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(BBSRC DTP) Integrating data intelligence and hybrid modelling to develop continuous CHO cell lines for recombinant protein production

   Department of Chemical Engineering

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  Dr D Zhang, Prof Alan Dickson  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Developing sustainable bio-manufacturing technologies for industrial production of pharmaceuticals is a top priority in growing and advancing the UK’s Bioeconomy. Chinese hamster ovary (CHO) cells are the leading production platform for manufacturing protein-based therapeutics, as they possess the appropriate machinery for the synthesis and processing of proteins with large molecular structures and complex humanlike post-translational modifications. However, a significant limitation of CHO cells as a manufacturing platform is their deficient secretory phenotype which leads to protein production that compares unfavourably to cells with a dedicated secretory machinery. To enable overexpression of target recombinant proteins and improve process performance of CHO cell lines at larger manufacturing scales, it is of critical importance to investigate the biological mechanisms and overcome the intrinsic limitations of the underlying bioprocess at each level from intracellular metabolism to bioreactor operation through a whole-systems approach. 

In particular, to accelerate the understanding and continuous operation of bio-based manufacturing processes, an innovative approach is to apply cutting-edge digital modelling techniques (machine learning, kinetic modelling, systems biology, and data analytics) to effectively analyse multi-omic bioprocess data and construct high-fidelity multiscale in-silico models to explore undetermined process knowledge and guide design of efficient CHO cell lines. This data-driven approach, bringing together wet and dry scientific approaches, will greatly help generate new understandings at cellular basis of the CHO cell-based manufacturing systems and promote the translation of bioscience into novel biotechnologies at industrial scales. 

At present, we have developed a number of digital tools for bioprocess multiscale modelling, metabolic flux analysis, machine learning based data analytics, and bioreactor upscaling. We have also collected substantial experimental data from the CHO cells recombinant protein production processes that replicate industrial processes. Together with these achievements, this PhD project aims to develop an in-silico model for CHO cells recombinant protein production. The model will be used to identify metabolic and culture conditions that will be manipulated experimentally to assess the consequences on cell growth and the capacity to support recombinant protein synthesis (yield, specific productivity and quality of industrially-relevant exemplar antibodies). The knowledge will be exploited to discover critical parameters and optimal operation strategies that can maximise expression level and total production of the protein, thus leading to the design and optimisation of continuous CHO cell production processes. This is a multidisciplinary research project integrating bioprocess systems engineering, systems/synthetic biology, machine learning, and metabolic engineering, with clear potential for the development of next-generation industrial biopharmaceutical technology. 


Applicants must have obtained or be about to obtain a First or Upper Second class UK honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science, engineering or technology. 

Before you Apply

Applicants must make direct contact with preferred supervisors before applying. It is your responsibility to make arrangements to meet with potential supervisors, prior to submitting a formal online application.

How To Apply

To be considered for this project you MUST submit a formal online application form - full details on eligibility how to apply can be found on the BBSRC DTP website

Your application form must be accompanied by a number of supporting documents by the advertised deadlines. Without all the required documents submitted at the time of application, your application will not be processed and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered. If you have any queries regarding making an application please contact our admissions team [Email Address Removed]

Equality, Diversity and Inclusion

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website


Funding Notes

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. Therefore, full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme.


1. K. Jing, Y. Tang, C. Yao, E. A. del Rio‐Chanona, X. Ling, D. Zhang, ‘Overproduction of L-tryptophan via Simultaneous Feed of Glucose and Anthranilic Acid from Recombinant Escherichia coli W3110: Kinetic Modeling and Process Scale-up’, Biotechnology and Bioengineering, 2018.

2. P. Petsagkourakis, I. O. Sandoval, E. Bradford, D. Zhang, E. A. del Rio-Chanona, ‘Reinforcement learning for batch bioprocess optimization’, Computers & Chemical Engineering, 2020.

3. M. Torres, A. J. Dickson, ‘Combined Gene and Environmental Engineering Offers A Synergetic Strategy to Enhance R‐protein Production in Chinese Hamster Ovary Cells’, Biotechnology and Bioengineering, 2021.
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