In Silico Models on Algal Cultivation and Processing: An Approach for Engineered Optimization

Introduction

The attention given to algae in different commercial applications for biofuels, vital nature components production, etc. make it necessary to develop system-level metabolic engineering. The bioinformatics tools in understanding and guidance of the metabolic pathway and algal bioactive components regulation, as microalgae are considered as bio-cell factories. Therefore, a synergistic approach to in silico analysis and then conditioning in vivo will improve robustness and enhance the marketability of carbon-neutral fuels from algae (Banerjee et al., 2020).

In silico analysis and tools have been developed in the field of genome scale metabolic reconstructions (GEMs) and microbial metabolic engineering, which give a clear prediction of algal genes, sequences and allow driving the pathway to produce high contents of a certain compounds such as triacyl-glycerides (TAGs), the precursor for biodiesel (http://genome.jgi-psf.org/Chlre4/Chlre4.home.html). Despite, there is no available genome scale reconstruction (GEMs) for algae, several studies aimed the improving of in silico analysis and 'omics' databases (Dal’Molin et al., 2011). Chlamydomonas reinhardtii was used as a first trial remodel a large metabolic reconstruction of algae, which demonstrated 484 reactions and 458 metabolites sited in the chloroplast, cytosol and mitochondria (Boyle and Morgan, 2009; Herrera-Valencia et al., 2012).

This chapter provides an insight into the different in silico models for the growth and cultivation of microalgae. Various factors such as light intensity/distribution, the temperature during cultivation and nutrient concentration are considered. The chapter also summarises the role of different Photobioreactors (PBRs) in optimising algae-based products using genome scale models.