Outer approximation-based algorithm for biotechnology studies in systems biology

TL;DR: The approach is based on reformulating the set of ordinary differential equations into an equivalent set of algebraic equations through the use of orthogonal collocation methods, giving rise to a nonconvex nonlinear programming (NLP) problem.

TL;DR: This methodology can be used with a special class of nonlinear kinetic models known as GMA models and it allows for a systematic characterization of the physiological requirements that may underlie the evolution of adaptive strategies.

TL;DR: The identification of the enzymatic profile that achieves a maximal production rate of a given metabolite is an important problem in the biotechnological industry, especially if there is a limit on the production rate.

TL;DR: This work proposes a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values.

TL;DR: Application of recombinant DNA methods to restructure metabolic networks can improve production of metabolite and protein products by altering pathway distributions and rates.

TL;DR: This work presents a graphical representation of biochemical systems, a sequence of models describing purine metabolism, and a model of the tricarboxylic acid cycle in Dictyostelium discoideum, which shows the importance of knowing the initial steps of the Glycolytic-Glycogenolytic pathway.

TL;DR: A power-law approximation technique based on the non-linear nature of these reactions is presented, which is considerably greater than in the linear case, while the effort necessary to obtain steady-state solutions is about the same.

TL;DR: This paper presents an overview of the research progress in deterministic global optimization during the last decade (1998-2008).