A model-based optimization of microalgal cultivation strategies for lipid production under photoautotrophic condition

OptMSP: A toolbox for designing optimal multi-stage (bio)processes

One central goal of bioprocess engineering is to maximize the production of specific chemicals using microbial cell factories. Many bioprocesses are one-stage (batch) processes (OSPs), in which growth and product synthesis are coupled. However, OSPs often exhibit low volumetric productivities due to the competition for substrate for biomass and product synthesis implying trade-offs between biomass and product yields. Two-stage or, more generally, multi-stage processes (MSPs) offer the potential to tackle this trade-off for improved efficiency of bioprocesses, for example, by separating growth and production. MSPs have recently gained much attention, also because of a rapidly growing toolbox for the dynamic control of metabolic fluxes. Despite these promising advancements, computational tools specifically tailored for the optimal design of MSPs in the field of biotechnology are still lacking. Here, we present OptMSP, a new Python-based toolbox for identifying optimal MSPs maximizing a user-defined process metrics (such as volumetric productivity, yield, and titer or combinations thereof) under given constraints. In contrast to other methods, our framework starts with a set of well-defined modules representing relevant stages or sub-processes. Experimentally determined parameters (such as growth rates, substrate uptake and product formation rates) are used to build suitable ODE models describing the dynamic behavior of each module. OptMSP finds then the optimal combination of those modules, which, together with the optimal switching time points, maximize a given objective function. We demonstrate the applicability and relevance of the approach with three different case studies, including the example of lactate production by E. coli in a batch setup, where an aerobic growth phase can be combined with anaerobic production phases with or without growth and with or without enhanced ATP turnover.

Characterization of Neutral Lipids of the Oleaginous Alga Micractinum inermum

An oleaginous microalga Micractinum inermum isolated from Mariana Lake, AB, Canada was cultured in a 1000 L photobioreactor with an f/2 medium to study its lipid content and neutral lipid profile. Algal biomass was collected at the stationary phase contained a significant amount of lipids (44.2%), as determined by Folch's method. The lipid was fractionated into neutral lipid, glycolipid and phospholipid fractions. The neutral lipid constitutes almost 77.3% of the total lipid species and is mainly composed of triacylglycerols (TAGs) determined by a proton NMR study. UHPLC-HRMS analysis allows us for the first time to identify 81 TAGs in the neutral lipid fraction of M. inermum. The fatty acid acyl side chains were identified based on fragment ions observed in MSMS analysis. TAGs with fatty acid acyl chains 18:1/18:1/18:1, 18:1/18:1/16:0, 18:2/18:1/16:0, and 18:2/18:2/18:0 were the major ones among the identified TAGs. Fatty acid analysis further supports the fact that oleic acid was the major fatty acid present in the neutral lipid fraction of M. inermum constituting 41.7%, followed by linoleic acid at 21.5%, and palmitic acid at 21.2%. The saturated and monounsaturated fatty acids were 67.8% or higher in the lipid fraction. Long-chain fatty acids were only present in a minor quantity. The results clearly demonstrate that M. inermum is an excellent source for TAGs.

Toward a modeling, optimization, and predictive control framework for fed-batch metabolic cybergenetics

Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes extracellular process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, and so on. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity.

Microalgae and biogas: a boon to energy sector

The global energy generation market immensely depends on fossil fuels which balances our survival on this planet. Energy can be called as the "master element" for our daily needs, starting from household power supply, agricultural purpose, automobile and transportation, industrial workload to economic and research domains. Fuel switching initiatives are being adapted by environmentalist and scientists to bring a novel sustainable source of energy. An environment and renewable alternative to fossil fuels are a must. Over the years, the world has shifted toward generating green fuels immensely. One such potential alternative to fossil fuels are biogases. Being versatile and renewable in nature, it has drawn immense attention globally. Despite having such potentials there exist some major drawbacks which mainly deal with the starting material. One such source for biogases can be microalgae. Microalgae based biogas production can produce huge amount of energy and that has been implemented by many foreign countries and their companies. Despite being in use in many countries, there are issues which needs to be addressed which will overall improve the biogas potential from microalgae even more. This review mainly focuses on generation of biogas from microalgae as a feedstock which are very economical and sustainable in its nature, presenting improvement strategies which can be impended to boost the over biogas sector globally.

Effect of light intensity and wavelength on nitrogen and phosphate removal from municipal wastewater by microalgae under semi-batch cultivation

Domestic, agricultural and industrial water activities lead to organic and inorganic pollution of the environment. Biotreatment of municipal wastewater with the potential production of biomass is a valuable feature of microalgae. In this study we evaluated the effects of wavelength and light intensity on phosphate and ammonium removal on the one hand, and biomass and protein production on the other hand by Spirulina platensis in municipal wastewater treatment under semi batch cultivation. S. platensis was inoculated at 40% in artificial wastewater open pond system. Red, blue and purple light with 3800, 4800 and 5800 lux light intensity under 12 h light and 12 h darkness were investigated. Cultivation was conducted in semi-batch conditions; after four days cultivation, one third of the culture was replaced with fresh medium. The highest biomass and protein concentrations were observed under blue light at 5800 lux light intensity, 5.45 and 3 g/l respectively cumulatively; while the highest amount of phosphate and ammonium removal were about 145 and 218 mg/l under purple light at 5800 lux intensity, respectively. The amounts of biomass and protein produced, as well as phosphate and ammonium removed, are therefore impacted by wavelength, light intensity, results show that light intensity and wavelength can be customized to reach on the one hand the highest biomass and protein production, and on the other hand to maximize the removal of phosphorous and ammonium.

Phosphorus uptake, distribution and transformation with Chlorella vulgaris under different trophic modes

Phosphorus (P) uptake, distribution and transformation are important processes associated with the growth and metabolism of microalgae. In this study, the fate of P in soluble microbial products (SMP), extracellular polymeric substances (EPS), and intracellular polymeric substances (IPS) of Chlorella vulgaris C7 in the form of inorganic P (IP) and organic P (OP) was studied under different trophic modes, including photoautotrophy, heterotrophy and mixotrophy. The results showed that mixotrophic cultivation of microalgae brought highest biomass yield, which was 2.09 times and 11.10 times higher than that of the photoautotrophic and heterotrophic conditions. Regarding P distribution and transformation, the trophic modes affected the transformation trends and rates of P in the form of IP and OP among SMP, EPS and IPS. Under photoautotrophic condition, EPS was the main P pool, and most of P in C. vulgaris was IP. While, under mixotrophic condition, IPS was the main P pool, and most of P was transformed to OP. The addition of glucose promoted the uptake of P by algal cells and the transformation of IP to OP, which accounted for 79.32% of total phosphorus at the end of cultivation. Collectively, mixotrophic cultivation was regarded as the optimum approach for microalgae cultivation and nutrient recovery.

Integrated Approach for Wastewater Treatment and Biofuel Production in Microalgae Biorefineries

The increasing world population generates huge amounts of wastewater as well as large energy demand. Additionally, fossil fuel’s combustion for energy production causes the emission of greenhouse gases (GHG) and other pollutants. Therefore, there is a strong need to find alternative green approaches for wastewater treatment and energy production. Microalgae biorefineries could represent an effective strategy to mitigate the above problems. Microalgae biorefineries are a sustainable alternative to conventional wastewater treatment processes, as they potentially allow wastewater to be treated at lower costs and with lower energy consumption. Furthermore, they provide an effective means to recover valuable compounds for biofuel production or other applications. This review focuses on the current scenario and future prospects of microalgae biorefineries aimed at combining wastewater treatment with biofuel production. First, the different microalgal cultivation systems are examined, and their main characteristics and limitations are discussed. Then, the technologies available for converting the biomass produced during wastewater treatment into biofuel are critically analyzed. Finally, current challenges and research directions for biofuel production and wastewater treatment through this approach are outlined.

Recent advancements in mixotrophic bioprocessing for production of high value microalgal products

Recently, microalgal biomass has become an attractive and sustainable feedstock for renewable production of various biochemicals and biofuels. However, attaining required productivity remains a key challenge to develop industrial applications. Fortunately, mixotrophic cultivation strategy (MCS) is leading to higher productivity due to the metabolic ability of some microalgal strain to utilise both photosynthesis and organic carbon compared to phototrophic or heterotrophic processes. The potential of MCS is being explored by researchers for maximized biochemicals and biofuels production however it requires further development yet to reach commercialization stage. In this review, recent developments in the MCS bioprocess for selective value-added (carotenoids) products have been reviewed; synergistic mechanism of carbon and energy was conferred. Moreover, the metabolic regulation of microalgae under MCS for utilized carbon forms and carbon recycling was demonstrated; Additionally, the opportunities and challenges of large-scale MCS have been discussed.

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.