The use of computer software in the fields of engineering, technology and management has become inevitable these days. Engineering problems are tedious and time consuming to solve manually due to higher complexity. In this perspective mathematical bioscience is an emerging field that involves formulation of biological concepts in terms of equations and application of computers to solve them. Though the biological systems are very complex and beautifully constructed they obey the rules of chemistry and physics that make them susceptible to engineering analysis. This forms the basis for bioprocess modeling optimization and simulation which can be accomplished using software. In the microbial world, varieties of species are available and both in natural systems and commercial applications mixed culture operations play a vital role. In such case interaction among them decides the output of the system and five patterns of interactions (namely neutralism, amensalism, competition, commensalism, mutualism, predation and parasitism) are observed so far. In the present work an innovative and unified approach is developed to characterize these patterns of interactions among microorganisms for two species interaction. The models for pure and mixed culture growth were derived from experimental data in batch mode using CFTOOL kit in MATLAB 7.1. The differential equations were solved using ODE SOLVER in MATLAB 7.1 and the simulation studies for continuous operations were carried out using C++ software. The simulated results and their interpretations are obtained using surface plots drawn using MINITAB software.

1. Atlas, R. M., and Bartha, R. 2005. Microbial Ecology, Pearson Education, New Delhi.
2. Aziza, M., Couriol, C., Amrane, A. and Boutrou, R. 2005. Evidences for synergistic effects of Geotrichum candidum on Penicillium camembertii growing on cheese juice. Enzyme and Microbial Technology, 37, 218-224.
3. Baltzis B. C. and Fredrickson, A. G. 1984. Competition of two suspension-feeding protozen populations for a growing bacterial population. Microbial Ecology.
4. Bailey, E. James and David F. Ollis, 1986. Biochemical Engineering Fundamentals. McGraw-Hill, New Delhi.
5. Catherine Chapuis and Flandrois, J. P. 1994. Mathematical model of the interactions between Micrococcus spp. and Pseudomonas aeruginosa on agar surface. Journal of Applied Bacteriology. 77, 727-732.
6. Fredrickson, A. G. and Stephanopoulos, G. 1981. Microbial competition. Science. 213, 972- 979.
7. Gause, G. F., 1935. Experimental demonstration of Volterra’s periodic oscillation in the numbers of animals. Journal of Experimental Biology. 12, 44-48.
8. Moon, J. Nancy and Reinbold, G. W. 1975. Commensalism and Competition in mixed cultures of Lactobacillus bulgaricus and Streptococcus thermophilus. Journal of Milk Food Tech. 39, 337-341.
9. Oberhofer, T. H. and Frazier, W. C. 1961.Competition of Staphylococcus aureus with other organisms. Journal of Milk Food Tech. 24, 172-175.
10. Picon, A. and Nuñez, M. 2007. Growth stimulation of a proteinase positive Lactococcus lactis strain by a proteinase negative Lactococcus lactis strain. Int. Journal of Food Microbiology. 119, 308-313.
11. Pommier, S., Strehaiano, M. and Delia, M.L. 2005. Modelling the growth dynamics of interacting mixed cultures: a case of amensalism. Int Journal of Food Microbiology. 100, 131-139.
12. Taillandier P., Martine Gilis and Pierre Strehaiano. 1995. Deacidification by Schizosaccharomyces: interactions with Saccharomyces. Journal of Biotechnology. 40,199-205.
13. Sivaprakash, B., Karunanithi ,T.and Jayalakshmi.S.2011. Application of Software in Mathematical Bioscience for Modelling and Simulation of the Behaviour of Multiple Interactive Microbial Populations. In Proceedings of the ICTSM 2011 Conference.

Interaction Chemostat Dilution rate cftool ode solver surface plot MATLAB MINITAB