Table of contentsApplication of mathematics in biotechnologyMathematics used in biotechnology, based on the physical way: metabolic network models and flow balance analysis (FBA )Reverse engineering of genetic regulatory networks (GRN) )Dynamic models based on a continuous ordinary differential equationSingle cell models and stochastic simulationsQualitative models: fuzzy logic and Petri netsConclusionReferences:Biotechnology is the use of living organisms for the well-being of humanity. Biotechnology refers to the understanding of cell metabolism and also considers the characteristics of individual biomolecules and their role in interaction networks. Mathematical modeling has become an important element in understanding the complexity of biology. Mathematical fields such as calculus, statistics, algebra, and various types of equations are now used in the field of biotechnology. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get the original essayMathematics plays a key role in many scientific disciplines, as a tool for mathematical modeling. Mathematical models tell us about past performance and predict future performance of biotechnology processes. Mathematics provides logic rather than faith and helps with quantification. Without mathematics, biology would never have been a modern science and biotechnology would never have taken the first step. Mathematics is used to perform routine laboratory activities like cloning, to run a gel or PCR, or to operate HPLC. We also want to expand the recombinant product or do genomic analysis of an isolated gene or to understand a reaction we must need a lot of mathematics for calculations or estimations. There is a mathematical field known as "biostatistics and probability" which has applications in the field of biotechnology. Basic statistics is also involved in topics such as genetics, bioinformatics, and research methodology. There are now many mathematical fields in biology, such as biomathematics. Application of Mathematics in Biotechnology Mathematics plays an important role in the field of biotechnology. Mathematics is a strong indicator of success in the field of biotechnology, in the industrial or academic field. The entire world of science and technology speaks, at some level, in the language of mathematics. Mathematics most certainly refers to areas of biotechnology like bioinformatics, biochemical engineering, systems biology, biostatistics, instrumentation, etc. Mathematics is also very useful for a deeper understanding of biotechnology itself. Because there are a lot of connections between each field, like a layer below biology is chemistry, after chemistry there is physics and to understand physics you have to understand mathematical concepts. There is a use of “TECHNOLOGY” in biotechnology which directly shows that there is a use of mathematics, physics, chemistry and everything else: Mathematics used in biotechnology, based on the physical way: To find the exact amount of DNA, mathematics is used for calculation. To calculate the composition of any culture medium. Find the morality, molality and normality of the solution. In industrial companies,mathematics is used a lot to estimate the percentage and pH of any solution. Mathematics plays a big role in bioinformatics, by matching or removing the arrangements of DNA during the process, biostatics is used regarding mathematics, as in finding old data from any research we find average, median. Mathematical modeling is most preferable in the field of biotechnology. Mathematical modeling is becoming an important tool, not only for the theories most needed in biology, but also for the application of knowledge gained about the genetic and molecular basis of life. There are the following models that play an important role in the field of biotechnology: Metabolic network models and flow balance analysis (FBA). Reverse engineering of genetic regulation Networks (GRN). Dynamic models based on differential equations ordinary continuous. Single-cell models and stochastic simulations. Qualitative models; fuzzy and Petri nets.Metabolic network models and flow balance analysis (FBA) Stoichiometric analysis and flow balance analysis (FBA) are the tools for modeling interaction networks. These models emerge as the most powerful tools that combine exterior cellular processes such as absorption, production rates, growth rate, yields, etc. with the distribution of internal cellular carbon and energy flow. FBA and stoichiometric models were used to calculate genomic scale. Dynamic flow balance analysis first proposed by Doyle and colleagues uses information about extracellular concentration to calculate maximum yield. Limitations of FBA include loss of dynamic metabolic information, inability of transient dynamic model, etc. FBA simulations have also been used to illuminate the concept of underlying biology. Flux distributions estimated by FBA are calculated by solving the mass balance equations at a constant rate. Reverse engineering gene regulatory networks (GRN) BAF is successful in many ways, but its power is limited because it does not include the regulation of gene expression or protein activity. At the cellular level, the activity of enzymes and other proteins is slightly regulated. A powerful use of gene regulatory networks lies in the combination with FBA. Covert and Palsson demonstrated the effects of gene regulation in the central metabolism of E. coli. In this study, gene regulation was represented as a logical Boolean network using the logical operators AND OR NOT. These include linear weight modeling, linear and nonlinear ordinary differential equations, etc. In the Boolean approach, genes are assumed to be turned on or off and the input-output relationships between them are expressed by logical functions (such as AND, OR, NOT, etc.). Dynamic models based on continuous ordinary differential equations Continuous dynamic models have become famous tools for modeling the temporal evolution of complex networks of protein-protein and protein-DNA interactions. The most common formulations like mass action which considers the reaction rate is proportional to the product of the reactants and Michaelis-Menten. The stoichiometric matrix and rate formulations are combined to form a network of ordinary differential equations (ODE), which describes the evolution of each species in the network. These systems are nonlinear and must therefore be solved numerically. There are also software packages produced specifically for modeling.., 71:225