ArticleJournal of theoretical biology2010
Parameter estimation and sensitivity analysis in an agent-based model of Leishmania major infection.
Article in Journal of theoretical biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 44 citations in OpenAlex.
- Sensitivity analysis of disease-information coupling propagation dynamics model parameters.PloS one · 2022Article
- Trends in mathematical modeling of host-pathogen interactions.Cellular and molecular life sciences : CMLS · 2020Review
- Parameter uncertainty quantification using surrogate models applied to a spatial model of yeast mating polarization.PLoS computational biology · 2018Article
- Modeling Immune Response toFrontiers in cellular and infection microbiology · 2017Article
- AGENT-BASED MODELS IN EMPIRICAL SOCIAL RESEARCH.Sociological methods & research · 2015Article
- Host-pathogen interactions between the human innate immune system and Candida albicans-understanding and modeling defense and evasion strategies.Frontiers in microbiology · 2015Review
- Article
- Modeling of leishmaniasis infection dynamics: novel application to the design of effective therapies.BMC systems biology · 2012Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Computer models of disease take a systems biology approach toward understanding host-pathogen interactions. In particular, data driven computer model calibration is the basis for inference of immunological and pathogen parameters, assessment of model validity, and comparison between alternative models of immune or pathogen behavior. In this paper we describe the calibration and analysis of an agent-based model of Leishmania major infection. A model of macrophage loss following uptake of necrotic tissue is proposed to explain macrophage depletion following peak infection. Using Gaussian processes to approximate the computer code, we perform a sensitivity analysis to identify important parameters and to characterize their influence on the simulated infection. The analysis indicates that increasing growth rate can favor or suppress pathogen loads, depending on the infection stage and the pathogen's ability to avoid detection. Subsequent calibration of the model against previously published biological observations suggests that L. major has a relatively slow growth rate and can replicate for an extended period of time before damaging the host cell.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.