Computational tools that help to improve synthetic biology have been developed and are currently being expanded. Improvement of algae production to increase biofuel yields through synthetic biology involves many distinct processes that can be aided by different computational tools. Genome-scale metabolic network reconstructions and models are available for a number of algal species. These can be useful for identifying and selecting gene targets for knockout and strain engi­neering. Some of the available tools and algorithms that are able to perform such tasks include (but are not restricted to) Optknock (Burgard et al. 2003) and Optstrain (Pharkya et al. 2004).

The standard approach for computing metabolic fluxes is flux balance analysis (FBA) through using toolboxes such as COBRA or Pathway Tools (these are discussed in the accompanying Chap. 10, Towards applications of Genomics and Metabolic Modeling to Improve Biomass Productivity). FBA allows prediction of optimal flux distribution throughout the network for a given cellular phenotypic state. Through the use of computational tools associated with FBA, consequences of changes introduced in a metabolic network can be predicted. For example, quantitative mapping of intracellular fluxes in relation to single or multiple gene deletions can easily be carried out by FBA. There are many strategies available to predict alterations that result in increased production of a desired metabolite. For instance, one method entails identifying key and relevant pathways that are impacted using simulated gene knockouts (Reed et al. 2010). The accuracy of this method can be enhanced by integrating experimental data; such as metabolite concentration, gene expression data, and uptake and secretion rates.

‘Pathway Tools’ (Karp et al. 2010) is an integrated reconstruction, analysis, and visualization software created by the Bioinformatics Research Group at SRI Inter­national (http://bioinformatics. ai. sri. com/ptools/). Pathway tools can automatically

generate organism-specific metabolic network databases and provides details of genes/proteins, reactions, and compound associations, as well as create pathway databases (called Pathway Genome Database, or PGDBs). To create a PGDB, one of the components of the Pathway Tools called PathoLogic is used. This tool allows users to create PGDBs using the genome annotation of an organism of interest directly from the organism’s GenBank annotation file. Users can manually adjust, edit, or add (new) content as needed. There are already many well developed and intensively curated pathway databases or PGDBs including BioCyc, EcoCyc and MetaCyc (Caspi et al. 2014), which aid metabolic analysis and network recon­structions. BioCyc alone has a collection of about 3530 PGDBs, which users can query, visualize, manage and analyze. Among these, algal PGDBs include Tha — lassiosira pseudonana, Nannochloropsis gaditana, Acaryochloris marina, Ana — baena cylindrica, Anabaena variabilis, Synechococcus elongatus and

Chlamydomonas reinhardtii. Other offered functionalities in Pathway Tools include tools that can be used in downstream analyses to identify the shortest path between two metabolites, identify dead-end metabolites, fill pathway gaps, identify choke — points (potential drug targets), and infer operons and transport reactions. Many new metabolic reactions have been added to EcoCyc using the dead-end metabolite analysis approach (Mackie et al. 2013). Pathway Tools can aid synthetic biology experiment designs by identifying potential pathways, which may be targets for alterations.