Did you know that there is a mathematical technique for measuring the diameter, area, flow rate (fluxes) and other physical properties of the red blood cell? Even more exciting is that, like the financial models of accounting and economics, metabolic engineers can adopt the concepts of price and cost derivatives for solving the challenges of metabolic systems. In this course, you examine the objective function that defines the flux balance optimization problem, and how it can be used to identify optimal solutions. Study the mathematical formulation of the objective functions from simple examples, which will consider the use of four metabolite fluxes for maximizing ATP (adenosine triphosphate) production. You will see the balanced set of metabolic demands that constitute the growth function of a microbial system. Learn about an important terminology known as ‘the shadow prices’ and its essentialities in optimum solutions interpretation, and decision making. Then, you will be shown how it attempts to address two linear programming questions: To what extent can metabolic fluxes be altered? and What will be the ensuing effects of flux altering on the cellular processes of interest, including growth and product formation? Similarly, identify the definition and applications of reduced cost analysis. Then, a concise review of the shadow prices and reduced costs will be presented.
Next, the module on robustness analysis examines the sensitivity of the optimal properties of a network by using the environmental and genetic parameters for calculating the optimal states. You will consider the four isocline regions of the phenotype phase plane (PhPP) and the effect of the variability of their alpha-values (∞-value) on the objective function. The characteristics of phase planes, including the infeasible and futile regions, line of optimality, and biomass optimization via flux balance analysis (FBA) will be highlighted. Then, the methods for characterizing metabolic solution spaces, and the effects of the constraints imposed on them will be studied via extreme pathway analysis and randomized sampling techniques. The computational methods for designing mutant strains, as well as the problem statements of each model, will be examined. Likewise, the four stepwise systems for strain design (also known as optstrain) will be presented. You will discern the development of the networked-based pathway paradigm by considering how advancements in biochemical technologies have positively impacted metabolic stoichiometry and genome annotation. The module on linear basis for null space will explain the dimensions, stoichiometric matrix and steady-state flux solution properties of the subject. Examine the extreme pathways, as well as their significance in convex analysis, and application in inequalities and linear equation studies. Study the definition of a convex space and the distinctions between the linear and convex spaces.
Furthermore, the distinguishing features of convex analysis and cellular biology, as well as the three types of extreme pathways, are outlined. Learn about the elementary modes, the convex solution, and exchange cores, and examine their relationship with the flux distribution and system boundaries of a metabolic network. The 13C technique used for quantifying the intracellular fluxes during metabolic flux analysis (MFA) is discussed. Likewise, the crucial steps in 13C MFA formulation and experimentation, as well as the challenges of 13C-assisted MFA, are presented. The concepts of chemical derivatization, 13C fingerprinting, as well as important case studies of the applications of MFA will also be discussed. What does sampling a space mean? How can the objective function be chosen? These questions will be addressed in this course. If you are seeking a career path in computational biology, metabolic engineering, or related fields, then you will find this course rewarding. Your application of the knowledge acquired in this subject could lead to improvements in the development of natural metabolites and products for the pharmaceutical, bioenergy, biochemical or biotechnological industries. So why wait? Start today! Start Course Now
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