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  Revolutionizing Synthetic Biology with AI-Enhanced Manufacturing Workflows


   Department of Chemistry

This project is no longer listed on FindAPhD.com and may not be available.

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  Prof Patrick Cai  No more applications being accepted  Competition Funded PhD Project (UK Students Only)

About the Project

The UKRI AI Centre for Doctoral Training (CDT) in Decision Making for Complex Systems is a joint CDT between The University of Manchester and the University of Cambridge. The CDT provides funding for four years of advanced studies towards a PhD. The first year consists of a taught program at Manchester that will cover the fundamentals of Machine Learning. This year is followed by three years of research at either at Manchester or Cambridge. Please note the research element of the PhD will take place at the host institution of the supervisor listed for each project.

Synthetic biology is a multidisciplinary field applying engineering principles to the rational design of living systems. Engineering biology can offer fundamental biological insights unattainable through traditional means, and provide new-to-nature functionalities for applications in biotechnology. The synthetic biology workflow of “Design, Build, Test and Learn” (DBTL) reflects the iterative nature of engineering living systems for which genomic data is incomplete for even the simplest organisms1, and often involves trial and error due to the complex nature of living systems.

A workflow to engineer organisms meets manufacturing challenges at each stage. Chemical DNA synthesis is limited to short oligonucleotides due to errors and yield declines inherent to the chemistry2. Sequence features such as repeats, secondary structure, and high %GC content are difficult to synthesise accurately. Synthesis providers have specific constraints and proprietary screening services, and submitting sequences for synthesis could entail rejection and recoding (where possible), time delays, or price premiums. Synthesis providers often provide tools to optimize codon usage for expression in a given organism, yet recoding non-coding DNA, which may harbour regulatory functionality, is hence unpredictable.

Different approaches exist to assemble short synthetic DNA into larger constructs3.Quality control, error correction, and integration into the host cell must occur before design specifications have been met, and the phenotypic viability of the final organism assessed. Differences in assembly methods and laboratory capabilities represent challenges to optimization, standardization and reproducibility. Two labs may assemble the same DNA construct through different methods. Sub-optimal methodologies would scale poorly for large manufacturing projects, hence accurate sequence-based prediction of approaches to determine the optimal represents a valuable goal of computer-aided manufacturing for synthetic biology.

Artificial intelligence and synthetic biology naturally complement each other 4,5. Biologists have been producing high volumes of multi-omic datasets which could train predictive models, generate novel designs and optimal experiments. This project will investigate AI applications to manufacturing, or ‘Build’ (the ‘B’ in DBTL) workflows in synthetic biology. Under the supervision of prominent experts in both synthetic biology and AI, the researcher will help develop methodologies to directly address some of the greatest technical challenges in synthetic biology.

The researcher will investigate automated decision-making systems for the conversion of discrete manufacturing tasks into machinable and manual instructions specific to a given lab’s resources and equipment, thereby increasing the accessibility and uptake of collaborators in distributed manufacturing projects. Meeting this objective will leverage the supervisors successes in translating assembly designs to human and machine-readable protocols6,7 for automation, and expertise in employing AI to address challenges in the biosciences8. Quantifiable metrics will inform the scheduling of jobs best suited for robotic or manual execution, based on cost-benefit analyses. We will define assembly experiments and dynamically produce “hardware agnostic” instructions for automated liquid handling robots to circumvent time lost manually entering instructions.

An AI-assisted virtual laboratory for engineering biology is in development, based on digital twins of the cellular entities and processes involved, and the human designers, with the goal to develop human-centric AIs and human-AI collaborations. formulating assistants which are useful to human scientists while leaving them in full control. For this, the assistants will need to infer their user’s goals and then recommend actions in a way they understand. In other words they would need models of human users to efficiently collaborate with them.

Entry requirements

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

How to Apply

As the CDT has only recently been awarded we strongly encourage you to contact the supervisor of the project you are interested in with your CV and supporting documents. You will have a chance to meet with prospective supervisors prior to submitting an application - further details will be provided.

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).

AI_CDT_DecisionMaking

Business & Management (5) Computer Science (8) Engineering (12) Mathematics (25)

Funding Notes

Funding includes tuition fees and stipend at the UKRI minimum rate, currently £18,622/annum (see https://www.ukri.org/apply-for-funding/studentships-and-doctoral-training/changes-to-the-minimum-stipend-from-1-october-2023/ for further information). Studentship funding is for 4 years. This scheme is open to both the UK and international applicants. We are only able to offer a limited number of studentships to applicants outside the UK.

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