Computational Biology and Bioinformatics

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

Received: 03 January 2014    Accepted:     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.

DOI 10.11648/j.cbb.20140201.12
Published in Computational Biology and Bioinformatics (Volume 2, Issue 1, February 2014)
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), 2024. Published by Science Publishing Group

Keywords

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

References
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[2] CAST, C.f.A.S.a.T., Mycotoxins: risks in plant, animal, and human systems. 2003, Council for Agricultural Science and Technology: Ames,Iowa.
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[4] Glass, N.L., Rasmussen, C., Roca, M. G., Read, N. D., Hyphal homing, fusion and mycelial interconnectedness. Trends in Microbiology, 2004. 12(3): p. 135-141.
[5] Olsson, S., Uptake of Glucose and Phosphorus by Growing Colonies of Fusarium oxysporum as Quantified by Image Analysis. Experimental Mycology, 1994. 18(1): p. 33-47.
[6] Davidson, F.A., Modelling the qualitative response of fungal mycelia to heterogeneous environments. Journal of Theoratical Biology, 1998. 195: p. 281-292.
[7] Jacobs, H., et al., Solubilization of metal phosphates by Rhizoctonia solani. Mycological Research, 2002. 106(12): p. 1468-1479.
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[20] Webster, J., Weber, R. , Introduction to Fungi. 2007: New York: Cambridge University Press.
[21] Lemons, R.A., Quate, C. F., , Acoustic Microscopy: Biomedical Applications. Science, 1972. 188(4191): p. 905-911.
[22] Hildebrand, J.A., Rugar, D., , Measurement of cllular elastic property by acoustic microscopy J. Micros., 1984. 134: p. 245-260.
[23] Kundu, T., Bereiter-Hahn, J. and Karl, I.,, Cell Property Determination from the Acoustic Microscope Generated Voltage Versus Frequency Curves. Biophysical Journal, 2000. 78: p. 2270-2279.
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Author Information
  • Dept. of Mechanical Engineering, University of South Carolina, Columbia, South Carolina, USA

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

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

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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://download.sciencepg.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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