1996
DOI: 10.1016/0098-1354(96)00022-1
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Design of process-compatible biological agents

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Cited by 10 publications
(4 citation statements)
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“…This optimization task involves both discrete 0-1 variables (e.g., whether a gene is targeted for deletion or modulation of expression) as well as continuous variables (e.g., metabolic fluxes and concentrations). The mathematical formulation of this task gives rise to mixed-integer nonlinear programming (MINLP) problems with a nonconvex continuous part (42,43). Although there exist algorithms (44) for globally solving this class of problems, the solution procedure is highly dependent on the type of nonlinearities and typically cannot handle problems of the targeted size and complexity.…”
Section: Mathematical Descriptionmentioning
confidence: 99%
“…This optimization task involves both discrete 0-1 variables (e.g., whether a gene is targeted for deletion or modulation of expression) as well as continuous variables (e.g., metabolic fluxes and concentrations). The mathematical formulation of this task gives rise to mixed-integer nonlinear programming (MINLP) problems with a nonconvex continuous part (42,43). Although there exist algorithms (44) for globally solving this class of problems, the solution procedure is highly dependent on the type of nonlinearities and typically cannot handle problems of the targeted size and complexity.…”
Section: Mathematical Descriptionmentioning
confidence: 99%
“…This representation leads to the simultaneous determination of the optimal structure of a network and its optimum operating parameters. Thus MINLPs find applieations in engineering design sueh as heat exehanger network synthesis, reaetor-separator-reeycle network synthesis or pump network synthesis (Floudas, 1995;Grossmann, 1996), in metabolie pathway engineering (Hatzimanikatis et al, 1996a,b;Dean and Dervakos, 1996), or in moleeular design (Maranas, 1996;Churi and Aehenie, 1996). The solution of MINLPs is not a trivial matter.…”
Section: Introductionmentioning
confidence: 99%
“…Also, most of the current methods deal with the parallel optimization of enzyme and knockout expressions by employing Mixed Integer Non-Linear Programming methods [17] that are unable to solve problems with hundreds of equations, or rely on the approximation of the non-linear dynamic model around a reference state (usually a steady state) and a posteriori use a MILP formulation [19]. The approximation of the non-linear dynamic model around a reference state also enforces the use of reaction and metabolite ranges around the reference state that may exclude valid solutions of interest.…”
Section: A Aims and Overview Of The Approachmentioning
confidence: 99%
“…Therefore, and in spite of the lack of information to build large-scale dynamic models, a few attempts have been made regarding their use in ME applications. In [17] a Mixed Integer Non-Linear Progamming (MINLP) method for finding optimal modulation strategies was developed. The main limitations of this method are computational tractability [18].…”
Section: Introductionmentioning
confidence: 99%