Control of Engineered Metabolism by Flowering and Temperature Triggered Plant Regulatory Networks (2015 – 2018)
Project No. ERASynBio ID:2-23
Source of funding: FP7 EraSynBio (2nd call)
Project period: 18.08.2015 – 17.08.2018 (36 months)
Total budget: 1 481 000 EUR (210 000 EUR for CSBG)
Project coordinator: Pr. Dr. Alain Tissier, Leibniz Institute of Plant Biochemistry, Germany
Project coordinator from our group: Professor, Dr.sc.ing. Egils Stalidzāns, email@example.com
A major goal of plant synthetic biology is to create smart plants that are able to respond to key cues and display a variety of agronomically valuable traits such as enhanced stress resilience or the biosynthesis of high-value compounds. The objectives of the SMARTPLANTS consortium are to develop parallel regulatory networks (PaRNets) that are based on cues that plants normally encounter in their growth cycle, namely flowering and temperature changes, and translate these into metabolic engineering-based outputs to produce high value or stress-protecting compounds. Flowering is accompanied by a dramatic metabolic switch leading to the massive transfer of resources from the leaves to the seeds or the fruits. However, there is still significant biomass remaining in the leaves and stems. By developing a PaRNet that uses flowering as a trigger, we will capture part of this biomass to convert it to a high-value compound, the diterpene cis-abienol. Our flowering PaRNet will be based on the florigen signal encoded by the conserved FT gene. Similarly, we will develop a PaRNet based on temperature fluctuations to induce upon higher temperatures the production of isoprene, a compound conferring heat-stress protection.
A major goal of plant synthetic biology is to create smart plants that are able to respond to key cues and display a variety of agronomically valuable traits such as enhanced stress resilience or the biosynthesis of high value compounds. The objectives of the SmartPlants consortium coordinated by Prof. Alain Tissier are to develop parallel regulatory networks (PaRNets) that are based on cues that plants normally encounter in their growth cycle and to translate these into metabolic engineering-based outputs to produce high value or stress-protecting compounds. Both the regulatory network and the metabolic engineering optimisation procedures will be assisted by modelling in iterative rounds.
Leibniz Institute of Plant Biochemistry, Germany
University of Cambridge, Sainsbury Laboratory, United Kingdom
A kinetic model of the A. thaliana 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway is developed including the production of plastoquinone, chlorophyll side chains and carotenoids. For the needs of optimisation, it was extended with additional reactions leading to the production of cis-abienol (using geranylgeranyl diphosphate as a precursor) and isoprene (using dimethylallyl pyrophosphate as a precursor).
A total optimisation potential (TOP) based search of reasonable enzyme concentration adjustments yielded sets of parameter combinations, from which those were selected that gave the largest increase in the product flux compared to parameter sets with a smaller number of parameters. Three designs (sets of required concentration changes of particular enzymes) are suggested for each product, based on optimisation of the 2048 possible combinations of adjustable parameters (enzyme concentrations). The resulting optimization of the model forecasts a cis-abienol production of 0.016 nmol.s-1.ml-1 and an isoprene production of 0.091 nmol.s-1.ml-1 in plastids of A. thaliana.
Petrovs R., Stalidzans E., Pentjuss A. (2021) IMFLer: A Web Application for Interactive Metabolic Flux Analysis and Visualization, Journal of Computational Biology, 28(10), 1-12. https://doi.org/10.1089/cmb.2021.0056