Review of Literature
2.8 Systems biology of microalgae
serious need to reduce the costs involved in these transesterification step so that they can be suitable for industrial scale applications.
the way to understand the systems biology of different algal strains grown under different cultivation conditions. For instance, comparison of the transcriptomic data of Chlamydomonas reinhardtii grown under nutrient replete and starved condition yielded valuable information on regulation and controls involved in neutral lipid biosynthesis (Miller et al., 2010). The study confirmed that expression of TAG biosynthetic pathway genes are high only during the nutrient starved conditions. Differential gene expression analysis in Micracitinium pusillum under nitrogen starvation conditions revealed that the carbon sources available for TAG biosynthesis were largely derived from the carbohydrate resource in the biomass and not from the new carbon fixed during photosynthesis (Li et al., 2012). De novo transcriptomic and proteomic analyses was employed in an unsequenced microalga Chlorella vulgaris for the examination of TAG biosynthesis which bypassed the necessity of genome sequence for such analyses (Guarnieri et al., 2011). Another interesting discovery is the identification of the nitrogen response regulator NRR1 in Chlamydomonas reinhardtii induced under nitrogen starvation conditions and during neutral lipid accumulation (Boyle et al., 2012). The NRR1 was found to be a regulatory gene involved in nitrogen assimilation and TAG accumulation. The TAG accumulation was reduced to half in the NRR1- mutant when compared with the wild type strain which confirms NRR1 as an important regulator involved in TAG accumulation (Boyle et al., 2012). The regulatory mechanisms and novel strain-engineering targets for simultaneous enhancement of neutral lipid biosynthesis and growth were determined using shotgun proteomic analyses in Chlorella vulgaris (Guarnieri et al., 2013). The study showed that around 175 proteins down regulated and 282 proteins are up-regulated in case of nutrient depleted condition when compared with the nutrient replete condition. The same study revealed AMP-activated kinase (AMP-Kinase) as a regulatory protein that inhibits the ACCase through phosphoregulation and therefore, knock out mutants of AMP-Kinase may enhance lipid
biosynthesis through ACCase. The study also showed possibility to increase the lipid biosynthesis capacity by increasing the NADPH pool in the cells via overexpression of malic enzyme.
Although various systems biology studies have been performed to identify the key regulatory pathways and proteins, it remains difficult to understand or quantitatively predict the dynamic responses of these molecules. One approach is to build up a kinetic model that focus on very small metabolic pathway in a network (Shastri and Morgan, 2005). However, with increase in model complexity the number of unknown kinetic parameter increases which makes it difficult for a holistic approach. Alternate is the constraint based metabolic models which can quantitatively predict the dynamic responses of the cells with respect to the changes in environmental conditions (Kauffman et al., 2003). Flux balance analysis (FBA) is one of such constraint based mathematical modelling tool often used to quantify and simulate the flow of metabolites through a metabolic network of an organism with stoichiometry as an constraint (Shastri and Morgan, 2005). FBA works with an assumption that all the metabolic pathways will reach a steady state at any growth condition satisfying the constraint stoichiometry thereby, yielding multiple steady state solutions in an underdetermined nature (Kauffman et al., 2003). Further optimization of the solution space with an appropriate objective function yields a meaningful steady state solution. The simulation results obtained from the model can be experimentally validated and the predictive model for cellular metabolism can be improved further. Such tools can also be used to determine the metabolic state of the cells under varying growth conditions and is the key for uncovering the stoichiometric and regulatory principles in the metabolic network of any microorganism (Shastri and Morgan, 2005).
FBA was carried out in green eukaryotic microalga Chlorella pyrenoidosa cultivated under photoautotrophic, mixotrophic, heterotrophic and cyclic culture (light-autotrophic
and dark-heterotrophic) conditions to determine the influence of illumination on carbon metabolism and energetics (Yang et al., 2000). The model comprised a total of 67 reactions with 61 metabolites involved in central metabolism. Evaluation of the energy economy of the cells grown under different cultivation conditions showed that heterotrophic condition yielded energy efficient growth while under photoautotrophic condition the biomass yield on energy supplied was low. Interestingly the cyclic culture grown in light autotrophic and dark heterotrophic yielded the efficient energy utilization for biomass production. Similarly, FBA was employed to understand the interactions between biochemical energy, carbon fixation and assimilation pathways during the photoautotrophic cultivation of Synechocystis sp. PCC 6803 (Shastri and Morgan, 2005). A metabolic network was reconstructed for the green algae C. reinhardtii involving three compartments, 484 metabolic reactions and 458 intracellular metabolites using the genomic and biochemical information available in the literatures (Boyle and Morgan, 2009). This was the first model for microalgae that included three metabolically active intracellular compartments. However, construction of a genome scale metabolic model that includes all reaction involved in the metabolism will yield a better predictable model for state evaluation. A comprehensive metabolic model (iSyn669) of a cyanobacteria Synechocystis sp. PCC 6803 was developed with 882 metabolic reactions and 790 metabolites by Montagud et al. (2010). The model included a detailed biomass equation expressed in terms of elementary molecules required for cellular growth and a detailed stoichiometric representation of photosynthesis. The model was also successfully used as a platform to simulate and identify the knock outs required for maximization of succinate production. Similarly, iRC1080 is another comprehensive genome scale model constructed for the eukaryotic green alga Chlamydomonas reinhardtii with 1080 genes, 2190 reactions and 1068 metabolites and includes 83 subsystems in 10 compartments (Chang et al., 2011). The main highlight of this model is that it is incorporated with
additional lipid metabolic pathways and reactions that has importance in the field of algal biofuels. Even though many genome scale metabolic models were constructed for microalgal systems, no model has highlighted the pathways or enzymes that has to be altered for maximization of net lipid productivity and more research in this field may allow us to determine the sensible targets for metabolic engineering towards biodiesel production.