Use of Approximate Bayesian Computation to Assess and Fit Models of Mycobacterium leprae to Predict Outcomes of the Brazilian Control Program

Smith, Rebecca L.; Gröhn, Yrjö T.

doi:10.1371/journal.pone.0129535

Cited by 6 publications

(9 citation statements)

References 19 publications

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“…The second data fitting method is a statistical approach known as approximate Bayesian computation (ABC). This is implemented using ABC‐SysBio, a Python software package designed specifically for statistical parameter inference in biological systems research [51–53]. The programme employs sequential Monte Carlo (SMC) simulations to construct an accurate approximation to the posterior probability distribution defined by Bayes’ theorem:

italicP ()(, italicA | italicB) = \frac{italicP ()(, italicB | italicA) italicP ()(, A)}{italicP ()(, B)}, P (italicB) > 0.

…”

Section: Resultsmentioning

confidence: 99%

Mechanistic modelling of tyrosine recombination reveals key parameters determining the performance of a CAR T cell switching circuit

et al. 2020

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italicP ()(, italicA | italicB) = \frac{italicP ()(, italicB | italicA) italicP ()(, A)}{italicP ()(, B)}, P (italicB) > 0.

…”

Section: Resultsmentioning

confidence: 99%

Mechanistic modelling of tyrosine recombination reveals key parameters determining the performance of a CAR T cell switching circuit

et al. 2020

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“…In order to assess the potential of the 48 candidate architectures to reproduce the known characteristics of the system, we perform model selection based on approximate Bayesian computation (ABC) using the ABC-SysBio software package. ABC-SysBio combines Bayes’ rule with sequential Monte Carlo (SMC) approaches to solve parameter inference and model selection problems in systems biology [ 21 – 23 ]. The procedure determines the model, from a set of candidate models, that is most likely to have produced the associated experimental data.…”

Section: Resultsmentioning

confidence: 99%

“…ABC-SysBio is a Python software package that is designed specifically for parameter inference and model selection in biological systems research using the approach of approximate Bayesian computation (ABC) [ 21 ]. The program enables ABC inference of mathematical models via sequential Monte Carlo (SMC) approaches [ 21 – 23 ]. Monte Carlo approaches to computational simulations involve generating random candidate solutions, testing their fitness against a desired output and repeating until a viable solution can be identified.…”

Section: Methodsmentioning

confidence: 99%

Modeling the architecture of the regulatory system controlling methylenomycin production in Streptomyces coelicolor

et al. 2017

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BackgroundThe antibiotic methylenomycin A is produced naturally by Streptomyces coelicolor A3(2), a model organism for streptomycetes. This compound is of particular interest to synthetic biologists because all of the associated biosynthetic, regulatory and resistance genes are located on a single cluster on the SCP1 plasmid, making the entire module easily transferable between different bacterial strains. Understanding further the regulation and biosynthesis of the methylenomycin producing gene cluster could assist in the identification of motifs that can be exploited in synthetic regulatory systems for the rational engineering of novel natural products and antibiotics.ResultsWe identify and validate a plausible architecture for the regulatory system controlling methylenomycin production in S. coelicolor using mathematical modeling approaches. Model selection via an approximate Bayesian computation (ABC) approach identifies three candidate model architectures that are most likely to produce the available experimental data, from a set of 48 possible candidates. Subsequent global optimization of the parameters of these model architectures identifies a single model that most accurately reproduces the dynamical response of the system, as captured by time series data on methylenomycin production. Further analyses of variants of this model architecture that capture the effects of gene knockouts also reproduce qualitative experimental results observed in mutant S. coelicolor strains.ConclusionsThe mechanistic mathematical model developed in this study recapitulates current biological knowledge of the regulation and biosynthesis of the methylenomycin producing gene cluster, and can be used in future studies to make testable predictions and formulate experiments to further improve our understanding of this complex regulatory system.

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“…These models can be used to compare competing causal structures (e.g. Smith and Gröhn (2015)). We show that, for a set of problems, DAGs and compartmental model diagrams can both express causation, mediation, confounding, and collider bias, while compartmental model diagrams can explicitly depict interaction and depict feedback cycles.…”

Section: Discussionmentioning

confidence: 99%

Compartmental Model Diagrams as Causal Representations in Relation to DAGs

Ackley

Mayeda

Worden

et al. 2017

Epidemiologic Methods

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Compartmental model diagrams have been used for nearly a century to depict causal relationships in infectious disease epidemiology. Causal directed acyclic graphs (DAGs) have been used more broadly in epidemiology since the 1990s to guide analyses of a variety of public health problems. Using an example from chronic disease epidemiology, the effect of type 2 diabetes on dementia incidence, we illustrate how compartmental model diagrams can represent the same concepts as causal DAGs, including causation, mediation, confounding, and collider bias. We show how to use compartmental model diagrams to explicitly depict interaction and feedback cycles. While DAGs imply a set of conditional independencies, they do not define conditional distributions parametrically. Compartmental model diagrams parametrically (or semiparametrically) describe state changes based on known biological processes or mechanisms. Compartmental model diagrams are part of a long-term tradition of causal thinking in epidemiology and can parametrically express the same concepts as DAGs, as well as explicitly depict feedback cycles and interactions. As causal inference efforts in epidemiology increasingly draw on simulations and quantitative sensitivity analyses, compartmental model diagrams may be of use to a wider audience. Recognizing simple links between these two common approaches to representing causal processes may facilitate communication between researchers from different traditions.

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Use of Approximate Bayesian Computation to Assess and Fit Models of Mycobacterium leprae to Predict Outcomes of the Brazilian Control Program

Cited by 6 publications

References 19 publications

Mechanistic modelling of tyrosine recombination reveals key parameters determining the performance of a CAR T cell switching circuit

Mechanistic modelling of tyrosine recombination reveals key parameters determining the performance of a CAR T cell switching circuit

Modeling the architecture of the regulatory system controlling methylenomycin production in Streptomyces coelicolor

Compartmental Model Diagrams as Causal Representations in Relation to DAGs

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