Page 116 - The-5th-MCAIT2021-eProceeding

parameters (Maia, Rocha and Rocha 2016). Regardless of these imperfections, FBA can determine the steady-
state fluxes of organisms as it does not require the above-mentioned parameters that are difficult to obtain.

References

Budinich, M., Bourdon, J., Larhlimi, A., & Eveillard, D. (2017). A multi-objective constraint-based approach
or modeling genome-scale microbial ecosystems. PloS one, 12(2), e0171744.
Daud, K. M., Zakaria, Z., Shah, Z. A., Saberi, M., Mohamad, S. D., Omatu, S., & Corchado, J. M. (2018). A
hybrid of Differential Search Algorithm and Flux Balance Analysis to Identify Knockout Strategies for in silico
Optimization of Metabolites Production. Int. J. Advance Soft Compu. Appl, 10(2).
Doshi, P., Shri, M., Bhargava, P., Joshi, C. G., & Joshi, M. (2020). Microbial Production of Industrial Proteins
and Enzymes Using Metabolic Engineering. In Engineering of Microbial Biosynthetic Pathways (pp. 189-204).
Springer, Singapore.
Fong, S. S., & Palsson, B. Ø. (2004). Metabolic gene–deletion strains of Escherichia coli evolve to
computationally predicted growth phenotypes. Nature genetics, 36(10), 1056-1058.
Klamt, S., Müller, S., Regensburger, G., & Zanghellini, J. (2018). A mathematical framework for yield (vs.
rate) optimization in constraint-based modeling and applications in metabolic engineering. Metabolic
engineering, 47, 153-169.
Maia, P., Rocha, M., & Rocha, I. (2016). In silico constraint-based strain optimization methods: the quest for
optimal cell factories. Microbiology and Molecular Biology Reviews, 80(1), 45-67.
Suthers, P. F., Foster, C. J., Sarkar, D., Wang, L., & Maranas, C. D. (2020). Recent advances in constraint and
machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility
and kinetic law formalisms. Metabolic engineering, 63, 13-33.
Stalidzans, E., Seiman, A., Peebo, K., Komasilovs, V., & Pentjuss, A. (2018). Model-based metabolism design:
constraints for kinetic and stoichiometric models. Biochemical Society Transactions, 46(2), 261-267.
Tomar, N., & De, R. K. (2013). Comparing methods for metabolic network analysis and an application to
metabolic engineering. Gene, 521(1), 1-14.
Vasilakou, E., Machado, D., Theorell, A., Rocha, I., Nöh, K., Oldiges, M., & Wahl, S. A. (2016). Current state
and challenges for dynamic metabolic modeling. Current opinion in microbiology, 33, 97-104.

E- Proceedings of The 5th International Multi-Conference on Artificial Intelligence Technology (MCAIT 2021) [103]
Artificial Intelligence in the 4th Industrial Revolution