Computational Modelling and Prediction of Microalgae Growth Focused Towards Improved Lipid Production

Avik Banerjee; Niwas Kumar; Sunita J. Varjani; Chandan Guria; Rajib Bandopadhyay; Pratyoosh Shukla; Chiranjib Banerjee

doi:10.1007/978-981-10-7434-9_13

Computational Modelling and Prediction of Microalgae Growth Focused Towards Improved Lipid Production

Avik Banerjee, Niwas Kumar, Sunita J. Varjani, Chandan Guria, Rajib Bandopadhyay, Pratyoosh Shukla, Chiranjib Banerjee^*

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 12 - Chapter in an edited book (Author) › peer-review

15 Citations (Scopus)

Abstract

In response to compelling demands worldwide for sources of renewable and eco-friendly energy feedstock, research and development in microalgae as a sustainable alternative has garnered interest. In order to make microalgae-derived fuel more competitive than fossil fuels in terms of cost, bottlenecks like scalability, better biomass production and enhanced lipid production without nutritional stress need to be resolved. In this chapter, the various computational modelling methods applied to microalgae growth in various environmental conditions have been reviewed. The possibility and potential of employing these models for better lipid production have also been highlighted, as better predictability of models can lead to better transgenic algal platform. Moreover, the upcoming models integrating omics data with flux analysis have also been discussed that has resulted in updated simulation due to the incorporation of data about novel genes. Lastly, the need for close collaboration between biochemical engineers, molecular biologists and modellers have been emphasised to validate the models on natural environment apart from laboratory conditions. © 2018, Springer Nature Singapore Pte Ltd.

Original language	English
Title of host publication	Biosynthetic Technology and Environmental Challenges
Editors	Sunita J. Varjani, Binod Parameswaran, Sunil Kumar, Sunil K. Khare
Publisher	Springer Singapore
Chapter	13
Pages	223-232
Number of pages	10
ISBN (Electronic)	978-981-10-7434-9
ISBN (Print)	978-981-10-7433-2, 978-981-13-5633-9
DOIs	https://doi.org/10.1007/978-981-10-7434-9_13
Publication status	Published - 2018
Externally published	Yes

Publication series

Name	Energy, Environment, and Sustainability
ISSN (Print)	2522-8366
ISSN (Electronic)	2522-8374

Research Keywords

Computational modelling
Flux balance analysis
Lipid and biomass production
Microalgae
Product optimisation

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10.1007/978-981-10-7434-9_13

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Banerjee, A., Kumar, N., Varjani, S. J., Guria, C., Bandopadhyay, R., Shukla, P., & Banerjee, C. (2018). Computational Modelling and Prediction of Microalgae Growth Focused Towards Improved Lipid Production. In S. J. Varjani, B. Parameswaran, S. Kumar, & S. K. Khare (Eds.), Biosynthetic Technology and Environmental Challenges (pp. 223-232). (Energy, Environment, and Sustainability). Springer Singapore. https://doi.org/10.1007/978-981-10-7434-9_13

Banerjee, Avik ; Kumar, Niwas ; Varjani, Sunita J. et al. / Computational Modelling and Prediction of Microalgae Growth Focused Towards Improved Lipid Production. Biosynthetic Technology and Environmental Challenges. editor / Sunita J. Varjani ; Binod Parameswaran ; Sunil Kumar ; Sunil K. Khare. Springer Singapore, 2018. pp. 223-232 (Energy, Environment, and Sustainability).

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abstract = "In response to compelling demands worldwide for sources of renewable and eco-friendly energy feedstock, research and development in microalgae as a sustainable alternative has garnered interest. In order to make microalgae-derived fuel more competitive than fossil fuels in terms of cost, bottlenecks like scalability, better biomass production and enhanced lipid production without nutritional stress need to be resolved. In this chapter, the various computational modelling methods applied to microalgae growth in various environmental conditions have been reviewed. The possibility and potential of employing these models for better lipid production have also been highlighted, as better predictability of models can lead to better transgenic algal platform. Moreover, the upcoming models integrating omics data with flux analysis have also been discussed that has resulted in updated simulation due to the incorporation of data about novel genes. Lastly, the need for close collaboration between biochemical engineers, molecular biologists and modellers have been emphasised to validate the models on natural environment apart from laboratory conditions. {\textcopyright} 2018, Springer Nature Singapore Pte Ltd.",

keywords = "Computational modelling, Flux balance analysis, Lipid and biomass production, Microalgae, Product optimisation",

author = "Avik Banerjee and Niwas Kumar and Varjani, {Sunita J.} and Chandan Guria and Rajib Bandopadhyay and Pratyoosh Shukla and Chiranjib Banerjee",

year = "2018",

doi = "10.1007/978-981-10-7434-9_13",

language = "English",

isbn = "978-981-10-7433-2",

series = "Energy, Environment, and Sustainability",

publisher = "Springer Singapore",

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Banerjee, A, Kumar, N, Varjani, SJ, Guria, C, Bandopadhyay, R, Shukla, P & Banerjee, C 2018, Computational Modelling and Prediction of Microalgae Growth Focused Towards Improved Lipid Production. in SJ Varjani, B Parameswaran, S Kumar & SK Khare (eds), Biosynthetic Technology and Environmental Challenges. Energy, Environment, and Sustainability, Springer Singapore, pp. 223-232. https://doi.org/10.1007/978-981-10-7434-9_13

Computational Modelling and Prediction of Microalgae Growth Focused Towards Improved Lipid Production. / Banerjee, Avik; Kumar, Niwas; Varjani, Sunita J. et al.
Biosynthetic Technology and Environmental Challenges. ed. / Sunita J. Varjani; Binod Parameswaran; Sunil Kumar; Sunil K. Khare. Springer Singapore, 2018. p. 223-232 (Energy, Environment, and Sustainability).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 12 - Chapter in an edited book (Author) › peer-review

TY - CHAP

T1 - Computational Modelling and Prediction of Microalgae Growth Focused Towards Improved Lipid Production

AU - Banerjee, Avik

AU - Kumar, Niwas

AU - Varjani, Sunita J.

AU - Guria, Chandan

AU - Bandopadhyay, Rajib

AU - Shukla, Pratyoosh

AU - Banerjee, Chiranjib

PY - 2018

Y1 - 2018

N2 - In response to compelling demands worldwide for sources of renewable and eco-friendly energy feedstock, research and development in microalgae as a sustainable alternative has garnered interest. In order to make microalgae-derived fuel more competitive than fossil fuels in terms of cost, bottlenecks like scalability, better biomass production and enhanced lipid production without nutritional stress need to be resolved. In this chapter, the various computational modelling methods applied to microalgae growth in various environmental conditions have been reviewed. The possibility and potential of employing these models for better lipid production have also been highlighted, as better predictability of models can lead to better transgenic algal platform. Moreover, the upcoming models integrating omics data with flux analysis have also been discussed that has resulted in updated simulation due to the incorporation of data about novel genes. Lastly, the need for close collaboration between biochemical engineers, molecular biologists and modellers have been emphasised to validate the models on natural environment apart from laboratory conditions. © 2018, Springer Nature Singapore Pte Ltd.

AB - In response to compelling demands worldwide for sources of renewable and eco-friendly energy feedstock, research and development in microalgae as a sustainable alternative has garnered interest. In order to make microalgae-derived fuel more competitive than fossil fuels in terms of cost, bottlenecks like scalability, better biomass production and enhanced lipid production without nutritional stress need to be resolved. In this chapter, the various computational modelling methods applied to microalgae growth in various environmental conditions have been reviewed. The possibility and potential of employing these models for better lipid production have also been highlighted, as better predictability of models can lead to better transgenic algal platform. Moreover, the upcoming models integrating omics data with flux analysis have also been discussed that has resulted in updated simulation due to the incorporation of data about novel genes. Lastly, the need for close collaboration between biochemical engineers, molecular biologists and modellers have been emphasised to validate the models on natural environment apart from laboratory conditions. © 2018, Springer Nature Singapore Pte Ltd.

KW - Computational modelling

KW - Flux balance analysis

KW - Lipid and biomass production

KW - Microalgae

KW - Product optimisation

U2 - 10.1007/978-981-10-7434-9_13

DO - 10.1007/978-981-10-7434-9_13

M3 - RGC 12 - Chapter in an edited book (Author)

SN - 978-981-10-7433-2

SN - 978-981-13-5633-9

T3 - Energy, Environment, and Sustainability

SP - 223

EP - 232

BT - Biosynthetic Technology and Environmental Challenges

A2 - Varjani, Sunita J.

A2 - Parameswaran, Binod

A2 - Kumar, Sunil

A2 - Khare, Sunil K.

PB - Springer Singapore

ER -

Banerjee A, Kumar N, Varjani SJ, Guria C, Bandopadhyay R, Shukla P et al. Computational Modelling and Prediction of Microalgae Growth Focused Towards Improved Lipid Production. In Varjani SJ, Parameswaran B, Kumar S, Khare SK, editors, Biosynthetic Technology and Environmental Challenges. Springer Singapore. 2018. p. 223-232. (Energy, Environment, and Sustainability). Epub 2017 Dec 1. doi: 10.1007/978-981-10-7434-9_13

Computational Modelling and Prediction of Microalgae Growth Focused Towards Improved Lipid Production

Abstract

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