Biotechnology Research International

Volume 2015, Article ID 238082, 11 pages

http://dx.doi.org/10.1155/2015/238082

*In Silico* Analysis of Bioethanol Overproduction by Genetically Modified Microorganisms in Coculture Fermentation

Department of Chemical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721 302, India

Received 29 August 2014; Accepted 28 January 2015

Academic Editor: Michael Hust

Copyright © 2015 Lisha K. Parambil and Debasis Sarkar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Lignocellulosic biomass is an attractive sustainable carbon source for fermentative production of bioethanol. In this context, use of microbial consortia consisting of substrate-selective microbes is advantageous as it eliminates the negative impacts of glucose catabolite repression. In this study, a detailed *in silico* analysis of bioethanol production from glucose-xylose mixtures of various compositions by coculture fermentation of xylose-selective *Escherichia coli* strain ZSC113 and glucose-selective wild-type *Saccharomyces cerevisiae* is presented. Dynamic flux balance models based on available genome-scale metabolic networks of the microorganisms have been used to analyze bioethanol production and the maximization of ethanol productivity is addressed by computing optimal aerobic-anaerobic switching times. A set of genetic engineering strategies for ethanol overproduction by *E. coli* strain ZSC113 have been evaluated for their efficiency in the context of batch coculture process. Finally, simulations are carried out to determine the pairs of genetically modified *E. coli* strain ZSC113 and *S. cerevisiae* that significantly enhance ethanol productivity in batch coculture fermentation.

#### 1. Introduction

Environmental concerns and energy security issues have renewed our interest in bioethanol as a substitute for petroleum derived liquid transportation fuel. Bioethanol is mainly produced from edible starch crops or sugar cane. The use of this first generation feedstock is uneconomical and leads to food versus fuel dispute. Lignocellulosic biomasses, the most abundant biological material on earth, are an attractive alternative feedstock for bioethanol production. It essentially contains cellulose (~45% of dry weight), hemicellulose (~30% of dry weight), and lignin (~25% of dry weight) [1]. Hydrolysis of cellulose produces easily fermentable hexose sugar (glucose) and hydrolysis of hemicellulose produces a mixture of hexose (glucose) and pentose sugars (xylose, arabinose). Due to this complex composition, the commercial utilization of lignocelluloses as bioethanol feedstock faces many technical and economic challenges. The proper selection of microorganisms for the fermentation step is thus very important. The productivity of the fermentation step can be enhanced by genetic manipulation of traditional strains for consumption of both glucose and xylose [2, 3] or by carrying out coculture fermentation of specialized microbes [4, 5]. This second alternative is particularly advantageous as it leads to simultaneous consumption of both glucose and xylose sugars.

Genome-scale metabolic networks are now available for a number of organisms and the availability of these models offers new approaches to improve the understanding of complex biological processes. A successful approach to genome-scale modelling is the constraint-based modelling approach which attempts to explore feasible phenotypes of an organism at given pseudo steady-state condition. Flux balance analysis (FBA) is an efficient constraint-based approach to analyze a genome-scale metabolic network. It uses linear programming to determine the intracellular fluxes that optimize a given objective function [6, 7]. Most of the modelling techniques that have been developed for systemic understanding of cellular functions require detailed information regarding reaction kinetics as well as enzyme and metabolite concentrations. But FBA requires minimal amount of biological knowledge and kinetic data to make quantitative predictions about metabolic phenotype [8]. The dynamic flux balance analysis (dFBA) models are obtained by combining stoichiometric equations for intracellular metabolism with dynamic mass balances on key extracellular substrates/products under the assumption of fast intracellular dynamics and are applicable for accounting the unsteady-state situation in batch/fed-batch fermentation [9].

Microbial coculture (fermentation with two or more microorganisms) appears to be advantageous over single-organism culture for bioethanol production from lignocelluloses due to the potential of synergistic utilization of metabolic capabilities of involved microbes for the cofermentation of glucose and xylose [5]. The superiority of coculture of substrate-selective microbes (engineered* Escherichia coli* and wild-type* Saccharomyces cerevisiae*) over single-organism culture (recombinant* S. cerevisiae* RWB218) in improving the utilization of glucose/xylose mixtures for enhanced bioethanol production from batch fermentation using dFBA modelling technique has been reported by Hanly and Henson [10] and Hanly et al. [11]. By using the genome-scale metabolic network of* S. cerevisiae* (*i*FF708), Bro et al. [12] reported ten genetic engineering strategies for enhancing ethanol yield at the expense of reduced glycerol production. Recently, Lisha and Sarkar [13, 14] analysed the impact of ten genetic engineering strategies reported by Bro et al. [12] on* S. cerevisiae* for their efficiency in enhancing the ethanol productivity in the context of batch/fed-batch coculture and monoculture fermentation. Simulations were carried out with various glucose/xylose mixtures and, for the 50/50 glucose/xylose (%/%) mixture, the batch coculture fermentation using genetically modified* S. cerevisiae* (consumes only glucose) and engineered* E. coli* strain ZSC113 (consumes only xylose) enhanced the ethanol productivity by 40.7% compared to the monoculture (*S. cerevisiae* strain RWB218) fermentation. The enhancement in ethanol productivity of coculture system of substrate-selective microbes is due to the simultaneous conversion of both glucose and xylose sugars, high substrate utilization rate, and reduced fermentation time compared to monoculture system of* S. cerevisiae* strain RWB218. The authors suggested that genetic modification on xylose-selective* E. coli* strain ZSC113 should also be explored as an alternative approach for enhanced bioethanol production from coculture system. Bioethanol production potential of* Scheffersomyces *(*Pichia*)* stipitis* from glucose/xylose mixtures has been investigated using dFBA analysis [15, 16].

The objective of the present study is to conduct* in silico* analysis of the effect of various genetic engineering strategies on xylose-selective* E. coli* strain ZSC113 towards enhanced production of ethanol from glucose/xylose mixtures in batch coculture fermentation with wild-type* S. cerevisiae*. Next, pairs of genetically modified* E. coli* strain ZSC113 and* S. cerevisiae* are determined through simulations of genome-scale models, which significantly enhance ethanol production. Batch ethanol productivity is taken as a measure of fermentation performance and the maximization of ethanol productivity is sought with respect to the optimal aerobic to anaerobic switching time. In order to investigate the effect of various lignocellulosic feedstocks that contain glucose/xylose mixture in varying proportions, simulations are carried out with 50/50, 60/40, and 70/30 glucose/xylose (weight%/weight%) mixtures.

#### 2. Methods

##### 2.1. Dynamic Flux Balance Model

The stoichiometric models used in the present study are adapted from* i*ND750* S. cerevisiae* [17] and* i*AF1260* E. coli* [18] genome-scale metabolic networks. The wild-type* S. cerevisiae* model* i*ND750 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 is formed. The wild-type* E. coli* model* i*AF1260 consists of 1261 genes and 2083 intracellular reactions. The dimensions of the stoichiometric matrix for model* i*AF1260 are with 299 membrane exchange fluxes. To simulate xylose-selective* E. coli* strain ZSC113, the glucose exchange and glucokinase fluxes are constrained to zero in* i*AF1260. 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 [10]. 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:
where is the matrix of stoichiometric coefficients, is vector of reaction fluxes including exchange fluxes, is the cellular growth rate, and is a vector of weights that represent the contribution of each flux to cellmass formation. The stoichiometric matrix is the mathematical representation of the reaction list. It is an matrix where is the number of metabolites and is the number of reactions. Each element of () represents the stoichiometric coefficient of the th metabolite in the th 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: where , , , and are the glucose, xylose, ethanol, and dissolved oxygen concentrations, respectively. , , and are the uptake rates of glucose, xylose, and oxygen, respectively. , , and are the half-saturation constants and is the ethanol inhibition constant.

The dynamic mass balances for the extracellular environment are described by the usual ordinary differential equations: where is the cellmass concentration and 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 (), where is the oxygen saturation concentration. It is reported by Lisha and Sarkar [13] 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.

##### 2.2. Model Parameters and Dynamic Simulation

All the simulations are performed in Matlab environment using ode23 to integrate the extracellular dynamic mass balance equations and the COBRA Toolbox [19] 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 Table 1. 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.