Seed corn projects run for 6 months. Autumn 2015 (Round 2) projects are listed in the drop down panes below.

Lead Academic

Co- InvestigatorsCentre Fellow(s)Project title

Tim Etheridge (CLES)

Michael Weedon

(UEMS)

Ryan Ames The role of mechano-transduction in the regenerative capacity of young and ageing human skeletal muscle

As people grow older their muscles gradually becomes smaller and weaker. The result is loss of independence, increased risk of falls, early death, and an annual cost to the NHS of £5.7 billion. A way of preventing this could be to exercise because in young people muscles get bigger and stronger. As we get older this muscle growth effect is substantially lower but we don't understand why this happens. One way to study this may be to look at molecules that specifically respond to exercise. However, there are so many potential molecules involved in this process that we cannot examine them all. In this project we will use computational methods to identify a list of molecules that might be involved in muscle growth in ageing people. We will then test each of these molecules for their role in ageing muscle growth after exercise using microscope techniques. This research will identify new molecules that are involved in abnormal ageing muscle growth. These can then be used to developed effective treatments, such as new exercise or drug methods. Therefore, these results may help reduce the negative effects of ageing on muscle.

Lead Academic

Co- InvestigatorsCentre Fellow(s)Project title
Marc Goodfellow (CEMPS) Jon Brown (UEMS), Krasmira Tsaneva-Atanasova (CEMPS) Kyle Wedgwood Multi-scale dynamical systems analysis of neural network deficits in a mouse model of Alzheimer’s disease

One of the most common symptoms of Alzheimer’s disease is ‘wandering’, where patients become disorientated and lost, even in familiar places. One of the parts of the brain important for remembering where you are is damaged at an early stage in dementia.

Evidence from human brain imaging studies of patients with Alzheimer’s disease suggest that nerve cells in this brain region and the connections between them (synapses), malfunction and then die. To study how these cells, synapses and brain regions behave, it is necessary to use mice which have some of the symptoms of Alzheimer’s, since these detailed experiments cannot be ethically performed on humans.

In non-Alzheimer’s brains, we have recently shown that electrical activity changes along the length of this particular brain area. These changes are important as they provide a sense of scale to our internal representation of the world around us, similar to how Ordnance Survey maps provide different levels of navigational information. In mice with Alzheimer’s disease these important changes are lost and this can account for the ‘wandering’ behaviour associated with the disease.

A key aspect to understanding Alzheimer’s, and brain function in general, is learning how individual nerve cells communicate with one another in networks. It is only when behaving in concert that nerve cells perform meaningful tasks. However, the complexity of the brain makes understanding these relationships very difficult.

One way of overcoming this complexity is to use mathematical equations, created in computer programs, to approximate the activity of brain cells and networks. In these computer programs, we can perform experiments that we cannot perform in real life, by modifying different properties of the cells. For example, we could alter the properties of individual cells, or the strength of communication between them. By performing these computer experiments, we can understand how these modifications affect the way in which the brain network behaves and crucially, we will be able to investigate what might be causing the changes seen in mice with Alzheimer’s disease.

Ultimately, by understanding these individual and network level mechanisms, we will be able to consider new potential drug therapies to treat disruptions to brain activity that are associated with Alzheimer’s disease.

Lead Academic

Co- InvestigatorsCentre Fellow(s)Project title

Tim Harries (CEMPS)

Alison Curnow (UEMS), Clare Thorn (UEMS) Sid Visser A virtual laboratory for optimising light dosage in photodynamic therapy

Non-melanoma skin cancer (NMSC) is the most common type of cancer worldwide and is predominantly caused by light from the Sun. About one in three people will develop NMSC during their lives, and over 80,000 new cases are reported in the UK each year. Treating NMSC costs the NHS approximately £200 million per year.

Photodynamic therapy (PDT) is a new way to treat this type of cancer. A cream containing a drug is put on the skin and the cancer cells make it light sensitive. Shining light then kills the cancer, without damaging the healthy skin. This means that healing occurs without the disfigurement or scarring (unlike the standard surgical treatment).

PDT is therefore greatly beneficial to patients, but unfortunately in its current form it cannot be used on thicker skin cancers (about half of all NMSC). The aim of this project is to widen the use of this already effective therapy.

The effectiveness of PDT strongly depends on the amount of light penetrating to the correct depth in the skin, but this is difficult to determine in the body. However the path of light through the skin, and hence the PDT dosage, can be effectively modelled on a computer.

Software, originally developed to model how light is transported through dust clouds in space, will be adapted to model how light passes through the skin. (Despite the vast differences in size, the way light behaves is the same.) The new code will allow us to create a ‘virtual’ PDT laboratory, where the intensity and colour of light delivered to the skin can be varied and the light dose at any depth quickly determined.

Our work will have clear, direct benefits to public health. Patients with thicker skin cancers will be able to benefit from PDT, and as PDT is provided by nurses rather than surgeons patients can be treated more cost effectively. This in turn means that the need for surgical interventions will be reduced, shortening surgical waiting lists.

This project is beautiful example of how scientific knowledge from blue-sky research (or in this case, the dark skies of outer space) can be applied to cancer research, which is real world human health issue of ever increasing importance.

Lead Academic

Co- InvestigatorsCentre Fellow(s)Project title
Francesca Palombo (CEMPS)

Francesco Tamagnini (UEMS), Jon Brown (UEMS), Andy Randall (UEMS), Nick Stone (CEMPS)

Charlie Jeynes A new approach to investigate Alzheimer’s disease using vibrational spectroscopic imaging

Alzheimer’s disease is the most common form of dementia. The disease progresses by gradually destroying nerve cells in the brain. Alzheimer’s disease is becoming more common throughout the world with more than one million people in the UK by 2025 if trends continue.

One of the major hallmarks of Alzheimer’s disease is the accumulation of clumps of a sticky protein fragment in the brain. Small amounts of this fragment are found in the brain and in blood and spinal fluids. We are developing light-imaging methods which study changes in levels of this protein in order to track progress of the disease. We are developing this method using mice, as these animals develop Alzheimer’s quickly enough to be able to detect measurable changes in this sticky protein. If this were done in humans we would need to wait decades. Our techniques, using very powerful microscopes, are among the emerging tools in medical research that have already been successfully employed for the diagnosis of cancer and stroke.

This will be a team effort between physicists, chemists and biologists to find out if this technique can be successfully employed in monitoring Alzheimer’s disease, with the potential to be used as an innovative tool for early diagnosis, leading to increased effectiveness of treatment, quality of life, and decreased healthcare costs.

Lead Academic

Co- InvestigatorsCentre Fellow(s)Project title
Joel Tabak (UEMS) Jamie Walker (CEMPS) David Richards The role of channel stochasticity in regulating the electrical activity of endocrine pituitary cells

The pituitary is the body’s master hormone gland situated just below the brain. Hormones produced by the pituitary regulate many essential functions, including growth, reproduction, and our response to emotional and physical stress. The cells that produce these hormones generate electrical activity, exactly like neurons, and this electrical activity controls the amount of hormone that is released. Signals from the brain and other organs tell the cells how much hormone to release by changing their electrical activity. It is therefore critical to understand how this electrical activity is regulated by these signals. We can use mathematical equations to build models that represent the activity of these cells, allowing us to perform manipulations that we cannot do experimentally. The goal of this project is to develop and analyse a new type of mathematical model that takes into account the random processes within the cell, which to date have been largely neglected. This new model could profoundly improve our understanding of how these cells function, helping us better understand how therapeutic drugs affect pituitary hormone production.

Lead Academic

Co- InvestigatorsCentre Fellow(s)Project title
Craig Williams (CLES) Krasi Tsaneva-Atanasova (CEMPS) Kyle Wedgwood, Sid Visser Developing exercise management protocols for paediatric cystic fibrosis patients using integrated cardiopulmonary modelling

Cystic Fibrosis (CF) is a genetic disorder affecting the lung, pancreas, and sweat glands. CF is diagnosed from birth and currently, there is no cure for CF and management of the disease is a key aspect of the work undertaken by clinicians and their support teams. CF is the most common life shortening genetic disease in the Caucasian population. In the UK CF affects over 10,000 people with about half of them expected to die before the age of 40 years.

Besides drug therapies, rehabilitative exercise programmes form an important aspect of CF treatment and long-term exercise programmes are considered positive treatment strategies but all lack any detailed prescriptive information. Exercise programmes start during childhood and adolescence in order to improve their heart, lung and muscle fitness. Editors in the Journal of Cystic Fibrosis highlighted one of our exercise studies and stated a greater need for understanding the role of exercise in therapeutic interventions. Specifically, it has been shown that the higher the fitness level, the longer a patient can live and the better their quality of life. To measure fitness we use a stationary bike test that shows how fit the patient is and where improvements might be needed in terms of lung, heart or muscle health.

We aim to use pre-existing data on children and adolescents aged 10-18 years from our Children’s Health and Exercise Research Centre (CHERC) and develop computer models to determine the limitations of exercise. This project will use existing patient data to develop new treatment protocols, which poses minimal burden on the patients and is cost-efficient. Using data from our annual tests of patients we will be able to use the computer model to identify limitations in how the heart, lungs and muscles work during exercise.

The data from the model will then be passed onto the physiotherapists who can then identify these limits and they can target and individualise the exercise programmes. This programme will help patients and their families because it will save time by exercising in a more focused way. The implementation of an improved exercise prescription programme based on evidence will improve the quality of clinical care and practice.