Chandan Guria

Metabolic pathways for lipid synthesis under nitrogen stress in Chlamydomonas and Nannochloropsis

Microalgae are currently being considered as a clean, sustainable and renewable energy source. Enzymes that catalyse the metabolic pathways for biofuel production are specific and require strict regulation and co-ordination. Thorough knowledge of these key enzymes along with their regulatory molecules is essential to enable rational metabolic engineering, to drive the metabolic flux towards the desired metabolites of importance. This paper reviews two key enzymes that play their role in production of bio-oil: DGAT (acyl–CoA:diacylglycerol acyltransferase) and PDAT (phospholipid:diacylglycerol acyltransferase). It also deals with the transcription factors that control the enzymes while cell undergoes a metabolic shift under stress. The paper also discusses the association of other enzymes and pathways that provide substrates and precursors for oil accumulation. Finally a futuristic solution has been proposed about a synthetic algal cell platform that would be committed towards biofuel synthesis.

The elitist non-dominated sorting genetic algorithm with inheritance (i-NSGA-II) and its jumping gene adaptations for multi-objective optimization

Like elitism, parent inheritance plays an important role to decide the quality of offspring and it is believed that the parents with high intelligence quotient (IQ) like to produce children with high IQ. Inspiring this concept, the improved pool of an initial random population involving the best set of chromosomes are incorporated in the framework of multi-objective optimization genetic algorithm. The effects of parent inheritance in the elitist non-dominated sorting genetic algorithm (called, i-NSGA-II) on the speed of convergence to the global Pareto-optimal front is compared with the binary coded NSGA-II using different benchmark multi-objective optimization problems. The parent inheritance is also incorporated in several jumping gene (JG) adapted NSGA-II algorithms. The efficacy of inheritance in NSGA-II and its several JG adaptations is tested by quantifying several indicators, namely, generational distance, spacing and hyper-volume ratio using different benchmark multi-objective optimization problems from the literature. The inclusion of the inheritance operator improves the speed of convergence to global Pareto-optimal front significantly with a minimum number of generations over existing NSGA-II and several JG adapted NSGA-II algorithms. The effectiveness of the proposed operator is further established by solving real-life robust multi-objective optimization problems involving the drilling of oil-well and synthesis of sal oil biodiesel.

Biokinetic model-based multi-objective optimization of Dunaliella tertiolecta cultivation using elitist non-dominated sorting genetic algorithm with inheritance

Algal model based multi-objective optimization using elitist non-dominated sorting genetic algorithm with inheritance was carried out for batch cultivation of Dunaliella tertiolecta using NPK-fertilizer. Optimization problems involving two- and three-objective functions were solved simultaneously. The objective functions are: maximization of algae-biomass and lipid productivity with minimization of cultivation time and cost. Time variant light intensity and temperature including NPK-fertilizer, NaCl and NaHCO3 loadings are the important decision variables. Algal model involving Monod/Andrews adsorption kinetics and Droop model with internal nutrient cell quota was used for optimization studies. Sets of non-dominated (equally good) Pareto optimal solutions were obtained for the problems studied. It was observed that time variant optimal light intensity and temperature trajectories, including optimum NPK fertilizer, NaCl and NaHCO3 concentration has significant influence to improve biomass and lipid productivity under minimum cultivation time and cost. Proposed optimization studies may be helpful to implement the control strategy in scale-up operation.

Computational Modelling and Prediction of Microalgae Growth Focused Towards Improved Lipid Production

In response to compelling demands worldwide for sources of renewable and eco-friendly energy feedstock, research and development in microalgae as a sustainable alternative has garnered interest. In order to make microalgae-derived fuel more competitive than fossil fuels in terms of cost, bottlenecks like scalability, better biomass production and enhanced lipid production without nutritional stress need to be resolved. In this chapter, the various computational modelling methods applied to microalgae growth in various environmental conditions have been reviewed. The possibility and potential of employing these models for better lipid production have also been highlighted, as better predictability of models can lead to better transgenic algal platform. Moreover, the upcoming models integrating omics data with flux analysis have also been discussed that has resulted in updated simulation due to the incorporation of data about novel genes. Lastly, the need for close collaboration between biochemical engineers, molecular biologists and modellers have been emphasised to validate the models on natural environment apart from laboratory conditions.

Recent developments in synthetic biology and metabolic engineering in microalgae towards biofuel production

In the wake of the uprising global energy crisis, microalgae have emerged as an alternate feedstock for biofuel production. In addition, microalgae bear immense potential as bio-cell factories in terms of producing key chemicals, recombinant proteins, enzymes, lipid, hydrogen and alcohol. Abstraction of such high-value products (algal biorefinery approach) facilitates to make microalgae-based renewable energy an economically viable option. Synthetic biology is an emerging field that harmoniously blends science and engineering to help design and construct novel biological systems, with an aim to achieve rationally formulated objectives. However, resources and tools used for such nuclear manipulation, construction of synthetic gene network and genome-scale reconstruction of microalgae are limited. Herein, we present recent developments in the upcoming field of microalgae employed as a model system for synthetic biology applications and highlight the importance of genome-scale reconstruction models and kinetic models, to maximize the metabolic output by understanding the intricacies of algal growth. This review also examines the role played by microalgae as biorefineries, microalgal culture conditions and various operating parameters that need to be optimized to yield biofuel that can be economically competitive with fossil fuels.