Multi-objective mixed integer strategy for the optimisation of biological networks
Multi-objective mixed integer strategy for the optimisation of biological networks
- Author(s): J.O.H. Sendín ; O. Exler ; J.R. Banga
- DOI: 10.1049/iet-syb.2009.0045
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- Author(s): J.O.H. Sendín 1 ; O. Exler 2 ; J.R. Banga 1
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View affiliations
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Affiliations:
1: Process Engineering Group, IIM-CSIC (Spanish National Research Council), Vigo, Spain
2: Department of Computer Science, University of Bayreuth, Bayreuth, Germany
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Affiliations:
1: Process Engineering Group, IIM-CSIC (Spanish National Research Council), Vigo, Spain
- Source:
Volume 4, Issue 3,
May 2010,
p.
236 – 248
DOI: 10.1049/iet-syb.2009.0045 , Print ISSN 1751-8849, Online ISSN 1751-8857
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In this contribution, the authors consider multi-criteria optimisation problems arising from the field of systems biology when both continuous and integer decision variables are involved. Mathematically, they are formulated as mixed-integer non-linear programming problems. The authors present a novel solution strategy based on a global optimisation approach for dealing with this class of problems. Its usefulness and capabilities are illustrated with two metabolic engineering case studies. For these problems, the authors show how the set of optimal solutions (the so-called Pareto front) is successfully and efficiently obtained, providing further insight into the systems under consideration regarding their optimal manipulation.
Inspec keywords: biology computing; complex networks; Pareto optimisation; nonlinear programming
Other keywords:
Subjects: Optimisation techniques; Systems theory applications in biology and medicine; General, theoretical, and mathematical biophysics
References
-
-
1)
- J. Handl , D.B. Kell , J. Knowles . Multiobjective optimization in bioinformatics and computational biology. IEEE/ACM Trans. Comput. Biol. Bioinf. , 2 , 279 - 291
-
2)
- R.M. Alexander . (1982) Optima for animals.
-
3)
- H.J. Greenberg , W.E. Hart , G. Lancia . Opportunities for combinatorial optimization in computational biology. INFORMS J. Comput. , 3 , 211 - 231
-
4)
- L. Regan , I.D.L. Bogle , P. Dunnill . Simulation and optimization of metabolic pathways. Comput. Chem. Eng. , 627 - 637
-
5)
- P. Mendes , D.B. Kell . Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. Bioinformatics , 10 , 869 - 883
-
6)
- J. Koski , R. Silvennoinen . Norm methods and partial weighting in multicriterion optimization of structures. Int. J. Numer. Methods Eng. , 6 , 1101 - 1121
-
7)
- F.G. Vital-Lopez , A. Armaou , E.V. Nikolaev , C.D. Maranas . A computational procedure for optimal engineering intervention using kinetic models of metabolism. Biotechnol. Prog. , 6 , 1507 - 1517
-
8)
- O. Exler , L.T. Antelo , J.A. Egea , A.A. Alonso , J.R. Banga . A Tabu search-based algorithm for mixed-integer nonlinear problems and its application to integrated process and control system design. Comput. Chem. Eng. , 8 , 1877 - 1891
-
9)
- K. Deb . (2001) Multi-objective optimization using evolutionary algorithms.
-
10)
- J.O.H. Sendín , I. Otero-Muras , A.A. Alonso , J.R. Banga . Improved optimization methods for the multiobjective design of bioprocess. Ind. Eng. Chem. Res. , 25 , 8594 - 8603
-
11)
- J.E. Bailey . Toward a science of metabolic engineering. Science , 5013 , 1668 - 1675
-
12)
- W.J. Sutherland . The best solution. Nature , 7042 , 569 - 569
-
13)
- P. Pharkya , C.D. Maranas . An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. Metabolic Eng. , 1 , 1 - 13
-
14)
- N.V. Torres , E.O. Voit , C. González-Alcón . Optimization of nonlinear biotechnological processes with linear programming: application to citric acid production by Aspergillus niger. Biotechnol. Bioeng. , 3 , 247 - 258
-
15)
- K.R. Patil , I. Rocha , J. Förster , J. Nielsen . Evolutionary programming as a platform in silico metabolic engineering. BMC Bioinf.
-
16)
- G. Stephanopoulos , D.E. Stafford . Metabolic engineering: a new frontier of chemical reaction engineering. Chem. Eng. Sci. , 14 , 2595 - 2602
-
17)
- F. Shiraishi , M.A. Savageau . The tricarboxylic acid cycle in Dictyostelium discoideum. Systemic effects of including protein turnover in the current novel. J. Biol. Chem. , 23 , 16917 - 16928
-
18)
- V. Hatzimanikatis , C.A. Floudas , J.E. Bailey . Analysis and design of metabolic reaction networks via mixed-integer linear optimization. AIChE J. , 5 , 1277 - 1290
-
19)
- I. Das , J.E. Dennis . Normal-boundary intersection: a new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM J. Optim. , 3 , 631 - 657
-
20)
- S. Schuster , R. Schuster , R. Heinrich . Minimization of intermediate concentrations as a suggested optimality principle for biochemical networks. II. Time hierarchy, enzymatic rate laws, and erythrocyte metabolism. J. Math. Biol. , 5 , 443 - 455
-
21)
- O. Exler , K. Schittkowski . A trust region SQP algorithm for mixed-integer nonlinear programming. Optim. Lett. , 3 , 269 - 280
-
22)
- A.P. Burgard , P. Pharkya , C.D. Maranas . OptKnock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. , 6 , 647 - 657
-
23)
- J.O.H. Sendin , J. Vera , N.V. Torres , J.R. Banga . Model-based optimization of biochemical systems using multiple objectives: a comparison of several solution strategies. Math. Comput. Model. Dyn. Syst. , 5 , 469 - 487
-
24)
- P. Larranaga , B. Calvo , R. Santana . Machine learning in bioinformatics. Briefings Bioinf. , 1 , 86 - 112
-
25)
- P.M. Schlosser , J.E. Bailey . An integrated modeling-experimental strategy for the analysis of metabolic pathways. Math. Biosci. , 1 , 87 - 114
-
26)
- J.R. Banga . Optimization in computational systems biology. BMC Syst. Biol.
-
27)
- V. Hatzimanikatis , C.A. Floudas , J.E. Bailey . Optimization of regulatory architectures in metabolic reaction networks. Biotechnol. Bioeng. , 4 , 485 - 500
-
28)
- J.P. Dean , G.A. Dervakos . Design of process-compatible biological agents. Comput. Chem. Eng. , S67 - S72
-
29)
- F. Rodríguez-Acosta , C.M. Regalado , N.V. Torres . Non-linear optimization of biotechnological processes by stochastic algorithms. Applications to the maximization of the production rate of ethanol, glycerol and carbohydrates by Saccharomyces cerevisiae. J. Biotechnol. , 1 , 15 - 28
-
30)
- E.O. Voit . Optimization in integrated biochemical systems. Biotechnol. Bioeng. , 5 , 572 - 582
-
31)
- K.M. Miettinen . (1999) Nonlinear multiobjective optimization.
-
32)
- H. Kacser , L. Acerenza . A universal method for achieving increases in metabolite production. Eur. J. Biochem. , 2 , 361 - 367
-
33)
- Shukla, P.K.: `On the normal boundary intersection method for generation of efficient front', Proc. Seventh Int. Conf. on Computational Science, Part I: ICSS 2007, Beijing, China, p. 310–317.
-
34)
- R. Heinrich , S. Schuster . (1996) The regulation of cellular systems.
-
1)
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