Efficient solution of ordinary differential equations with a parametric lexicographic linear program embedded

This work analyzes the initial value problem in ordinary differential equations with a parametric lexicographic linear program (LP) embedded. The LP is said to be embedded since the dynamics depend on the solution of the LP, which is in turn parameterized by the dynamic states. This problem formulation finds application in dynamic flux balance analysis, which serves as a modeling framework for industrial fermentation reactions. It is shown that the problem formulation can be intractable numerically, which arises from the fact that the LP induces an effective domain that may not be open. A numerical method is developed which reformulates the system so that it is defined on an open set. The result is a system of semi-explicit index-one differential algebraic equations, which can be solved with efficient and accurate methods. It is shown that this method addresses many of the issues stemming from the original problem’s intractability. The application of the method to examples of industrial fermentation processes demonstrates its effectiveness and efficiency.

[1]  M. N. Vrahatis,et al.  Ordinary Differential Equations In Theory and Practice , 1997, IEEE Computational Science and Engineering.

[2]  J. Lambert Numerical Methods for Ordinary Differential Systems: The Initial Value Problem , 1991 .

[3]  S. M. Robinson,et al.  A sufficient condition for continuity of optimal sets in mathematical programming , 1974 .

[4]  Linda R. Petzold,et al.  Numerical solution of initial-value problems in differential-algebraic equations , 1996, Classics in applied mathematics.

[5]  Carlos Romero,et al.  GOAL PROGRAMMING AND MULTIPLE CRITERIA DECISION-MAKING IN FARM PLANNING: SOME EXTENSIONS , 1985 .

[6]  P. I. Barton,et al.  Parametric sensitivity functions for hybrid discrete/continuous systems , 1999 .

[7]  P. Saint-Pierre Approximation of slow solutions to differential inclusions , 1990 .

[8]  Xinyuan Wu,et al.  Piecewise Integration of Differential Variational Inequality , 2009 .

[9]  Jong-Shi Pang,et al.  Differential variational inequalities , 2008, Math. Program..

[10]  Paul I. Barton,et al.  State event location in differential-algebraic models , 1996, TOMC.

[11]  Bernhard O. Palsson,et al.  BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions , 2010, BMC Bioinformatics.

[12]  B. Palsson Systems Biology: Properties of Reconstructed Networks , 2006 .

[13]  David G. Luenberger,et al.  Linear and Nonlinear Programming: Second Edition , 2003 .

[14]  Paul I. Barton,et al.  Modeling, simulation, sensitivity analysis, and optimization of hybrid systems , 2002, TOMC.

[15]  Lorenz T. Biegler,et al.  Modeling and simulation of metabolic networks for estimation of biomass accumulation parameters , 2009, Discret. Appl. Math..

[16]  H. Isermann Linear lexicographic optimization , 1982 .

[17]  Jeffrey D Orth,et al.  What is flux balance analysis? , 2010, Nature Biotechnology.

[18]  M. Kanat Camlibel,et al.  Convergence of Time-Stepping Schemes for Passive and Extended Linear Complementarity Systems , 2009, SIAM J. Numer. Anal..

[19]  Timothy J. Hanly,et al.  Dynamic metabolic modeling of a microaerobic yeast co-culture: predicting and optimizing ethanol production from glucose/xylose mixtures , 2013, Biotechnology for Biofuels.

[20]  Timothy J. Hanly,et al.  Dynamic flux balance modeling of microbial co‐cultures for efficient batch fermentation of glucose and xylose mixtures , 2011, Biotechnology and bioengineering.

[21]  Aleksej F. Filippov,et al.  Differential Equations with Discontinuous Righthand Sides , 1988, Mathematics and Its Applications.

[22]  P I Barton,et al.  A reliable simulator for dynamic flux balance analysis , 2013, Biotechnology and bioengineering.

[23]  Markus J. Herrgård,et al.  Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. , 2004, Genome research.

[24]  Paul I. Barton,et al.  Parametric mixed-integer 0-1 linear programming: The general case for a single parameter , 2009, Eur. J. Oper. Res..

[25]  Marc Despontin,et al.  Multiple Criteria Optimization: Theory, Computation, and Application, Ralph E. Steuer (Ed.). Wiley, Palo Alto, CA (1986) , 1987 .

[26]  Vincent Acary,et al.  Time-Stepping via Complementarity , 2012 .

[27]  B. Palsson,et al.  An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR) , 2003, Genome Biology.

[28]  P. Korhonen,et al.  Using Lexicographic Parametric Programming for Searching a Non‐dominated Set in Multiple‐Objective Linear Programming , 1996 .

[29]  P. I. Barton,et al.  DAEPACK: An Open Modeling Environment for Legacy Models , 2000 .

[30]  John N. Tsitsiklis,et al.  Introduction to linear optimization , 1997, Athena scientific optimization and computation series.

[31]  M A Henson,et al.  Steady-state and dynamic flux balance analysis of ethanol production by Saccharomyces cerevisiae. , 2009, IET systems biology.

[32]  M. Zarepisheh,et al.  A dual-based algorithm for solving lexicographic multiple objective programs , 2007, Eur. J. Oper. Res..

[33]  R. Mahadevan,et al.  The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. , 2003, Metabolic engineering.

[34]  J. G. Evans,et al.  Postoptimal Analyses, Parametric Programming, and Related Topics , 1979 .

[35]  M. A. Henson,et al.  Genome‐scale analysis of Saccharomyces cerevisiae metabolism and ethanol production in fed‐batch culture , 2007, Biotechnology and bioengineering.

[36]  Jean-Pierre Aubin,et al.  Viability theory , 1991 .

[37]  Lorenz T. Biegler,et al.  Parameter estimation in metabolic flux balance models for batch fermentation—Formulation & Solution using Differential Variational Inequalities (DVIs) , 2006, Ann. Oper. Res..

[38]  Asen L. Dontchev,et al.  Difference Methods for Differential Inclusions: A Survey , 1992, SIAM Rev..

[39]  Mihai Anitescu,et al.  An iterative approach for cone complementarity problems for nonsmooth dynamics , 2010, Comput. Optim. Appl..

[40]  John K. Reid,et al.  The design of MA48: a code for the direct solution of sparse unsymmetric linear systems of equations , 1996, TOMS.