SpringerLink Metadata of the chapter that will be...

Metadata of the chapter that will be visualized inSpringerLink

Book Title Machine Learning, Optimization, and Big DataSeries Title

Chapter Title A Nash Equilibrium Approach to Metabolic Network Analysis

Copyright Year 2016

Copyright HolderName Springer International Publishing AG

Corresponding Author Family Name LuciaParticle

Given Name AngeloPrefix

Suffix

Division Department of Chemical Engineering

Organization University of Rhode Island

Address Kingston, RI, 02881, USA

Email [email protected]

Author Family Name DiMaggioParticle

Given Name Peter A.Prefix

Suffix

Division Department of Chemical Engineering

Organization Imperial College London

Address London, SW7 2AZ, UK

Email

Abstract A novel approach to metabolic network analysis using a Nash Equilibrium formulation is proposed.Enzymes are considered to be players in a multi-player game in which each player attempts to minimizethe dimensionless Gibbs free energy associated with the biochemical reaction(s) it catalyzes subject toelemental mass balances. Mathematical formulation of the metabolic network as a set of nonlinearprogramming (NLP) sub-problems and appropriate solution methodologies are described. A small examplerepresenting part of the production cycle for acetyl-CoA is used to demonstrate the efficacy of theproposed Nash Equilibrium framework and show that it represents a paradigm shift in metabolic networkanalysis.

A Nash Equilibrium Approach to MetabolicNetwork Analysis

Angelo Lucia1(B) and Peter A. DiMaggio2

1 Department of Chemical Engineering, University of Rhode Island,Kingston, RI 02881, USA

[email protected] Department of Chemical Engineering, Imperial College London,

London SW7 2AZ, UK

Abstract. A novel approach to metabolic network analysis using a NashEquilibrium formulation is proposed. Enzymes are considered to be play- AQ1

ers in a multi-player game in which each player attempts to minimize thedimensionless Gibbs free energy associated with the biochemical reac-tion(s) it catalyzes subject to elemental mass balances. Mathematicalformulation of the metabolic network as a set of nonlinear program-ming (NLP) sub-problems and appropriate solution methodologies aredescribed. A small example representing part of the production cyclefor acetyl-CoA is used to demonstrate the efficacy of the proposed NashEquilibrium framework and show that it represents a paradigm shift inmetabolic network analysis.

1 Introduction

Flux balance analysis (FBA) has been the mainstay for understanding and quan-tifying metabolic networks for many years. See, for example, [1–6]. The basicidea behind FBA is to represent a given metabolic network at steady-state inthe form of a graph with nodes that define specific biochemical reactions andfluxes that connect nodes. The constraints for the network constitute a set ofunder-determined steady-state linear mass balance equations. To complete therepresentation, a linear objective function relevant to the particular biologicaltask at hand [e.g., maximizing the output flux [5]; minimizing the cardinality ofthe flux vector [4]; minimum nutrient intake; minimum ATP production mini-mal knockout, etc.] is selected, which together with the mass balance constraints,results in a linear programming (LP) formulation. Many variants and extensionsto FBA have also been proposed over the years, including Mixed Integer Lin-ear Programming (MILP) formulations [7], the incorporation of linearized ther-modynamic constraints [8] or thermodynamic metabolic flux analysis (TMFA),dynamic FBA [9], and others [10,11].

In addition to FBA, the other main approach to metabolic network analysisinvolves a time-dependent or dynamic formulation that incorporates chemicalreaction kinetics.

c© Springer International Publishing AG 2016P.M. Pardalos et al. (Eds.): MOD 2016, LNCS 10122, pp. 1–14, 2016.DOI: 10.1007/978-3-319-51469-7 4

Au

tho

r P

roo

f

2 A. Lucia and P.A. DiMaggio

1.1 Motivation

Both the kinetic model and FBA approaches suffer from a number of significantmodeling and biological limitations. Kinetic models require a large number ofparameters that are not ‘directly’ measurable and thus must be determined bymodel regression. Also, all FBA-based approaches are constrained only by reac-tion stoichiometry, which often results in degenerate solutions that lie on theuser-specified flux bounds. Additional modeling limitations stem from the inabil-ity of either approach to accurately capture the inherent complexities within acrowded cellular environment. In particular, natural competition/cooperationamong enzymes exists for the pool of continuously produced metabolites andneither approach takes into consideration the population distribution and/orheterogeneity of protein-metabolite interactions that form the basis for thesereactions. From a biological perspective, these approaches also fail to model thephenotypic consequence of overproducing a given product and the subsequentregulation of overproduction in these organisms. For instance, it is well knownthat excess quantities of a given protein or small molecule can lead to ‘higherorder’ interactions, which can be non-specific or the result of adaptive evolution-ary pressures that activate alternative pathways to deplete these excess pools.

1.2 Nash Equilibrium Approach

This paper takes a radically different approach to metabolic network analysisby formulating the problem as a Nash Equilibrium (NE) problem using firstprinciples (i.e., conservation of mass and rigorous reaction thermodynamics).The key idea behind our proposed NE approach is to view enzymes as ‘players’in a multi-player game, in which each enzyme pursues a strategy that can bequantified using a payoff or objective function. The collection of enzymes orplayers has a solution called a ‘NE point’, which is a point that maximizes the‘payoff’ of all players. Thus a NE point is the best solution for all players takentogether and not necessarily a point that is best for any one player. This NEapproach, in our opinion, is a more accurate representation of the evolutionary-defined competition/cooperation observed in complex metabolic pathways.

The remainder of this paper is organized in the following way. In Sect. 2,the optimization formulations, input and other data required, size of data, etc.for FBA and the Nash Equilibrium approach to metabolic pathway analysis arepresented. In Sect. 3, the mathematical tools needed to solve the NE problemare described in some detail. Section 4 provides numerical results for a proof-of-concept example based on acetyl-CoA metabolism in E. Coli and both NEand FBA predictions of key fluxes are compared to experimental data. Finally,Sect. 5 draws conclusions from this work.

2 Mathematical Modeling

In this section, optimization formulations, input and other data required, size ofdata, and other aspects of FBA and the proposed NE approach are described indetail.

Au

tho

r P

roo

f

A Nash Equilibrium Approach to Metabolic Network Analysis 3

2.1 FBA Formulation

It is well known that conventional FBA is formulated as a linear programming(LP) problem, which in general is given by:

optimize cT vsubject to Sv = 0

vL ≤ v ≤ vU(1)

where cT v is a linear objective function in which c are considered weighting coef-ficients, v is a vector of unknown metabolic fluxes, S is a stoichiometric matrixassociated with the chemical reactions in the network, and the superscripts Land U denote lower and upper bounds, respectively.

Data Required for Flux Balance Analysis. The data typically requiredfor FBA includes: (1) stoichiometric coefficients for all chemical reactions inthe network, which are placed in the stoichiometric matrix, S, (2) fluxes forall inputs to the given network, (3) lower and upper bounds on all unknownmetabolic fluxes.

Amount of Data Required for Flux Balance Analysis. The amount ofdata required for FBA is directly proportional to the number of species andchemical reactions in the network.

2.2 NE Formulation

Let the unknown variables, v, be partitioned into N subsets, v = [v1, v2, ..., vN ].Each variable partition, vj , has nj unknown variables. The Nash Equilibrium(NE) formulation for an arbitrary metabolic network is quite different for thatfor FBA and is given by a collection of j = 1, 2, ..., N nonlinear programming(NLP) sub-problems of the form:

min Gj(vj ,v∗−j)

RTsubject to conservation of mass

v∗−j

(2)

where Gj

RT , the dimensionless Gibbs free energy, is the objective function asso-ciated with the appropriate enzymes involved in a particular set (or number)of metabolic reactions at a given node j in the network, R is the gas constant,and T is the temperature. The conservation of mass constraints are elementalmass balances and vj represents the flow of metabolic material in and out of anynode. Finally, the vector, v∗

−j , denotes the minima of all other sub-problems,k = 1, 2, .., j −1, j +1, ..., N . In this article the words “sub-problem” and “node”mean the same thing.

Au

tho

r P

roo

f


The Gibbs free energy for sub-problem j is given by:

Gj

RT=

Cj∑

i=1

xij

[ΔG0

ij

RT+ lnxij + lnφij

](3)

where ΔG0ij are the standard state Gibbs free energies of the components for

the metabolic reactions associated with sub-problem j, xij are mole fractions,which are related to the fluxes, φij are fugacity coefficients, i is a componentindex, and Cj and Rj are the number of components and number of reactionsassociated with a sub-problem j in the network.

Temperature effects can be taken into effect using the van’t Hoff equation,which is given by:

ΔG0ij(T )

RT=

ΔG0ij(T0)

RT0+

ΔH0ij(T0)R

[T − T0

TT0

](4)

where T0 is the reference temperature (usually 25 ◦C), T is the temperatureat which the reaction takes place (usually 37 ◦C), and ΔH0

ij(T0) is the standardstate enthalpy of component i at node j in the network. Equations (3) and (4) areequivalent to the Gibbs free energy changes of reaction, ΔGR

ij(T ), which can alsobe computed from Gibbs free energies and enthalpies of formation, temperatureeffects, and reaction stoichiometry.

ΔGR0ij =

np(ij)∑

k=1

skΔG0f,ijk −

nr(ij)∑

k=1

skΔG0f,ijk (5)

where the sk’s are the stoichiometric numbers and np(ij) and nr(ij) are the num-ber of products and number of reactants, respectively, associated with reactioni and node j.

The key attributes that distinguish the proposed NE approach from otherformulations and make the problem challenging are: (a) the objective functionsin all sub-problems are nonlinear and (b) chemical reaction equilibrium can benon-convex and therefore multiple solutions can exist.

Data Required for Nash Equilibrium Metabolic Pathway Analysis.The amount of data needed for the NE approach to metabolic pathway analysisis greater than that for the equivalent FBA formulation. In addition to all stoi-chiometric coefficients of all chemical reactions, the NE approach requires Gibbsfree energies and enthalpies of formation at the reference conditions. However,numerical data for ΔG0

f,ijk(T0) and ΔH0f,ijk(T0) are generally available in vari-

ous text books [12,13] and journal articles [14,15].

Amount of Data Required for NE Metabolic Pathway Analysis. Aswith FBA, the amount of data required for metabolic pathway analysis usingNash Equilibrium is directly proportional to the number of species and chemicalreactions in the network.

Au

tho

r P

roo

f


3 Solutions Methods

A summary of the solution methods for FBA and NE approaches to metabolicpathway analysis are presented in this section.

3.1 Solution Methods Used to Solve FBA Problems

As discussed in Sect. 2.1, FBA problems are formulated as LP problems andtherefore linear programming methods are commonly used to solve FBA prob-lems as well as for thermodynamic metabolic flux analysis (TMFA) problemsoriginally proposed by [8]. LP methods are well established in the open litera-ture and not discussed in any detail in this paper. For a well posed LP problem,it is well known that the solution must lie at a vertex of the feasible regionand while the optimum value of the objective function in the LP solution isunique, the values of the fluxes need not be. Other approaches to solving FBAproblems include MILP methods, in which binary variables are added to theproblem formulation in order to find all bases that give vertex solutions and todetermine degenerate (or multiple) solutions [7] and protein abundance designusing multi-objective optimization to automatically adjust flux bounds [10].

3.2 Solution Methods for Nash Equilibrium Problems

The premise of Nash Equilibrium is to find a solution to Eq. 2 and that this solu-tion may not represent the best solution for each individual sub-problem in Eq. 2.Many of the methodologies for solving Nash Equilibrium problems are rooted ingame theory where continuity and/or continuous differentiability are difficult toguarantee and thus are not considered. However, through reformulation a numberof standard techniques from complementarity problems, variational inequalities,and nonlinear programming have been adapted for solving NE problems.

Nonlinear Programming Methods. In general, NE problems are not smoothand therefore require some reformulation to apply nonlinear programming meth-ods such as those that use the Nikaido-Isoda function and Jacobi-type trustregion methods [16]. Surveys of numerical methodologies applied to NE prob-lems can be found in Facchinei and Kanzow [17] and von Heusinger [18].

Terrain Methods. The metabolic network formulation within the framework ofNash Equilibrium (i.e., Eq. 2) does not suffer from non-differentiability. There-fore, in this work, we propose the use of the class of methods known as ter-rain methods [19–21] for solving the sub-problems in Eq. 2. Terrain methods areNewton-like methods that are based on the simple idea of moving up and downvalleys on the surface of the objective function to find all stationary points (i.e.,minima, saddles, and singular points). We only briefly summarize the terrainmethodology. Additional details can be found in the references.

Given an objective function, f(Z) : �n → �n, where f(Z) is C3, the basicsteps of the terrain method are as follows:

Au

tho

r P

roo

f


1. Initialize Z.2. Compute a solution (i.e., minima, saddle point or singular point) using a trust

region method.3. Perform a (partial) eigenvalue-eigenvector decomposition of the matrix HT H,

where H is the Hessian matrix of the function f(Z).4. Move uphill in the eigen-direction associated with the smallest eigenvalue of

HT H using predictor-corrector steps to follow the valley. Predictor steps aresimply uphill Newton steps that can actually drift from the valley. Therefore,corrector steps are used to return iterates to the valley by solving the nonlinearprogramming problem given by:

V = opt gT HT Hg such that gT g = L ∀L ∈ L (6)

where g is the gradient of f(Z), L is a given level set and L is a set of levelcurves.

5. Repeat step 4 using predictor-corrector movement uphill until a saddle pointon f(Z) is found.

6. Perform an eigenvalue-eigenvector decomposition of HT H and choose theeigen-direction associated with the smallest eigenvalue to initiate the nextdownhill search. Go to step 2.

There are considerably more details to the terrain method than are summarizedin the foregoing steps (e.g., perturbations from a given solution to initiate thenext search, the way in which corrector steps are initiated, keeping track ofsolutions, overall termination criteria, etc.). See [19–21] for details.

4 Numerical Example

Numerical results are presented for a small proof-of-concept example to illustratethat the formulation of metabolic pathways as a Nash Equilibrium problemrepresents a paradigm shift and to allow the reader interested in the detailsto reproduce our results. All computations in this section were performed ona Dell Inspiron laptop computer in double precision arithmetic using the LF95compiler.

4.1 Problem Statement

Consider the network shown in Fig. 1, which has 9 unknown fluxes as shownTable 4 in the Appendix.

4.2 Nash Equilibrium

The Nash Equilibrium formulation for the metabolic network in Fig. 1 hasfour players (i.e., four enzymes: pyruvate dehydrogenase, acetyl-CoA synthetase,phosphotransacetylase, and acetate kinase; see Appendix for further details) andthree nonlinear programming (NLP) sub-problems that describe the competition

Au

tho

r P

roo

f


Fig. 1. Simplified metabolic network for the production of acetyl CoA based on theiJO1366 network for E. coli [22]. The metabolic reactions considered are representedusing black arrows for acetyl-CoA, acetyl phosphate and acetate.

between those enzymes under the assumption that the goal of each enzyme isto minimize the dimensionless Gibbs free energy for the biochemical reaction itcatalyzes subject to appropriate elemental mass balances (i.e., conservation ofmass of carbon, hydrogen, oxygen, nitrogen, phosphorous and sulfur). Thus theoverall goal of the network is find the best value of:

G(v)RT

=3∑

j=1

minGj(vj , v

∗−j)

RT(7)

Note that the node in Fig. 1 for acetyl-CoA production is modeled by the sum oftwo biochemical reactions while each of the other nodes is modeled by a singlebiochemical reaction. The specific biochemical reactions and the enzymes (orplayers) used in this NE network model are illustrated in Fig. 1 and given inthe Appendix, along with the flux numbers and species to which the numberscorrespond.

Au

tho

r P

roo

f


4.3 NLP Sub-problems

All mass balance constraints shown in the sub-problem descriptions are linearand have been simplified as much as possible.

1. minG1(v1, v2, v3, v4, v5, v6)

RT(8)

subject to element balances given by3v1 + 3v2 + 32v3 + 34v4 + 2v6 = H1 (9)3v1 + 2v2 + 16v3 + 17v4 + 2v5 + v6 = O1 (10)7v3 + 7v4 = N1 (11)3v1 + 2v2 + 21v3 + 23v4 + v5 = C1 (12)

where H1, O1, N1 and C1 are the amounts of elemental hydrogen, oxygen, nitro-gen, and carbon respectively and can change from NE iteration to NE iteration.

2. minG2(v3, v4, v7, v8)

RT(13)

subject to element balances given by32v3 + 34v4 + v7 + 3v8 = H2 (14)7v3 + 7v4 = N2 (15)3v3 + 3v4 + v7 + v8 = P2 (16)

where H2, N2, and P2 are the amounts of elemental hydrogen, nitrogen, andphosphorous, respectively, which can change at each NE iteration.

3. minG3(v2, v8, v9)

RT(17)

subject to element balances given by2v2 + 2v8 = C3 (18)v8 + v9 = P3 (19)

where C3 and P3 are any amount of elemental carbon and phosphorous in theacetyl phosphate pool. Note that each sub-problem has some degrees of freedom.Sub-problem 1 has six unknowns, four linear constraints, and two degrees offreedom. Sub-problems 2 and 3 each have one degree of freedom.

4.4 Numerical Solution of the Nash Equilibrium

It is important for the reader to understand that the right hand sides of thelinear mass balance constraints in the NLP sub-problems will vary from NEiteration to NE iteration as one cycles through the sub-problems until the NashEquilibrium problem is converged.

The numerical strategy for solving the Nash Equilibrium problem defined inSects. 4.1 and 4.2 consists of the following steps:

Au

tho

r P

roo

f


1. Break the feedback in the network and estimate the appropriate fluxes.2. Solve each NLP sub-problem for its associated minimum in G/RT.3. Compare calculated feedback fluxes with estimated fluxes.

(a) If they match within a certain tolerance, stop.(b) If not, replace the estimated feedback fluxes with the calculated ones and

go to step 2.

The proposed approach is a direct (or successive) substitution strategy for con-verging the NE problem.

4.5 Numerical Results

This section provides details for the solution of the individual NLP sub-problemsand the Nash Equilibrium problem. The Nash Equilibrium problem can beviewed as a master problem.

NLP Sub-problem Solution. The individual NLP sub-problem are straight-forward to solve. Table 1 provides an illustration for the conversion of pyru-vate and acetate to acetyl-CoA. Since the constraints are linear, four of thesix unknown fluxes can be eliminated by projecting G/RT onto the mass bal-ance constraints, leaving two unknown fluxes [e.g., vco2, vh2o]. As a result, thesequence of iterates generated is feasible and all other flux values can be com-puted using projection matrices. Table 1 shows that the NLP solution method-ology is robust, generates a monotonically decreasing sequence of dimensionlessGibbs free energy values, and convergence is quite fast (10 iterations) to a tighttolerance (i.e., 10−12) in the two-norm of the component chemical potentials.

Table 1. NLP solution to acetyl-CoA production

Iteration no. Unknown variables (vco2, vh2o) G/RT

0 (0.00001, 0.00001) −4.39621

1 (1.41971 × 10−5, 1.37181 × 10−5) −4.39640

2 (2.75153 × 10−5, 2.53197 × 10−5) −4.39695

3 (8.23974 × 10−5, 7.13924 × 10−5) −4.39897

4 (3.98558 × 10−4, 3.22080 × 10−4) −4.40867

5 (3.02694 × 10−3, 2.25533 × 10−3) −4.46949

6 (2.18630 × 10−2, 1.48783 × 10−2) −4.76591

7 (0.111113, 0.0706099) −5.63168

8 (0.337213, 0.228526) −6.81803

9 (0.523871, 0.423184) −7.21853

10 (0.548037, 0.459889) −7.22650

11 (0.548251, 0.459857) −7.22650

Au

tho

r P

roo

f


Solution to the Nash Equilibrium Problem. The solution to the NashEquilibrium formulation of the metabolic network shown in Fig. 1 is determinedby specifying the input flux to the network (i.e., the pyruvate flux), breakingthe acetate feedback to the acetyl-CoA pool, providing an initial estimate ofthe acetate flux, and regulating (or constraining) the fluxes of water and co-factor co-enzyme A (CoA). This allows each of the three NLP sub-problemsto be solved, in turn, in the order shown in Sect. 4.3: (1) acetyl-CoA produc-tion, (2) acetyl phosphate production, and (3) acetate production. The acetatebiochemical equilibrium provides a new value for the acetate flux, which is com-pared to the initial estimate. If the initial and calculated values do not match,the calculated value is used for the next iteration, and this process is repeateduntil convergence.

Table 2 gives a summary of the NE iterations and solution per nmol/h ofpyruvate starting from an initial estimate of acetate flux of v2 = vac = 0 nmol/hand regulated fluxes of v3 = vCoA = 2 nmol/h and v6 = vH2O = 1 nmol/h.Note that (1) our NE approach takes 33 iterations to converge, (2) convergenceis linearly, and (3) the sequence of G/RT values is monotonically decreasing.

Table 2. Nash Equilibrium iterates and solution to acetyl-CoA production.

Iter. G/RT ∗ vactp vac vPO3 vaccoa vcoa vpi vco2 vh2o Error

1 −10.34 0.0532 0.5045 0.0661 0.4330 1.5670 0.4330 0.5143 1.0380 0.0478

2 −14.19 0.0978 0.8200 0.1249 0.7845 1.6484 0.3296 0.9032 1.0660 0.3155

3 −16.69 0.1257 1.0057 0.1625 1.0651 1.7194 0.2641 1.1833 1.0660 0.2806

4 −18.36 0.1415 1.1185 0.1833 1.2777 1.7874 0.2275 1.3800 1.0524 0.1128

5 −19.50 0.1501 1.1912 0.1943 1.4317 1.8460 0.2080 1.5163 1.0373 0.0727

6 −20.29 0.1548 1.2403 0.1999 1.5399 1.8918 0.1976 1.6099 1.0251 0.0491

7 −20.84 0.1575 1.2743 0.2030 1.6147 1.9253 0.1918 1.6739 1.0165 0.0340

8 −21.21 0.1592 1.2979 0.2047 1.6658 1.9489 0.1885 1.7175 1.0108 0.0236

9 −21.46 0.1602 1.3143 0.2056 1.7006 1.9652 0.1866 1.7471 1.0071 0.0164

10 −21.64 0.1608 1.3256 0.2062 1.7242 1.9764 0.1853 1.7673 1.0047 0.0113

.

..

31 −22.01 0.1620 1.3501 0.2073 1.7740 1.9999 0.1830 1.8098 1.0000 3 × 10−5

32 −22.01 0.1620 1.3501 0.2073 1.7740 2.0000 0.1830 1.8098 1.0000 2 × 10−5

33 −22.01 0.1620 1.3501 0.2073 1.7740 2.0000 0.1830 1.8098 1.0000 1 × 10−5

∗G/RT =∑3

j=1 min Gj/RT

Comparisons to Experiments. To assess the predictive capability of the NEapproach for metabolic pathway analysis, experimental data for the metabolicfluxes for E. coli were examined. These fluxes have been experimentally measuredusing a number of technologies, such as GC-MS and 13C-fluxomics, and areavailable in the recently curated CeCaFDB database [23] that contains data forE. coli strains from 31 different studies over varying conditions. However, onlyone study reports experimental fluxes for the metabolic sub-network containingacetyl-CoA, acetyl phosphate and acetate [24] shown in Fig. 1.

Au

tho

r P

roo

f


Table 3. Comparison of NE (from Table 2) and FBA predicted relative fluxes∗ withexperimental data [24].

Component Experimentallymeasured fluxes

NE predictedfluxes

FBA predictedfluxes

Acetate 0.4557 0.4109 Upper bound

Acetyl phosphate 0.0000 0.0493 0

Acetyl-CoA 0.5443 0.5398 2∗Relative fluxes are considered since the experimental data isreported per unit of cell dry weight.

Table 3 presents the relative fluxes for the E. coli strain ML308 using glucoseas a carbon source [24]. In [24] it was found that 45.57% of acetyl-CoA (gener-ated from the pyruvate flux) was converted to acetyl phosphate, which in turnwas entirely consumed to produce acetate. Table 3 shows that normalized NEpredicted fluxes are in good agreement with experiment, while those for FBA arequite poor. The NE model also predicts that acetyl phosphate constitutes 4.93%of the pool of three metabolites at equilibrium, implying that acetyl phosphateproduced from acetyl-CoA was mostly converted into acetate. While the exper-imental data estimated that acetyl phosphate was completely converted intoacetate (Table 3), recent reports have shown that intracellular acetyl phosphatedoes exist in concentrations up to 3mM in E. coli [25], which is consistent with AQ2

the NE predictions. This hypothesis is further supported by the excellent agree-ment between experimental and the NE predicted relative flux of acetyl-CoA.While the relative flux of acetate shows the largest deviation, the predictions ofthe NE model are in good agreement with experimental data and far superiorto those predicted by FBA for the small sub-network and modeling assumptionsconsidered.

5 Conclusions

A paradigm shift in metabolic network analysis based on Nash Equilibrium (NE)was proposed. The key idea in this NE approach is to view enzymes as playersin a multi-player game, where each player optimizes a specific objective func-tion. Governing equations, data requirements, solution methodologies for the NEformulation and conventional FBA formulation were described and compared.A proof-of-concept example consisting of a simplified metabolic network for theproduction of acetyl-CoA with four players was presented.

Nomenclature

c objective function coefficients in any LP formulationC number of chemical speciesf objective function

Au

tho

r P

roo

f


g gradientG Gibbs free energyH enthalpy, Hessian matrixL level set valuen dimension of spacenp number of productsnr number of reactantsN number of sub-problemsR gas constant, number of reactions�n real space of dimension ns stoichiometric numbersS stoichiometric coefficientsT temperaturex mole fractionZ unknown variables

Greek Symbols

v unknown fluxesφ fugacity coefficient

Subscripts

f formationi component indexj sub-problem or node index−j excluding j0 reference state

Superscripts

R reactionL lower boundU upper bound∗ optimal value

Appendix

Biochemical Reactions for a Simplified Metabolic Network for AcetylCoA Production

The biochemical reactions involved at each node in the metabolic network areas follows:

Au

tho

r P

roo

f


1. Acetyl CoA production from pyruvate and acetate

C3H3O3 + C2H3O2 + 2C21H32N7O16P3S � 2C23H34N7O17P3S + CO2 + H2O(20)

Pyruvate → acetyl CoA: pyruvate dehydrogenase (genes: lpd, aceE, and aceF)[26]Acetate → acetyl CoA: acetyl CoA synthetase (genes: acs) [26]

2. Acetyl phosphate production from acetyl CoA

C23H34N7O17P3S + HO4P � C2H3O5P + C21H32N7O16P3S (21)

Acetyl CoA ↔ acetyl phosphate: phosphotransacetylase (genes: pta or eutD)[26]

3. Acetate production from acetyl phosphate

C2H3O5P � C2H3O−2 + PO3−

3 (22)

Acetyl phosphate ↔ acetate: acetate kinase (genes: ackA, tdcD, or purT) [26]

Table 4. Unknown fluxes, components and Gibbs energy of formation.

Flux no. Species Symbol† Chemical formula ΔG0f (kJ/mol)

1 Pyruvate pyr C3H3O3 −340.733 [14]

2 Acetate ac C2H3O−2 −237.775 [14]

3 Coenzyme A coa C21H32N7O16P3S −4.38624 [14]

4 Acetyl CoA accoa C23H34N7O17P3S −48.1257 [14]

5 Carbon Dioxide co2 CO2 −394.4 [13]

6 Water h2o H2O −228.6 [13]

7 Phosphate pi HO4P −1055.12 [14]

8 Acetyl Phosphate actp C2H3O5P −1094.02 [14]

9 Phosphonate PO3 PO3−3 −866.93‡

†Using nomenclature in BiGG [26]‡Difference between ATP and ADP, using ΔG0

f ’s from [14]

References

1. Varma, A., Palsson, B.O.: Metabolic flux balancing: basic concepts, scientific andpractical use. Nat. Biotechnol. 12, 994–998 (1994)

2. Kauffman, K.J., Prakash, P., Edwards, J.S.: Advances in flux balance analysis.Curr. Opin. Biotechnol. 14, 491–496 (2003)

3. Holzhutter, H.G.: The principles of flux minimization and its application to esti-mate stationary fluxes in metabolic networks. Eur. J. Biochem. 271, 2905–2922(2004)

4. Julius, A.A., Imielinski, M., Pappas, G.J.: Metabolic networks analysis using con-vex optimization. In: Proceedings of the 47th IEEE Conference on Decision andControl, p. 762 (2008)

5. Smallbone, K., Simeonidis, E.: Flux balance analysis: a geometric perspective. J.Theor. Biol. 258, 311–315 (2009)

Au

tho

r P

roo

f


6. Murabito, E., Simeonidis, E., Smallbone, K., Swinton, J.: Capturing the essenceof a metabolic network: a flux balance analysis approach. J. Theor. Biol. 260(3),445–452 (2009)

7. Lee, S., Phalakornkule, C., Domach, M.M., Grossmann, I.E.: Recursive MILPmodel for finding all the alternate optima in LP models for metabolic networks.Comput. Chem. Eng. 24, 711–716 (2000)

8. Henry, C.S., Broadbelt, L.J., Hatzimanikatis, V.: Thermodynamic metabolic fluxanalysis. Biophys. J. 92, 1792–1805 (2007)

9. Mahadevan, R., Edwards, J.S., Doyle, F.J.: Dynamic flux balance analysis indiauxic growth in Escherichia coli. Biophys. J. 83, 1331–1340 (2002)

10. Patane, A., Santoro, A., Costanza, J., Nicosia, G.: Pareto optimal design for syn-thetic biology. IEEE Trans. Biomed. Circuits Syst. 9(4), 555–571 (2015)

11. Angione, C., Costanza, J., Carapezza, G., Lio, P., Nicosia, G.: Multi-target analysisand design of mitochondrial metabolism. PLOS One 9, 1–22 (2015)

12. Alberty, R.A.: Thermodynamics of Biochemical Reactions. Wiley, Hoboken (2003)13. Elliott, J.R., Lira, C.T.: Introductory Chemical Engineering Thermodynamics, 2nd

edn. Prentice Hall, Upper Saddle (2012)14. Kummel, A., Panke, S., Heinemann, M.: Systematic assignment of thermodynamic

constraints in metabolic network models. BMC Bioinform. 7, 512–523 (2006)15. Flamholz, A., Noor, E., Bar-Even, A., Milo, R.: eQuilibrator - the biochemical

thermodynamics calculator. Nucleic Acids Res. 40 (2011). doi:10.1093/nar/gkr87416. Yuan, Y.: A trust region algorithm for nash equilibrium problems. Pac. J. Optim.

7, 125–138 (2011)17. Facchinei, F., Kanzow, C.: Generalized nash equilibrium problems. Ann. Oper.

Res. 175, 177–211 (2010)18. von Heusinger, A.: Numerical methods for the solution of generalized nash equilib-

rium problems. Ph.D. thesis, Universitat Wurzburg, Wurzburg, Germany (2009)19. Lucia, A., Feng, Y.: Global terrain methods. Comput. Chem. Eng. 26, 529–546

(2002)20. Lucia, A., Feng, Y.: Multivariable terrain methods. AIChE J. 49, 2553–2563 (2003)21. Lucia, A., DiMaggio, P.A., Depa, P.: A geometric terrain methodology for global

optimization. J. Global Optim. 29, 297–314 (2004)22. Orth, J.D., Conrad, T.M., Na, J., Lerman, J.A., Nam, H., Feist, A.M., Palsson,

B.O.: A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Mol. Syst. Biol. 11(7), 535 (2011). doi:10.1038/msb.2011.65

23. Zhang, Z., Shen, T., Rui, B., Zhou, W., Zhou, X., Shang, C., Xin, C., Liu, X.,Li, G., Jiang, J., Li, C., Li, R., Han, M., You, S., Yu, G., Yi, Y., Wen, H., Liu,Z., Xie, X.: CeCaFDB: a curated database for the documentation, visualizationand comparative analysis of central carbon metabolic flux distributions exploredby 13C-fluxomics. Nucleic Acids Res. 43 (2015). doi:10.1093/nar/gku1137

24. Holms, H.: Flux analysis and control of the central metabolic pathways inEscherichia coli. FEMS Microbiol. Rev. 19, 85–116 (1996)

25. Klein, A.H., Shulla, A., Reimann, S.A., Keating, D.H., Wolfe, A.J.: The intracellu-lar concentration of acetyl phosphate in Escherichia coli is sufficient for direct phos-phorylation of two-component response regulators. J. Bacteriol. 189(15), 5574–5581 (2007)

26. King, Z.A., Lu, J.S., Drager, A., Miller, P.C., Federowicz, S., Lerman, J.A.,Ebrahim, A., Palsson, B.O., Lewis, N.E.: BiGG models: a platform for integrating,standardizing, and sharing genome-scale models. Nucleic Acids Res. (2015). doi:10.1093/nar/gkv1049

Au

tho

r P

roo

f

http://dx.doi.org/10.1093/nar/gkr874

http://dx.doi.org/10.1038/msb.2011.65

http://dx.doi.org/10.1093/nar/gku1137

http://dx.doi.org/10.1093/nar/gkv1049

http://dx.doi.org/10.1093/nar/gkv1049

Author Queries

Chapter 4

QueryRefs.

Details Required Author’sresponse

AQ1 Please check and confirm if the corresponding author andthe e-mail ID are correctly identified. Amend if necessary.

AQ2 Please check the usage of the unit “3mM”.

Au

tho

r P

roo

f

MARKED PROOF

Please correct and return this set

Instruction to printer

Leave unchanged under matter to remain

through single character, rule or underline

New matter followed by

or

or

or

or

or

or

or

or

or

and/or

and/or

e.g.

e.g.

under character

over character

new character

new characters

through all characters to be deleted

through letter or

through characters

under matter to be changed





Encircle matter to be changed

(As above)

(As above)

(As above)

(As above)

(As above)

(As above)

(As above)

(As above)

linking characters

through character or

where required

between characters or

words affected

through character or

where required

or

indicated in the margin

Delete

Substitute character or

substitute part of one or

more word(s)Change to italics

Change to capitals

Change to small capitals

Change to bold type

Change to bold italic

Change to lower case

Change italic to upright type

Change bold to non-bold type

Insert ‘superior’ character

Insert ‘inferior’ character

Insert full stop

Insert comma

Insert single quotation marks

Insert double quotation marks

Insert hyphen

Start new paragraph

No new paragraph

Transpose

Close up

Insert or substitute space

between characters or words

Reduce space betweencharacters or words

Insert in text the matter

Textual mark Marginal mark

Please use the proof correction marks shown below for all alterations and corrections. If you

in dark ink and are made well within the page margins.

wish to return your proof by fax you should ensure that all amendments are written clearly

SpringerLink Metadata of the chapter that will be...

Documents

Transcript of SpringerLink Metadata of the chapter that will be...