Mathematical optimization of laccase activity of Phlebiopsis flavidoalba

Abstract

Laccase is an enzyme produced by fungi with great market demand in biotechnological, and industrial applications. However, laccase production by fungi under natural conditions is insufficient. Wet lab experiments have found that factors like carbon, nitrogen, and metal ion sources affect laccase secretion. This study focuses on the mathematical optimization of the laccase activity of Phlebiopsis flavidoalba in the presence of the above sources. Woodchips, starch, cellulose, lignin, and glucose were used as carbon sources, NH4Cl, NH4NO3, peptone, urea, and yeast were used as nitrogen sources, and CuSO4, FeSO4, and ZnSO4 were used as metal ion sources. Liquid Potato Dextrose Broth mediums amended with each C, N, or metal ions were incubated separately for 3, 6, 9, 12 and 14 days and laccase activities were determined. The objectives of this study were to optimize the incubation period mathematically, and culture medium composition for the best laccase activity. Graphical analysis was done using Microsoft Excel by drawing scatter plots and trend lines. Linear regression equations were obtained to predict the activity on a given day for a source. Statistical analysis was done by R programming. Carbon and metal ion sources had the highest activity on the 14th day. Generalized linear models (gamma regression) were developed for each source to determine the optimum medium on the 14th day where woodchips, urea, and CuSO4 were found as key components. By the coefficients of the regression model, a regression equation was formed by introducing two dummy variables such that combinations of optimum mediums can be obtained. It was predicted that if the media is amended with CuSO4 and woodchips, it will enhance laccase activity by 43-fold. However, if CuSO4, woodchips, and urea were used, it would reduce the laccase activity by 2-fold. Mathematical optimization could be used in predicting and for effective in-vitro assay designs.

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YSCMR-2022
Proceedings of the
Young Scientists’ Conference on
Multidisciplinary Research - 2022
Virtual International Conference
10th November 2022
“Multidisciplinary Research for Tomorrow’s Challenges”

ISSN 2815-0260
Proceedings of the Young Scientists’ Conference on Multidisciplinary Research-2022
Young Scientists’ Association, National Institute of Fundamental Studies, Sri Lanka
10th November 2022
50
YSCMR - 2022
Paper ID: CMT-095
Mathematical optimization of laccase activity of Phlebiopsis flavidoalba
H.H.K.D.C. Wickrama1*, B.D.H.N. Dharmasiri2, R.N. Attanayake2, G.S. Wijesiri1
1Department of Mathematics, University of Kelaniya, Kelaniya, Sri Lanka
2Department of Plant and Molecular Biology, University of Kelaniya, Kelaniya, Sri Lanka
*chamodini1996@gmail.com
Laccase is an enzyme produced by fungi with great market demand in biotechnological, and
industrial applications. However, laccase production by fungi under natural conditions is
insufficient. Wet lab experiments have found that factors like carbon, nitrogen, and metal ion
sources affect laccase secretion. This study focuses on the mathematical optimization of the
laccase activity of Phlebiopsis flavidoalba in the presence of the above sources. Woodchips,
starch, cellulose, lignin, and glucose were used as carbon sources, NH4Cl, NH4NO3, peptone,
urea, and yeast were used as nitrogen sources, and CuSO4, FeSO4, and ZnSO4 were used as
metal ion sources. Liquid Potato Dextrose Broth mediums amended with each C, N, or metal
ions were incubated separately for 3, 6, 9, 12 and 14 days and laccase activities were
determined. The objectives of this study were to optimize the incubation period
mathematically, and culture medium composition for the best laccase activity. Graphical
analysis was done using Microsoft Excel by drawing scatter plots and trend lines. Linear
regression equations were obtained to predict the activity on a given day for a source. Statistical
analysis was done by R programming. Carbon and metal ion sources had the highest activity
on the 14th day. Generalized linear models (gamma regression) were developed for each source
to determine the optimum medium on the 14th day where woodchips, urea, and CuSO4 were
found as key components. By the coefficients of the regression model, a regression equation
was formed by introducing two dummy variables such that combinations of optimum mediums
can be obtained. It was predicted that if the media is amended with CuSO4 and woodchips, it
will enhance laccase activity by 43-fold. However, if CuSO4, woodchips, and urea were used,
it would reduce the laccase activity by 2-fold. Mathematical optimization could be used in
predicting and for effective in-vitro assay designs.
Keywords: Gamma regression, laccase activity, linear regression