Development of a combined mathematical and experimental approach for the design, evaluation and optimization of algae based bio-refinery processes
Lead Research Organisation:
University College London
Department Name: Biochemical Engineering
Abstract
-Studying, understanding and quantifying the characteristics/traits that render an algae species a good candidate for bio-refinery type applications
-Understanding algae cell metabolism and identifying both Metabolic and Bioprocess Engineering strategies that can increase yields and profitability of algae-based bio-refineries
-Exploring the metabolic capabilities of algae and identifying environmental and process conditions that favour/optimize the synergistic generation of multiple product streams
Background. Although a number of algal biomass conversion technologies have been recently commercialized (e.g., bio-diesel production, dry and wet algal biomass conversion technologies, etc.), it is clear that major economic and technical barriers still exist regarding the commercial feasibility of algal-based bioprocessing. Towards that end, the concept of algae based biorefineries is being pursued in order to enable the generation of secondary (and tertiary) fermentation product streams in an attempt to exploit the wide range of chemicals that can be produced by algae. Understanding algal metabolism and how it responds to varying environmental and bioprocessing conditions is an integral part of designing and optimizing an algae-based biorefinery. The aim of this project is the development of an integrated methodology combining Multi-scale Modelling, Metabolic Engineering and wet-lab experiments in order to: (i) Study algae cell metabolism under varying environmental and bioprocessing conditions (ii) Identify environmental conditions (i.e., media composition, feeding schedule, osmotic pressure) that cause shifts in the utilised catabolic pathways and affect product yields and diversity (iii) Combine metabolic modelling, (global) sensitivity analysis and design of experiments in order to derive fast, cost-effective, reliable and robust model-aided tests for the screening of multiple algal species and the selection of species better suited for industrial scale biorefinery applications (iv) Use of Systems Biology and Model Analysis techniques for the design and optimisation of cell culture media/conditions and feeding policies that lead to improved product yields.
-Understanding algae cell metabolism and identifying both Metabolic and Bioprocess Engineering strategies that can increase yields and profitability of algae-based bio-refineries
-Exploring the metabolic capabilities of algae and identifying environmental and process conditions that favour/optimize the synergistic generation of multiple product streams
Background. Although a number of algal biomass conversion technologies have been recently commercialized (e.g., bio-diesel production, dry and wet algal biomass conversion technologies, etc.), it is clear that major economic and technical barriers still exist regarding the commercial feasibility of algal-based bioprocessing. Towards that end, the concept of algae based biorefineries is being pursued in order to enable the generation of secondary (and tertiary) fermentation product streams in an attempt to exploit the wide range of chemicals that can be produced by algae. Understanding algal metabolism and how it responds to varying environmental and bioprocessing conditions is an integral part of designing and optimizing an algae-based biorefinery. The aim of this project is the development of an integrated methodology combining Multi-scale Modelling, Metabolic Engineering and wet-lab experiments in order to: (i) Study algae cell metabolism under varying environmental and bioprocessing conditions (ii) Identify environmental conditions (i.e., media composition, feeding schedule, osmotic pressure) that cause shifts in the utilised catabolic pathways and affect product yields and diversity (iii) Combine metabolic modelling, (global) sensitivity analysis and design of experiments in order to derive fast, cost-effective, reliable and robust model-aided tests for the screening of multiple algal species and the selection of species better suited for industrial scale biorefinery applications (iv) Use of Systems Biology and Model Analysis techniques for the design and optimisation of cell culture media/conditions and feeding policies that lead to improved product yields.
Organisations
People |
ORCID iD |
| Alexandros Kiparissides (Primary Supervisor) | |
| Lukmaan Kolia (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509577/1 | 01/10/2016 | 24/03/2022 | |||
| 1807432 | Studentship | EP/N509577/1 | 01/10/2016 | 25/12/2020 | Lukmaan Kolia |
| Description | Microalgae are photosynthetic organisms which are increasingly being used in bioprocessing. Light is required for photosynthesis, and in the case of Chlamydomonas reinhardtii, the colour of light used for growth has an effect on the organism's behaviour. C. reinhardtii divides by multiple fission: each mother cell can divide into 2, 4, 8, or 16 daughter cells depending on the size achieved by the mother cell. Earlier literature shows that blue light inhibits cell division, whereas red light lifts this inhibition. Literature has already shown that blue light will result in larger mother cells which result in a larger number of daughter cells per cell cycle. However, combining this knowledge into a bioprocessing optimisation framework was missing. Experimental results from this award have shown that by using an informed light colour switching mechanism at the appropriate time during the cell cycle, C. reinhardtii cells under blue light can be forced to grow larger than they normally would before initiating division. This is then followed by a period of red light to reduce the time delay in initiating division due to division inhibition from blue light. The result is a net increase in productivity compared to plain light conditions (control conditions). The award also aimed to develop a mathematical model which would inform exact light switching timepoints. This is still in development and should be completed during the Completing Research Status (CRS) period. |
| Exploitation Route | Industrial bio-processing companies making use of green microalgae could use the findings to inform their process optimisation strategies. |
| Sectors | Manufacturing, including Industrial Biotechology |