engineering of microorganisms for the production of biofuels and perspectives based

Biotechnology Advances xxx (2011) xxx–xxx

JBA-06478; No of Pages 12

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Biotechnology Advances

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Research review paper

Engineering of microorganisms for the production of biofuels and perspectives basedon systems metabolic engineering approaches

Yu-Sin Jang a, Jong Myoung Park a, Sol Choi a, Yong Jun Choi a, Do Young Seung c,Jung Hee Cho c, Sang Yup Lee a,b,⁎a Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Program), BioProcess Engineering Research Center,Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, KAIST, Daejeon, Republic of Koreab Department of Bio and Brain Engineering and Bioinformatics Research Center, KAIST, Daejeon, Republic of Koreac GS Caltex Corporation Value Creation Center, Daejeon, Republic of Korea

⁎ Corresponding author at: Department of Chemical3930; fax: +82 42 350 3910.

E-mail address: [email protected] (S.Y. Lee).

0734-9750/$ – see front matter © 2011 Elsevier Inc. Alldoi:10.1016/j.biotechadv.2011.08.015

Please cite this article as: Jang Y-S, et al, Enmetabolic engineering approaches, Biotech

a b s t r a c t
a r t i c l e i n f o
Available online xxxx

Keywords:Systems metabolic engineeringSystems biologySynthetic biologyBiofuelEthanolButanolAlkaneBiodieselHydrogenMicrobial cell factory

The increasing oil price and environmental concerns caused by the use of fossil fuel have renewed our inter-est in utilizing biomass as a sustainable resource for the production of biofuel. It is however essential to de-velop high performance microbes that are capable of producing biofuels with very high efficiency in order tocompete with the fossil fuel. Recently, the strategies for developing microbial strains by systems metabolicengineering, which can be considered as metabolic engineering integrated with systems biology and synthet-ic biology, have been developed. Systems metabolic engineering allows successful development of microbesthat are capable of producing several different biofuels including bioethanol, biobutanol, alkane, and biodie-sel, and even hydrogen. In this review, the approaches employed to develop efficient biofuel producers bymetabolic engineering and systems metabolic engineering approaches are reviewed with relevant examplecases. It is expected that systems metabolic engineering will be employed as an essential strategy for the de-velopment of microbial strains for industrial applications.

and Biomolecular Engineering, KAIST, 291 Daehak-ro, Yu

rights reserved.

gineering of microorganisms for the producnol Adv (2011), doi:10.1016/j.biotechadv.201

© 2011 Elsevier Inc. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 02. Metabolic engineering of microorganisms for the production of biofuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0

2.1. Considerations on strain development for the production of biofuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 02.2. Ethanol producers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 02.3. Butanol and higher alcohol producers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 02.4. Gasoline and diesel producers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 02.5. Algal fuel and hydrogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 02.6. Isoprenoid-based fuels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0

3. Future perspectives on systems metabolic engineering for biofuel production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 03.1. Tools and approaches employed systems metabolic engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 03.2. Production of biofuels using systems metabolic engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0

4. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0Acknowledgments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0

seong-gu, Daejeon 305-701, Republic of Korea. Tel.: +82 42 350

tion of biofuels and perspectives based on systems1.08.015

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2 Y.-S. Jang et al. / Biotechnology Advances xxx (2011) xxx–xxx

1. Introduction

Due to the limited nature of fossil resources, oil price is vulnerableto many factors and is expected to increase more rapidly from now on(Kerr, 2007). Recent sharp increase in oil price caused by the turmoilin north African countries proves that this is the case. Also, climatechange issue is becoming more and more serious, and thus therehas been a global movement toward reduced use of fossil resources.Thus, bio-based production of fuels and chemicals from renewablebiomass is becoming increasingly attractive and will be essential forour future. Bioethanol and biodiesel are already commercialized as al-ternative fuels in the market worldwide. Bio-based production of bu-tanol and alkane is also being pursued around the world. It is clearthat biofuels will be increasingly used to replace some of fossil fuelfor our sustainable future (Farrell et al., 2006; Lynd et al., 2008).

In order to efficiently produce fuels from renewable resources,micro-organisms having superior metabolic capabilities need to be developed.Traditionally microbial strains for industrial applications have been de-veloped by the random mutagenesis and screening processes (Labarreet al., 2001). However, this approach results in unknown genotypic/phenotypic changes and can cause unwanted alterations in the genomethat might become problematic when production condition needs tobe modified; further metabolic engineering of the random mutant cellsis often difficult (Park et al., 2008). Consequently, rational metabolic en-gineering has become a standard strategy for strain development overthe last couple of decades (Lee and Papoutsakis, 1999; Stephanopouloset al., 1998). However, these approaches also attained to some limitations

Fig. 1. Summary of key approaches taken to the development of strains for the production ofas gray ovals. Small circles located within each oval together with references indicate approin silico analysis, and synthetic biology; these small circles are arranged in a way that they

Please cite this article as: Jang Y-S, et al, Engineering of microorganismmetabolic engineering approaches, Biotechnol Adv (2011), doi:10.1016

in order to improve cellular performances dramatically because thescope of engineering cells is often local rather than system-wide.Recently, systems metabolic engineering has been developed towardengineering the organism at the systems-level beyond traditionalmetabolic engineering (Lee et al., 2005a; Palsson and Zengler, 2010).Systems metabolic engineering has been employed for developingstrains for the production of bioproducts, including biofuels (Jarboeet al., 2007; Lee et al., 2009; Mukhopadhyay et al., 2008; Otero etal., 2007; Ranganathan and Maranas, 2010; Schirmer et al., 2010;Steen et al., 2010; Tang and Zhao, 2009). Several techniques includinghigh-throughput screening, in silico modeling and simulation, omics(genome, transcriptome, proteome, metabolome, and fluxome), genesynthesis, and synthetic regulatory circuits, and enzyme and pathwayengineering have been employed during systems metabolic engineer-ing (Kim et al., 2008a; Lee et al., 2005b; Lee and Papoutsakis, 1999;Palsson and Zengler, 2010; Park et al., 2008, 2009). Here, we reviewseveral recent case studies of metabolic engineering for the produc-tion of biofuels, and suggest future perspectives on successful straindevelopment using a more refined version of metabolic engineering,namely systems metabolic engineering.

2. Metabolic engineering of microorganisms for the productionof biofuels

For industrial applications of microorganisms for the productionof desired biofuels, the metabolic performance needs to be improvedby metabolic engineering. There have recently been reports on the

biofuels including ethanol, butanol, gasoline, diesel, and algal fuels, which are indicatedaches taken for strain improvement: random mutagenesis, rational engineering, omics,are grouped to large circles in the background.

s for the production of biofuels and perspectives based on systems/j.biotechadv.2011.08.015


3Y.-S. Jang et al. / Biotechnology Advances xxx (2011) xxx–xxx

use of metabolically engineered microorganisms for the production ofseveral different biofuels. General strategies for metabolic engineer-ing of microorganisms and specific examples on the use of these strat-egies for the production of biofuels are described below, andsummarized in Fig. 1. Some examples of systems metabolic engineer-ing approaches are also discussed in next section with future perspec-tives, and summarized in Table 1.

2.1. Considerations on strain development for the production of biofuels

Constructing microorganisms toward desired fuel production shouldtake into account several considerations, including enhancement of prod-uct concentration, yield and productivity, simplification of downstream

Table 1Representative systems metabolic engineering strategies of the strain development for the

Fuels Strain Strategies for systematic app

Ethanol S. cerevisiaeE. coliZ. mobilis

Construction of genome-scafor ethanologenic organisms

Ethanol S. cerevisiae Genome-scale metabolic moimprovement of ethanol proyield and decrease glycerolconditions using glucose as

Ethanol E. coli OptReg method has suggestengineering strategies of actelimination of reactions for

Ethanol and higher alcohols E. coli OptORF method has been deknockout and amplificationnetworks with transcriptionfor the production of ethano

Ethanol E. coli Transcriptome analysis to idethanologenic E. coli; Identifserve as protective osmolyte

Butanol C. acetobutylicumC. beijerinckii

Genome sequencing of clostmore systematic approachesof butanol producers

Butanol C. acetobutylicum In silico genome-scale metabreconstructed, which allowand other simulations for deengineering strategies

Butanol E. coli OptForce has considered theand possible flux ranges forbioproduct-overproducing nthe wild type metabolic netproduction of butanol

Butanol – Computational algorithms hmetabolic pathways basedpredict butanol production

Butanol C. acetobutylicum Genome-wide transcriptomin studies on stress responsegenes (groES, dnaKJ, hsp18, a

Butanol C. acetobutylicumE. coli

Genomic library screening oimprove butanol tolerance iand CAC1869 (in C. acetobut(in E. coli) have been identifi

Butanol C. acetobutylicum Genome-wide non-coding Rbutyrate tolerance of C. acet

Isobutanol E. coli Genome-wide systems analyfollowed by gene repair and(acrA, gatY, tnaA, yhbJ, and misobutanol tolerance have b

Isobutanol E. coli Evolution combined with ge(marC, hfq, mdh, acrAB, gatYincreased tolerance to isobu

Alternative biofuels includinggeraniol, geranyl acetate,limonene, and farnesylhexanoate

E. coli Identification of novel biofubacterial genomes based onof 43 pumps has been heterand tested by using a compaidentified pumps have restoof biofuels including geranioand farnesyl hexanoate; nonidentified for improving tole


processes, and utilization of inexpensive substrates, which are dis-cussed in next sections. Besides, the control of redox balance andthe fuel tolerance also deserve attention for enhanced fuel production.Control of redox balance is also one of the most important obstacles instrain development for the production of fuels at high yields. In general,high redox potential is needed to efficiently produce ethanol and buta-nol, which are all relatively reduced products. To increase the yieldand productivity, cofactormanipulation has been applied as an essentialand powerful tool for metabolic engineering. For examples, over-expression of the NAD+-dependent Fdh doubled the maximum yieldof NADH from 2 to 4 mol NADH/mol glucose consumed, resulting in in-creased cell density and ethanol production (Berrios-Rivera et al.,2002). As a result, ethanol production by Escherichia coli could be

production of biofuels.

roach References

le metabolic models Feist and Palsson (2008); Lee et al.(2010); Mo et al. (2009)

del of S. cerevisiae aided theduction to increase ethanolproduction under anaerobica carbon source

Bro et al. (2006); Hjerstedet al. (2007)

ed the optimal metabolicivation, repression, andthe overproduction of ethanol

Pharkya and Maranas (2006)

veloped to identify optimal genetargets by incorporating metabolical regulatory networks; employedl and higher alcohols

Kim and Reed (2010)

entify expression changes onying both glycine and betaine cans

Gonzalez et al. (2003)

ridia, which has given chancefor metabolic engineering

Nolling et al. (2001)(http://genome.jgi-psf.org/mic_cur1.html)

olic models have beengenome-scale flux analysissigning the metabolic

Lee et al. (2008a); Senger andPapoutsakis (2008a,2008b)

change of computationalall metabolic reactions inetworks compared withwork; used in the increased

Ranganathan et al. (2010); Ranganathanand Maranas (2010)

ave been suggested to predicton chemical structure; used topathway

Cho et al. (2010); Li et al. (2004)

e and proteome analysis; usedand sporulation; stress responsend hsp90) have been identified

Alsaker et al. (2004, 2010); Jones et al.(2008); Mao et al. (2010); Schwarz et al.(2007); Sullivan and Bennett (2006);Tomas et al. (2003a,2003b, 2004)

f C. acetobutylicum and E. coli ton genome-wide manner; CAC0003ylicum) and the entC and feoAed to increase butanol tolerance

Borden and Papoutsakis (2007); Reyeset al. (2011)

NAs study; RDNA7 increasedobutylicum

Borden et al. (2010)

ses; whole genome sequencingknockout study; genesarCRAB) related to

een identified

Atsumi et al. (2011)

nomic study; genesZABCD and rph) leadtanol

Minty et al. (2011)

el efflux pumps from sequencedbioinformatics; identified libraryologously expressed in E. colirative growth assay; somered growth in the presencel, geranyl acetate, limonene,e of the pumps have beenrance to butanol and isopentanol

Dunlop et al. (2011)


http://genome.jgi-psf.org/mic_cur1.html



dramatically increased by 22-fold relative to the control strain underanaerobic condition (Berrios-Rivera et al., 2002). In another example,metabolic engineering was performed to increase NADH level in E. coliby disrupting the ldh, adh, and frd genes, while the Candida boidiniifdh gene was overexpressed. This metabolic engineering strategyresulted in the construction of E. coli strain having capability to produce15 g/L of butanol (Shen et al., 2011).

Achievinghigh titers of target fuels is closely related to the endprod-uct stress to the host cell (Taylor et al., 2008). The cellular stress to thesebiofuels can be reduced by amplification of tolerance related-proteinsor addition of protectant in the culture media, which often increasesthe final titer of the target product. One approach to identify targetgenes to be manipulated for increasing tolerance against ethanol istranscriptome analysis, which is discussed in the last section. Anotherapproach to increase the product tolerance is directed or adaptive evo-lution. The E. coli EMFR9 strain could be evolved to be able to grow inthe presence of furfural at high concentration (Miller et al., 2009). Sim-ilarly, Saccharomyces cerevisiae M25 strain evolved with transcriptionfactors could become more tolerant to ethanol (Zhao et al., 2010).More recently, butanol tolerance of E. coli could be increased by usingartificial transcription factor (ATF) libraries which consist of zinc fingerDNA-binding proteins and E. coli cyclic AMP receptor protein (Lee et al.,

Fig. 2.Major pathways for the production of (A) ethanol, (B) butanol, (C) biodiesel, (D) algal fuedehydrogenase; PFL, pyruvate formate lyase; ACTDH, acetaldehyde dehydrogenase; PFOR, pyrnase; CRT, crotonase; BCD, butyryl-CoA dehydrogenase; AAD, butyraldehyde dehydrogenasefatty acyl-CoA reductase; WS/DGAT, acyltransferase; HMG-CoA, hydroxymethylglutaryl-CoA;MEP, 2-C-methyl-D-erythritol 4-phosphate; CDP-MEP, 4-diphosphocytidyl-2-C-methyl-D-erythdimethylallyl diphosphate; GPP, geranyl diphosphate; FPP, farnesyl diphosphate; GGPP, geraCoA synthase; HMGR, hydroxymethylglutaryl-CoA reductase; MK, mevalonate kinase; PMK, pxylulose-5-phosphate synthase; DXR, 1-deoxy-D-xylulose-5-phosphate reductoisomerase; CMCS, 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase; HDS, hydroxy-methylbutenyl didiphosphate synthase; FPPS, farnesyl diphosphate synthase; GGPPS, geranylgeranyl diphospha


2011b). The selected butanol-tolerant E. coli strain could grow in thepresence of up to 1.5% (v/v) of butanol (Lee et al., 2011b).

2.2. Ethanol producers

Ethanol is currently the only renewable biofuel that is produced atsufficiently large scale, and used as an additive or alternative for gas-oline. The amount of worldwide ethanol production for transport fuelis continuously increasing (Bringezu et al., 2009). It is forecasted thatthe global use of biofuel including bioethanol is expected to nearlydouble in 2017 (Bringezu et al., 2009; RFA, 2010). The United Statesand Brazil produced and used most ethanol for transportation, asmuch as 89% of the worldwide ethanol production in 2009, and man-dated the blending ethanol with gasoline (RFA, 2010; Worldwatch,2006). In order to achieve higher yield and productivity due to theimportance of ethanol, many different metabolic engineering studieshave been performed.

S. cerevisiae and Zymomonas mobilis are native ethanol producersthat can efficiently convert glucose to ethanol (Fig. 2A), but cannotuse pentose sugars as carbon sources (Almeida et al., 2011; Matsushikaet al., 2009; Seo et al., 2005). Because of the concerns on the possible in-crease of food prices due to the use of edible biomass for ethanol

l, and (E) fuels from isoprenoid. Abbreviations: PDC, pyruvate decarboxylase; ADH, alcoholuvate:ferredoxin oxidoreductase; THL, thiolase; HBD, 3-hydroxybutyryl-CoA dehydroge-; BDH, butanol dehydrogenase; TES, thioesterase; FADD, fatty acyl-CoA synthase; FAR,MEV, mevalonate; IPP, isopentenyl diphosphate; DXP, 1-deoxy-D-xylulose 5-phosphate;ritol 2-phosphate; HMB-PP, (E)-4-hydroxy-3-methyl-but-2-enyl pyrophosphate; DMAP,nylgeranyl diphosphate; AtoB, acetoacetyl-CoA thiolase; HMGS, hydroxymethylglutaryl-hosphomevalonate kinase; MVD, diphosphomevalonate decarboxylase; DXS, 1-deoxy-D-MS, C-methyl-erythritol cyclodiphosphate synthase; CMK, C-methyl-erythritol kinase;phosphate synthase; HDR, hydroxy-methylbutenyl diphosphate reductase; GPPS, geranylte synthase; PPI, diphosphate.




production, lignocellulosics have been generally considered as a goodcarbon source. In contrast to S. cerevisiae and Z. mobilis, E. coli can uti-lize most carbohydrate components present in lignocellulosics butproduce only a small amount of ethanol during fermentation (Neid-hardt et al., 1996). Accordingly, E. coli strains have been metabolicallyengineered for enhanced ethanol production through the introductionof foreign genes, elimination of competitive pathways, and disruptionof byproducts formation (Jarboe et al., 2007). The resulting strain, E.coli KO11, was constructed based on E. coli W stain by introducing for-eign genes encoding pyruvate decarboxylase and alcohol dehydroge-nase (PET operon) from Z. mobilis and disrupting fumarate reductase(Jarboe et al., 2007). The E. coli KO11 strain was able to produce eth-anol at about 95% of the theoretical yield in a complex medium, whichis as good as S. cerevisiae. In addition, to increase ethanol tolerance ina complex medium, the LY01 strain was constructed by adaptive evo-lution and selection. However, the dependence of both E. coli KO11and LY01 on complex nutritional supplement increased the cost ofethanol production. Thus, a new ethanologenic E. coli strain startingfrom SZ110 strain, a derivative of KO11, was constructed to improvethe yield of ethanol production in minimal medium (Zhou et al.,2005). The E. coli SZ110 strain was redesigned to produce lactic acidby removing PET operon, alcohol dehydrogenase, and acetate kinasefrom E. coli KO11 strain. For ethanol production in minimal medium,the SZ110 strain was reprogrammed again by eliminating lactate de-hydrogenase and inserting pyruvate formate lyase and PET operonfrom Z. mobilis (Jarboe et al., 2007). The resulting strain, LY168,could produce 0.5 g ethanol per gram of xylose, which is close tothe theoretical maximum yield of 0.51, in a minimal medium contain-ing betaine. Furthermore, a homo-ethanol producer, SE2378 (ΔldhΔpfl), which is constructed by mutagenesis, was able to produce eth-anol with the productivity of 2.24 g/h/g-cells at approximately 80% ofthe theoretical ethanol yield (Kim et al., 2007). About 88% of fermen-tation metabolites produced by SE2378 was ethanol. Another homo-ethanol producer, SZ420 (ΔfrdBC Δldh ΔackA ΔfolA-pfl ΔpdhR::pflBp6-aceEF-lpd), which is constructed by eliminating the competingfermentation pathways in E. coli B and highly expressing pyruvate de-hydrogenase complex (aceEF-lpd, a typical aerobically-expressed op-eron) under anaerobic condition, could achieve a 90% ethanol yieldfrom xylose (Zhou et al., 2008). More recently, an adaptive evolutionof nontransgenic E. coli KC01 (ldhA pflB ackA frdBC pdhR::pflBp6-aceEF-lpd) has been performed to achieve homo-ethanol production,which results in improvement of ethanol production with 94% yieldfrom xylose (Wang et al., 2010b).

2.3. Butanol and higher alcohol producers

Butanol, a four carbon primary alcohol (C4H10O), has recentlybeen attracting much interest due to its great potential to be usedas an alternative fuel in addition to its existing applications as a sol-vent (Durre, 2007, 2008; Lee et al., 2008b; Papoutsakis, 2008). It hasbeen estimated that 10–12 billion pounds of butanol is produced an-nually, which accounts for 7–8.4 billion dollar market at current price(Lee et al., 2008b). It is notable that butanol and higher alcohols canbe used as a direct replacement of gasoline or as a fuel additive. Buta-nol can be used directly in any gasoline engine without modificationand/or substitution, because it has sufficiently similar characteristicsto gasoline as a liquid fuel. For example, in 2005, a gasoline car withunmodified engine was fueled with 100% butanol and successfullyran almost 10,000 miles across the USA. This result clearly demon-strates that biobutanol is one of the most powerful alternative fuel.Clostridium (Durre, 2008; Jiang et al., 2009; Lee et al., 2009; Qureshiet al., 2008; Sillers et al., 2008) and some engineered E. coli (Atsumiet al., 2008a,2008b) strains are two popular butanol (or isobutanol)producers being studied intensively.

One of the characteristics of the butanol-producing clostridia is bi-phasic fermentation (Lee et al., 2008b). During the first phase, acetate


and butyrate are produced as major products and pH goes downbelow 5.0, which is known as acidogenic phase. Then solventogenicphase follows, during which acetate and butyrate are reassimilatedto form solvents, butanol, acetone and ethanol. The major objectivesof metabolic engineering of clostridia include efficient butanol pro-duction with respect to the titer, yield, and selectivity of butanol. Be-fore genome sequencing of clostridia, several interesting examples ofrandom mutagenesis and rational metabolic engineering of clostridiahave been reported (Bennett and Rudolph, 1995; Formanek et al.,1997; Green et al., 1996; Green and Bennett, 1998). C. beijerinckiiBA101 strain was isolated from the mutant pool of C. beijerinckiiNCIMB 8052 treated with a mutagen N-methyl-N'-nitro-N-nitroso-guanidine (NTG), and selected on nonmetabolizable glucose analog,2-deoxyglucose (Formanek et al., 1997). C. beijerinckii BA101 is ableto produce higher level of butanol (18.6 g/L) than its parent strain(9.2 g/L).

There have been several metabolic engineering approaches takento improve butanol production in C. acetobutylicum. The PJC4BK strainwas constructed by the disruption of the buk gene encoding butyratekinase involved in butyrate formation pathway in C. acetobutylicum(Green et al., 1996). This strain was able to produce butanol up to16.7 g/L, which is much higher than that (11.7 g/L) obtained withthe wild-type strain (Harris et al., 2000). Another engineering ap-proach taken was increasing the butanol titer through enhancingthe end-products tolerance. Overexpression of the molecular chaper-one GroESL, which allowed the solventogenic enzymes to be in moreactive states, resulted in an increase of the final solvent titer (Tomaset al., 2003b).

High butanol selectivity is important as it can reduce the recoverycost (Jiang et al., 2009; Lee et al., 2009). C. acetobutylicumM5 strain isa derivative of C. acetobutylicum ATCC 824, and it does not producebutanol due to the lack of the megaplasmid pSOL1 (Clark et al.,1989). C. acetobutylicumM5was metabolically engineered to increasethe butanol selectivity to total solvents (Lee et al., 2009; Sillers et al.,2008). The adhE1 gene was overexpressed under the control of theptb promoter in the M5 strain, which restored butanol productionto the level that can be achieved with ATCC 824 while without pro-ducing acetone (Sillers et al., 2008). The introduction of the adhE1-ctfAB genes into the M5 strain also increased the butanol selectivity(Lee et al., 2009). The metabolically engineered C. acetobutylicumM5(pIMP1E1AB) was able to produce butanol with high butanol se-lectivity to total solvents (0.84), which is much higher than that(0.57) typically obtained with the ATCC 824 strain. In another study,the butanol selectivity could be increased to 0.82 by disrupting theadc gene encoding acetoacetate decarboxylase in C. acetobutylicumand regulating electron flow by the addition of methyl viologen intoculture medium (Jiang et al., 2009).

It should be noted that genetic manipulations such as gene am-plification and gene knockout, particularly in native butanol produc-er Clostridium sp., is extremely difficult. Due to this reason, E. coliand yeast have been attracted as alternative hosts for butanol pro-duction. Introduction of clostridial genes including thl, hbd, crt,bcd, etfAB and adhE2, responsible for butanol formation in C. aceto-butylicum (Fig. 2B), resulted in the production of 139 mg/L of buta-nol in E. coli under anaerobic condition (Atsumi et al., 2008a).Butanol production by the metabolically engineered E. coli straincould be increased up to 1.2 g/L by changing the alcohol dehydroge-nase gene from adhE2 to adhE1 (Inui et al., 2008). In another ap-proach, 2-keto acids, the metabolic intermediates in amino acidbiosynthetic pathway, were used as precursors to produce butanoland isobutanol by overexpressing the 2-keto acid decarboxylasesand alcohol dehydrogenases in E. coli (Atsumi et al., 2008b). Whenthe ilvD gene was additionally disrupted, 0.7 g/L butanol could beproduced by supplementing 8 g/L of L-threonine. To construct isobu-tanol producer, 2-ketoisovalerate biosynthesis was additionally en-hanced by disrupting the adhE, ldhA, frdAB, fnr, pta, pflB genes




while the alsS (Bacillus subtilis) and ilvCD (E. coli) genes were over-expressed in E. coli strain. This metabolic engineering strategy led tothe production of 22 g/L isobutanol by culturing E. coli in a 250-mLscrew-cap conical flask (Atsumi et al., 2008b). In a very recent re-port on application of metabolic engineering approaches for biobu-tanol production in E. coli, construction of a chimeric pathway byassembling the required genes from three different organisms hasbeen reported (Bond-Watts et al., 2011). For this purpose, the com-bination of phaA (Ralstonia eutropha), hbd, crt, and adhE2 (C. aceto-butylicum), and ter (Treponema denticola) genes was introduced intothe host E. coli. Additionally, the native aceEF-lpd operon of E. coliwas also overexpressed in order to provide reducing equivalents.The metabolically engineered E. coli strain could produce a maxi-mum of 4.7 g/L butanol in a 250-mL baffled flask sealed with paraf-ilm (Bond-Watts et al., 2011).

2.4. Gasoline and diesel producers

Alkanes composed of 5 to 9 carbons, which are liquid at roomtemperature, can be used as a good fuel in internal combustion en-gine (Peralta-Yahya and Keasling, 2010). Alkanes composed of 10 to16 carbons are more suitable for their use as diesel and aviationfuel. Some eukaryotes naturally produce alkanes. For examples,plants synthesize wax, which is an alkane consisting of more than30 carbon atoms, to prevent evaporation of water, while insects pro-duce pheromones are mainly hydrocarbons. Furthermore, biosyn-thesis of alkanes (heptadecane as the most abundant one) has alsobeen reported in a diversity of microorganisms including photosyn-thetic cyanobacteria (Dembitsky and Srebnik, 2002; Winters et al.,1969).

Recently, an alkane-producing E. coli was developed by the intro-duction of the alkane operon from cyanobacteria (Schirmer et al.,2010). During this study, initially the genes responsible for alkanebiosynthesis in Synechococcus elongatus PCC7942 (orf1594 encodingan acyl-acyl carrier protein reductase and orf1593 encoding an alde-hyde decarbonylase) were identified and characterized. Subsequent-ly, both orf1593 and orf1594 from S. elongatus were coexpressed inE. coli. The resulting metabolically engineered E. coli strain could pro-duce a mixture of alkanes (~ 0.3 g/L) including tridecane, pentade-cene, pentadecane, and heptadecene at the approximate ratio of10:10:40:40 (Schirmer et al., 2010).

Biodiesel could also be produced by E. coli after metabolic engi-neering and synthetic biology for introducing new biochemical reac-tion (Fig. 2C) (Steen et al., 2010). The metabolically engineeredE. coli strain could produce biodiesel through the acyltransferase ac-tivity from acyl-CoA and ethanol, which are produced from the mod-ified fatty acid biosynthetic pathway and the ethanol producingpathways from Z. mobilis, respectively (Fig. 2C). The fatty acid biosyn-thetic pathway of E. coli was modified to increase production of freefatty acids and acyl-CoAs by disrupting the fadE gene encoding thefirst enzyme involved in beta-oxidation, and by overexpressinggenes for thioesterase and acyl-CoA ligase (Fig. 2C). To establish theefficient biosynthetic pathway for ethanol production in E. coli, theZ. mobilis pdc and adhB genes encoding pyruvate decarboxylase andalcohol dehydrogenase, respectively, were introduced (Fig. 2C). Themetabolically engineered E. coli strain could produce fatty acid ethylesters (biodiesel) up to 674 mg/L, which corresponds to 9.4% of thetheoretical yield (Steen et al., 2010). When the genes encodingendoxylanase catalytic domain from Clostridium stercorarium and axylanase from Bacteroides ovatus were introduced, of the engineeredE. coli could produce 11.6 mg/L of biodiesel from 2% (w/v) xylan(Steen et al., 2010).

Through this approach, biogasoline and biodiesel, which are non-native products, could be produced by engineered E. coli. Further-more, systems metabolic engineering approach will become more


and more important for the development of strains capable of effi-ciently producing biofuels and other products of interest.

2.5. Algal fuel and hydrogen

Photosynthetic algae have also been considered either as a bio-mass source or host strain for the production of biofuel (Schmidtet al., 2010). Some microalgae produce good raw materials, such astriacylglycerol and starch, for their further conversion to biofuelssuch as biodiesel and ethanol, or to hydrogen (Fig. 2D).

Hydrogen has long been considered as a good energy due to itshigh energy density and sustainability. Hydrogen has been mainlyproduced by algae and cyanobacteria, but at rather low efficiencies.Either hydrogenase or nitrogenase catalyzes hydrogen productionin photosynthetic algae and cyanobacteria (Benemann and Weare,1974; Gaffron and Rubin, 1942). A highly efficient hydrogen produc-er, Chlamydomonas reinhardtii Stm6, was isolated by random screen-ing with the goal of increasing H+ and e− supply to thehydrogenase (Kruse et al., 2005). The resulting strain was able to pro-duce hydrogen with 5 times higher yield than its parent strain. Met-abolic engineering studies on the Smt6 strain, including theintroduction of a hexose symporter system from Chlorella kessleri(Doebbe et al., 2007) and manipulation of the expression rates oflight harvesting protein subunits (Beckmann et al., 2009), were per-formed to efficiently produce hydrogen by water photolysis and toenhance light capture efficiency, respectively. The metabolically engi-neered C. reinhardtii Smt6 strain showed 20–30% higher growth ratesat high light (800 mE) condition and 1.5 times higher hydrogen pro-duction rate compared to its parent strain (Beckmann et al., 2009).

In recent years, algal biodiesel has also attracted worldwide atten-tion as carbon substrate other than carbon dioxide does not need tobe fed for fuel production. Various algal species produce intracellulartriacylglycerides to relatively high concentrations (Fig. 2D), whichvary from 20 to 77% of dry cell weight, depending on algal species.Some of the best oil producing algal species include Botryococcusbraunii, Nannochloropsis sp., Schizochytrium sp. in which the triglycer-ides content can vary between 25–75%, 31–68%, and 50–77% of theirdry cell weights, respectively (Chisti, 2007; Hu et al., 2008). The tri-glycerides produced by these algal species are subsequently used forbiodiesel production by trans-esterification process. Based on the bio-chemical studies on fatty acid production, silicon deficiency wasfound to result in the accumulation of neutral lipids by increased ac-tivity of acetyl-CoA carboxylase (ACCase) in Cyclotella cryptica(Roessler, 1988). This ACCase catalyzes the conversion of acetyl-CoAto malonyl-CoA, which is used as the substrate for fatty acid synthasein this organism (Roessler, 1988). Based on this finding, metabolic en-gineering of C. cryptica was attempted to enhance the expressionlevels of the ACCase and UDP-glucose pyrophosphorylase (UGPase)genes, which are important for lipid biosynthesis. However, lipid pro-duction could not be enhanced despite of the overexpression of theseenzymes (Sheehan et al., 1998). Several attempts to improve triglyc-eride production by metabolic engineering of green algae and dia-toms did not result in much successful outcomes (Roessler, 1988,1990; Roessler and Ohlrogge, 1993; Sheehan et al., 1998). More stud-ies are needed to more systematically perform metabolic engineeringafter improving our understanding on these organisms with respectto genetics and physiology.

Ethanol is another class of liquid fuel that can be produced bycyanobacteria by direct conversion of CO2 via their inherent pho-toautotrophic metabolism. Introduction of the Z. mobilis pdc andadhII genes encoding pyruvate decarboxylase and alcohol dehy-drogenase II into cyanobacteria resulted in ethanol production(Deng and Coleman, 1999) (Fig. 2D). Recently, an ethanol produc-ing Synechocystis strain has been developed by the chromosomalintegration of the Z. mobilis pdc and adhII genes (Dexter and Fu,2009). While grown in photobioreactor, This metabolically




engineered Synechocystis PCC 6803 strain could produce a maxi-mum of 0.2 g ethanol/OD730 unit/L/Day (Dexter and Fu, 2009).

The limited success on improving the performance of algal specieseither by random and targeted mutagenesis or metabolic engineeringsuggests that more rational and systematic approach needs to be ap-plied for development of an efficient biofuel producing algal strain.Thus, systems metabolic engineering needs to play its role to fully ex-ploit the potential of biofuel production by algae. Several nuclear ge-nome sequencing projects have been currently completed (http://genome.jgi-psf.org/mic_cur1.html), which will allow us to better un-derstand genome-wide metabolic and regulatory characteristics andprovide us with the opportunity to redesign the metabolic pathwaysystematically as done in many bacteria. In the near future, it isexpected that algal strains with improved capability of producing al-cohols, diesels, alkanes, and hydrogen will be developed by systemsmetabolic engineering concepts.

2.6. Isoprenoid-based fuels

Isoprenoids are derived from combination of two isomeric me-tabolites, isoprenyl pyrophosphate (IPP) and dimethylallyl pyro-phosphate (DMAPP), which are synthesized through two differentpathways, namely mevalonate (MEV) pathway and 1-deoxy-D-xylulose 5-phosphate (DXP) pathway (Kirby and Keasling, 2008;Muntendam et al., 2009; Peralta-Yahya and Keasling, 2010; Zhanget al., 2011). Combination of IPP and DMAPP produces geranyl py-rophosphate (GPP, C10), followed by farnesyl pyrophosphate(FPP, C15), and geranylgeranyl pyrophosphate (GGPP, C20) via pre-nyltransferases (Fig. 2E). These isoprenoids can be directed to twodifferent kinds of molecules that can be used as biofuels, dependingon the different enzymes utilized. One class of the corresponding en-zymes is terpene synthases, which convert isoprenoids to cyclic al-kenes or branched chains, while pyrophosphatase, the otherenzyme type, hydrolyzes isoprenoids to produce alcohols (Fig. 2E).These metabolic pathways are of great importance in biofuels de-velopment because all these metabolic products, including cyclicalkenes (monoterpenes and diterpenes), branched alkanes (sesqui-terpenes) and alcohols (farnesol, geraniol, and isopentenol) havebeen proposed as gasoline substitutes, while long carbon chainsare better suited for diesel and jet fuel substitutes (Fig. 2E). Recent-ly, E. coli has been engineered to enhance the production of farne-sol (Wang et al., 2010a). Introduction of the foreign mevalonate(MVA) pathway and overexpression of native FPP synthase in E.coli have resulted in the production of 135.5 mg/L farnesol, whileno production of farnesol has been observed in the parent strain.Recently, several metabolic engineering strategies have beenemployed to overproduce isoprenoids in different hosts, includingplants and microorganisms, which have been well reviewed inthe recent papers (Kirby and Keasling, 2008; Muntendam et al.,2009). The ideal concept to overproduce isoprenoids is the completetransfer of a plant terpenoid pathway to microorganisms. Then, thebiosynthetic pathways need to be optimized in the engineered mi-croorganisms. However, the regulation of the terpenoid pathway isnot simple, without mentioning the already complex networks ofthe microbial host itself. Both heterologous and homologous net-works can be properly regulated for enhancing the production of iso-prenoids by systems metabolic engineering approaches.

3. Future perspectives on systems metabolic engineering forbiofuel production

To manufacture microbial strains for biofuel production, randommutagenesis and metabolic engineering have been employed as stan-dard strategies over the last couple of decades. However, the ap-proach of random mutation and selection is difficult for furtherimprovement of cellular performance due to the complexity


associated with identifying modified gene as a consequence of ran-dom mutation. Metabolic engineering, on the contrary, aims at im-proving cellular performance by considering metabolic pathway inentirety and manipulating specific genes that are targeted based onengineering tools, which improved our knowledge of cellular physiol-ogy and our subsequent engineering results (Lee et al., 2011a; Park andLee, 2008). In recent years, however, additional aspects of strain im-provement had to be further considered for successful biochemical pro-duction. High-throughput technologies and parallel advances ofcomputational and systems biology have enabled analyzing largeamount of omics data for investigating cellular metabolism and physiol-ogy at systems-level. Also, engineering factors associated with up- anddown-stream processes have to be carefully examined. Metabolic engi-neering integrated with systems-level analyses and computationaltools for the issues beyond simply engineering cells is termed systemsmetabolic engineering, and is providing a newparadigm on the develop-ment of strains for biofuel production (Lee et al., 2005a,2005b; Palssonand Zengler, 2010; Park et al., 2008).

3.1. Tools and approaches employed systems metabolic engineering

Cellular functions can be mostly characterized by interrogatingthree major networks in the cell: metabolic, gene regulatory and sig-naling networks. Various tools have been developed, inexpensive yetvery fast genome sequencing, genome-wide profiling of transcrip-tome and proteome, DNA synthesis, and genome-scale metabolic net-work simulation (Fig. 3). Having complete genome sequences andannotations for the organisms of interest, comparative genome anal-ysis is possible to identify the genes or regulatory regions that needto be introduced, deleted, down- or up-regulated to attain a desiredmetabolic phenotype. Accordingly, a strain to be engineered can becompared with the genomes of other strains that contain interestingmetabolic and cellular phenotypes to identify the genes and regionsin the genome to be engineered (Lee et al., 2005a).

Transcriptome profiling allows examination of the genome-wideexpression levels of mRNAs that can vary with genetic and environ-mental conditions using DNA microarrays. Based on transcriptomeprofiling, potential target genes to be manipulated and strategies forstrain improvement can be identified by comparing the expressionlevels of genes between the samples of same strain cultured underdifferent environmental conditions or between strains of different ge-notypes under identical or different environmental conditions (Hibiet al., 2007; Sindelar and Wendisch, 2007).

Similarly, proteome profiling allows examination of the levels ofproteins in a cell by using two-dimensional gel electrophoresis(2DGE) or chromatography-coupled mass spectrometry. Comparativeanalysis of proteome profiles between different samples under genet-ically or environmentally different conditions can be used to identifythose proteins showing altered expression levels and those proteinswhich are post-translationally modified (Lee and Lee, 2010), andhas been employed for strain improvement (Aldor et al., 2005; Hanet al., 2001, 2003). Recently, genome, transcriptome, proteome, andother large-scale data were combined for better understanding oncells to elucidate the transcriptional unit architecture of E. coli (Choet al., 2009). By integrating the information on organizational compo-nents, such as RNA polymerase (RNAP)-binding regions, RNAP-guided transcript segment, transcript start sites, and open readingframes, the transcriptional structure of genomes in E. coli was deci-phered and found to be much more complex than previously thought(Cho et al., 2009). More recently, RNA sequencing data presenting thetranscriptional state has been algorithmically integrated into agenome-scale metabolic model of Clostridium thermocellum (Gowenand Fong, 2010).

Metabolomics allows profiling metabolites, which are substrates,products, and intermediates of cellular metabolism, under desiredculture conditions by using chromatography coupled with mass





Fig. 3. Future perspectives for biofuel production using systems metabolic engineering and overall schematic procedure for strain development and application of the developedstrain. (A) Systems metabolic engineering strategy for the development of biofuel producers by integrating high-throughput experimental data with systematic analyses basedin silico genome-scale metabolic model and its simulations. (B) Overall strategy and procedure for strain development by systems metabolic engineering. Traditional methods todevelop improved strains, such as randommutagenesis or targeted metabolic engineering of selected genes, have limitations in dramatically improving the performance of a strain.Systems biology allows us to investigate the organism at the systems-level using various genome-wide tools, including high-throughput analytical techniques, computational an-alyses, and omics analyses that cover genome, transcriptome, proteome, metabolome, and fluxome. Information obtained from such studies can be applied in an integrated mannerduring the strain development by metabolic engineering. The whole process usually needs iteration until the desired phenotype and performance are obtained.


spectrometry and nuclear magnetic resonance (NMR). The profiles ofmetabolites represent the metabolic status of a cell and thus provideinformation on the physiological changes under genetic and environ-mental perturbations. Metabolic flux profiles, which can be consid-ered as one of the ultimate phenotypes of a cell, are related closelywith cellular metabolic performance (Liebeke et al., 2011; Wittmannand Heinzle, 2001). Fluxomics quantifies metabolic fluxes and collec-tively represent the metabolic characteristics of a cell under a givencondition. Two methods, 13C-based flux analysis and constraints-based flux analysis, have been used to estimate the metabolic fluxesand understand the physiological and metabolic states of a cell in


the conditions of interest (Feng et al., 2010; Kim et al., 2008a; Rossellet al., 2011). The 13C-based flux analysis uses an isotope labeled sub-strate, usually 13C-labeled glucose, to quantify 13C distribution pat-terns of metabolites with NMR, liquid chromatography–massspectrometry (LC–MS), or gas chromatography–mass spectrometry(GC–MS) (Sauer, 2006; Zamboni and Sauer, 2009). The computation-al model and optimization techniques are integrated with the mea-sured 13C-pattern data and exchange flux data, such as substrateuptake rates and product excretion rates. Then, the intracellular met-abolic fluxes are estimated by minimizing the differences betweenthe simulated fluxes and the experimentally measured data.




Although 13C-based flux analysis calculates intracellular metabolicfluxes accurately, the scale of model is limited to rather small-scale.Constraints-based flux analysis is based on an optimization techniquethat calculates fluxes by maximizing or minimizing one or more ob-jective functions while satisfying the mass balances of metabolitesand stoichiometry of metabolic reactions under pseudo-steady stateassumption (Orth et al., 2010; Park et al., 2009). The methods ofconstraints-based flux analysis can be applied to genome-scale meta-bolic models. The in silico genome-scale metabolic models have beenreconstructed for many bacteria, archaea, and eukarya and more inprogress (Duarte et al., 2007; Durot et al., 2009; Feist and Palsson,2008; Kim et al., 2008b; Schellenberger et al., 2010; Senger, 2010;Thiele and Palsson, 2010). Also, several in silico algorithms havebeen developed to predict the cellular physiology more accurately(e.g. addition of physiological constraints) and identify target genesto be deleted, introduced, and up-/down- regulated. The flux solutionspace of in silico genome-scale metabolic model that represents theall of feasible states of a cell is larger than physiologically possibleand feasible flux solution space of the real cell because the real celloperates under various levels of cellular regulatory mechanisms,such as transcriptional regulation, translational regulation, and ho-meostasis. Thus, the addition of physiological constraints, represent-ing experimental flux data (Sauer, 2006), transcriptional regulation(Covert et al., 2008), thermodynamics (Henry et al., 2007), and phys-iological characteristics of a cell (Beg et al., 2007; Park et al., 2010),into the model, can improve the accuracy of predictions by reducingthe scope of broad flux solution space of a model (Park et al., 2009).To identify target genes to manipulate for the overproduction of thedesired product, several in silico algorithms and strategies havebeen introduced, including minimization of metabolic adjustment(MOMA) (Segre et al., 2002), OptKnock (Burgard et al., 2003), OptReg(Pharkya and Maranas, 2006), OptForce (Ranganathan et al., 2010),and flux scanning based on enforced objective flux (FSEOF) (Choiet al., 2010). Furthermore, in silico genome-scale modeling and simu-lation can incorporate several omics data to promote our capabilityfor understanding the metabolic characteristics of a cell under any ge-netic and environmental perturbations, and then develop metabolicengineering strategies for strain improvement at systems-level.

3.2. Production of biofuels using systems metabolic engineering

Systems metabolic engineering allows systematic changes of met-abolic pathways toward desired goals including enhancement ofproduct concentration, yield and productivity. Accordingly, therehave recently been several reports on the use of systems metabolicengineering in developing microbial hosts for the production of bio-fuels (Alsaker et al., 2010; Bro et al., 2006; Hjersted et al., 2007; Kimand Reed, 2010; Ranganathan and Maranas, 2010). Some of these ex-amples are described below, which are also summarized in Table 1.

Systems metabolic engineering can provide novel solutions andstrategies for further enhancing ethanol production. Recently, in silicogenome-scale metabolic models for ethanologenic organisms, such asE. coli (Feist and Palsson, 2008), Z. mobilis (Lee et al., 2010), and S. cer-evisiae (Mo et al., 2009), have been developed. In silico genome-scalemetabolic models are powerful and promising tools to explore cellu-lar characteristics at systems-level and design metabolic engineeringstrategies for the improved and desired properties of a cell (Blazeckand Alper, 2010; Kim et al., 2008b, 2011; Milne et al., 2009; Oberhardtet al., 2009). For example, the genome-scale metabolic model of S.cerevisiae aided the improvement of ethanol production to increaseethanol yield and decrease glycerol production under anaerobic con-ditions using glucose as a carbon source (Bro et al., 2006; Hjerstedet al., 2007). OptReg method suggests the optimal metabolic engi-neering strategies of activation, repression, and elimination of reac-tions for the overproduction of ethanol in E. coli (Pharkya andMaranas, 2006). Also, OptORF method was developed to identify


optimal gene knockout and amplification targets for strain improve-ment by considering transcriptional regulatory network in additionto metabolic network, and employed for the production of ethanoland higher alcohols including isobutanol in E. coli (Kim and Reed,2010). One approach to identify target genes to be manipulated forincreasing tolerance against ethanol is transcriptome analysis. In-creased expression of genes involved in glycine and betaine degrada-tion showed beneficial effects on ethanol tolerance (Gonzalez et al.,2003). Addition of glycine and betaine increased ethanol toleranceby over 2-fold in engineered E. coli strain (Gonzalez et al., 2003).More examples of applying genome-scale metabolic simulation andanalysis on the development of ethanol producers are expected.

In the case of the butanol production, after the genome sequenc-ing of clostridia, more systematic approaches became available forthe metabolic engineering. The complete genome sequences of C.acetobutylicum (Nolling et al., 2001) and C. beijerinckii NCIMB 8052(http://genome.jgi-psf.org/mic_cur1.html) have been reported.More recently, in silico genome-scale metabolic models have beenreconstructed by two independent research groups (Lee et al.,2008a; Senger and Papoutsakis, 2008a,2008b), which will allowgenome-scale flux analysis and other simulations for designing themetabolic engineering strategies. In silico simulation methods devel-oped for identifying engineering targets were also applied for the pro-duction of butanol. The method called OptForce examines possibleflux ranges for all metabolic reactions in bioproduct-overproducingnetworks compared with the wild type metabolic network, and se-lects genes to be amplified, repressed, and eliminated. This strategywas implemented to investigate the metabolic network for the in-creased production of butanol in E. coli (Ranganathan et al., 2010;Ranganathan and Maranas, 2010). Additionally, in silico approachesin E. coli were applied for the high level production of L-valine andL-threonine (Lee et al., 2007; Park et al., 2007, 2011) whose biosyn-thetic pathways could be used for the production of butanol andhigher alcohols (Atsumi et al., 2008b; Shen and Liao, 2008). In otherstudies, computational algorithms have been suggested to predictmetabolic pathways feasible for the production of the target chemi-cals, including butanol, on the basis of chemical structure changes,enzymatic information, thermodynamic constraints, and reactionmechanisms (Cho et al., 2010; Li et al., 2004). These powerful toolsfor the prediction of pathways will greatly contribute constructing ar-tificial metabolic pathways for more efficient production of butanoland other biofuels.

Based on the complete genome sequence, genome-wide transcrip-tome (Tomas et al., 2003b, 2004) and proteome studies on C. acetobu-tylicum (Alsaker et al., 2010; Mao et al., 2010; Schwarz et al., 2007;Sullivan and Bennett, 2006) have been performed. Microarray exper-iments have been used to investigate stress responses by several me-tabolites, such as butanol, butyrate, and acetate (Alsaker et al., 2010),and sporulation of C. acetobutylicum (Alsaker et al., 2004; Jones et al.,2008; Tomas et al., 2003a). Indeed, investigation on the responses tosolvent stress in C. acetobutylicum by transcriptome analysis identi-fied molecular chaperones including groES, dnaKJ, hsp18, and hsp90as potential target genes for improving solvent tolerance coupledwith genes involved in sporulation, fatty acid synthesis and transcrip-tional regulators (Tomas et al., 2004). Genomic library screening ofC. acetobutylicum (Borden and Papoutsakis, 2007) and E. coli (Reyeset al., 2011) have also been conducted to improve butanol tolerancein a genome-wide manner. In these studies, CAC0003 and CAC1869genes in C. acetobutylicum (Borden and Papoutsakis, 2007) and theentC and feoA genes in E. coli (Reyes et al., 2011) were identified to in-crease butanol tolerance. Furthermore, it was recently reported thatnon-coding RNAs (ncRNAs) might affect metabolite tolerance inC. acetobutylicum (Borden et al., 2010); the ncRNA named RDNA7 in-creased butyrate tolerance of C. acetobutylicum.

Genome-wide systems analyses were also applied to the produc-tion studies of isobutanol. Whole genome sequencing followed by





gene repair and knockout identified several genes (acrA, gatY, tnaA,yhbJ, andmarCRAB) involved in isobutanol tolerance of E. coli (Atsumiet al., 2011). More recently, evolution combined with genomic studyidentified that themarC, hfq,mdh, acrAB, gatYZABCD and rph genes ledto increased tolerance to isobutanol stress in E. coli (Minty et al.,2011).

Consequently, systems metabolic engineering for biofuel produc-tion will allow us to comprehend the cellular physiology more accu-rately, expand the understandable scope of engineering, andproduce new information on the biological systems of cell. Employingthe systems metabolic engineering for biofuel production is expectedto improve the attainable performance of cell and become a standardapproach for worldwide fuel production using microbial platforms(Fig. 3).

4. Conclusions

Limited fossil oil resources and increasing environmental concernsare urging us to establish biorefinery systems for sustainable and eco-nomical production of alternative fuels. In order to develop econom-ical and sustainable processes for biofuel production, the metabolicpathways of biofuel producers need to be optimally redesigned toachieve high performance. The major goals of metabolic pathwayredesigning for biofuel producer include improved product yield,higher product concentration and productivity, and product toler-ance. Also, the production strain should be designed in a way thatthe whole process becomes operationally inexpensive by system-wide optimization of midstream and downstream processes. We areready for taking a second leap toward efficient strain developmentby systems metabolic engineering that integrates traditional meta-bolic engineering and bioprocess engineering with systems biologyand synthetic biology. It is expected that this combined strategy willresult in the development of microorganisms capable of producingvarious biofuels cost effectively on industrial scale.

Acknowledgments

We thank Dr. Jin Hwan Park for his valuable comments to themanuscript. This work was supported by the Advanced BiomassR&D Center of Korea (ABC-2010-0029799) through the Global Fron-tier Research Program of the Ministry of Education, Science and Tech-nology (MEST). Further support by the GS Caltex, BioFuelChem, EEWSprogram of KAIST, and the World Class University program (R32-2008-000-10142-0) of the MEST are appreciated.

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