Improving our Mechanistic Understanding of Cell Cycle Dynamics

Lead Research Organisation: University of Oxford
Department Name: Synthetic Biology DTC


This project falls within the EPSRC "Synthetic Biology" and "Systems Biology" research areas.

The past decades saw enormous advances in biological sciences, resulting in a staggering amount of detailed knowledge about the biochemical reactions and mechanisms in a cell. This knowledge enabled rational design of biological systems, giving rise to synthetic biology as a new engineering discipline. Despite synthetic biology can provide solutions to pressing environmental, medical and ethical problems, intricate interactions of the designed system with its (cellular) environment limit the complexity of synthetic biological systems. An important strategy to overcome this limitation, is to integrate the ever-increasing amount of biological knowledge to a mechanistic, systems level understanding of the processes in a cell.
In this project we focus on the processes that regulate cell cycle progression using the following a four-step strategy. (1) We cast knowledge about biochemical reaction networks into ordinary differential equations (ODEs). (2) We obtain highly multiplexed snapshot measurements of cell cycle regulators in asynchronous cell populations. (3) We computationally reconstruct cell cycle regulator time-courses from our snapshot measurements. (4) We develop optimisation and model reduction/selection toolboxes to fit unknown kinetic parameters of the ordinary differential equations from (1) to predict experimental data from (2, 3).
By building a human cell cycle model as described above, we want to improve our mechanistic understanding of cell cycle dynamics. The cell cycle model shall help synthetic biologists, researchers and medical practitioners alike, for high throughput design and hypotheses testing in silico.

Planned Impact

The emerging and dynamic field of Synthetic Biology has the potential to provide solutions to some of the key challenges faced by society, ranging across the healthcare, energy, food and environmental sectors. The UK government has recently a "Synthetic Biology Roadmap", which presents a vision and direction for Synthetic Biology in the UK. The report projects that the global Synthetic Biology market will grow from $1.6bn in 2011 to $10.8bn by 2016. It highlights that there is an urgent need for the UK to develop the interdisciplinary skills required to take advantage of the opportunities provided by Synthetic Biology.

The challenge to the academic and industrial research communities is to develop new translational approaches to ensure that these potential benefits are realised. These new approaches will range across the design and engineering of biologically based parts, devices and systems as well as the re-design of existing, natural biological systems across all scales from molecules to organisms. The techniques will encompass not only individual cells, but also self-assembled biomimetic systems, engineered microbial communities and multicellular organisms, combining multiple perspectives drawn from the engineering, life and physical sciences.

Realising these goals will require a new generation of skilled interdisciplinary scientists, and the training of these scientists is the primary goal of the SBCDT. Our programme will give the breadth of coverage to produce a "skilled, energized and well-funded UK-wide synthetic biology community", who will have "the opportunity to revolutionise major industries in bio-energy and bio-technology in the UK" (David Willetts, Minister for Universities and Science) in their future careers. This will be made possible through genuine inter-institutional collaboration in partnership with key industrial, academic and public facing institutions.

The potential impact of the SBCDT, and its potential national importance, are very therefore high, and the potential benefits to society are significant.


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Description We build a mathematical model that describes progression though the M-phase of the cell division cycle. In particular the model mechanistically explains the experimental finding that the protein cyclin A triggers mitosis either via greatwall or cyclin B.

We build the software tool BpForms. BpForms provides a human readable way to unambiguously describe all chemical bonds that occur in biopolymers like DNA, RNA or proteins. Importantly, this includes (epigenetically/posttrancriptionally/posttranslationally) modified biopolymers. Such an unambiguous representation of biopolymers is essential to communicate knowledge in the field of biosciences. In particular it helps to annotate and communicate mathematical models of biological signalling pathways.
Exploitation Route Continue on the intended path and publish the results in scientific journals. Should we believe that any of the custom code we develop during this project also be useful for the wider community, polish it up and release it on GitHub.
Sectors Digital/Communication/Information Technologies (including Software),Healthcare,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology

Title BpForms software 
Description BpForms is a software that unambiguously describes the primary structure of (modified) biopolymers. This allow modellers to unambiguously describe and communicate the molecular species they are modelling. 
Type Of Material Improvements to research infrastructure 
Year Produced 2019 
Provided To Others? Yes  
Impact BpForms helped to curate the protein ontology of the PRO consortium. These corrections will be released with the next PRO version, 60.0. BpForms integrates with several other standard formats (PDB, FASTA, BioPAX, CellML, SBML and SBOL). 
Title SBML2Julia: interfacing SBML with efficient nonlinear Julia modelling and solution tools for parameter optimization 
Description Motivation: Estimating model parameters from experimental observations is one of the key challenges in systems biology and can be computationally very expensive. While the Julia programming language was recently developed as a high-level and high-performance language for scientific computing, systems biologists have only started to realise its potential. For instance, we have recently used Julia to cut down the optimization time of a microbial community model by a factor of 140. To facilitate access of the systems biology community to the efficient nonlinear solvers used for this optimisation, we developed SBML2Julia. SBML2Julia translates optimisation problems specified in SBML and TSV files (PEtab format) into Julia for Mathematical Programming (JuMP), executes the optimization and returns the results in tabular format. Availability and implementation: SBML2Julia is freely available under the MIT license. It comes with a command line interface and Python API. Internally, SBML2Julia calls the Julia LTS release v1.0.5 for optimisation. All necessary dependencies can be pulled from Docker Hub. Source code and documentation are available on GitHub ( 
Type Of Material Improvements to research infrastructure 
Year Produced 2020 
Provided To Others? Yes  
Impact None 
Title SBMLToolkit.jl 
Description SBMLToolkit.jl is a lightweight tool to import models specified in the Systems Biology Markup Language (SBML) into the Julia SciML ecosystem. More specifically, SBMLToolkit.jl extracts reactions, initial conditions and parameter values from SBML files. 
Type Of Material Improvements to research infrastructure 
Year Produced 2021 
Provided To Others? Yes  
Impact We do not have user statistics, but the tool is well embedded into the Julia Scientific Machine Learning ecosystem and essential for importing SBML models into this ecosystem. 
Description Integrating a cell cycle model into the Karr lab whole-cell modelling framework 
Organisation Icahn School of Medicine at Mount Sinai
Department Icahn Institute for Genomics and Multiscale Biology
Country United States 
Sector Academic/University 
PI Contribution I helped to write a software that unambiguously describes the primary structure of (modified) biopolymers. I helped to write the paper that describes the software.
Collaborator Contribution My collaborators conceptualised the work, helped write the software and paper.
Impact We developed a software called BpForms. We wrote a paper about BpForms.
Start Year 2019
Description Multiplexed Imaging for large scale cell cycle modelling 
Organisation Institute of Cancer Research UK
Country United Kingdom 
Sector Academic/University 
PI Contribution I initiated the collaboration to generate data for my model. I am helping with planning the experiments and analysing the data.
Collaborator Contribution My partners are helping with planning the experiments, executing the experiments and analysing the data. They are about to purchase the needed reagents.
Impact The planned process of generating data works on a small scale. This convinced another laboratory in the UK to try adapt the same workflow. We are about to scale up the pipeline. The collaboration is multidisciplinary: our collaborators are specialising in microscopy, we are specialising in mathematical modelling.
Start Year 2019
Description Parameter optimisation for cell cycle model 
Organisation Spanish National Research Council (CSIC)
Country Spain 
Sector Public 
PI Contribution I am providing exact parameter optimisation problem formulations in the PEtab format. I am discussing/troubleshooting the optimisation results with them.
Collaborator Contribution They adapted the optimisation algorithm to meet the requirements of the problem, run the problem on their cluster and discuss/troubleshoot the optimisation results with me.
Impact We have established that the problem is theoretically solvable if data were perfect.
Start Year 2021