Uncertainty Quantification Driven Predictive Multi-Scale Model for Synthesis of Mycotoxins

Sourav Banerjee; Gabriel Terejanu; Anindya Chanda

doi:doi:10.11648/j.cbb.20140201.12

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Uncertainty Quantification Driven Predictive Multi-Scale Model for Synthesis of Mycotoxins

Sourav Banerjee, Gabriel Terejanu, Anindya Chanda

Published in Computational Biology and Bioinformatics (Volume 2, Issue 1)

Received: 3 January 2014 Published: 30 January 2014

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Abstract

Many toxic molds synthesize and release an array of poisons, termed mycotoxins that have an enormous impact on human health, agriculture and economy [1]. These molds contaminate our buildings, indoor air and crops, cause life threatening human and animal diseases and reduce agricultural output [2]. In order to design appropriate approaches to minimize the detrimental effects of these fungi, it is essential to develop diagnostic methodologies that can rapidly and accurately determine based on fungal strains and their growth patterns, the extent of mycotoxin mediated damage caused to the environment.Here we developed a novel multi-scale predictive mathematical model that could reliably estimate aflatoxin synthesis from growth features extracted fromAspergillusparasiticus, a well-characterized model for studying mycotoxin biosynthesis. We conducted acoustic imaging experiments to observe and extract the growth features from the biomass profiles of the growing Aspergillus colony growing on an aflatoxin-inducing solid growth medium. We employed the probability-based representation of uncertainty and used Bayes’ theorem to infer the uncertain parameters in our mathematical model using biomass observations of the colony at 24h (aflatoxin is not synthesized yet at this time-point) and 48 hours (aflatoxin synthesis occurs at peak levels). We demonstrate that our model could successfully predict with quantified uncertainties the levels of aflatoxin secreted to the environment by the fungus.

Published in	Computational Biology and Bioinformatics (Volume 2, Issue 1)
DOI	10.11648/j.cbb.20140201.12
Page(s)	7-12
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

Predictive Multi-scale Model, Aflatoxin synthesis, Fungi, Aspergillus, Scanning Acoustic Microscopy, Uncertainty Quantification

References

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APA Style

Sourav Banerjee, Gabriel Terejanu, Anindya Chanda. (2014). Uncertainty Quantification Driven Predictive Multi-Scale Model for Synthesis of Mycotoxins. Computational Biology and Bioinformatics, 2(1), 7-12. https://doi.org/10.11648/j.cbb.20140201.12

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ACS Style

Sourav Banerjee; Gabriel Terejanu; Anindya Chanda. Uncertainty Quantification Driven Predictive Multi-Scale Model for Synthesis of Mycotoxins. Comput. Biol. Bioinform. 2014, 2(1), 7-12. doi: 10.11648/j.cbb.20140201.12

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AMA Style

Sourav Banerjee, Gabriel Terejanu, Anindya Chanda. Uncertainty Quantification Driven Predictive Multi-Scale Model for Synthesis of Mycotoxins. Comput Biol Bioinform. 2014;2(1):7-12. doi: 10.11648/j.cbb.20140201.12

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@article{10.11648/j.cbb.20140201.12,
  author = {Sourav Banerjee and Gabriel Terejanu and Anindya Chanda},
  title = {Uncertainty Quantification Driven Predictive Multi-Scale Model for Synthesis of Mycotoxins},
  journal = {Computational Biology and Bioinformatics},
  volume = {2},
  number = {1},
  pages = {7-12},
  doi = {10.11648/j.cbb.20140201.12},
  url = {https://doi.org/10.11648/j.cbb.20140201.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20140201.12},
  abstract = {Many toxic molds synthesize and release an array of poisons, termed mycotoxins that have an enormous impact on human health, agriculture and economy [1]. These molds contaminate our buildings, indoor air and crops, cause life threatening human and animal diseases and reduce agricultural output [2]. In order to design appropriate approaches to minimize the detrimental effects of these fungi, it is essential to develop diagnostic methodologies that can rapidly and accurately determine based on fungal strains and their growth patterns, the extent of mycotoxin mediated damage caused to the environment.Here we developed a novel multi-scale predictive mathematical model that could reliably estimate aflatoxin synthesis from growth features extracted fromAspergillusparasiticus, a well-characterized model for studying mycotoxin biosynthesis. We conducted acoustic imaging experiments to observe and extract the growth features from the biomass profiles of the growing Aspergillus colony growing on an aflatoxin-inducing solid growth medium. We employed the probability-based representation of uncertainty and used Bayes’ theorem to infer the uncertain parameters in our mathematical model using biomass observations of the colony at 24h (aflatoxin is not synthesized yet at this time-point) and 48 hours (aflatoxin synthesis occurs at peak levels). We demonstrate that our model could successfully predict with quantified uncertainties the levels of aflatoxin secreted to the environment by the fungus.},
 year = {2014}
}

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TY  - JOUR
T1  - Uncertainty Quantification Driven Predictive Multi-Scale Model for Synthesis of Mycotoxins
AU  - Sourav Banerjee
AU  - Gabriel Terejanu
AU  - Anindya Chanda
Y1  - 2014/01/30
PY  - 2014
N1  - https://doi.org/10.11648/j.cbb.20140201.12
DO  - 10.11648/j.cbb.20140201.12
T2  - Computational Biology and Bioinformatics
JF  - Computational Biology and Bioinformatics
JO  - Computational Biology and Bioinformatics
SP  - 7
EP  - 12
PB  - Science Publishing Group
SN  - 2330-8281
UR  - https://doi.org/10.11648/j.cbb.20140201.12
AB  - Many toxic molds synthesize and release an array of poisons, termed mycotoxins that have an enormous impact on human health, agriculture and economy [1]. These molds contaminate our buildings, indoor air and crops, cause life threatening human and animal diseases and reduce agricultural output [2]. In order to design appropriate approaches to minimize the detrimental effects of these fungi, it is essential to develop diagnostic methodologies that can rapidly and accurately determine based on fungal strains and their growth patterns, the extent of mycotoxin mediated damage caused to the environment.Here we developed a novel multi-scale predictive mathematical model that could reliably estimate aflatoxin synthesis from growth features extracted fromAspergillusparasiticus, a well-characterized model for studying mycotoxin biosynthesis. We conducted acoustic imaging experiments to observe and extract the growth features from the biomass profiles of the growing Aspergillus colony growing on an aflatoxin-inducing solid growth medium. We employed the probability-based representation of uncertainty and used Bayes’ theorem to infer the uncertain parameters in our mathematical model using biomass observations of the colony at 24h (aflatoxin is not synthesized yet at this time-point) and 48 hours (aflatoxin synthesis occurs at peak levels). We demonstrate that our model could successfully predict with quantified uncertainties the levels of aflatoxin secreted to the environment by the fungus.
VL  - 2
IS  - 1
ER  -

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Author Information

Sourav Banerjee

Dept. of Mechanical Engineering, University of South Carolina, Columbia, South Carolina, USA
Gabriel Terejanu

Dept. of Computer Science and Engineering, University of South Carolina, Columbia, South Carolina, USA
Anindya Chanda

Dept. of Environmental Health Sciences, University of South Carolina, Columbia, South Carolina, USA

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Plain Text BibTeX RIS

APA Style

Sourav Banerjee, Gabriel Terejanu, Anindya Chanda. (2014). Uncertainty Quantification Driven Predictive Multi-Scale Model for Synthesis of Mycotoxins. Computational Biology and Bioinformatics, 2(1), 7-12. https://doi.org/10.11648/j.cbb.20140201.12

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ACS Style

Sourav Banerjee; Gabriel Terejanu; Anindya Chanda. Uncertainty Quantification Driven Predictive Multi-Scale Model for Synthesis of Mycotoxins. Comput. Biol. Bioinform. 2014, 2(1), 7-12. doi: 10.11648/j.cbb.20140201.12

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AMA Style

Sourav Banerjee, Gabriel Terejanu, Anindya Chanda. Uncertainty Quantification Driven Predictive Multi-Scale Model for Synthesis of Mycotoxins. Comput Biol Bioinform. 2014;2(1):7-12. doi: 10.11648/j.cbb.20140201.12

Copy | Download

@article{10.11648/j.cbb.20140201.12,
  author = {Sourav Banerjee and Gabriel Terejanu and Anindya Chanda},
  title = {Uncertainty Quantification Driven Predictive Multi-Scale Model for Synthesis of Mycotoxins},
  journal = {Computational Biology and Bioinformatics},
  volume = {2},
  number = {1},
  pages = {7-12},
  doi = {10.11648/j.cbb.20140201.12},
  url = {https://doi.org/10.11648/j.cbb.20140201.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20140201.12},
  abstract = {Many toxic molds synthesize and release an array of poisons, termed mycotoxins that have an enormous impact on human health, agriculture and economy [1]. These molds contaminate our buildings, indoor air and crops, cause life threatening human and animal diseases and reduce agricultural output [2]. In order to design appropriate approaches to minimize the detrimental effects of these fungi, it is essential to develop diagnostic methodologies that can rapidly and accurately determine based on fungal strains and their growth patterns, the extent of mycotoxin mediated damage caused to the environment.Here we developed a novel multi-scale predictive mathematical model that could reliably estimate aflatoxin synthesis from growth features extracted fromAspergillusparasiticus, a well-characterized model for studying mycotoxin biosynthesis. We conducted acoustic imaging experiments to observe and extract the growth features from the biomass profiles of the growing Aspergillus colony growing on an aflatoxin-inducing solid growth medium. We employed the probability-based representation of uncertainty and used Bayes’ theorem to infer the uncertain parameters in our mathematical model using biomass observations of the colony at 24h (aflatoxin is not synthesized yet at this time-point) and 48 hours (aflatoxin synthesis occurs at peak levels). We demonstrate that our model could successfully predict with quantified uncertainties the levels of aflatoxin secreted to the environment by the fungus.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Uncertainty Quantification Driven Predictive Multi-Scale Model for Synthesis of Mycotoxins
AU  - Sourav Banerjee
AU  - Gabriel Terejanu
AU  - Anindya Chanda
Y1  - 2014/01/30
PY  - 2014
N1  - https://doi.org/10.11648/j.cbb.20140201.12
DO  - 10.11648/j.cbb.20140201.12
T2  - Computational Biology and Bioinformatics
JF  - Computational Biology and Bioinformatics
JO  - Computational Biology and Bioinformatics
SP  - 7
EP  - 12
PB  - Science Publishing Group
SN  - 2330-8281
UR  - https://doi.org/10.11648/j.cbb.20140201.12
AB  - Many toxic molds synthesize and release an array of poisons, termed mycotoxins that have an enormous impact on human health, agriculture and economy [1]. These molds contaminate our buildings, indoor air and crops, cause life threatening human and animal diseases and reduce agricultural output [2]. In order to design appropriate approaches to minimize the detrimental effects of these fungi, it is essential to develop diagnostic methodologies that can rapidly and accurately determine based on fungal strains and their growth patterns, the extent of mycotoxin mediated damage caused to the environment.Here we developed a novel multi-scale predictive mathematical model that could reliably estimate aflatoxin synthesis from growth features extracted fromAspergillusparasiticus, a well-characterized model for studying mycotoxin biosynthesis. We conducted acoustic imaging experiments to observe and extract the growth features from the biomass profiles of the growing Aspergillus colony growing on an aflatoxin-inducing solid growth medium. We employed the probability-based representation of uncertainty and used Bayes’ theorem to infer the uncertain parameters in our mathematical model using biomass observations of the colony at 24h (aflatoxin is not synthesized yet at this time-point) and 48 hours (aflatoxin synthesis occurs at peak levels). We demonstrate that our model could successfully predict with quantified uncertainties the levels of aflatoxin secreted to the environment by the fungus.
VL  - 2
IS  - 1
ER  -

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