles in English presenting a new or adapted model of SDM.Information sourcesAcademic Search Premier, Cochrane, Embase, Emcare, PsycINFO, PubMed, and Web of Science were systematically searched for articles published up to and including September 2, 2019.ResultsForty articles were included, each describing a unique SDM-model. Twelve models were generic, the others were specific to a healthcare setting. Fourteen were based on empirical data, 26 primarily on analytical thinking. Fifty-three different elements were identified and clustered into 24 components. Overall, Describe treatment options was the most prominent component across models. Components present in >50% of models were: Make the decision (75%), Patient preferences (65%), Tailor information (65%), Deliberate (58%), Create choice awareness (55%), and Learn about the patient (53%). In the majority of the models (27/40), both healthcare professional and patient were identified as actors. Over time, Describe treatment options and Make the decision are the two components which are present in most models in any time period. Create choice awareness stood out for being present in a markedly larger proportion of models over time.ConclusionsThis review provides an up-to-date overview of SDM-models, showing that SDM-models quite consistently share some components but that a unified view on what SDM is, is still lacking. Clarity about what SDM constitutes is essential though for implementation, assessment, and research purposes. A map is offered to identify SDM-components seen as key.Trial registrationPROSPERO registration CRD42015019740.

Project description:The steady states of cells affect their response to perturbation. Indeed, diagnostic markers for predicting the response to therapeutic perturbation are often based on steady state measurements. In spite of this, no method exists to systematically characterize the relationship between steady state and response. Mathematical models are established tools for studying cellular responses, but characterizing their relationship to the steady state requires that it have a parametric, or analytical, expression. For some models, this expression can be derived by the King-Altman method. However, King-Altman requires that no substrate act as an enzyme, and is therefore not applicable to most models of signal transduction. For this reason we developed py-substitution, a simple but general method for deriving analytical expressions for the steady states of mass action models. Where the King-Altman method is applicable, we show that py-substitution yields an equivalent expression, and at comparable efficiency. We use py-substitution to study the relationship between steady state and sensitivity to the anti-cancer drug candidate, dulanermin (recombinant human TRAIL). First, we use py-substitution to derive an analytical expression for the steady state of a published model of TRAIL-induced apoptosis. Next, we show that the amount of TRAIL required for cell death is sensitive to the steady state concentrations of procaspase 8 and its negative regulator, Bar, but not the other procaspase molecules. This suggests that activation of caspase 8 is a critical point in the death decision process. Finally, we show that changes in the threshold at which TRAIL results in cell death is not always equivalent to changes in the time of death, as is commonly assumed. Our work demonstrates that an analytical expression is a powerful tool for identifying steady state determinants of the cellular response to perturbation. All code is available at http://signalingsystems.ucsd.edu/models-and-code/ or as supplementary material accompanying this paper.

Dataset Information

Assumptions on decision making and environment can yield multiple steady states in microbial community models.

Background

Results

Conclusions

Publications

Assumptions on decision making and environment can yield multiple steady states in microbial community models.

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