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

Experimental Study of Substrate Limitation and Light Acclimation in Cultures of the Microalgae Scenedesmus obliquus—Parameter Identification and Model Predictive Control

In this study, the parameters of a dynamic model of cultures of the microalgae Scenedesmus obliquus are estimated from datasets collected in batch photobioreactors operated with various initial conditions and light illumination conditions. Measurements of biomass, nitrogen quota, bulk substrate concentration, as well as chlorophyll concentration are achieved, which allow the determination of parameters with satisfactory confidence intervals and model cross-validation against independent data. The dynamic model is then used as a predictor in a nonlinear model predictive control strategy where the dilution rate and the incident light intensity are simultaneously manipulated in order to optimize the cumulated algal biomass production.

Microalgae with artificial intelligence: A digitalized perspective on genetics, systems and products

With recent advances in novel gene-editing tools such as RNAi, ZFNs, TALENs, and CRISPR-Cas9, the possibility of altering microalgae toward designed properties for various application is becoming a reality. Alteration of microalgae genomes can modify metabolic pathways to give elevated yields in lipids, biomass, and other components. The potential of such genetically optimized microalgae can give a "domino effect" in further providing optimization leverages down the supply chain, in aspects such as cultivation, processing, system design, process integration, and revolutionary products. However, the current level of understanding the functional information of various microalgae gene sequences is still primitive and insufficient as microalgae genome sequences are long and complex. From this perspective, this work proposes to link up this knowledge gap between microalgae genetic information and optimized bioproducts using Artificial Intelligence (AI). With the recent acceleration of AI research, large and complex data from microalgae research can be properly analyzed by combining the cutting-edge of both fields. In this work, the most suitable class of AI algorithms (such as active learning, semi-supervised learning, and meta-learning) are discussed for different cases of microalgae applications. This work concisely reviews the current state of the research milestones and highlight some of the state-of-art that has been carried out, providing insightful future pathways. The utilization of AI algorithms in microalgae cultivation, system optimization, and other aspects of the supply chain is also discussed. This work opens the pathway to a digitalized future for microalgae research and applications.

Models of microalgal cultivation for added-value products - A review

Microalgae are considered a promising feedstock for biorefineries given that their chemical composition - rich in carbohydrate and lipid - can be directed towards the co-production of various value-added fuels and chemicals. Production of microalgal biomass for biorefinery purposes requires the identification and establishment of optimal cultivation systems, a crucial yet complicated task due to the numerous factors (e.g. media composition, light, temperature) that simultaneously regulate biomass growth and intracellular composition. Modelling these biological processes, taking into account a single or multiple growth-limiting factors, offers a valuable tool to simulate, design and optimise the dynamics of microalgae cultivation. This review provides an overview of existing models developed to describe microalgal growth processes at the macroscopic scale (also termed black-box models) and discusses their formulation in detail. The black-box kinetic modelling frameworks are compiled into single-factor (6 formulations) and multiple-factor (32 formulations - further divided into non-interactive, additive, and interactive) growth kinetic models, as reported in more than 80 studies, for the prediction of biomass growth as a function of major operational factors such as media composition (e.g. nutrient concentration) and environmental factors (e.g. transient light and temperature). In addition, the review focuses on those models that further account for the production dynamics of two microalgal intracellular products with renowned potential as biorefinery substrates: carbohydrate and lipid molecules. Models of microalgal cultivation dynamics offer a robust engineering tool to understand the natural yet complex responses of microalgae to their growing environment and can help - if used appropriately - to optimise microalgae cultivation and increase the economic viability and sustainability of microalgal systems.

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.