Materials, Methods & Technologies ISSN 1314-7269, Volume 9, 2015 Journal of International Scientific Publications www.scientific-publications.net INTERCRITERIA ANALYSIS FOR IDENTIFICATION OF ESCHERICHIA COLI FED- BATCH MATHEMATICAL MODEL Tatiana S. Ilkova, Mitko M. Petrov Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 105 Acad. George Bonchev Str., Sofia 1113, Bulgaria Abstract Escherichia coli produces insulin, interferons, growth factors, exo- and endo- products, e.g. enzymes, etc. In this paper we present a new approach for multicriteria decision making – InterCriteria Analysis to mathematical modelling of a fermentation process. It is based on the apparatus of index matrices and intuitionistic fuzzy sets. The approach for multicriteria analysis makes it possible to compare certain criteria or estimated by them objects. In this paper we apply the ideas in an E. coli fed-batch laboratory process. We explore the basic dependencies between different criteria in fed- batch fermentation – biomass, substrate, oxygen and carbon dioxide. As a result, we develop structural and parametric identification of the process. Key words: intercriteria analysis, e. coli, fed-batch process, mathematical modelling 1. INTRODUCTION Cultivation of recombinant micro-organisms, e.g., Escherichia coli, in many cases is the only economical way to produce pharmaceutical biochemical's such as interleukins, insulin, interferon's, enzymes and growth factors. Simple bacteria like E. coli are manipulated to produce these chemicals so that they are easily harvested in vast quantities for use in medicine. E. coli is still the most important host organism for recombinant protein production. Scientists probably know more about E. coli than they do about any other species on Earth. Research on E. coli accelerated even more after 1997, when its entire genome was published. Scientists were able to survey all 4,288 of its genes and to discover how groups of them worked together to break down food, make new copies of DNA and do other tasks. But, despite decades of research, there is a lot more we need to know about E. coli, and in order to research it further, E. coli experts have been joining forces. In 2002, they formed the International E-coli Alliance to organize projects that many laboratories could carry out together. As knowledge of E. coli keeps growing, scientists are starting to build models of the microbe that capture certain aspects of its behaviour. It is important to be able to predict how fast the microbe will grow on various sources of food, as well as how its growth changes if individual genes are knocked out. Here is the place of mathematical modelling (Roeva et al. 2012). Bioprocesses such as Escherichia coli cultivation have advanced tremendously in recent years. Due to their multidisciplinarity, they have attracted significant interest from microbiologists, biochemists, molecular biologists, bioengineers, chemical engineers, food and pharmaceutical chemists, etc. (Pencheva et al. 2006). Cultivation processes are characterized by a complicated structure of organization and independent characteristics which determine their nonlinearity and non-stationary. The model formulation for a bioprocess is traditionally performed under conditions of a well-defined medium with single-substrate limitations, conditions that are not applied to most industrial cultivations, typically running in a complex medium. In many cases, the globally valid conventional numeric models which describe the overall process behaviour cannot be used in on-line monitoring and control, either because they do not describe the process well enough or contain too many poorly known parameters (Roeva et al. 2007). Atanassov et al. (2014) introduced a new approach for multicriteria decision making, namely InterCriteria Analysis for decision making. It is based on the apparatus of index matrices (IMs) (Atanassov 1987, 2010a, 2010b) and intuitionistic fuzzy sets (IFs) (Atanassov 2012, Atanassov et al. 2013) and can be applied for decision making in different areas of science and practice. The new approach for multicriteria decision making makes it possible to compare certain criteria or objects Page 598