Bioprocess modelling for the design and optimization of lignocellulosic biomass fermentation
 Pornkamol Unrean^{1}Email author
Received: 24 September 2015
Accepted: 16 December 2015
Published: 4 January 2016
Abstract
Lignocellulosic feedstocks, which are currently underexploited, can be used for the production of biofuels, such as ethanol, and for biorefinery applications to produce a variety of valueadded products. Although bioconversion of lignocellulose by microbial or yeast fermentation have been reported, efficient and economical lignocellulosic fermentation process is still a challenge due to multiple process parameters involved for bioprocess design, optimization and scaleup. Bioprocess modelling strategies have been proven effective for achieving highproduction process* efficiency in yield, productivity or titer of desired product. Several types of bioprocess modelling for lignocellulosic application have been developed and successfully validated as a promising alternative for rapid design, optimization and scaling up of biomassbased process. This review aims to summarize the important development of bioprocess modelling for lignocellulosic bioprocess applications towards the success of biorefineries and biobased economy. In particular, we discuss modelling relevant to lignocellulosic bioprocess including cell modelling based on kinetics, stoichiometry and integrative approaches and fermentation kinetic modelling for process performance assessment. An overview of these modelling approaches and their application for systematic design of efficient and economical lignocellulosebased bioprocesses are given.
Keywords
Background
Lowpriced, abundant and renewable lignocellulosic biomass has become an attractive alternative feedstock to significantly supplement corn and starch as a fermentation feedstock for biobased production (FitzPatrick et al. 2010; Kircher 2012). These substrates can be obtained from agricultural, industrial and municipal solid wastes and forestry residues. The use of lignocellulose resources for the production of biochemicals and biofuels is considered as costeffective and environmentally sustainable serving biobased economy (Binod et al. 2010; Lopes 2015). The optimization of the technology and scaleup for lignocellulosic bioprocess is rapidly developing by several biotech companies and pilot plants in Europe and the US. Bioconversion of lignocellulose to bioproducts requires lignocellulosic biomass to be hydrolysed in order to generate monomeric sugars for the fermentation step. Hydrolysis of lignocellulose is usually achieved by means of a thermal and/or chemical pretreatment followed by enzyme hydrolysis. Many studies have demonstrated the feasible production of bioproducts by both bacteria (e.g. Zymomonas mobilis, Escherichia coli) and yeasts (e.g. Saccharomyces cerevisiae, Scheffersomyces stipitis) using lignocellulosic feedstock (Geddes et al. 2015; Zhang and Lynd 2010; Van Zyl et al. 2007; Unrean and Nguyen 2012). However, several challenges remain for achieving the efficient hydrolysis and fermentation of lignocellulose. Studying enzymatic and chemical hydrolysis of lignocellulosic biomass based on experimental and modelling approaches has been extensively reviewed elsewhere (Van Dyk and Pletschke 2012; Bansal et al. 2009; Meng and Ragauskas 2014; Hodge et al. 2009; Geddes et al. 2010; Sun and Cheng 2002). Hence, this review focuses on the fermentation step of lignocellulosic bioprocess based upon integrative cell and fermentation kinetic modelling framework.
One of the challenges of lignocellulose fermentation is the presence of sugar mixture (mainly glucose and xylose) released during the pretreatment and enzyme hydrolysis of lignocellulosic materials. From an economic point of view, these sugars must be efficiently fermented by organisms into desired product (Bera et al. 2010; Konishi et al. 2015; Unrean and Srienc 2010). The fluctuation of sugar composition, 30–50 % and 10–25 % of dry weight for glucose and xylose content, respectively, in different biomass feedstock strongly affects fermentation performance since an organism may not be able to optimally adjust its fermentation capacity to match with the change in sugar composition resulting in long fermentation time. A culture system that is able to handle the variation of sugar composition and efficiently ferment the sugar mixture is therefore required in order to meet the technical and economic requirements of industrial lignocellulosebased process. Another challenge for lignocellulosic fermentation is the presence of inhibitory substances (such as acetic acid and furans) generated during the pretreatment strongly inhibiting growth and fermentation performance of fermenting organism (Almeida et al. 2007; Allen et al. 2010; Klinke et al. 2004). These inhibitors are significant hurdles for the implementation of largescale lignocellulosebased bioprocess. Removal of the inhibitors by physical and chemical means significantly adds to the overall process cost and causes loss of sugars (Liu and Blaschek 2010). Therefore, the use of inhibitortolerant microorganisms in the fermentation or the use of optimized process configuration to minimize inhibitory effects is required to improve process efficiency. The development of inhibitortolerant cell factory is previously reviewed by Liu (2006, 2011) describing the mechanisms of action of known inhibitors as well as metabolic and evolutionary engineering strategies for tolerant strain development. Thus, this review focuses on fermentation process configuration to overcome inhibition issue caused by the inhibitors and fermentative end products. Moreover, problems with viscosity and partial insolubility of lignocellulosic biomass can cause poor mixing and limited mass and heat transfer especially at high solid operation of fermentation process. Design of fedbatch process configuration with sufficient mixing is required to improve process efficiency (Nguyen et al. 2015; Geddes et al. 2010; Unrean et al. 2015).
Hence, this review article discusses the development of cell and bioprocess modelling to provide a comprehensive update of the modelbased approach for the design, optimization and scaleup of biomassbased processes. Specific modelling strategies for optimizing fermentation control in lignocellulosic bioprocess based on integrative cell modelling and fermentation kinetics are discussed.
Cell modelling for growth and fermentation of lignocellulosic hydrolysate
Different type of cell modelling that is relevant to cell growth and fermentation of lignocellulosic bioprocesses is explored as follows:
Monod cell growth kinetics
An unsegregated and unstructured model based on Monod kinetic has been the most commonly used model to describe the overall cell growth and fermentation in batch, fedbatch or continuous lignocellulosic biomass processes. The Monod’s cell modelling which considers cell growth as one, single reaction is typically composed of the kinetics of (1) cell growth determined by limiting substrate (i.e. glucose or xylose present in biomass feedstock), and (2) cell death due to the endogenous metabolism as well as toxicity caused by end product or inhibitors (e.g. furfural, HMF or acetic acid) present in hydrolysates. A generalized Monod’s cell growth equation with competitive and noncompetitive inhibition of inhibitors and cell growth inhibition of end product is
In addition, cell death kinetics is typically applied to prevent an overprediction of cell viability in lignocellulosic process (Zhang et al. 2009a, b). Both cell death rate caused by end product and cultivation temperature can be described by Arrheniustype kinetics (Mutturi and Lidén 2014). Furthermore, two distinct population of cells: one is active population able to replicate, Type I cell, and another is stalled population unable to replicate due to toxicity of inhibitors, Type II cell, can also be included in the cell growth model (Wang et al. 2014). The predicted cell death rates due to endogenous metabolism, temperature, end product and toxicity of inhibitors present in hydrolysate are given as follows:
This equation is commonly applied for predicting cell growth during the fermentation of lignocellulosic hydrolysate.
Stoichiometric metabolic model
Cell growth can also been simulated based on a steadystate flux balance model which solves the stoichiometric mass balance of metabolic reaction network within cell. The model allows for quantification of carbon flux occurring within the cell by coupling extracellular fluxes for cell growth, substrate uptake and product secretion with the intracellular flux distribution in matrix form:
The most common mathematical tool used for solving these balance equations is flux balance analysis (FBA). The reader is referred to Maarleveld et al. (2013) for the thorough review of concept and application of this computation tool. Briefly, FBA yields a single flux solution that satisfies specified objective and constraints based on linear program (LP) optimization. The commonly used objectives are as follows:
\( \begin{array}{*{20}l} {{\text{Objective}}:} & {\max \mu ,q_{P} \,{\text{or}}\,q_{{ATP}} } \\ {{\text{Subject to}}:} & {q_{{\min }} \le q \le q_{{\max }} } \\ {} & {J_{i} = J_{i} ,i \in E} \\ {} & {J_{i} = 0,i \in N} \\ {} & {J_{{i,\min }} \le J_{i} \le J_{{i,\max }} ,i \in M} \\ \end{array} \)
The stoichiometry metabolic model has been utilized to study the response of cell metabolism to different environmental and genetic perturbations or different stresses caused by inhibitors during lignocellulosic fermentation process (Heer et al. 2009; Hanly and Henson 2014). By constraining fluxes associated with corresponding genes, the stoichiometric model can be applied to guide genetic engineering for increasing production of biorefinery products such as ethanol, malic acid and succinic acid (Pizarro et al. 2007; Oberhardt et al. 2009) as well as to aid process development, optimization and scaleup (Baart et al. 2007). Integration of stoichiometric metabolic model with dynamic model, regulatory and signalling network in the future could significantly increase the usefulness of the model for guiding cell engineering and optimizing lignocellulosic bioprocesses.
Fermentation kinetic model
Kinetic model to describe fermentation profile of lignocellulosic hydrolysate can be developed by taking into account growthlimiting factor such as sugar and/or nitrogen content, product titer and temperature influenced fermentation process. The proposed lignocellulosic fermentation kinetic model typically comprises (1) the sugar uptake equation and (2) the fermentation equation of secreting products. Sugar uptake model following Michaelis–Menten kinetics considers the uptake rate of hexose and pentose sugars (e.g. glucose or xylose) for cell growth, product synthesis and for maintenance process, the competitive inhibition between hexoses for each transporter (Pizarro et al. 2007) and the noncompetitive inhibition between hexoses and pentoses (Zhang et al. 2009a, b). The noncompetitive inhibition of sugar transport caused by increasing concentration of end product and by the presence of inhibitors (e.g. acetic acid, furfural or HMF) are also commonly included in the model to capture the adverse effects of these compounds on sugar fermentation (Hanly and Henson 2014). A generalized kinetics of sugar uptake is
The balance equation of sugar during lignocellulosic hydrolysate is as follows:
Balance equation of sugar:
Some yeast cells such as S. cerevisiae have ability to convert inhibitors (e.g. furfural or HMF) present in lignocellulosic hydrolysate into less toxic compounds. Thus, kinetics of inhibitor conversion should also be included when describing cell growth and fermentation. The conversion kinetics of inhibitors can be defined similarly to that of sugar uptake. A model developed by Hanly and Henson (2014) has described the detoxification of furfural and HMF from hydrolysate media by S. cerevisiae. The kinetics of fermentation describing growthassociated production based on Monod kinetics and nongrowthassociated production for fermentation products can be written in a general form as follows:
Combining cell growth and fermentation kinetic model then permits the prediction of time profiles for the production of bioproducts, such as ethanol, during lignocellulosic hydrolysate fermentation.
Integrative dynamic model for cell growth and fermentation
Most modelling frameworks describing cell growth and fermentation are based on a simple unstructured Monod kinetic model or a steadystate stoichiometric flux balance model. Integrative dynamic model framework has recently been proposed through incorporation of kinetic model and stoichiometric metabolic model for the prediction of dynamic wholecell metabolism as the culture environment dynamically changes with time. Integrative dynamic model allows the thorough studies of a dynamic interaction of cell metabolism occurring during culture environment changes or genetic alternation by predicting optimal metabolic flux distribution at each instant time throughout the process. Such model may enable an expanded platform to design process or genetic modification candidates that may enhance the efficiency in batch or fedbatch of lignocellulosic bioprocesses. Two types of integrative dynamic model capable of simulating dynamics of cell growth and fermentation in batch or fedbatch fermentation of lignocellulosic biomass have been developed: (1) dynamic flux balance model and (2) cybernetic model.
Dynamic flux balance model
Cybernatic model
Cybernetic modeling framework is based on the incorporation of internal dynamics of simplified regulated metabolic network of cell and the effects of external environment (Murthy et al. 2012). Similar to dFBA model, the cybernetic model can be divided into two distinct but interlinked models. The first model determines reaction rates of cybernetic metabolic network model consisting of simplified catabolic and anabolic pathways that produce energy, catabolic and anabolic precursors necessary for cell growth and fermentation. These pathways are optimally utilized by cell for maximizing cell growth. The second model determines kinetics for cell growth, substrate uptake and product secretion based on the metabolite balance equations. The cybernetic model is typically described by set of equations for reaction rates following Monod kinetics by assuming to vary directly with the relative enzyme concentration and to exhibit saturation dependence on all substrates.
The enzyme balance equation is given by
The balance equations for cell growth, substrate, product and intracellular metabolite can be described as follows:
The cybernetic modelling approach has been used to accurately simulate yeast cell growth, ethanol fermentation and energy consumption in batch, fedbatch and continuous fermentation of lignocellulosic biomass (Straight and Ramakrishna 1994; Ko et al. 2010; Murthy et al. 2012).
Modelbased process design and optimization
Cell consortium model for optimizing coculture fermentation
The process using cell consortia holds promise for a better exploitation of individual species capabilities leading to an efficient fermentation of pentose and hexose sugars that compose lignocellulosic biomass. A mixture of multiple substrateselective microbial or yeast strains is expected to act in concert to simultaneously uptake pentose and hexose sugars and efficiently convert to valueadded bioproducts (Suriyachai et al. 2013; Henson and Hanly 2014). Several studies have developed cell consortium model based on cell growth, fermentation kinetic model and dynamic flux balance model to study the capability of coculture system and to optimize cell growth and mixed sugar fermentation performance by coculture (Unrean and Srienc 2010; Unrean and Khajeeram 2015; Hanly and Henson 2013). Using the coculture of multiple strains enhances ethanol titer, production rate, shorten fermentation time, and reduce process costs making the coculture process a promising technology for industrial applications (Chen 2011; Wan et al. 2012; Yadav et al. 2011; Li et al. 2011; Hickert et al. 2013). The dynamic coculture model has been applied to optimize the inoculum cell concentration and aeration level that maximized fermentation process efficiency (Unrean and Srienc 2010; Hanly and Henson 2013). Coculture model has also been used to predict the optimal relative cell ratio of each strain that yields simultaneous consumption of different sugar mixture with minimal fermentation time enabling improved productivity and less production cost (Hanly et al. 2012; Unrean and Khajeeram 2015). The coculture model also demonstrates the flexibility of the cell consortia for optimally handling any sugar mixture available in different biomass feedstock. Additionally, Hanly and Henson (2013) applied the cell consortium modelling strategy for predicting targeted gene manipulation in the xylosefermenting yeast cell in order to further improve ethanol fermentation by coculture. The cell consortium modelling framework could, therefore, provide strategies for rapid process optimization of the multiplestrain culture by optimally adjusting each strain distribution based on the model prediction to match with varying sugar composition in lignocellulosic biomass feedstock for efficient and sustainable production of bioproducts.
Fedbatch lignocellulosic bioprocess optimization
Fedbatch cultivation strategy by controlling the substrate feeding can be applied (1) to overcome inhibitory effects by maintaining the inhibitors at low concentrations, (2) to avoid accumulation of undesired byproducts caused by overflow metabolism and (3) to ensure a balanced feeding of mixed hexose and pentose sugar available in biomass feedstock for achieving high yield, titer and productivity of the desired product (AbdelRahman et al. 2015; Rudolf et al. 2007; Petersson and Lidén 2007; Johnsson et al. 2013). A kinetic model based upon a system of linear differential equations can be formulated to design and optimize various process configurations such as batch and fedbatch process for efficient fermentation of biomassderived sugars. The modelbased process optimization was demonstrated in designing feed strategy with optimal specific cell growth rate of fedbatch for efficient mixed glucose–xylose fermentation (Unrean and Nguyen 2012). The optimized batch with cell recycle or with in situ ethanol removal was also simulated based on the kinetic model (Slininger et al. 2014). Besides application of the integrative dynamic model to study wholecell metabolism during batch and fedbatch processes, the modeling approach can be used for in silico determination of the optimal operating conditions, such as feed rate or feed medium composition, for fedbatch fermentation of lignocellulosic hydrolysate (Unrean and Franzen 2015).
Future prospect
The utilization of lignocellulosic feedstocks as substrate in biobased processes has increased considerably in recent years for a sustainable development of biobased economy. Design and optimization of lignocellulosic bioprocesses to improve yield, titer and productivity of desired bioproducts is key to the success of bioprocesses and biorefineries. Modelbased bioprocess design and optimization appears as a promising approach that can be used, in combination with genetic engineering and fermentation control, to facilitate the systematic design and optimization efforts aimed at rapidly improving efficiency of lignocellulosic biomass process for the production of valueadded products. Integrative cell and fermentation kinetic modelling can assist in designing fermentation strategies or identifying genetic modification candidates for enhanced lignocellulosebased bioprocess efficiency to meet the current technical and economical demand. However, the current models do not include the regulatory and signalling network or stress response mechanisms of the cell when being cultured in lignocellulosic hydrolysate which also play important roles in determining the process efficiency. Inclusion of highthroughput omics data to describe cellular regulation and genomewide kinetics is a future trend to further improve the accuracy of the integrative modelling framework for lignocellulosic bioprocess design, optimization and scaling up.
Nomenclature
 \( C_{{S_{i} }} \) :

concentration of sugar S _{ i }
 \( C_{{S_{j} }} \) :

concentration of sugar S _{ j }
 \( C_{{S_{i} ,\,{\text{feed}}}} \) :

concentration of sugar S _{ i } in feed media
 C _{ P } :

concentration of end product P
 C _{ P, max} :

maximum concentration of end product P
 \( C_{{m_{j} }} \) :

concentration of intracellular metabolite m _{ j }
 C _{ mexj } :

concentration of extracellular metabolite m _{ ex,j }
 C _{ mexj,feed} :

concentration of metabolite m _{ ex,j } in feed media
 I _{ i } :

concentration of competitive inhibitor i
 I _{ j } :

concentration of noncompetitive inhibitor j
 I _{ k } :

concentration of inhibitor k
 X :

biomass concentration
 t :

fermentation time
 \( \mu_{{S_{i} }} \) :

specific cell growth rate
 \( \mu_{{{ \rm{max} },\,S_{i} }} \) :

maximum specific growth rate on sugar S _{ i }
 D :

dilution rate of continuous culture mode
 q :

metabolite flux vector of enzymatic reaction
 \( q_{{S_{i} }} \) :

specific uptake rate of sugar S _{ i }
 \( q_{{P_{i} }} \) :

specific production rate of product P _{ i }
 q _{ATP} :

synthesis rate of ATP
 \( V_{{{ \rm{max} },\,S_{i} }} \) :

maximum rate of sugar S _{ i } uptake
 \( V_{{{ \rm{max} },\,P_{i} }} \) :

maximum specific production rate of product P _{ i }
 \( v_{\text{T}}^{\rm{max} } \) :

maximum specific transformation rate
 \( K_{{m,\,S_{i} }} \) :

saturation constant of sugar S _{ i } uptake
 \( K_{{S_{j} }} \) :

noncompetitive inhibition rate constant of sugar S _{ j } on sugar S _{ i }
 \( K_{{{\text{mu}},\,S_{i} }} \) :

saturation constant for growth on sugar S _{ i }
 \( K_{{P_{i} }} \) :

saturation constant of product P _{ i }
 K _{ i } :

competitive inhibition rate constant of inhibitor i
 K _{ j } :

noncompetitive inhibition rate constant of inhibitor j
 K _{trf} :

specific transformation rate from type I to type II cells
 k _{T} :

inhibitor saturation constant of type I–type II cell transformation
 n :

exponential constant of ethanol inhibition to growth on sugar S _{ i }
 \( m_{{S_{i} }} \) :

maintenance coefficient for growth on sugar S _{ i }
 \( Y_{{X,\,S_{i} }}^{\rm{max} } \) :

maximum cell yield on sugar S _{ i }
 \( Y_{{P_{i} ,\,S_{i} }} \) :

yield of product P _{ i } based on consumed sugar S _{ i }
 A :

frequency factor for Arrhenius equation
 E :

activation energy for Arrhenius equation
 T:

culture temperaturef
 a :

ethanol death coefficient
 b :

ethanol death rate constant
 H :

coefficient for cooperative transformation from type I to type II cells
 S :

m by n stoichiometric matrix of metabolite m in enzymatic reaction n
 J :

vector of accumulation and exchange rates
 E :

set of intracellular metabolites with externally determined exchange flux
 N :

set of intracellular metabolites with no accumulation
 M :

set of extracellular metabolites based on experimental measurement
 r _{ i } :

specific rate of reaction i for synthesis or degradation of metabolite
 r _{ ek } :

synthesis rate of enzyme k
 k _{ i } :

rate constant of reaction i
 ɛ _{ i } :

relative concentration of enzyme catalysing reaction r _{ i }
 \( K_{{m_{j} }} \) :

saturation constant of metabolite m _{ j }
 e _{ k } :

concentration of enzyme k
 β _{ k } :

firstorder degradation constant of enzyme k
 v _{ i } :

cybernetic variable for activity of enzyme i
Declarations
Acknowledgements
We greatly acknowledge Thailand Research Fund (Grant no. P1551025) and National Center for Genetic Engineering and Biotechnology, Thailand (Grant no. P1550042), for funding support.
Competing interests
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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