Abstract
Elevated costs and long implementation times of bio-based processes for producing chemicals represent a bottleneck for moving to a bio-based economy. A prospective analysis able to elucidate economically and technically feasible product targets at early research phases is mandatory. Computational tools can be implemented to explore the biological and technical spectrum of feasibility, while constraining the operational space for desired chemicals. In this chapter, two different computational tools for assessing potential for bio-based production of chemicals from different perspectives are described in detail. The first tool is GEM-Path: an algorithm to compute all structurally possible pathways from one target molecule to the host metabolome. The second tool is a framework for Modeling Sustainable Industrial Chemicals production (MuSIC), which integrates modeling approaches for cellular metabolism, bioreactor design, upstream/downstream processes, and economic impact assessment. Integrating GEM-Path and MuSIC will play a vital role in supporting early phases of research efforts and guide the policy makers with decisions, as we progress toward planning a sustainable chemical industry.
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References
Pollak P (2011) Fine chemicals: the industry and the business. John Wiley & Sons, Hoboken, NJ
Herrgard M, Sukumara S, Campodonico M et al (2015) A multi-scale, multi-disciplinary approach for assessing the technological, economic and environmental performance of bio-based chemicals. Biochem Soc Trans 43:1151â1156
Campodonico MA, Andrews BA, Asenjo JA et al (2014) Generation of an atlas for commodity chemical production in Escherichia coli and a novel pathway prediction algorithm, GEM-Path. Metab Eng 25:140â158
Zhuang KH, HerrgĂ„rd MJ (2015) Multi-scale exploration of the technical, economic, and environmental dimensions of bio-based chemical production. Metab Eng 31:1â12
Zhuang K, Bakshi BR, HerrgĂ„rd MJ (2013) Multi-scale modeling for sustainable chemical production. Biotechnol J 8:973â984
Dai Z, Nielsen J (2015) Advancing metabolic engineering through systems biology of industrial microorganisms. Curr Opin Biotechnol 36:8â15
Hadadi N, Hatzimanikatis V (2015) Design of computational retrobiosynthesis tools for the design of de novo synthetic pathways. Curr Opin Chem Biol 28:99â104
Carbonell P, Planson A-G, Fichera D et al (2011) A retrosynthetic biology approach to metabolic pathway design for therapeutic production. BMC Syst Biol 5:122
Cho A, Yun H, Park JH et al (2010) Prediction of novel synthetic pathways for the production of desired chemicals. BMC Syst Biol 4:35
Henry CS, Broadbelt LJ, Hatzimanikatis V (2010) Discovery and analysis of novel metabolic pathways for the biosynthesis of industrial chemicals: 3-hydroxypropanoate. Biotechnol Bioeng 106:462â473
Yim H, Harry Y, Robert H et al (2011) Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol. Nat Chem Biol 7:445â452
Orth JD, Ines T, Palsson BĂ (2010) What is flux balance analysis? Nat Biotechnol 28:245â248
Bordbar A, Monk JM, King ZA et al (2014) Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 15:107â120
McCloskey D, Palsson BĂ, Feist AM (2013) Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli. Mol Syst Biol 9:661
Mahadevan R, Edwards JS, Doyle FJ 3rd (2002) Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J 83:1331â1340
Zhuang K, Kai Z, Laurence Y et al (2013) Dynamic strain scanning optimization: an efficient strain design strategy for balanced yield, titer, and productivity. DySScO strategy for strain design. BMC Biotechnol 13:8
Varma A, Palsson BO (1994) Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl Environ Microbiol 60:3724â3731
Caspeta L, Luis C, Jens N (2013) Economic and environmental impacts of microbial biodiesel. Nat Biotechnol 31:789â793
Hermann BG, Patel M (2007) Todayâs and tomorrowâs bio-based bulk chemicals from white biotechnology. Appl Biochem Biotechnol 136:361â388
Smart B (1992) Industry as a metabolic activity. Proc Natl Acad Sci U S A 89:804â806
OâBoyle NM, Banck M, James CA et al (2011) Open babel: an open chemical toolbox. J Chem 3:33
Steinbeck C, Han Y, Kuhn S et al (2003) The chemistry development kit (CDK): an open-source java library for chemo- and bioinformatics. J Chem Inf Comput Sci 43:493â500
G. Landrum RDKit. http://www.rdkit.org.
James CA, Weininger D, Delany J (1995) Daylight theory manual. Daylight Chemical Information Systems, Irvine, CA
Mu F, Unkefer CJ, Unkefer PJ et al (2011) Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds. Bioinformatics 27:1537â1545
Kanehisa M (2006) From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 34:D354âD357
Curran KA, Alper HS (2012) Expanding the chemical palate of cells by combining systems biology and metabolic engineering. Metab Eng 14:289â297
Machado D, Daniel M, HerrgĂ„rd MJ (2015) Co-evolution of strain design methods based on flux balance and elementary mode analysis. Metab Eng Commun 2:85â92
biosustain biosustain/MuSIC-PDO-3HP. https://github.com/biosustain/MuSIC-PDO-3HP
cdanielmachado cdanielmachado/framed. https://github.com/cdanielmachado/framed
Overbeek R (2005) The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res 33:5691â5702
Henry CS, Broadbelt LJ, Hatzimanikatis V (2007) Thermodynamics-based metabolic flux analysis. Biophys J 92:1792â1805
King ZA, Lu J, DrĂ€ger A et al (2016) BiGG models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res 44:D515âD522
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Campodonico, M.A., Sukumara, S., Feist, A.M., HerrgÄrd, M.J. (2018). Computational Methods to Assess the Production Potential of Bio-Based Chemicals. In: Jensen, M.K., Keasling, J.D. (eds) Synthetic Metabolic Pathways. Methods in Molecular Biology, vol 1671. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7295-1_7
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DOI: https://doi.org/10.1007/978-1-4939-7295-1_7
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