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Uncertainty Quantification Driven Predictive Multi-Scale Model for Synthesis of Mycotoxins
Computational Biology and Bioinformatics
Volume 2, Issue 1, February 2014, Pages: 7-12
Received: Jan. 3, 2014; Published: Jan. 30, 2014
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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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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.
Predictive Multi-scale Model, Aflatoxin synthesis, Fungi, Aspergillus, Scanning Acoustic Microscopy, Uncertainty Quantification
To cite this article
Sourav Banerjee, Gabriel Terejanu, Anindya Chanda, Uncertainty Quantification Driven Predictive Multi-Scale Model for Synthesis of Mycotoxins, Computational Biology and Bioinformatics. Vol. 2, No. 1, 2014, pp. 7-12. doi: 10.11648/j.cbb.20140201.12
Ingold, C.T., The biology of fungi. 1973: Hutchins Education Ltd. 54-83.
CAST, C.f.A.S.a.T., Mycotoxins: risks in plant, animal, and human systems. 2003, Council for Agricultural Science and Technology: Ames,Iowa.
Moss, S.T., The Biology of Marine Fungi. 1986, Cambridge, UK: Cambridge University Press.
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.
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.
Davidson, F.A., Modelling the qualitative response of fungal mycelia to heterogeneous environments. Journal of Theoratical Biology, 1998. 195: p. 281-292.
Jacobs, H., et al., Solubilization of metal phosphates by Rhizoctonia solani. Mycological Research, 2002. 106(12): p. 1468-1479.
Boswell, G.P., et al., A mathematical approach to studying fungal mycelia. Mycologist, 2003. 17(4): p. 165-171.
Falconer, R.E., et al., Biomass recycling: a key to efficient foraging by fungal colonies. Oikos, 2007. 116: p. 1558-1568.
Heaton, L.L.M., et al., Growth-induced mass flows in fungal networks. Proceedings of the Royal Society of London, Biological Sciences, 2010. 277: p. 3265-3275.
Lee, L.W., C.H. Chiou, and J.E. Linz, Function of native OmtA in vivo and expression and distribution of this protein in colonies of Aspergillus parasiticus. Applied and Environmental Microbiology, 2002. 68: p. 5718-5727.
Boswell, G.P., et al., Growth and Function of Fungal Mycelia in Heterogeneous Environments. Bulletin of Mathematical Biology, 2003. 65(3): p. 447-477.
Oh, K., et al., Flow sensing in mycelial fungi. Journal of Biotechnology, 1997. 58(3): p. 197-204.
Carlile, M.J., S.C. Watkinson, and G.W. Gooday, The Fungi 1994: Academic Press.
Molin, P., et al., Direction of hyphal growth: a relevant parameter in the development of filamentous fungi. Research in Microbiology, 1992. 143: p. 777-784.
Knudsen, G.R., et al., Individual-based approach to modeling hyphal growth of a biocontrol fungus in soil. Phytopathology, 2006. 96: p. 1108-1115.
Carver, I. and G.P. Boswell, A Lattice-Free Model Of Translocation-Induced Outgrowth In Fungal Mycelia.International Journal of Applied Mathematics, 2008. 38(4): p. 173.
Davidson, F.A., et al., Travelling waves and pattern formation in a model for fungal development. Journal of Mathematical Biology, 1997. 35(5): p. 589-608.
Falconer, R.E., et al., Biomass recycling and the origin of phenotype in fungal mycelia. Fungal Ecology, 2005. 1: p. 57-68.
Webster, J., Weber, R. , Introduction to Fungi. 2007: New York: Cambridge University Press.
Lemons, R.A., Quate, C. F., , Acoustic Microscopy: Biomedical Applications. Science, 1972. 188(4191): p. 905-911.
Hildebrand, J.A., Rugar, D., , Measurement of cllular elastic property by acoustic microscopy J. Micros., 1984. 134: p. 245-260.
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.
Edelstein, L. and L.A. Segel, Growth and metabolism in mycelial fungi. Journal of Theoretical Biology, 1983. 104(2): p. 187-210.
Kennedy, M.C. and A. O'Hagan, Bayesian calibration of computer models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2001. 63(3): p. 425-464.
Eaton, J.W., Bateman, D., Hauberg, S., GNU Octave Manual Version 3.0, ed. N.T. Limited. 2008.
Haario, H., Laine, M., Mira, A., Saksman, E., DRAM: Efficient adaptive MCMC. Statistics and Computing, 2006. 16: p. 339-354.
Gelman, A., Shalizi, C. R., Philosophy and the practice of Bayesian statistics. British Journal of Mathematical and Statistical Psychology, 2013. 66: p. 8-38.
Box, G.E.P., Bayesian Inference in Statistical Analysis. 1973: New York: Wiley Classics.
Hyndman, R.J., Computing and graphing highest density regions. The American Statistician, 1996. 50(2): p. 120-126.
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