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Identifying Key In Silico Knockout for Enhancement of Limonene Yield Through Dynamic Metabolic Modelling

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Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2745))

Abstract

Living cells display dynamic and complex behaviors. To understand their response and to infer novel insights not possible with traditional reductionist approaches, over the last few decades various computational modelling methodologies have been developed. In this chapter, we focus on modelling the dynamic metabolic response, using linear and nonlinear ordinary differential equations, of an engineered Escherichia coli MG1655 strain with plasmid pJBEI-6409 that produces limonene. We show the systems biology steps involved from collecting time-series data of living cells, to dynamic model creation and fitting the model with experimental responses using COPASI software.

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Acknowledgement

This work was supported by the Intra-create Thematic Grant “Cities” (grant number: NRF2019-THE001-0007) under the EcoCTs project. The EcoCTs research project is supported by the National Research Foundation, Prime Minister’s Office, Singapore, under its campus for Research Excellence and Technological Enterprise (CREATE) programme. In addition, we are thankful to Dr. Floriant Bellvert from MetaToul (Metabolomics & Fluxomics Facilities, Toulouse, France) and its staff members for their experimental guidance and insights. We would also like to acknowledge Dr. Wee Chew from the Singapore Institute of Food and Biotechnology Innovation (SIFBI) for technical support and discussion.

Author information

Author notes

  1. Jasmeet Kaur Khanijou and Yan Ting Hee contributed equally with all other contributors.

Authors and Affiliations

  1. Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore

    Jasmeet Kaur Khanijou

  2. Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore

    Yan Ting Hee & Kumar Selvarajoo

  3. Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore (NUS), Singapore, Republic of Singapore

    Kumar Selvarajoo

  4. School of Biological Sciences, Nanyang Technological University (NTU), Singapore, Republic of Singapore

    Kumar Selvarajoo

Authors
  1. Jasmeet Kaur Khanijou
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  2. Yan Ting Hee
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  3. Kumar Selvarajoo
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Corresponding author

Correspondence to Kumar Selvarajoo .

Editor information

Editors and Affiliations

  1. Department of Experimental Medicine, Sapienza University, Rome, Italy

    Mariano Bizzarri

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© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Khanijou, J.K., Hee, Y.T., Selvarajoo, K. (2024). Identifying Key In Silico Knockout for Enhancement of Limonene Yield Through Dynamic Metabolic Modelling. In: Bizzarri, M. (eds) Systems Biology. Methods in Molecular Biology, vol 2745. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3577-3_1

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  • DOI: https://doi.org/10.1007/978-1-0716-3577-3_1

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3576-6

  • Online ISBN: 978-1-0716-3577-3

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