Seed corn projects

Centre for Systems Modelling and Quantitative Biomedicine

Seedcorn projects 2021/22

The next round of seedcorn projects will start in October 2021 and run for 6 months.  

The call for project teams is now OPEN. Application forms, and further information is available from Rebecca Ward. The deadline for applications is 4pm, Thursday 1st July 2021.

These projects will support new and emerging interdisciplinary collaborations and address a biological or healthcare challenge using mathematical and computational approaches. Investigators from complementary disciplines (typically a biomedical or clinical scientist, and a quantitative discipline), as well as other stakeholders from industry and the clinic, are paired with one or more of our Centre Fellows to undertake a focused project.

Publications and Onward funding are the key objectives of seed corn awards and by the end of the project time, preliminary results suitable for first publications and future funding applications will have been obtained.

Applications will be assessed by the Centre Steering Group and all successful team will be notified by end of July.

Successful applicants will be required to attend a two-day incubator event on 6/7th October where projects are further developed by the team, supported by professional services colleagues.

The scheme provides a budget of up to £10K for consumables, essential travel and public engagement activities, as well as up to six months' research time from the Centre Fellow. Budgets are agreed at the incubator event.

There is an opportunity to have an Artist in Residence embedded in the team to develop a legacy artwork around your project. Not all projects will necessarily be matched with artists, as this depends on suitable quality artists’ proposals coming forward. Funding for creative artwork is provided in addition to resources needed to deliver the scientific project.

If your project is suitable, there is also an opportunity for help to identify industry involvement in the research, working with Business Engagement. 

Hear Centre Director Professor John Terry explain SMQB’s funding opportunities

Case study one presented by Dr Leandro Junges

Case study two presented by Dr Yolanda Hill
Case study three presented by Dr Wessel Woldman

Seed corn projects 2020/21

The first round of projects began in February 2020 and ran until March 2021 (extended due to the coronavirus pandemic). Four out of the six projects also included an Artist in Residence. Our residents consisted of artists and creative practitioners who collaborated with our researchers on the projects to bring on board new perspectives and insights, whilst developing exciting, novel creative outputs responding to the research. Find out more about these exciting Sci-Art collaborations.

Uncovering the links between stress hormones and inflammatory mediators
during and after cardiac surgery

PI: Ben Gibbison (University of Bristol). Co-Is: Eder Zavala, Jamie Walker (University of Exeter), Gianni Angelini (University of Bristol), Stafford Lightman (University of Bristol), Daniel Galvis. Artist in Residence: Pietro Bardini

35,000 people a year have heart surgery in the UK. Two-thirds of people make a straightforward recovery and go home quickly. About 1 in 4 people stay in the intensive care unit (ICU) longer than usual. The major cause of this is inflammation - similar to the inflammation that occurs after a sprained ankle. Instead of occurring in a small area, as in a sprained ankle – it occurs across the whole body. Uncontrolled inflammation can lead to failure of the body’s organs. One of the things that protects the body from excessive inflammation is the steroid hormone cortisol, which increases after heart surgery. Doctors sometimes give steroids to patients who are on ICU to prevent severe inflammation. Because we do not know how cortisol is controlled, we cannot produce tests to work out who may need steroids or design the best therapies to reduce inflammation.

We want to model the interactions between inflammation and cortisol. Using data from heart surgery patients, we will then use the model to predict the dynamics of inflammatory responses and look at the differences between people who recover quickly after heart surgery and those who do not. In the future, these models would be useful to inform whether giving steroids at different times and doses could reduce recovery times from surgery.

As part of the project, the team produced this video explaining their work.

Modelling Inflammation After Heart Surgery

This project is funded by the NIHR Bristol Biomedical Research Centre, a partnership between the University of Bristol and University Hospitals Bristol NHS Foundation Trust.

Predicting, with Optical Coherence Tomography, Papilloedema – the POP
study group

PI: Susan Mollan (University Hospitals Birmingham) Co-Is: Alex Sinclair, Wessel Woldman, John Terry, Leandro Junges. Artist in Residence: Mellissa Fisher.

Swelling of the eye nerves (papilloedema) is a frightening diagnosis, which is often found on routine eye examinations in an optician’s shop. Papilloedema is caused by raised brain pressure and can be as a result of serious life-threatening causes such as brain tumours. Sometimes it is difficult for the eye care team to be certain that the swelling is there or not.

Our plan is to use a computer assisted analysis of eye scans and statistical modelling to predict those who have papilloedema and those who do not.

This type of algorithm could have the potential to transform healthcare, by offering earlier and more accurate diagnoses. It would reduce unnecessary emergency admissions by predicting the likelihood of having papilloedema.

Precision Antithyroid Therapy

PI: David Smith. Co-Is: Zaki Hassan-Smith, Neil Gittoes, Meurig Gallagher. Artist in Residence: Vicky Roden

University of Birmingham research governance reference number: RG_20-088

Hyperthyroidism is a common condition affecting approximately 1 million people in the UK. The thyroids (glands located in the neck) produce too much of certain hormones called ‘T3’ and ‘T4’. This condition can occur for a number of reasons, including growths and the immune system being over-active. If these hormones cannot be controlled properly, they can have serious effects such as heart failure or osteoporosis.

Doctors treat hyperthyroidism with drugs to reduce how much hormone is made. The amount of drug needed is hard to predict, so patients have to return to check and vary their dose, often several times. This is inconvenient and costs a lot of money. It can be bad for the patients if their hormones are too high or too low.

We will solve this problem by developing an app to enable doctors to predict the best dose. The app will take into account the available data: hormone levels at the beginning and early stages of treatment, age, sex, and weight.

The app will then say what is likely to happen to help the doctor decide what to do. The maths inside the app will be based on patient records from Queen Elizabeth Hospital Birmingham.

We will also try out new ways to help patients track their condition when they are not visiting the doctor. An example device is a fitbit – a watch that measures heart rate, sleep and activity. This will help say how well the dose is working for the patient. High heart rate, lack of sleep, over activity then crashing, are signs the dose is too low. Low heart rate, tiredness all the time and lack of activity are signs the dose is too high. All of these symptoms have a big effect on quality of life, and might tell us more than just looking at numbers from blood tests.

Impact of spatio-temporal deregulation of mitochondria on cell death in acute myocardial infarction

PI: Fabian Spill. Co-Is: Melanie MadhaniDaniel Tennant, Peter Ashwin (University of Exeter), Tanja Zerenner (University of Exeter)

Heart attack is one of the major factors of death in the UK and worldwide; yet, we still have no drugs to help survival. Heart attack occurs because the blood supply to the heart muscle is cut off. This causes the heart cells to die due to lack of oxygen and nutrients. Within each heart cell, there are organelles called the “mitochondria”. These mitochondria provide the heart cell with the energy to contract and pump blood around the body in normal conditions. When a heart attack limits oxygen supply, these mitochondria can trigger the death of a cell. Paradoxically, cell death can also be triggered after doctors have resupplied blood to the heart following a heart attack.

Mitochondria are very dynamic, and, for example, can change their shape and their localisation within a heart cell in response to a lack of oxygen. We suspect that this dynamism is related to the triggering of cell death. We are therefore developing mathematical models that can predict how cell death is triggered and use it to uncover how drugs targeting mitochondria may stop cell death. We will also perform microscopy of the mitochondria to investigate how their shape affects drug response.

Understanding dynamic steroid biosynthesis in health and disease through machine learning in the space of mechanistic models

PI: Peter Tino. Co-Is: Thomas Upton (University of Bristol), Georgina Russell (University of Bristol), Eder Zavala, Krasimira Tsaneva-Atanasova (University of Exeter), Stafford Lightman (University of Bristol), Yuan Shen, Diane Fraser (University of Exeter), Xinyue Chen.

Hormones, including the stress hormone cortisol, are released in rhythmic patterns. This means that there are variations in normal levels across time and between individuals. Consequently, traditional, one-off hormone measurements are extremely difficult to interpret, which can lead to delay in diagnosis and treatment.

To address this problem, we need to understand hormone behaviour over the day. We will explicitly model biological mechanisms involved in hormone dynamics. Given hormone measurements taken from patients and healthy volunteers over 24 hours, we will investigate how the data could be explained and classified with the help of our modelling. This will be achieved via the development of a mathematical framework that will enable us to quantify to what degree the data from a particular subject “is explainable” by models related to normal or pathological cases. In other words, to what degree the subject should be diagnosed as a normal or pathological case.

The primary effect will be to benefit patients. Diagnosis will be understandable in terms of the underlying biology, faster, more reliable and convenient. Treatment will be easier to monitor and tailored to the individual. The overall effect will be to reduce the burden on our healthcare system.

Beta cell heterogeneity: the benefits of a diverse workforce

PI: Kyle Wedgwood (University of Exeter). Co-Is: David Hodson, Isabella Marinelli, Daniel Galvis. Artist in Residence: Carol Breen.

Diabetes is characterised by a loss of control over blood sugar levels. If not properly managed, high blood sugar levels can lead to damage to the heart, kidneys, feet and eyes. One of the key regulators of blood sugar levels is a hormone called insulin. Insulin is made naturally by our bodies by cells in the pancreas called ‘beta cells’. If beta cells do not work properly, they cannot produce enough insulin to keep blood sugar levels under control. Over time, this leads to diabetes.

We often think of beta cells as being the same as one another, since this makes them easier to understand and treat when they become diseased. However, recent studies have shown that differences between beta cells are important for them to work properly. In fact, forcing the beta cells to all be the same can actually accelerate diabetes progression. In this project, we will create a mathematical representation of groups of beta cells and the differences between them. We will use this representation to explore exactly why these differences are important to beta cells. A better understanding of this will allow us to create new ways to treat and prevent diabetes.