Computational Approaches for Microalgal Biofuel Optimization: A Review

Table 4

A comparative table contrasting major constraint based modeling tools (adapted from Blazier and Papin 2012 [53]).

	GIMME	iMAT	MADE	E-Flux	SIMUP	MTA
Description	Determines sets of active versus inactive reactions comparing expression levels to a set threshold optimizing the model towards a set objective function	Categorizes reactions into high, moderate, and low expression and solves mathematical equation to optimize for an objective function	Establishes a differential expression profile using several datasets originating from different growth conditions	Sets upper bounds for lowly expressed reactions using an externally set threshold to evaluate expression data sets	Identifies bioengineering strategies that force the cell to coutilize substrates achieving a state of “synthetic survival”	Predicts gene knockout strategies that would alter the metabolic fluxes in a cell in order to achieve the objective function assumed
Advantages	Requires one set of expression data	Requires no knowledge of metabolic functions	Requires no externally set threshold for expression levels	Requires no reduction of expression data to an on/off categorization	Achieves the coutilization of two sugars	Categorizes cell metabolism as “source” or “target” with no necessary a priori knowledge of functionalities
Disadvantages	Requires an externally set threshold for mRNA transcript values	Categorizes genes into high, moderate, and low expression	Requires more than one dataset of expression data to establish differential expression profiles	Sets an upper bound on fluxes using a specific function converting expression data	So far only applicable to sugars	Requires gene expression profiles in order to identify knockout strategies