Abstract
The various pathways and mechanisms behind fatty acid, lipid, and membrane biosynthesis together form a complex network. Moreover, lipid metabolism does not operate in isolation but rather functions in the context of a whole cell, surrounded by all its other metabolic pathways, a situation that results in additional connectivity and complexity. Computational models can aid to provide understanding of these complex networks and to make sense of interactions on a whole cell or genomic scale. In particular, these models have proven to be valuable to answer biotechnological questions, such as how to increase the biosynthesis of fatty acids. This chapter discusses the development and usage of genome-scale metabolic models in the light of lipid biosynthesis. A special focus is placed on baker’s yeast Saccharomyces cerevisiae and the oleaginous yeast Yarrowia lipolytica and the use of genome-scale metabolic models to answer biotechnological questions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agren R, Liu L, Shoaie S et al (2013) The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum. PLoS Comput Biol 9:e1002980. https://doi.org/10.1371/journal.pcbi.1002980
Asadollahi MA, Maury J, Patil KR et al (2009) Enhancing sesquiterpene production in Saccharomyces cerevisiae through in silico driven metabolic engineering. Metab Eng 11:328–334. https://doi.org/10.1016/j.ymben.2009.07.001
Aung HW, Henry SA, Walker LP (2013) Revising the representation of fatty acid, glycerolipid, and glycerophospholipid metabolism in the consensus model of yeast metabolism. Ind Biotechnol 9:215–228. https://doi.org/10.1089/ind.2013.0013
Beg QK, Vazquez A, Ernst J et al (2007) Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc Natl Acad Sci U S A 104:12663–12668. https://doi.org/10.1073/pnas.0609845104
Bordel S, Agren R, Nielsen J (2010) Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes. PLoS Comput Biol 6:e1000859. https://doi.org/10.1371/journal.pcbi.1000859
Burgard AP, Maranas CD (2003) Optimization-based framework for inferring and testing hypothesized metabolic objective functions. Biotechnol Bioeng 82:670–677. https://doi.org/10.1002/bit.10617
Chakrabarti A, Miskovic L, Soh KC, Hatzimanikatis V (2013) Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiological constraints. Biotech J 9:1043-1057. https://doi.org/10.1002/biot.201300091
Chumnanpuen P, Zhang J, Nookaew I, Nielsen J (2012) Integrated analysis of transcriptome and lipid profiling reveals the co-influences of inositol-choline and Snf1 in controlling lipid biosynthesis in yeast. Mol Gen Genomics 287:541–554. https://doi.org/10.1007/s00438-012-0697-5
Feist AM, Palsson BO (2010) The biomass objective function. Curr Opin Microbiol 13:344–349. https://doi.org/10.1016/j.mib.2010.03.003
Förster J, Famili I, Fu P et al (2003) Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res 13:244–253. https://doi.org/10.1101/gr.234503
Heavner BD, Price ND (2015) Comparative analysis of yeast metabolic network models highlights progress, opportunities for metabolic reconstruction. PLoS Comput Biol 11:e1004530. https://doi.org/10.1371/journal.pcbi.1004530
Henry CS, DeJongh M, Best AA et al (2010) High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol 28:977–982. https://doi.org/10.1038/nbt.1672
Herrgård MJ, Swainston N, Dobson P et al (2008) A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat Biotechnol 26:1155–1160. https://doi.org/10.1038/nbt1492
Kavšček M, Bhutada G, Madl T, Natter K (2015) Optimization of lipid production with a genome-scale model of Yarrowia lipolytica. BMC Syst Biol 9:72. https://doi.org/10.1186/s12918-015-0217-4
Kerkhoven EJ, Pomraning KR, Baker SE, Nielsen J (2016) Regulation of amino-acid metabolism controls flux to lipid accumulation in Yarrowia lipolytica. npj Syst Biol Appl 2:16005. https://doi.org/10.1038/npjsba.2016.5
Ledesma-Amaro R, Nicaud J-M (2016) Yarrowia lipolytica as a biotechnological chassis to produce usual and unusual fatty acids. Prog Lipid Res 61:40–50. https://doi.org/10.1016/j.plipres.2015.12.001
Lewis NE, Nagarajan H, Palsson BO (2012) Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 10:291–305. https://doi.org/10.1038/nrmicro2737
Loira N, Dulermo T, Nicaud J-M, Sherman DJ (2012) A genome-scale metabolic model of the lipid-accumulating yeast Yarrowia lipolytica. BMC Syst Biol 6:35. https://doi.org/10.1186/1752-0509-6-35
Mahadevan R, Schilling CH (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 5:264–276. https://doi.org/10.1016/j.ymben.2003.09.002
Nookaew I, Jewett MC, Meechai A et al (2008) The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism. BMC Syst Biol 2:71. https://doi.org/10.1186/1752-0509-2-71
Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotechnol 28:245–248. https://doi.org/10.1038/nbt.1614
Patil KR, Nielsen J (2005) Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc Natl Acad Sci U S A 102:2685–2689. https://doi.org/10.1073/pnas.0406811102
Patil KR, Rocha I, Förster J, Nielsen J (2005) Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinforma 6:308. https://doi.org/10.1186/1471-2105-6-308
Pitkänen E, Jouhten P, Hou J et al (2014) Comparative genome-scale reconstruction of gapless metabolic networks for present and ancestral species. PLoS Comput Biol 10:e1003465. https://doi.org/10.1371/journal.pcbi.1003465
Price ND, Reed JL, Palsson BØ (2004) Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol 2:886–897. https://doi.org/10.1038/nrmicro1023
Sánchez BJ, Nielsen J (2015) Genome scale models of yeast: towards standardized evaluation and consistent omic integration. Integr Biol 7:846–858. https://doi.org/10.1039/C5IB00083A
Sánchez BJ, Zhang C, Nilsson A et al (submitted) Improving phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints
Schuetz R, Zamboni N, Zampieri M et al (2012) Multidimensional optimality of microbial metabolism. Science 336:601–604. https://doi.org/10.1126/science.1216882
Tai M, Stephanopoulos G (2013) Engineering the push and pull of lipid biosynthesis in oleaginous yeast Yarrowia lipolytica for biofuel production. Metab Eng 15:1–9. https://doi.org/10.1016/j.ymben.2012.08.007
Thiele I, Palsson BØ (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc 5:93–121. https://doi.org/10.1038/nprot.2009.203
Vongsangnak W, Klanchui A, Tawornsamretkit I et al (2016) Genome-scale metabolic modeling of Mucor circinelloides and comparative analysis with other oleaginous species. Gene 583:121–129. https://doi.org/10.1016/j.gene.2016.02.028
Väremo L, Gatto F, Nielsen J (2014) Kiwi: a tool for integration and visualization of network topology and gene-set analysis. BMC Bioin 15:408. https://doi.org/10.1186/s12859-014-0408-9
Ye C, Xu N, Chen H et al (2015) Reconstruction and analysis of a genome-scale metabolic model of the oleaginous fungus Mortierella alpina. BMC Syst Biol 9:1. https://doi.org/10.1186/s12918-014-0137-8
Acknowledgments
The author would like to acknowledge support from the US Department of Energy, Office of Science, Office of Biological and Environmental Research, Genomic Science program (DE-SC0008744), and the Novo Nordisk Foundation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this entry
Cite this entry
Kerkhoven, E.J. (2019). Modeling Lipid Metabolism in Yeast. In: Geiger, O. (eds) Biogenesis of Fatty Acids, Lipids and Membranes. Handbook of Hydrocarbon and Lipid Microbiology . Springer, Cham. https://doi.org/10.1007/978-3-319-50430-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-50430-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50429-2
Online ISBN: 978-3-319-50430-8
eBook Packages: Biomedical and Life SciencesReference Module Biomedical and Life Sciences