TABLE 2.

Overview of some key contributions of systems biology and AI in microalga research

Methodology	Key contributions	Reference
Computational analysis of MFA data: using mathematical and statistical approaches to process and analyze MFA data	Identify suppressor proteins of methionine and cysteine biosynthesis	[140]
	Increase carbon fixation and biomass through the identification of an alternative pathway for isoleucine synthesis	[141]
	Investigate and increase the astaxanthin synthesis	[142]
	Control the hydrogen production through the regulation of hydrogenase and poly-β-hydroxybutyrate synthase	[143]
Genome-scale metabolic model: a mathematical approach for simulating metabolism in genome-scale reconstructions. It uses steady-state assumptions and stoichiometry of all known reactions of the metabolic pathway	Predict alternative metabolic routes for fixed carbon through an analysis of all possible double reaction knockouts	[144]
Dynamic or kinetic modeling: computational modeling of metabolic pathways using enzymes kinetics and rate laws	Control the growth of the microalgae in the bioreactors with artificial lights	[145]
	Predict the photosynthetic apparatus status (open, closed, and damaged reaction centers) under different lighting conditions	[146]
	Optimize the growth of microalgae for increased biomass through the simulation of the production in a virtual system	[147]
Machine learning: data analytics, bioinformatics, and deep learning using artificial neural networks	Analyze microscopic imaging for the classification, identification, and growth stage estimation of microalgae	[148]
	Analyze microscopic imaging for the identification of individual microalgae in a free or symbiosis state	[149]
	Improve the design of a semicontinuous algal cultivation to overcome the mutual shading that limited the growth	[150]
	Define and diagnose algal cultures under stress conditions to save them from imminent crashing by utilizing 4 biomarkers	[151]
	Predict phosphorylation site from mass spectrometry–based proteomics data	[152]
Bioinformatics analysis of gene expression data	Identify the phytoene synthase gene of microalgae	[153]
	Identify the sequence encoding P-type ATPases from RNA-Seq transcriptomic data	[154]

MFA, metabolic flux analysis