Developing machine learning and gene-editing tools to make better crops

The rapid growth of human population commands to increase crop yields by 50 - 70 % by 2050 in order to feed the predicted 9 - 10 billion people. Extra food and biofuel production has to be achieved using the shrinking supply of arable land making it a key global challenge that requires "thinking outside the box". One of solutions for future prosperity of humankind is improved photosynthesis. All food production is based on photosynthesis either directly when growing crops or indirectly when plants used to feed livestock. However, despite being the most important biological process on the planet, photosynthesis is surprisingly inefficient with only 5 % of sun energy received by plants converted into biochemical energy of sugars.

One of the bottle necks that curbs crop productivity is the CO₂ fixation by ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco). Recently our lab showed how crop performance might be improved if crop enzymes would be substituted by Rubiscos from other species [1] and how machine learning (ML) could be used to predict Rubisco catalytic properties [2]. Possibility of chaperone assisted Rubisco transplantation between plant species was demonstrated by us earlier [3].

ML algorithms such as neural networks became a part of our daily lives and are integral part of the success of the leading IT companies. The AI program AlphaFold developed by Google's DeepMind made it into the world news in November 2020 with its predictions of protein structures. This project will use algorithms developed by AlphaFold and our own pipeline to predict Rubisco properties from sequences.

Project objectives:

1)     Use ML to (i) predict Rubisco kinetics from amino acid sequences; (ii) find superior Rubiscos; (iii) find amino acid substitutions that might improve Rubisco catalytic.

2)     Assemble potential superior Rubiscos with improved catalysis in E. coli [4].

3)     Perform catalytic assays of recombinant Rubiscos.

4)     Introduce superior Rubiscos into potato using carbon nanotubes – a method recently developed by us (Moore et al, unpublished) to engineer plants with improved photosynthesis/yield.

Training in both computational and molecular biology will be provided, and the project could be customised to balance ratio between dry and wet biology depending on student’s interests. The prospective candidates could be both biologists willing to learn ML or computer scientists willing to learn synthetic biology.

[1] Journal Experimental Botany (2021) 72:6066–6075; [2] Journal Experimental Botany (2022) erac368, https://doi.org/10.1093/jxb/erac368; [3] PNAS (2015) 112:3564-3569; [4] Science (2017) 358:1272-1278. 

HOW TO APPLY

Applications should be made by emailing [Email Address Removed] with:

·        a CV (including contact details of at least two academic (or other relevant) referees);

·        a covering letter – clearly stating your first choice project, and optionally 2^nd ranked project, as well as including whatever additional information you feel is pertinent to your application; you may wish to indicate, for example, why you are particularly interested in the selected project(s) and at the selected University;

·        copies of your relevant undergraduate degree transcripts and certificates;

·        a copy of your IELTS or TOEFL English language certificate (where required);

·        a copy of your passport (photo page).

A GUIDE TO THE FORMAT REQUIRED FOR THE APPLICATION DOCUMENTS IS AVAILABLE AT https://www.nld-dtp.org.uk/how-apply. Applications not meeting these criteria may be rejected.

In addition to the above items, please email a completed copy of the Additional Details Form (as a Word document) to [Email Address Removed]. A blank copy of this form can be found at: https://www.nld-dtp.org.uk/how-apply.

Informal enquiries may be made to [Email Address Removed]

The deadline for all applications is 12noon on Monday 9^th January 2023.