Biofuel production improvement with genome-scale … 2010 Biotechnology... · ·...

© 2010 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim 671

Biotechnol. J. 2010, 5, 671–685 DOI 10.1002/biot.201000007 www.biotechnology-journal.com

1 The challenges of liquid biofuels

The demand for more environmentally friendlyand renewable liquid transportation fuels has ledto an enormous research effort to engineer andharvest them from microbes [1–3]. Much of the ini-tial effort has focused on ethanol fermentationfrom plant-derived sugars and biodiesel from veg-etable and waste oils [4]. More recent efforts havetargeted butanol [5] due to its comparably high en-ergy content, compatibility with existing pipelineinfrastructure, and suitability for modern engines[6–8]. Novel advances in synthetic biology have en-abled the production of higher alcohols, such as

isobutanol, that are non-native to microbial speciesyet have appeal as potential biofuels [7–9]. Syn-thetic approaches are also being taken to producenovel fatty acid derivatives, alkanes and alkenes inmicrobes [10, 11]. Several other non-fermentivesources of potential liquid biofuels show promise.These include biocrude oils from thermochemicalconversions [12], algal biofuels [13, 14], and “on-de-mand” hydrogen production from sugar using acell-free synthetic pathway [15]. Despite the initialsuccesses, it is widely recognized that several chal-lenges still exist for the commercialization of mi-crobial-derived liquid biofuels. These include: (i)the identification of one or more chemicals to serveas optimal biofuel(s) [3, 4], (ii) metabolic engineer-ing to maximize production from a chosen host, (iii)production from lignocellulosic materials [1, 2, 16,17] in a consolidated bioprocess [17] or directlyfrom sunlight and CO2 [18], and (iv) production inhigh concentrations leading to improved down-stream processing and process economics.

Review

Biofuel production improvement with genome-scale models:The role of cell composition

Ryan S. Senger

Biological Systems Engineering Department, Virginia Tech, Blacksburg, VA, USA

Genome-scale models have developed into a vital tool for rational metabolic engineering. Thesemodels balance cofactors and energetic requirements and determine biosynthetic precursor avail-ability in response to environmental and genetic perturbations. In particular, allocation of addi-tional reducing power is an important strategy for engineering potential biofuel production frommicrobes. Many potential biofuel solvents induce biomolecular changes on the host organism thatare not yet captured by genome-scale models. Here, methods of construction for several biomassconstituting equations are reviewed along with potential changes to cellular composition withpotential biofuels exposure. The biomass constituting equations of potential host organisms withexisting genome-scale models are compared side-by-side to explore their evolution over the yearsand to explore differences that arise when these equations are compiled by different researchgroups. Genome-scale model simulation results attempt to address and provide guidance for fur-ther research into: (i) whether inconsistencies in the biomass constituting equations are relevantto predictions of solvent production, (ii) what level of detail is necessary to accurately describe cel-lular composition, and (iii) future developments that may enable more accurate characterizationsof biomolecular composition.

Keywords: Biofuels · Biomass constituting equations · Genome-scale model · Metabolic engineering · Systems biology

Correspondence: Dr. Ryan S. Senger, Virginia Tech, Biological SystemsEngineering Department, Blacksburg, VA 24061, USAE-mail: [email protected]

Abbreviation: DCW, dry cell weight

Received 12 January 2010Revised 25 April 2010Accepted 28 April 2010

Supporting information available online

BiotechnologyJournal Biotechnol. J. 2010, 5, 671–685

672 © 2010 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

2 Stoichiometric models for metabolicengineering

Metabolic engineering solutions to the challengesfor liquid biofuels production mentioned above areactively being sought [3]. To achieve this, both ra-tional and combinatorial metabolic engineeringapproaches are being applied. Rational approach-es involve the use of metabolic pathway recon-structions and models to identify specific gene andpathway targets to re-direct metabolic flux. Usual-ly this is done with the intention of increasing pro-duction of a targeted intracellular or secretedmetabolite [19, 20].The combinatorial approach, onthe other hand, involves selection of a desired cel-lular phenotype, which is most often multigenic,from a heterogeneous population of cells [21–25].In general, genetic manipulations are performed ina stochastic manner, and resulting cells are select-ed through growth competition [26, 27] or by high-throughput screening [28], depending on the selec-tion characteristics of the desired phenotype. Thefocus of this review is on rational metabolic engi-neering approaches leading to improved biofuelsproduction. In particular, this review considers theexisting genome-scale models for the following po-tential biofuel-producing microorganisms: Esche-richia coli K-12 MG1655 [29–31], Saccharomycescerevisiae [32, 33], Bacillus subtilis [34, 35], andClostridium acetobutylicum ATCC 824 [36–38].

Genome-scale models have developed largelyin the past 10 years in response to the numerousgenome sequencing projects and have been re-viewed in numerous places [39–41]. Genome-scalemodeling serves to provide the connection betweenan organism’s genotype and its expressed pheno-type [29].This is done by reconstructing a metabol-ic network from genomic annotation [42] to serveas a platform for systems-level data [43]. Flux bal-ance analysis is used for generating metabolic pre-dictions through elemental and cofactor balancingover the entire metabolic network [44–47]. To dothis, the allocation of macromolecules, organicsolutes, ionic species, reducing power, and energyto cell growth and division is necessary. Thus, a“biomass constituting equation” (also called a “bio-mass objective function”) is required in thegenome-scale model. Consideration of existingbiomass constituting equations are of importancebecause the exposure of many cell types to solventsthat could serve as potential biofuels are inhibitoryto cell growth and alter the chemical composition ofthe cell. In this review, the methods for construct-ing the biomass constituting equations of the po-tential biofuels-producing organisms listed aboveare discussed. Additionally, the biomass constitut-

ing equations from these organisms have been de-constructed and parsed so that they can be com-pared side-by-side.The evolving nature of the bio-mass constituting equation is illustrated throughmultiple genome-scale model developments for E.coli K-12 MG1655 [29–31]. This comparison alsodemonstrates the differences in biomass constitut-ing equations for genome-scale models preparedby different research groups at the same time (C.acetobutylicum [36–38]). A brief review of the ex-perimental findings that have correlated solventexposure with changes in cellular composition fol-lows. Experimental methods, including emergingspectroscopic techniques, used to probe genome-wide biomolecular changes are also discussed. Newsimulation results of a published C. acetobutylicumgenome-scale model [37, 38] are presented to illus-trate the impact of the biomass constituting equa-tion on the production of biobutanol. Finally, a dis-cussion is presented on how algorithmic approach-es may lead to more effective biomass constitutingequations that are responsive to environmentalstimuli and what measurements are truly impor-tant for composing effective biomass constitutingequations for new cell types.

3 Optimizing biofuels production with genome-scale models

A recent review outlined the applications and com-putational tools that have been developed using thegenome-scale model for E. coli K-12 MG1655 [40].In the span of a decade, genome-scale metabolicreconstructions and flux models have quicklygrown into the platform needed for high-through-put data interpretation, engineering target identifi-cation, and biological discovery [48]. Computation-al tools have been developed to predict “sub-opti-mal” phenotypes following a genetic perturbation,such as a gene knockout [49–51]. Further researchhas shown that these strains eventually evolve totheir predicted optimum growth rates [52]. Howev-er, there is room for improvement in the quantita-tive modeling of biological process dynamics. Ofcourse, it is a primary goal of genome-scale model-ing to develop truly predictive models of phenotyp-ic changes in response to genetic and environmen-tal perturbations. Ultimately, this will allow re-searchers to engineer genetic circuits and synthet-ic pathways in silico. This trend has already begunfor biofuels production [9, 53, 54] and will be great-ly aided when engineered using a genome-scalemodel. However, important developments areneeded to enable effective modeling of solvent pro-duction. For example, solvent tolerance or solvent-


induced biomolecular changes to a microorganismare not yet included in genome-scale models. It issuspected that to incorporate these factors, accu-rate cellular compositions will be needed by bio-mass constituting equations.

The biomass constituting equations of the po-tential biofuel producers: E. coli K-12 MG1655[29–31], S. cerevisiae [32, 33], B. subtilis [34, 35], andC. acetobutylicum ATCC 824 [36–38] have been de-constructed and arranged in a format to make themcomparable to one another.This is included as Sup-plementary Appendix 1.This table provides a snap-shot of how the biomass equation has evolved forthe multiple E. coli K-12 MG1655 models producedby the Palsson research group and how biomassconstituting equations compare for the same or-ganism when produced by different groups. Clear-ly, remarkable consistencies exist. However, the in-consistencies show the need for the developmentof standards and uniformity.The following sectionsof this review seek to address the question ofwhether the inconsistencies of biomass constitut-ing equations are important to predicting improvedsolvents production from genome-scale models. Ifso, what level of detail is important? And, how canthis be achieved in a dynamic environment?

4 Developing biomass constitutingequations

When the metabolic flux profile of a genome-scalemodel is optimized using linear programming (i.e.,flux balance analysis), an objective function mustbe maximized or minimized to result in cell growth.Traditionally, maximizing the rate of cell growthhas served as the objective function; however, re-search on this topic continues [55]. Thus, for mostcases, the rate at which macromolecules and cellu-lar “building-blocks” (e.g., DNA, RNA, lipids, etc.)are synthesized and assembled is maximized. Ob-viously, the identity and quantity of these new cel-lular building-blocks are based on observed cellu-lar physiology.These are specifically defined in thebiomass constituting equation of an organism.Published physiological data have long been thesource for data for compiling a computational ac-count of biomolecular composition of a cell. This isthe ideal choice for the systems biologist construct-ing a new metabolic network reconstruction andgenome-scale model. In absence of literature data,extrapolations are made to similar organisms, andseveral researchers have designed and performedexperiments to obtain specific data.This section re-views methods of biomass constituting equationdevelopment for well-studied and lesser-studied

organisms. The genome-scale-model builder oftenrelies on literature data to construct an accurateportrayal of the cellular composition. However, or-ganism-specific data are rarely available to buildthe entire biomass constituting equation, and par-allels must be drawn to closely related organisms.Thus, this section intends to provide several exam-ples of effective ways to integrate nonspecific datawhere necessary. Simulations in a following sectionseek to illustrate ramifications of these model-building assumptions.

The biomass constituting equation includesmacromolecules such as: (i) protein, (ii) DNA, (iii)RNA, (iv) lipids, and (v) cell wall (including pepti-doglycan, teichoic acids, and lipopolysaccharides).Solutes, ions, and cofactors found within the cyto-plasm are also included. The specific macromole-cules that populate these broad cellular buildingblocks are cell specific and are largely presented inSupplementary Appendix 1.The biomass constitut-ing equation also contains ATP hydrolysis termsrelated to the energetics of cell division and macro-molecule assembly.

To construct a biomass constituting equation fora new organism, an initial consideration should beits homology to an organism that already containsa well-established biomass constituting equation.E. coli K-12 seems to be the only organism with anestablished biomass constituting equation [29–31]that did not require some sort of comparative studyto another organism. Next, a literature search is re-quired to acquire relevant data regarding about (i)the membrane lipids profile (phosphatidyl headgroups and acyl chain lengths), (ii) the accumula-tion of organic solutes and ions in the cytosol, and(iii) the cell wall components (peptidoglycan con-tent, teichoic acids, lipopolysaccharides, etc.) forthe particular organism. This procedure was fol-lowed for the well-documented construction of abiomass constituting equation for the pathogenStaphylococcus aureus N315 [56]. Even though S.aureus is considered a “well-studied” organism inthe literature (with more than 60 000 publicationsin PubMed), Heinemann et al. [56] noted that dataon the specific biomass composition for strainN315 were not fully available in published litera-ture.Thus, cellular composition data from a varietyof S. aureus strains and complex media formula-tions were pooled to form a representative biomassconstituting equation for the N315 strain. This wasnecessary despite the authors’ acknowledgementthat these factors are known to impact cellularcomposition [56]. Further, energy requirements re-lated to biomass building-block (e.g., protein, DNA,and RNA) synthesis were inferred from E. coli datadue to the lack of adequate literature data. The

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DNA and RNA compositions were derived throughan analysis of the G+C content of the genome andassuming cellular contents of 0.03 g DNA/g dry cellweight (DCW) and 0.12 g RNA/g DCW.The averageamino acid composition of a protein was also de-rived from a sequence-based analysis of ORFs.Thederivation of lipid, cell wall, and solute pool con-tents in the biomass constituting equation werebased on literature data [56]. S. aureus is not a po-tential biofuels-producing organism; however, thisexample is presented because of the outstandinglevel of detail provided by Heinemann et al. [56] forformulation of their biomass constituting equation.The following example is also from a non-biofuels-producer, but it, too, provides the reader with an ex-cellent description.

Corynebacterium glutamicum ATCC 13032 is alesser-studied bacterium (with about 1100 publica-tions in PubMed) that is used industrially for theproduction of amino acids, L-lysine and L-gluta-mate in particular [57]. For the biomass constitut-ing equation of this organism [58], the followingbiomass building-blocks were considered: (i) pro-tein (0.52 g/g DCW), (ii) DNA (0.01 g/g DCW), (iii)RNA (0.05 g/g DCW), (iv) lipids (0.13 g/g DCW)consisting of mycolic acids and phospholipids, and(v) cell wall carbohydrates (0.19 g/g DCW) consist-ing of peptidoglycan and arabinogalactan. The av-erage amino acid content was obtained from liter-ature data [59]. The DNA composition was calcu-lated based on the G+C content of the genome. RNAwas assumed to consist of 5% mRNA, 75% rRNA,and 20% tRNA (molar). The mRNA compositionwas determined from a genomic analysis of G+Ccontent in ORFs.The rRNA composition was calcu-lated from sequences of 16S, 23S, and 5S ribosomalRNAs. The tRNA composition was calculated fromthe sequences of leucine- and glycine-transportingRNAs. Lipids composition was determined frompublished data, as were the major phospholipidconstituents. Peptidoglycan, arabinogalactan, andmycolic acid biosyntheses were described withlumped reactions derived from organism-specificand nonspecific data.

For the gram-positive butanol producer C. ace-tobutylicum, the existing genome-scale models[36–38] contain biomass constituting equationsthat were derived simultaneously, with neithergroup having knowledge of the other. Even thoughthese metabolic network reconstructions were re-markably similar (502 reactions and 479 metabo-lites for [36] and 552 reactions and 422 metabolitesfor [37, 38]), considerable differences are observedbetween the biomass constituting equations asso-ciated with these genome-scale models. Both ofthese biomass constituting equations are given in

Supplementary Appendix 1.The biomass constitut-ing equation for the model composed by Lee et al.[36] used the macromolecular composition andsolute pools generated for the well-studied gram-positive B. subtilis [35]. The nucleotide, lipid, andteichoic acid compositions were determined fromavailable literature data. The authors experimen-tally analyzed amino acids and cell wall composi-tions for this biomass constituting equation. Fur-ther, experiments were performed to obtain valuesof growth- and non-growth-associated mainte-nance values. The biomass constituting equationprepared by Senger and Papoutsakis [37, 38] wasderived from the S. aureus N315 model [56] dis-cussed above. The protein composition from theHeinemann et al. [56] model was used.The averageDNA composition was calculated from the genomesequence.The average RNA composition was heav-ily weighted on rRNA and tRNA sequences as wellas mRNA sequences highly expressed in unpub-lished DNA microarray studies. Considerable re-search has been performed on the lipid composi-tion of solventogenic clostridia [60–64], and this lit-erature was used to derive the lipid component ofthe biomass constituting equation.A single value ofacyl chain length of C16:0 (carbon chain length,number of double bonds) was used because it is thedominant experimental observation [62, 64]. Thelipoteichoic acid content was adapted from experi-mental studies in B. subtilis [65, 66].The lack of cellwall data available in the literature led to adapta-tion of B. subtilis and S. aureus data in the biomassconstituting equation. From a model-building per-spective, the two models for C. acetobutylicum are ofconsiderable interest. C. acetobutylicum is a rela-tively under-studied organism in the literature, andthese models were prepared simultaneously by twogroups that had no contact with one another re-garding the model building process. The two S. au-reus N315 models [56, 67] were also prepared un-der these circumstances.

5 Modeling an adaptive cell composition

Most biomass constituting equations are static en-tities. Regardless of environmental conditions andgenetic perturbations, the metabolic demands fornew biomass synthesis remain the same, accordingto the model. Of course, several successful genome-scale models have been constructed on this concept[40, 41]. Traditionally, changes in cellular functionhave been described using gene regulatory net-works, and good agreement between in silico pre-dictions and cellular phenotypes have been ob-served [68–71]. In an effort to determine regulato-ry mechanisms, an early stoichiometric E. coli mod-


el considered a dynamic biomass equation to fit ex-perimental data [72, 73]. Of course, this is differentfrom dynamic flux balance analysis (DFBA) [74]that has found use in industrial E. coli fermenta-tions [75]. The model by Pramanik and Keasling[72] predicted how cellular composition changeswith growth rate. The dependence of cell composi-tion on the growth rate was initially developed fromthe observations that, in E. coli, RNA content in-creases with growth rate, while DNA and proteincontents decrease [76, 77]. This led to an iterativeprocedure to balance biomass requirements for aparticular carbon and energy source, given a de-fined growth rate. Ultimately, this allowed the cor-relation of all biomass building blocks (e.g., protein,DNA, RNA, glycogen) and cell characteristics (e.g.,cell mass, radius, length, and surface area) as afunction of the specific growth rate. A sensitivityanalysis further confirmed the importance of thebiomass constituting equation on obtaining fluxvalues that correlate with experimental measure-ments for their model [72]. A later model by theseresearchers specifically investigated the averageamino acid composition of protein and the cellmembrane fatty acid composition on the changes tomeasured metabolic fluxes using a sensitivityanalysis [73]. The authors concluded that the com-position of fatty acids varied with the dilution rateand carbon source, but amino acid composition didnot change significantly with growth conditions. Itwas concluded that this particular metabolic mod-el showed a high degree of sensitivity to compo-nents of the biomass constituting equation [73].

More recent studies of E. coli K-12 MG1655 [30]and S. cerevisiae [33] also assessed the sensitivity offlux simulations against the macromolecular com-position of the cell. For the E. coli model, the growthrate and O2 uptake rates were calculated for speci-fied glucose uptake rates, while individually vary-ing cellular compositions of: (i) protein (50–80%),(ii) RNA (10–25%), (iii) lipids (7–15%), (iv) potentialP/O ratios (1.0–2.7), (v) non-growth-associatedmaintenance (±50% measured value), and (vi)growth-associated maintenance (±50% measuredvalue). Results showed that the P/O ratio, and themaintenance energy requirements are the mostsensitive components of this model [30]. For S.cerevisiae, the sensitivity analysis was performedfor aerobically grown cells under glucose- and am-monium-limited conditions [33]. In this model, the(i) protein, (ii) RNA, (iii) carbohydrate, and (iv) lipidcontents of the biomass equation were variedaround previously reported ranges [78–80].The au-thors reported that glucose- and ammonium-limit-ed cultures were most accurately modeled usingseparate biomass constituting equations for each

condition [33]. Further, the amino acid compositionof protein was found to more strongly influence up-take rates at a fixed growth rate. Carbohydrate,lipid, and RNA content had little influence with thismodel in sensitivity analysis of the biomass build-ing blocks of the biomass constituting equation.Flux profiles were validated against data from 13C-labeling experiments [33].

A recent approach with a genome-scale modelfor C. acetobutylicum also investigated the use ofdynamics in the biomass constituting equation tofit experimental data [38]. In this case, the mainte-nance ATP demand was optimized over discretestates of batch growth to fit observed metaboliteprofiles, optical density, and extracellular pH dropof the culture (calculated with a charge-balance-derived pH model). The values of ATP hydrolysisrequired to fit these data far exceeded (by morethan one order of magnitude) the maintenanceATP values experimentally determined for C. ace-tobutylicum [36] or other organisms. The authorsrecognized a changing (or dynamic) flux profileover the course of exponential growth of this or-ganism [38], long before the onset of transcription-al regulation leading to solventogenesis and sporu-lation [81–83]. The authors observed the highestoptimized maintenance ATP values during “lag-phase” events of batch growth and toward the on-set of the stationary phase, which is associated witha metabolic shift in solventogenic clostridia.

6 Solvent-induced biomolecular changes

The cellular response of microorganisms to etha-nol and other solvent exposure has been studiedfor decades and is well-documented. Here, some ofthese studies are revisited to determine if solvent-induced cellular composition changes are signifi-cant enough to be reflected in biomass constitutingequations. Relevant studies in E. coli are followedby those for S. cerevisiae and the solventogenicclostridia. It is noted the solvent toxicity is relatedto the portioning coefficient of a solvent in a de-fined octanol-water mixture [84] and the inherenttraits of the organism itself [85]. Thus, predicting acell’s solvent response mechanisms will ultimatelybe a function of the organism’s genotype as well asthe molecular traits of the solvent itself.

Several mechanisms of solvent tolerance havebeen deduced for gram-negative bacteria, and thefollowing prominent mechanisms have been re-viewed in detail [85]: (i) adaptation of membranefatty acids and phospholipid head groups, (ii) vesi-cle formation for toxic compounds, and (iii) up-reg-ulation of energy-dependent solvent efflux pumps.The adaptive response of the gram-negative bac-




terium is to alter membrane fluidity upon exposureto solvents by undergoing cis-to-trans isomeriza-tion of unsaturated fatty acids in the lipid bilayer(short-term solution) and by altering the saturat-ed-to-unsaturated fatty acid ratio and the ratio oflong-chain to short-chain fatty acids in the mem-brane (long-term response) [85]. The acidic phos-pholipids of phosphatidyl glycerol and cardiolipinhave been found to increase as a result of ethanolexposure; whereas the phosphatidyl ethanolaminecontent decreases. Research also showed that thetotal amount of phospholipids decreases withethanol exposure [86]. Ethanol exposure is alsomarked by a decrease in C16:0 palmitic acid and re-placed by C18:1 cis-11, vaccenic acid. Thesechanges in fatty acids have been associated with in-creased surface membrane fluidity but may alsocorrelate with increased rigidity of the center of theplasma membrane [87]. Ethanol exposure in E. colileads to higher levels of membrane proteins [88]that may have rigidifying effects on fatty acylchains [89]. Increased protein content and de-creased lipid/protein ratio may serve as a barrier toion leakage and cell death resulting from increasedmembrane fluidity [90].

Similar to E. coli, the lipid bilayer of S. ceriviseareplaces C16:0 palmitic acid with C18:1 acids [91].In particular, You et al. [92] determined that oleicacid (C18:1 cis-9) of the lipid membrane is directlytied to ethanol tolerance in S. cerevisiae. Similar toE. coli, significant changes in acyl chain length andsaturation are observed in lipid bilayer phospho-lipids in response to ethanol exposure [91, 93, 94].Sterols, particularly ergosterol, are particularly im-portant to solvent tolerance [91], and several stud-ies attempted to decode the mechanisms of ergos-terol protection and its genetic regulation. Like-wise, the effect of solvents (ethanol and butanol)has been studied on the membrane lipid content ofthe solventogenic clostridia (including C. aceto-butylicum).

Several solventogenic clostridia, such as C. ace-tobutylicum (formerly C. butyricum) and C. beijer-inckii can be identified by their lipid profiles [95].This makes lipids homology in deriving biomassconstituting equations for the clostridia difficult.The composition of the phosphatidyl head groupshave been determined for this organism as func-tions of the acyl chain length and exposure to de-fined solvents [63, 95]. Although not reviewed herein detail, cellular differentiation (e.g., sporulation)is also a significant contributor to changes in thecellular biomolecular composition [96]. Further,the accumulation of energy storage polymers, suchas granulose in C. acetobutylicum [97], require sig-nificant cellular resources and need to be included

in biomass constituting equations. Although sensi-tivity analyses have been performed on several cel-lular building blocks [30, 33, 72, 73], the degrees towhich smaller changes (e.g., altered phosphatidylhead groups) impact flux balance solutions needsto be determined.

Given the significant changes in cellular bio-molecular composition noted above, a high-throughput experimental study has emphasizedthe robustness of the metabolic network andmetabolome of E. coli K-12 in response to geneticand environmental perturbations [98]. The follow-ing high-throughput measurements were made toprobe the global response of this organism: DNAmicroarrays, two-dimensional differential gel elec-trophoresis (2D-DIGE) [99], capillary elec-trophoresis time-of-flight mass spectrometry (CE-TOFMS) for metabolome analysis [100, 101], andliquid chromatography tandem mass spectrometry(LC-MS/MS) for quantification of proteins [98].Al-though changes in enzyme, mRNA, and metabolitelevels were observed for the environmental and 24single-knockout strains examined, the degree towhich these variables changed was much less thanexpected, leading to the hypothesis of an inherentrobustness in the metabolic network. These find-ings, indeed, should have implications in deriving adynamic biomass constituting equation.

7 Measuring cell composition changes

Clearly, for potential biofuel-producing organisms,changes in membrane lipids in response to accu-mulating solvents are of concern when consideringa systems approach. The ability to easily measureand quantify cellular composition changes is sig-nificant for the eventual development of dynamicbiomass constituting equations that are responsiveto changes in the culture environment. Severaltechniques have developed in addition to tradition-al MS measurements.This section discusses poten-tially powerful experimental techniques that haveyet to be used in developing biomass constitutingequations. High-throughput research in this areahas been enabled with the rise of lipidomics[102–106]. Studies coming from this field estimatethat the eukaryotic lipid membrane may consist ofhundreds to thousands of lipid species [106]. Twomethods are highly utilized in the effort to identifyand quantify all molecular species of lipids in thecell: (i) LC/MS and (ii) shotgun lipidomics. Exam-ples involving LC/MS have been described else-where [102, 105, 107, 108]; this technique providesan accurate method of measurement with a draw-back being that “one only finds what one is lookingfor” [102]. Shotgun lipidomics, on the other hand,


have shown promise in the discovery of novel lipidstructures [102, 109]. The lipidome for S. cerevisiaewas recently elucidated using this approach to re-veal over 250 molecular lipid species in 21 lipidclasses. A comparative lipidomics study by theseauthors using a temperature perturbation yieldedwhat was referred to as a “ripple effect” to describemolecular composition changes over the entirelipidome [110]. What impact ethanol exposure willhave remains to be seen but is of tremendous in-terest to metabolic engineers interested in solventtolerance mechanisms.

The cellular biomolecular-level real-time re-sponse of a culture to an environmental perturba-tion is also of interest to building better biomassconstituting equations and defining regulatorymechanisms. Effective analysis methods are need-ed to evaluate metabolic engineering ventureswithout performing a full analysis of the lipidome.Fourier transform infrared (FT-IR) has been shownto be effective for bacterial strain identification andrecognition of biomolecular composition [111, 112].Further studies have shown that FT-IR can also bea useful tool for analyzing the phospholipids bilay-er, peptidoglycan, and lipopolysaccharides of thecell surface along with fatty acids, polysaccharides,and nucleotides of the cell cytoplasm [113, 114]. Arecent study examined the global transcriptionaland biomolecular responses to E. coli cultures ex-posed to a number of environmental perturbations,including acetic acid and ethanol [115]. FT-IR spec-troscopy was used to show that, along with coldstress, ethanol resulted in an increased signal inband ~3006 cm–1, which corresponds to the C=C-Hstretch present in unsaturated fatty acids. Heatstress and acids exposure was observed to decreasethis band signal [115]. These studies also allowedmeasurement of membrane fluidity using the posi-tion of the maximum of the symmetric vibrationband of the CH2 group (~2850 cm–1). Calculation ofthe CH3/CH2 ratio allowed assessment of fatty acidchain lengths. Shorter fatty acid-chain lengthswere observed, relative to the control, for the cul-ture exposed to ethanol [115]. Principle componentanalysis was used in this study to compare FT-IRspectra related to fatty acids, proteins, and carbo-hydrates. Ethanol exposure resulted in clusters forall three regions that were largely separated fromthose of the other stresses [115]. Further applica-tions of this analysis include monitoring the tran-sient chemical environment of the oxygen adaptiveresponse of an anaerobe [116] as well as monitor-ing the cell membrane behavior of E. coli duringwater removal and rehydration [117]. Similar re-search is being done to develop Raman spec-troscopy for microorganism identification, culture

heterogeneity, and cell composition [118–120]. Al-though these spectroscopic techniques have shownsignificant promise in distinguishing qualitativechanges between microorganisms and environ-mental perturbations, these data have not yet beenused to formulate a quantitative cellular composi-tion that could be used in a biomass constitutingequation.

8 Genome-scale model simulations

The genome-scale model of C. acetobutylicum bySenger and Papoutsakis [37, 38] was used to assessthe impact of changes in the biomass constitutingequation on biobutanol production at the onset ofthe stationary phase of batch growth.The impact ofthe biomass constituting equation was evaluatedon biobutanol production by considering (i) bio-mass constituting equations from other organisms(Fig. 1), (ii) the incorporation of the storage poly-mer granulose (Fig. 2), (iii) varying the total amountof lipids (Fig. 3), (iv) varying the total proton in-flux/efflux (Fig. 4), and (v) considering differentvalues of the ATP maintenance requirement(Fig. 5). Using Supplementary Appendix 1, the bio-mass constituting equations for the C. aceto-butylicum model prepared by Lee et al. [36] and thegram-positive B. subtilis [35] were installed in theC. acetobutylicum genome-scale model by Sengerand Papoutsakis [37, 38]. The specific growth ratewas maximized given specified biobutanol effluxvalues. Here, several values were constrained, in-cluding: (i) the acetate and butyrate influx/efflux,(ii) the butyrate concentration of the extracellularmedium (2 g/L), (iii) the specific proton influx/ef-flux (0 mmol h–1 g DCW–1), and (iv) the ATP main-tenance requirement (100 mmol/g DCW). Asshown in Fig. 1, the three different biomass consti-tuting equations produced different profiles ofbiobutanol efflux with specific growth rate. Re-markable symmetry is observed from the Lee et al.[36] and Senger and Papoutsakis [37, 38] biomassconstituting equations; however, the maximumpredicted growth rates differ by 13%. The biobu-tanol flux is unchanged at the maximum predictedgrowth rate from both biomass constituting equa-tions. Considerable differences are observed withthe B. subtilis biomass constituting equation.Thesesimulations suggest that caution must be exercisedwhen using an existing biomass constituting equa-tion from another organism. Using the Senger andPapoutsakis biomass constituting equation [37, 38],the metabolic impact of granulose (50-kDa glucosestorage polymer) accumulation on biobutanol pro-duction was assessed. Results are shown in Fig. 2.Wild-type accumulation levels result in a stoichio-




metric coefficient of ~1 × 10–3, which results in pre-dicted biobutanol production and growth very sim-ilar to a culture that does not accumulate granulose.Significant deviations are observed as this stoi-

chiometric coefficient approaches 1 × 10–2.The sto-ichiometric coefficient of total lipids in the biomassconstituting equation was varied between 0.001and 1. Results are shown in Fig. 3. Significant devi-

Figure 1. Relationship of specific butanolefflux with specific growth rate for theSenger and Papoutsakis [37, 38] C. aceto-butylicum genome-scale model with alter-native biomass constituting equationsfrom the Lee et al. [36] C. acetobutylicumand B. subtilis [35] genome-scale models.Constants: ATP maintenance(100 mmol g DCW–1), specific protonefflux (0 mmol h–1 g DCW–1), granuloseaccumulation (0 mmol g DCW–1).

Figure 2. Relationship of the specificbutanol efflux with specific growth ratefor the Senger and Papoutsakis [37, 38]genome-scale model given specifiedstoichiometric coefficients for granulosein the biomass constituting equation.Constants: ATP maintenance(100 mmol g DCW–1), specific protonefflux (0 mmol h–1 g DCW–1).


ations between the biobutanol and specific growthrate relationship were only observed as the totallipids stoichiometric coefficient approached 1. Ofcourse, these model results only take into account

the metabolic burden of increased lipids produc-tion. The model cannot anticipate any “protective”impact of increased lipids production in a high sol-vent concentration environment. Thus, adding sol-


Figure 3. Relationship of the specificbutanol efflux with specific growth ratefor the Senger and Papoutsakis [37, 38]genome-scale model given specifiedstoichiometric coefficients for lipids inthe biomass constituting equation.Constants: ATP maintenance(100 mmol g DCW–1), specific protonefflux (0 mmol h–1 g DCW–1), granuloseaccumulation (0 mmol g DCW–1).

Figure 4. Relationship of the specificbutanol efflux with specific growth rate forthe Senger and Papoutsakis [37, 38]genome-scale model given the specifiedspecific proton flux (mmol H+ h–1 g DCW–1).Negative values are proton efflux, andpositive values are influx. Constants: ATPmaintenance (100 mmol g DCW–1), granu-lose accumulation (0 mmol g DCW–1).



vent toxicity mechanisms to a genome-scale modelis a distinct and important area for future modeldevelopment. On the other hand, the total specificproton influx/efflux of the cell was shown to have adramatic impact on the predicted biobutanol pro-duction and specific growth rate relationship. Re-sults are shown in Fig. 4. As Senger and Papout-sakis [38] described previously, the specific protonflux is calculated from a summation of all mem-brane transport fluxes involving protons. Only asmall deviation in the proton influx/efflux was re-quired to elicit a significant response, as shown inFig. 4. Proton influx/efflux values range from–5 mmol H+ h–1 g DCW–1 (efflux) to 3 mmol H+ h–1 gDCW–1 (influx). By comparison, during the acido-genic stage of vegetative growth in C. aceto-butylicum, proton efflux values exceed –50 mmol H+ h–1 g DCW–1.The largest deviation in the growthrate in Fig. 4 is greater than 40% with predictedbiobutanol production differing by more than 30%.Finally, the impact of the maintenance ATP re-quirement is shown in Fig. 5. A strong influence isobserved from ATP maintenance on the relation-ship between the predicted biobutanol productionand the specific growth rate. These results suggestthat accurate quantification of cell maintenance re-quirements is of utmost importance for predictingproduction of potential biofuels compounds. How-ever, as described in this review, sensitivity analy-ses of the components of biomass constituting

equations from different models often yield con-flicting results.These are likely due to inherent dif-ferences in the respective metabolic networks.Likewise, the simulations reported here need to becarried out with other genome-scale models to pro-vide adequate comparisons and transparency be-tween individual models as well as to locate thesources of model-dependence.

9 How does this impact developing a biomass constituting equation?

It becomes increasingly clear that the biomassconstituting equation is an approximation of thecellular biomolecular composition, no matter howmany data sets and experimental measurementsare included in its construction. However, shouldthis matter for in silico predictions using genome-scale models? The number of genomes sequencedis increasing exponentially. With automated meth-ods of genome annotation [121, 122] and metabolicnetwork building [42], the number of metabolicnetwork reconstructions is expected to explode aswell. As discussed previously, these data are onlyavailable for very few organisms for constructingtruly representative biomass constituting equa-tions. So, is “biomolecular homology” sufficient toderive biomass constituting equations for all otherorganisms? Do costly laboratory measurements

Figure 5. Relationship of the specificbutanol efflux with specific growth ratefor the Senger and Papoutsakis [37, 38]genome-scale model given specifiedstoichiometric coefficients for main-tenance ATP in the biomass constitutingequation. Constants: specific protonefflux (0 mmol h–1 g DCW–1), granuloseaccumulation (0 mmol g DCW–1).


need to be performed to develop organism-specif-ic biomass constituting equations? The simulationresults presented here suggest that organism-spe-cific results are necessary but many broad approx-imations can be made. Even the biomass constitut-ing equation of the closely related B. subtilis is notadequate for accurately predicting biobutanol pro-duction in C. acetobutylicum. Additional studies arerequired to determine which components of thebiomass constituting equation must be organism-specific and which can be inferred from other or-ganisms. However, accurate ATP maintenance val-ues are of utmost importance to developing accu-rate genome-scale models.The specific proton fluxwas also significant in determining the relationshipbetween the predicted biobutanol production rateand the specific growth rate. Of course, the specificproton flux is ultimately determined by the protonmotive force, which is a function of intra- and ex-tracellular pH and ionic concentrations. Whilethese effects have not yet been fully quantified in agenome-scale model, it is suggested that the bio-mass constituting equation for an organism be de-termined under several environmental conditions.Until further research is done on this subject, thereader is directed to the examples provided in thisreview for methods on how to obtain a biomassconstituting equation for a single environmentalcondition. Finally, model simulations demonstrateda low (or virtually non-existent) impact of lipids onthe biobutanol and specific growth rate relation-ship. These results are not consistent with experi-mental findings.Thus, incorporation of solvent tox-

icity mechanisms into a genome-scale model mayultimately be required to realize the impact oflipids.

Ideally, the problem of the biomass constitutingequation will be solved computationally to con-serve time and laboratory resources. It has beendemonstrated that cellular composition and physi-ology is fluid, directly responding to environmentalconditions and environmentally driven geneticprograms. Thus, the biomass constituting equationresults from a complex function of the cellulargenotype and the culture environment. Obviously,the proposed function is very complex and cannotyet be fully resolved. However, this does not meanthat an optimal solution (or family of solutions)cannot be located. Of course, this would lead to aniterative development to locate metabolic engi-neering targets by perturbing a genome-scalemodel. This is shown in Fig. 6 for the production ofa solvent or potential biofuel. It is assumed herethat the solvent produced impacts cellular compo-sition. This algorithm demonstrates that a pertur-bation that is initially predicted to increase the pro-duction of a solvent with the original biomass con-stituting equation needs to be re-evaluated withthe biomass constituting equation that results fromthe altered cellular physiology induced by the per-turbation itself. It is anticipated that developmentsto produce an adaptive biomass constituting equa-tion will add to the computational toolbox ofgenome-scale models that have already provenwidely successful in biotechnology and biomedi-cine.


Figure 6. Flow diagram showing the useof an adaptive biomass constitutingequation for prediction of biofuelproduction.



This work was supported by the USDA AFRI Award2010-65504-20346 and the Biodesign and Biopro-cessing Research Center at Virginia Tech.

The author has declared no conflict of interest.

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Ryan S. Senger is an Assistant Profes-

sor of Biological Systems Engineering

at the Virginia Polytechnical Institute

and State University. He received his

PhD in Chemical Engineering from

Colorado State University in 2005.

From there, he was a postdoctoral re-

searcher at Texas Tech with M. Nazmul

Karim and then an NIH NRSA postdoc-

toral fellow with E. Terry Papoutsakis at

Northwestern University and the University of Delaware. Dr. Senger’s

research focuses on model-driven rational and combinatorial metabol-

ic engineering of microbes (and higher organisms) for production of

novel biofuels and valuable chemicals. Of particular interest to this

research is the ability to predict accumulation levels of compounds of

interest in organelles, cells, and tissues.


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