IndexApplication of mathematics in biotechnologyMathematics used in biotechnology, based on physical modalities: metabolic network models and flux balance analysis (FBA)Reverse engineering of gene regulatory networks (GRN) )Dynamic models based on continuous ordinary differential equationsUnicellular 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, it also considers the characteristics of individual biomolecules and their work in interaction networks. Mathematical modeling has become an important element in understanding the complexity of biology. Mathematical fields such as calculus, statistics, algebra, 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 Shouldn't Be Banned"? Get an original essay Mathematics plays a key role in many scientific disciplines, as a mathematical modeling tool. Mathematical models describe the past performance and predict the future performance of biotechnological processes. Mathematics provides logic rather than belief and helps in 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 tasks such as cloning or to run a gel or PCR or to operate an HPLC. We also want to zoom in on the recombinant product or do a genomic analysis of an isolated gene or to understand a reaction we need a lot of mathematics for calculations or estimates. There is a field of mathematics known as "Biostatistics and Probability" which has application in the field of biotechnology. Basic statistics is also involved in subjects such as genetics, bioinformatics and research methodology. There are many fields of mathematics now in biology such as biomathematics. Application of Mathematics to Biotechnology Mathematics plays an important role in the field of biotechnology. Mathematics is a strong indicator of success in biotechnology in industry or academia. The entire world of science and technology speaks, at some level, the language of mathematics. Mathematics definitely refers to areas of biotechnology such as bioinformatics, biochemical engineering, systems biology, biostatistics, instrumentation etc. Mathematics is also very useful for a deeper understanding of biotechnology itself. Because there is a lot of connection between each field, like a layer under biology there is chemistry, which after chemistry there is physics and to understand physics you need to understand the concepts of mathematics. There is a use of "TECHNOLOGY" in biotechnology which directly show that there is a use of mathematics, physics, chemistry and everything else: Mathematics used in biotechnology, based on physical methods: To find the exact amount of DNA, there is a use of mathematics for calculation. To calculate the composition of any culture medium. To find the morality, molality and normality of the solution. In industrial companies much use of mathematics is made to estimate the percentage and pH of any solution. There is a great role of mathematics in bioinformatics, matching or eliminating DNA arrangements During the process, biostatics is used than mathematics, such as finding the old data of any research we find mean, median. Mathematical modeling is preferable in the field ofbiotechnology. Mathematical modeling becomes an important tool, not only for the theories that biology most needs, but also for the application of the knowledge acquired on 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 flux balance analysis (FBA). Reverse engineering of gene regulation Networks (GRN). Dynamic models based on continuous ordinary differential equations. Single-cell models and stochastic simulations. Qualitative models; fuzzy and Petri networks. Metabolic network models and flux balance analysis (FBA) Stoichiometric and flux balance analysis (FBA) are the tools for modeling interaction networks. These models emerge as the most powerful tools that combine external cellular processes such as uptake, production rates, growth rate, yields, etc. with the distribution of carbon and energy flow within the cell. FBA and stoichiometric models were used to calculate the genomic scale. The dynamic flow balance analysis first proposed by Doyle and co-workers uses extracellular concentration information to calculate maximum yield. Limitations of FBA include loss of dynamic metabolic information, inability of dynamic transient model, etc. FBA simulations were also used to inform the underlying biology concept. The flow distributions estimated by FBA are calculated by solving the constant rate mass balance equations. Reverse engineering of gene regulatory networks (GRNs) FBA is successful in many ways but has limited power because it does not include regulation of gene expression or protein activity. At the cellular level the activity of enzymes and other proteins is slightly regulated. One powerful use of gene regulatory networks is in combination with FBA. Covert and Palsson demonstrated the effects of genetic 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 ON or OFF, and the input-output relationships between them are expressed through logical functions (such as AND, OR, NOT, etc.).Continuous dynamic models based on ordinary differential equationsContinuous dynamic models have become popular tools for modeling the temporal evolution of complex protein-protein and protein-DNA interaction networks. The most common formulations as mass action considering the reaction rate are proportional to the product of the reactants and Michaelis-Menten. The stoichiometric matrix and velocity formulations combine to form a network of ordinary differential equations (ODE), which describes the evolution of each species in the network. These systems are not linear, so they must be solved numerically. There are also software packages produced specifically for modeling biochemical reaction networks. Single-Cell Models and Stochastic Simulations Deterministic models depend on continuum mathematics and ignore discrete events. However, molecular reactions occur discretely between individual molecules. In SSA we once again have a species system, which can be described by a state vector. These species interact with each other through reaction channels characterized by propensity functions. Since SSA considers the occurrence of every reaction event in the system, the algorithm is inefficient for large systems. Many methods have been developed to improve the., 71:225