DOI : https://doi.org/10.18517/ijaseit.11.1.10872

The Linear Model of Saccharomyces cerevisiae Turbidity in Liquid Media

Ahmad Syauqi (1), Hari Santoso (2), Siti Nurul Hasana (3)
(1) Department of Biology, Mathematics and Natural Sciences Faculty, Islamic University of Malang, Malang, East Java 65144, Indonesia b
(2) Department of Biology, Mathematics and Natural Sciences Faculty, Islamic University of Malang, Malang, East Java 65144, Indonesia b
(3) Departement of Mathematical Education, Teacher’s Training and Education Faculty, Islamic University of Malang, Malang, East Java
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How to cite (IJASEIT) :
Syauqi, Ahmad, et al. “The Linear Model of Saccharomyces Cerevisiae Turbidity in Liquid Media”. International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 1, Feb. 2021, pp. 312-9, doi:10.18517/ijaseit.11.1.10872.
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The study aims to investigate the relationship between the turbidity and density or the total suspended inorganic particles have been obtained many models. The live cells as the homogeneous particle are presumed to cause turbid in liquid media, and that has a linear relationship that can be utilized on the cell counting. The method for the term of clouded liquid form is the measurement based on the reflection and scattered of light, i.e., the turbidimetry. Knowledge attainment of microbial cell counting should be answered how many Nephelometric Turbidity Unit of the one cell. We work to obtain a turbidity model of cells in water-based media for the estimation of cell numbers. This paper aims to construct the computational structure on the turbidity modeling of Saccharomyces cerevisiae in pure water and to test a consistent model in liquid nutrients medium. The modeling was performed in systematized stages of the diagnostic-analysis-test; the regression assurances, the simulation of the lowest error, and the coefficient value itself of turbidity factors. We constructed an optimal analysis and diagnosis to create a computational structure of cell turbidity modeling. The measurement and stopping bivariate elimination of the simulation is a subsystem of the algorithm of obtaining and testing models. The first mathematics model is a standard curve on turbidimetry, and the second, turbidity mathematics model of cell growth in liquid nutrients medium. Both models have an equal coefficient of cell turbidity. The turbidity coefficient of cell growth time interval in the carbonyl diamide - potato dextrose broth is significant.

D. Weintrop et al., “Defining Computational Thinking for Mathematics and Science Classrooms,” J. Sci. Educ. Technol., vol. 25, no. 1, pp. 127-147, Feb. 2016, doi: 10.1007/s10956-015-9581-5.

A. V. Aho, “Computation and Computational Thinking,” Comput. J., vol. 55, no. 7, pp. 832-835, Jul. 2012, doi: 10.1093/comjnl/bxs074.

I. D. Mienye, Y. Sun, and Z. Wang, “Prediction performance of improved decision tree-based algorithms: a review,” Procedia Manuf., vol. 35, pp. 698-703, 2019, doi: 10.1016/j.promfg.2019.06.011.

M. Nickaeen et al., “A free-boundary model of a motile cell explains turning behavior,” PLOS Comput. Biol., vol. 13, no. 11, p. e1005862, Nov. 2017, doi: 10.1371/journal.pcbi.1005862.

S. Waldherr, “Estimation methods for heterogeneous cell population models in systems biology,” J. R. Soc. Interface, vol. 15, no. 147, p. 20180530, Oct. 2018, doi: 10.1098/rsif.2018.0530.

Q. Ye and M. H. MacGregor, “Using simulation to test formally verified protocols in complex environments,” Math. Comput. Model., vol. 53, no. 3-4, pp. 538-551, Feb. 2011, doi: 10.1016/j.mcm.2010.03.039.

Z. Zukhri, Genetic Algorithm of Evolutionary Computational Method for Optimize Problem Solving, I. Yogyakarta: ANDI, 2014.

A. Syauqi, H. Santoso, and S. N. Hasana, “Measurement of Action Result of Eliminating Bivariate based Cook’s D: Cell Concentration and Turbidity,” in Proceeding of The 9th International Conference on Green Technology 2018, 2018, pp. 1-5, [Online]. Available: http://conferences.uin-malang.ac.id/index.php/ICGT/article/view/841/338.

J. Thomann and G. Eichfelder, “Numerical results for the multiobjective trust region algorithm MHT,” Data Br., vol. 25, p. 104103, Aug. 2019, doi: 10.1016/j.dib.2019.104103.

O. K. Oyebamiji et al., “Gaussian process emulation of an individual-based model simulation of microbial communities,” J. Comput. Sci., vol. 22, pp. 69-84, Sep. 2017, doi: 10.1016/j.jocs.2017.08.006.

D. Liao and T. D. Tlsty, “Evolutionary game theory for physical and biological scientists. II. Population dynamics equations can be associated with interpretations,” Interface Focus, vol. 4, no. 4, p. 20140038, Aug. 2014, doi: 10.1098/rsfs.2014.0038.

A. Syauqi, “Cell Quantity Determining of Saccharomyces cerevisiae by Turbidimetry,” J Biosaintropis, vol. 2, no. 2, pp. 1-9, 2017, [Online]. Available: http://biosaintropis.unisma.ac.id/index.php/biosaintropis/article/view/84/38.

A. Zapata and S. Ramirez-Arcos, “A Comparative Study of McFarland Turbidity Standards and the Densimat Photometer to Determine Bacterial Cell Density,” Curr. Microbiol., vol. 70, no. 6, pp. 907-909, Jun. 2015, doi: 10.1007/s00284-015-0801-2.

G. E. Lozano, S. R. Beatriz, F. M. Cervantes, G. N. P. Marí­a, and J. M. C. Francisco, “Low accuracy of the McFarland method for estimation of bacterial populations,” African J. Microbiol. Res., vol. 12, no. 31, pp. 736-740, Aug. 2018, doi: 10.5897/AJMR2018.8893.

B. Vestergaard, “Static and Dynamic Light Scattering for Biological Macromolecules in Solution,” Suwon Korea, 2016. [Online]. Available: https://www.embl-hamburg.de/biosaxs/courses/embo2016skku/slides/vestergaard-light-scattering.pdf.

J. Mauer, M. Peltomí¤ki, S. Poblete, G. Gompper, and D. A. Fedosov, “Static and dynamic light scattering by red blood cells: A numerical study,” PLoS One, vol. 12, no. 5, p. e0176799, May 2017, doi: 10.1371/journal.pone.0176799.

L. Z. H. Daphne, Low Hui Xiang;Utomo, Handojo Djati; Kenneth, “Correlation between Turbidity and Total Suspended Solids in Singapore Rivers,” J. Water Sustain., vol. 1, no. 3, pp. 313-322, 2011, [Online]. Available: http://www.jwsponline.com/uploadpic/Magazine/313-322.pdf.

T. Q. W. Real, “Establishing A Relationship between Turbidity And Total Suspended Solids - A Student Project.,” 2011.

A. Hannouche, G. Chebbo, G. Ruban, B. Tassin, B. J. Lemaire, and C. Joannis, “Relationship between turbidity and total suspended solids concentration within a combined sewer system,” Water Sci. Technol., vol. 64, no. 12, pp. 2445-2452, Dec. 2011, doi: 10.2166/wst.2011.779.

T. Wriedt, “Mie Theory: A Review,” in The Mie Theory, T. Hergert, W; Wriedt, Ed. Springer, 2012, pp. 53-71.

A. Syauqi, H. Santoso, and S. N. Hasana, “Turbidity of Saccharomyces cerevisiae : a proposed cell quantification method,” IOP Conf. Ser. Earth Environ. Sci., vol. 456, no. 1, p. 012031, Apr. 2020, doi: 10.1088/1755-1315/456/1/012031.

A. A. Duina, M. E. Miller, and J. B. Keeney, “Budding Yeast for Budding Geneticists: A Primer on the Saccharomyces cerevisiae Model System,” Genetics, vol. 197, no. 1, pp. 33-48, May 2014, doi: 10.1534/genetics.114.163188.

E. V. Soares, “Flocculation in Saccharomyces cerevisiae: a review,” J. Appl. Microbiol., vol. 110, no. 1, pp. 1-18, Jan. 2011, doi: 10.1111/j.1365-2672.2010.04897.x.

A. Syauqi, H. Santoso, and S. N. Hasana, “Agregate Dispersion of suspended cell of Saccharomyces cerevisiae in water by chemical-physical effect,” in National Seminar Proceeding of Biology 2018-Biodiversity: Learning, Research, and Application on Environmental Management, 2018, pp. 41-47.

R. K. Sembiring, Analisis Regresi, 3rd ed. Bandung: ITB, 2003.

U. of California-Davis, “Excel 2007: Multiple Regressions,” Web Page, 2009. http://cameron.econ.ucdavis.edu/excel/ex61multipleregression.html.

A. Majid, M. Aslam, and S. Altaf, “Efficient estimation of distributed lag model in presence of heteroscedasticity of unknown form: A Monte Carlo evidence,” Cogent Math. Stat., vol. 5, no. 1, Oct. 2018, doi: 10.1080/25742558.2018.1538596.

A. Bin Omar and M. Bin MatJafri, “Turbidimeter Design and Analysis: A Review on Optical Fiber Sensors for the Measurement of Water Turbidity,” Sensors, vol. 9, no. 10, pp. 8311-8335, Oct. 2009, doi: 10.3390/s91008311.

I. J. Georgescu, Ramona;Khismatullin, Damir;Holt, R. Glynn;Castagner, Jean Luc;A’amar, Ousama;Bigio, “Design of a system to measure light scattering from individual cells excited by an acoustic wave,” J. Opt. EXPRESS, vol. 16, no. 6, 2008, [Online]. Available: https://www.bu.edu/bmo/files/2011/01/Georgescu-2008.pdf.

C. Trihendradi, Enlighten Regression the Accurate Strategy for Causal Relation Analysis, I. Yogyakarta: ANDI, 2007.

R. Al-Ali, N. Kathiresan, M. El Anbari, E. R. Schendel, and T. A. Zaid, “Workflow optimisation of performance and quality of service for bioinformatics application in high performance computing,” J. Comput. Sci., vol. 15, pp. 3-10, Jul. 2016, doi: 10.1016/j.jocs.2016.03.005.

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