*library_books*

## Similar protocols

## Protocol publication

[…] The stoichiometric models used in the present study are adapted from iND750 S. cerevisiae [] and iAF1260 E. coli [] genome-scale metabolic networks. The wild-type S. cerevisiae model iND750 is a fully compartmentalized genome-scale metabolic network with 750 genes and 1150 intracellular reactions. From 1061 metabolites and 1266 fluxes, which include 116 membrane exchange fluxes, the stoichiometric matrix of size 1061 × 1266 is formed. The wild-type E. coli model iAF1260 consists of 1261 genes and 2083 intracellular reactions. The dimensions of the stoichiometric matrix for model iAF1260 are 1668 × 2382 with 299 membrane exchange fluxes. To simulate xylose-selective E. coli strain ZSC113, the glucose exchange and glucokinase fluxes are constrained to zero in iAF1260. If two microorganisms are assumed to be noninteracting and each species maximizes its own growth rate using available resources, the model for the coculture system can be developed by combining the **dFBA** models for individual species []. The standard linear program to solve the underdetermined flux balance model of coculture system of two microbial species, glucose-selective S. cerevisiae (SC) and xylose-selective E. coli strain ZSC113 (EC), can thus be formulated as follows:
(1)MaximizevSC,vEC μ=μSC+μEC=wSCTvSC+wECTvECSubject to: ASC00AECvSCvEC=00 vSC,minvEC,min≤vSCvEC≤vSC,maxvEC,max,
where A is the matrix of stoichiometric coefficients, v is vector of reaction fluxes including exchange fluxes, μ is the cellular growth rate, and w is a vector of weights that represent the contribution of each flux to cellmass formation. The stoichiometric matrix A is the mathematical representation of the reaction list. It is an m × n matrix where m is the number of metabolites and n is the number of reactions. Each element of A (A
ij) represents the stoichiometric coefficient of the ith metabolite in the jth reaction. The coefficient is positive when the metabolite is a product of the given reaction and negative when the metabolite is a substrate.Substrates uptake kinetics for the microorganisms are modelled as Michaelis-Menten kinetics with additional regulatory term to account for growth rate suppression at high ethanol concentration:
(2)vg,SC=vg,max,SCGKg,SC+G11+E/Kie,SC,vz,EC=vz,max,ECZKz,EC+Z11+E/Kie,EC,vo,SC=vo,max,SCOKo,SC+O,vo,EC=vo,max,ECOKo,EC+O,
where G, Z, E, and O are the glucose, xylose, ethanol, and dissolved oxygen concentrations, respectively. v
g, v
z, and v
o are the uptake rates of glucose, xylose, and oxygen, respectively. K
g, K
z, and K
o are the half-saturation constants and K
ie is the ethanol inhibition constant.The dynamic mass balances for the extracellular environment are described by the usual ordinary differential equations:
(3)dXSCdt=μSCXSC,dXECdt=μECXEC,dGdt=−vg,SCXSC,dZdt=−vz,ECXEC,dEdt=ve,SCXSC+ve,ECXEC,
where X is the cellmass concentration and v
e is the ethanol exchange flux from microbial species. Extracellular oxygen balances are omitted on the assumption that direct manipulation of dissolved oxygen is possible. The dissolved oxygen (DO) concentration is represented as the percent of saturation (O/O
sat), where O
sat is the oxygen saturation concentration. It is reported by Lisha and Sarkar [] that if the oxygen concentration is higher than 25% of the saturated concentration, the ethanol production is practically insensitive to oxygen concentration in the medium. For all aerobic simulations, the dissolved oxygen is considered to be regulated at 0.29 mM, which corresponds to 98% of the saturated oxygen concentration. [...] All the simulations are performed in Matlab environment using ode23 to integrate the extracellular dynamic mass balance equations and the **COBRA** Toolbox [] with Matlab interface to the GNU linear program code glpk to solve the inner linear program. The substrate uptake parameters and the operating conditions used for all the dynamic simulations are listed in . The differences between the substrate uptake rates under aerobic and anaerobic conditions are neglected. The final batch time is chosen as the time when the glucose concentration dropped below 0.1 g/L. Since the optimal growth rate is being determined by solving a linear program, there may exist many different flux distributions that produce the same optimal growth rate. The problem of such multiple optimal solutions with respect to ethanol production rate is checked by first solving the linear program for maximization of cellmass and then by constraining the cellmass at this maximum value and solving the linear program again for maximum ethanol production rate.Batch ethanol productivity (Preth) is defined as the overall rate of ethanol production:
(4)Preth=EVtftf,
where t
f is the fermentation time.The switching time (t
s) for aerobic to anaerobic condition for fixed final time t
f is determined optimally by solving the following single variable optimization problem using the bounded search algorithm fminbnd in Matlab:
(5)Maximizets EVtftfSubject to: dFBA model l tLB≤ts≤tUB,
where t
LB and t
UB are appropriate lower and upper bounds for switching time, respectively. […]

## Pipeline specifications

Software tools | DFBA, COBRA Toolbox |
---|---|

Application | Metabolic engineering |

Organisms | Escherichia coli, Saccharomyces cerevisiae |

Chemicals | Ethanol, Carbon, Glucose |