An Improved Geometric Programming Approach for Optimization of Biochemical Systems
This paper proposes an improved geometric programming approach to address the optimization of biochemical systems. In the proposed method we take advantage of a special and interesting class of nonlinear kinetic models known as generalized mass action (GMA) models. In most situations optimization pr...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/719496 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849405794516205568 |
|---|---|
| author | Gongxian Xu Lei Wang |
| author_facet | Gongxian Xu Lei Wang |
| author_sort | Gongxian Xu |
| collection | DOAJ |
| description | This paper proposes an improved geometric programming approach to address the optimization of biochemical systems. In the proposed method we take advantage of a special and interesting class of nonlinear kinetic models known as generalized mass
action (GMA) models. In most situations optimization problems with GMA models are nonconvex and difficult problems to solve for global optimality. To deal with this difficulty, in this work, some transformation strategy is first used to convert the optimization problem with GMA models into an equivalent problem. Then a convexification technique is applied to transform this resulting optimization problem into a series of standard geometric programming problems that can be solved to reach a global solution. Two case studies are presented to demonstrate the advantages of the proposed method in terms of computational efficiency. |
| format | Article |
| id | doaj-art-52fd91268db44f5eafe4bbc09d614d9b |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-52fd91268db44f5eafe4bbc09d614d9b2025-08-20T03:36:34ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/719496719496An Improved Geometric Programming Approach for Optimization of Biochemical SystemsGongxian Xu0Lei Wang1Department of Mathematics, Bohai University, Jinzhou 121013, ChinaDepartment of Mathematics, Bohai University, Jinzhou 121013, ChinaThis paper proposes an improved geometric programming approach to address the optimization of biochemical systems. In the proposed method we take advantage of a special and interesting class of nonlinear kinetic models known as generalized mass action (GMA) models. In most situations optimization problems with GMA models are nonconvex and difficult problems to solve for global optimality. To deal with this difficulty, in this work, some transformation strategy is first used to convert the optimization problem with GMA models into an equivalent problem. Then a convexification technique is applied to transform this resulting optimization problem into a series of standard geometric programming problems that can be solved to reach a global solution. Two case studies are presented to demonstrate the advantages of the proposed method in terms of computational efficiency.http://dx.doi.org/10.1155/2014/719496 |
| spellingShingle | Gongxian Xu Lei Wang An Improved Geometric Programming Approach for Optimization of Biochemical Systems Journal of Applied Mathematics |
| title | An Improved Geometric Programming Approach for Optimization of Biochemical Systems |
| title_full | An Improved Geometric Programming Approach for Optimization of Biochemical Systems |
| title_fullStr | An Improved Geometric Programming Approach for Optimization of Biochemical Systems |
| title_full_unstemmed | An Improved Geometric Programming Approach for Optimization of Biochemical Systems |
| title_short | An Improved Geometric Programming Approach for Optimization of Biochemical Systems |
| title_sort | improved geometric programming approach for optimization of biochemical systems |
| url | http://dx.doi.org/10.1155/2014/719496 |
| work_keys_str_mv | AT gongxianxu animprovedgeometricprogrammingapproachforoptimizationofbiochemicalsystems AT leiwang animprovedgeometricprogrammingapproachforoptimizationofbiochemicalsystems AT gongxianxu improvedgeometricprogrammingapproachforoptimizationofbiochemicalsystems AT leiwang improvedgeometricprogrammingapproachforoptimizationofbiochemicalsystems |