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J Theor Biol. 2016 Jun 7;398:85-95. doi: 10.1016/j.jtbi.2016.03.021. Epub 2016 Mar 19.

Bayesian model selection framework for identifying growth patterns in filamentous fungi.

Author information

1
Department of Computer Science and Engineering, University of South Carolina, 315 Main St, Swearingen Bldg. 3A01L, Columbia, SC 29208, USA. Electronic address: lin65@email.sc.edu.
2
Department of Computer Science and Engineering, University of South Carolina, 315 Main St, Swearingen Bldg. 3A01L, Columbia, SC 29208, USA. Electronic address: terejanu@cec.sc.edu.
3
Department of Mechanical Engineering, University of South Carolina, United States. Electronic address: sajan@email.sc.edu.
4
Department of Mechanical Engineering, University of South Carolina, United States. Electronic address: banerjes@cec.sc.edu.
5
Department of Environmental Health Sciences, University of South Carolina, United States. Electronic address: achanda@mailbox.sc.edu.

Abstract

This paper describes a rigorous methodology for quantification of model errors in fungal growth models. This is essential to choose the model that best describes the data and guide modeling efforts. Mathematical modeling of growth of filamentous fungi is necessary in fungal biology for gaining systems level understanding on hyphal and colony behaviors in different environments. A critical challenge in the development of these mathematical models arises from the indeterminate nature of their colony architecture, which is a result of processing diverse intracellular signals induced in response to a heterogeneous set of physical and nutritional factors. There exists a practical gap in connecting fungal growth models with measurement data. Here, we address this gap by introducing the first unified computational framework based on Bayesian inference that can quantify individual model errors and rank the statistical models based on their descriptive power against data. We show that this Bayesian model comparison is just a natural formalization of Occam׳s razor. The application of this framework is discussed in comparing three models in the context of synthetic data generated from a known true fungal growth model. This framework of model comparison achieves a trade-off between data fitness and model complexity and the quantified model error not only helps in calibrating and comparing the models, but also in making better predictions and guiding model refinements.

KEYWORDS:

Fungal growth; Model calibration; Model discrepancy; Statistical modeling

PMID:
27000772
DOI:
10.1016/j.jtbi.2016.03.021
[Indexed for MEDLINE]

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