Decision-making involves choosing between alternatives by considering the probability of each outcome and how rewarding it will be. In some situations, such as reading the side effects of a medication, these probabilities and outcomes are listed and in others, such discovering a new food allergy, we need to learn them from experience. What we remember influences the decisions we make. Some items, such as extremely rewarding events, are more likely to be remembered than others and therefore more likely to influence our decisions. How might memory for particular events influence decision-making? Imagine that you are on holiday and you go to buy milk. You can either turn left and walk 3 minutes to the newsagent or turn right and walk 3 minutes to the supermarket. Let's assume that the last four times you have been to a newsagent you paid the following for milk [£1.50, £0.95, 1.40, £1] and at the supermarket you paid [£1.23, £1.26, £1.25, £1.22]. So, on average you paid the same at both shops. To decide which way to turn, you might sample a few experiences from memory. Which items do you sample and how do you then compare these items? You may think of the time you found a bargain and paid £0.95 and happily turn right. However, you may just as easily end up over paying so you would do well to think of the time you paid £1.50. What you decide depends on the processes which have yet to be examined in the same context: 1) which events you originally encoded; 2) how many experiences you sample from your memory and their "value"; and 3) how you compare the items in your sample. Research has indicated that people are more likely to rely on extreme information when making decisions and this can increase risk-seeking behaviours. The precise memory mechanism underlying this phenomenon are not yet understood. This project investigates how our memory for rewarding events, including extreme events, contributes to decision-making and risky choices. The proposed research will address this question using a suite of theory driven experiments supported by computational models. In a series of experiments, we will assess how healthy individuals encode, store and retrieve rewards in memory and use these memories to make decisions. We will develop computer models to understand how memory guides risky-choice. The project will increase our understanding of the role of memory in risky decision-making and help us to identify novel approaches for therapeutic intervention. Many of people's everyday decisions and choices relating to health-related lifestyle, financial savings, purchasing behaviours and environmental choices are heavily influenced by memory for past experiences. This research will support the development of better, more effective choice architecture interventions. If we can develop a better understanding of how people retrieve information from memory, and how that retrieved information supports choice, we will be able to develop interventions that prompt or nudge memory to improve choice.

See Manchester document.

Worldwide, opioid use (e.g. heroin) is now viewed as a 'crisis'. Men are more likely to use opioids, but in recent years there has been a greater increase in the numbers of women using opioids. As a consequence, the numbers of pregnancies where opioid use is a factor is also increasing. There is already strong evidence that opioid use in pregnancy harms the unborn child, but very little is known about the long-term outcomes for these children: they are difficult to follow-up over a long time period using traditional methods, such as repeated face-to-face interviews, due to the complexity of their lives. Scotland is in a rare position of being able to link all health data across a person's lifetime, as well as linking mother and child data, not readily available in all countries, and having high levels of opioid use. As a first step, I have shown that we can identify women who use illicit or prescription opioids in pregnancy from the health data we already collect routinely (e.g. antenatal records, hospital records) in Scotland and this can be matched to equivalent data from their children. This gives us a way of exploring longer term outcomes. The proposed research will be the first to analyse a population-based cohort of children exposed to opioids in pregnancy through to adolescence. This will allow us to explore a range of health, education and justice outcomes for children in Scotland, and to explore the pathways they take to these outcomes. The outcomes of children who were exposed to opioids through their mother's substance use will be compared with children in two other groups: 1) mothers who used opioids for chronic pain in pregnancy, and 2) children who are from similar socio-economic backgrounds but who were not exposed to opioids. We also don't know much about how drugs pass through to the unborn child during pregnancy, as the effects on the child do not seem to be directly linked to the mother's consumption. I will work with a forensic chemist to develop a test which changes colour depending on the type and amount of drugs (e.g. heroin, morphine, methadone) detected in blood samples. In Phase 1 this will be developed using animal bloods. In phase 2 we plan to test this on blood collected from the umbilical cord. During Phase 1 I will work with mothers who use opioids to gather information on their views and potential concerns around the use of cord blood, in order to ensure this research is conducted sensitively in Phase 2. There are other countries with excellent data linkage systems, but with smaller numbers of babies exposed to opioids in pregnancy. The second stage of this study will be to develop international collaborations, in order to access datasets from some Scandinavian and Commonwealth countries (New Zealand, Australia) and to test the pathways found on the Scottish data on these international datasets. In the rest of the UK, it is difficult to bring data together to the same degree as in Scotland. We will therefore work with other UK nations to improve these data linkages. This will mean that later in the study we can analyse data for the whole of the UK in a similar way to the Scottish data. Throughout the project we will work with women who use opioids, young adults whose mothers used opioids, and the charity organisations and policy-makers who support these families. This will ensure that we are asking the right questions and will be able to create useful and meaningful recommendations for future research and intervention. The results of this study will help us to understand the effects on children of opioid use during pregnancy and allow us to explore promising approaches for interventions to improve the support that is currently provided to women, adoptive families, and their children. Our findings will ensure that women across the world are given accurate information about the impact of opioid use in pregnancy to help provide their children with the best start in life.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

The impact of computer science technology in microbiology has lead to the creation of online databases which now contain complete genome sequences for several hundred organisms, as well as detailed information for a wide variety of cell processes. Computers can also act as simulators to model the dynamic behaviour of these processes and the interactions between them. Simulation can provide guidance to scientists in the selection of useful experiments and can also provide predictions where experimentation is costly and difficult to perform. Systems biology is a rapidly advancing science that aims to capture knowledge of these processes and interactions and the creation of simulation models is a central activity. A medium term goal is the construction of a model of the whole cell, where the interactions of systems that are normally studied separately can be analysed. Computational Scientific Discovery is another emerging discipline where techniques from Artificial Intelligence (AI) are used to automate or greatly ease the difficult process of translating experimental results and data into scientific knowledge. This is especially important as the quantity of data far exceeds the ability of unaided human interpretation. In terms of systems biology scientific discovery often involves the construction and validation of computer models that provide explanations of experimental results. It is important that the resulting model accurately explains the results and is also biologically valid, i.e. the knowledge makes sense to a human expert. Machine Learning, a branch of AI, has seen the development of computer programs that can generate explanations from data. The last decade or more has seen increasing use of machine learning techniques for the acquisition of biological knowledge. However, a major drawback, preventing even wider acceptance of computational scientific discovery by the more general biology community, is the learning curve necessary for efficient use of the techniques and technology. Many systems biology scientists find it necessary to become experts in the mathematics of machine learning and model simulation as well as being experts in cell biology. The Modelling Apprentice seeks to overcome these obstacles by providing an easy to use, understandable tool to aid the construction, validation and improvement of biological models by removing the need for the scientist to understand or even interact with the underlying mathematical knowledge representation and machine learning. This is achieved by; 1) an intuitive graphical user interface where molecular and chemical interactions are displayed explicitly, and 2) separation of the scientific knowledge from the machine learning techniques that reason with the knowledge. The second of these also allows the Modelling Apprentice to be easily adapted to investigate other scientific applications by constructing a library that acts as a plug-in. The Modelling Apprentice will seek to improve the newly developed program Justaid - which already incorporates these features. As a test case, a model of the MAPK cell signalling network of yeast will be built using knowledge from expert biologists in Cambridge and Aberdeen. Cell signalling is the process by which cells respond to external and environmental stimuli and study of these networks is crucial to the understanding of human diseases such as cancer, diabetes, and immune and degenerative disorders. Modelling of cell signalling has also not progressed as fast as other biological processes such as metabolism. Suitability of the Modelling apprentice and the new MAPK model library will then be assessed by expert biologists who will use it to evaluate their latest experimental results. Insights gained from this testing will be used to further improve the Modelling Apprentice.