An outlook on metabolic pathway engineering in crop plants
https://doi.org/10.26832/24566632.2020.0503027
Abstract
To produce the essential secondary metabolites, plants are the major and important target source materials for conducting the high-profile metabolic engineering studies. Metabolic pathway engineering of both microorganism targets and plants target contribute towards important drug discovery. In order to efficiently work out in advanced plant metabolic pathway engineering techniques, a detailed knowledge and expertise is essentially needed regarding the plant cell physiology and the mechanics of plant metabolism. Mathematical and statistical models to scale and map the genome for integrative metabolic pathway activity, signal transduction mechanism in the genome, gene regulation and the networks of protein-protein interaction can provide the in-depth knowledge to work efficiently on plant metabolic pathway engineering studies. Incorporation of omics data into these statistical and mathematical models is crucial in the case of drug discovery using the plant system. Recently, artificial intelligence concept and approaches are experimentally applied for efficient and accurate metabolic engineering in plants.
Keywords:
Artificial intelligence, Cellular metabolism, Metabolic engineering, PlantsDownloads
References
Abernathy, M.H., He, L. and Tang, Y.J. (2017). Channelling in native microbial pathways: implications and challenges for metabolic engineering. Biotechnology Advances, 35 (6): 805-814.
Armah, C.N., Traka, M.H., Dainty, J.R., Defernez, M., Janssens, A., Leung, W., Doleman, J.F., Potter, J.F. and Mithen, R.F. (2013). A diet rich in high-glucoraphanin broccoli interacts with genotype to reduce discordance in plasma metabolite pro- files by modulating mitochondrial function. The American Journal of Clinical Nutrition, 98 (3): 712–722.
Bailey, J.E., Sburlati, A., Hatzimanikatis, V., Lee, K., Renner, W.A. and Tsai, P.S. (2002). Inverse metabolic engineering: a strategy for directed genetic engineering of useful phenotypes. Biotechnology and Bioengineering, 79 (5): 568–579.
Blazeck, J. and Alper, H. (2010). Systems metabolic engineering: genome-scale models and beyond. Biotechnology Journal, 5 (7): 647–659.
Boles, K.S., Kannan, K., Gill, J., Felderman, M., Gouvis, H., Hubby, B., Kamrud, K.I., Venter, J.C. and Gibson, D.C. (2017). Digital-to-biological converter for on-demand pro- duction of biologics. Nature Biotechnology, 35 (7): 672.
Boyle, P.M. and Silver, P.A. (2012). Parts plus pipes: synthetic biology approaches to metabolic engineering. Metabolic Engineering, 14 (3): 223–232.
Butelli, E., Titta, L., Giorgio, M., Mock, H.P., Matros, A., Peterek, S., Schi- jlen, E.G., Hall, R.D., Bovy, A.G. and Luo, J. (2008). Enrichment of tomato fruit with health-promoting anthocyanins by expression of select transcription factors. Nature Biotechnology, 26 (11): 1301–1308.
Carbonell, P., Jervis, A.J., Robinson, C.J., Yan, C., Dunstan, M., Swainston, N., Vinaixa, M., Hollywood, K.A., Currin, A. and Rattray, N.J. (2018). An automated design-build-test-learn pipeline for enhanced microbial production of fine chemicals. Communications Biology, 1 (1): 1–10.
Chao, R., Mishra, S., Si, T. and Zhao, H. (2017). Engineering biological systems using auto- mated biofoundries. Metabolic Engineering, 42: 98–108.
Chuai, G., Ma, H., Yan, J., Chen, M., Hong, N., Xue, D., Zhou, C., Zhu, C., Chen, K., and Duan, B. (2018). Deepcrispr: optimized CRISPR guide rna design by deep learning. Genome Biology, 19 (1): 80.
Clauwaert, J., Menschaert, G. and Waegeman, W. (2019). Deepribo: a neural network for precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patterns. Nucleic Acids Research 47 (6): e36-e36.
Collinge, D.B. (Ed.). (2016). Plant pathogen resistance biotechnology (pp. 978-1). John Wiley and Sons.
Dasgupta, A., Chowdhury, N. and De, R. K. (2020). Metabolic pathway engineering: Perspectives and applications. Computer Methods and Programs in Biomedicine, 192, 105436.
Delaunois, B., Cordelier, S., Conreux, A., Clement. and Jeandet, P. (2009). Molecular engineering of resveratrol in plants. Plant Biotechnology Journal, 7 (1): 2-12.
Groher, A.C., Jager, S., Schneider, C., Groher, F., Hamacher, K. and Suess, B. (2018). Tuning the performance of synthetic riboswitches using machine learning. ACS Synthetic Biology, 8(1): 34-44.
Kebeish, R., Niessen, M., Thiruveedhi, K., Bari, R., Hirsch, H. J., Rosenkranz, R. and Peterhänsel, C. (2007). Chloroplastic photorespiratory bypass increases photosynthesis and biomass production in Arabidopsis thaliana. Nature Biotechnology, 25(5): 593-599.
Kim, G.B., Kim, W.J., Kim, H.U., and Lee, S.Y. (2020). Machine learning applications in systems metabolic engineering. Current Opinion in Biotechnology, 64, 1-9.
Kim, H.U., Charusanti, P., Lee, S.Y. and Weber, T. (2016). Metabolic engineering with systems biology tools to optimize production of prokaryotic secondary metabolites. Natural Product Reports, 33(8): 933-941.
Krambeck, F. J., and Betenbaugh, M. J. (2005). A mathematical model of N‐linked glycosylation. Biotechnology and Bioengineering, 92(6): 711-728.
Kümmel, A., Panke, S. and Heinemann, M. (2006). Putative regulatory sites unraveled by network embedded thermodynamic analysis of metabolome data. Molecular Systems Biology, 2(1): 2006-0034.
Li, Y., Lin, Z., Huang, C., Zhang, Y., Wang, Z., Tang, Y.J. and Zhao, X. (2015). Metabolic engineering of Escherichia coli using CRISPR–Cas9 meditated genome editing. Metabolic Engineering, 31, 13-21.
Morris, C.E. and Sands, D.C. (2006). The breeder's dilemma—yield or nutrition? Nature Biotechnology, 24(9): 1078-1080.
Olson, D.G., Maloney, M., Lanahan, A.A., Hon, S., Hauser, L.J. and Lynd, L.R. (2015). Identifying promoters for gene expression in Clostridium thermocellum. Metabolic Engineering Communications, 2: 23-29.
Ryu, J.Y., Kim, H.U. and Lee, S.Y. (2019). Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers. Proceedings of the National Academy of Sciences, 116(28): 13996-14001.
Schuhmann, H., Lim, D.K., and Schenk, P.M. (2012). Perspectives on metabolic engineering for increased lipid contents in microalgae. Biofuels, 3(1): 71-86.
Segler, M. H., Preuss, M., and Waller, M. P. (2018). Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 555(7698): 604-610.
Shan, Q., Wang, Y., Li, J., Zhang, Y., Chen, K., Liang, Z. and Gao, C. (2013). Targeted genome modification of crop plants using a CRISPR-Cas system. Nature Biotechnology, 31(8): 686-688.
Stephanopoulos, G. and Sinskey, A.J. (1993). Metabolic engineering—methodologies and future prospects. Trends in Biotechnology, 11(9): 392-396.
Tang, G., Qin, J., Dolnikowski, G.G., Russell, R.M. and Grusak, M.A. (2009). Golden Rice is an effective source of vitamin A. The American Journal of Clinical Nutrition, 89(6): 1776-1783.
Tyo, K.E., Alper, H.S. and Stephanopoulos, G.N. (2007). Expanding the metabolic engineering toolbox: more options to engineer cells. Trends in Biotechnology, 25(3): 132-137.
Umaña, P. and Bailey, J.E. (1997). A mathematical model of N-linked glycoform biosynthesis. Biotechnology and Bioengineering, 55(6): 890-908.
Winzer, T., Gazda, V., He, Z., Kaminski, F., Kern, M., Larson, T.R. and Walker, C. (2012). A Papaver somniferum 10-gene cluster for synthesis of the anticancer alkaloid noscapine. Science, 336(6089): 1704-1708.
Ye, X., Al-Babili, S., Klöti, A., Zhang, J., Lucca, P., Beyer, P. and Potrykus, I. (2000). Engineering the provitamin A (β-carotene) biosynthetic pathway into (carotenoid-free) rice endosperm. Science, 287(5451): 303-305.
Zamboni, N., Kümmel, A., and Heinemann, M. (2008). anNET: a tool for network-embedded thermodynamic analysis of quantitative metabolome data. BMC Bioinformatics, 9(1): 199.
Zampieri, G., Vijayakumar, S., Yaneske, E. and Angione, C. (2019). Machine and deep learning meet genome-scale metabolic modeling. PLoS Computational Biology, 15(7): e1007084.
Published
How to Cite
Issue
Section
Copyright (c) 2020 Agriculture and Environmental Science Academy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
