Biodiversity is in decline and conservation is becoming increasingly important in today's society. The process of speciation, how new species evolve, is fundamental to biodiversity maintenance but is not fully understood. Phylogenetic trees show the origins of present day diversity, pinpointing when species evolved and describing their relatedness. They are also used in modeling the different strains of diseases such as flu. A lot of research has taken place with the aim of interpreting speciation and phylogenetic trees by evaluating various models. In this project, we use neutral models, which make the controversial assumption that all individuals interact with the system in an identical manner regardless of their species. Despite their assumptions, these models match ecological data with astounding precision, attracting a lot of research attention. For modeling phylogenetic trees, neutral models improve on many earlier models because the probability of a species becoming extinct becomes proportional to the number of representative individuals. Point mutation is one of three modes of speciation regularly used in these models. It states that every newborn individual has a constant probability being a new species. A powerful computational method based on coalescence traces the ancestry of individuals backwards in time. This solves the problems of waiting for equilibrium and restrictive simulation sizes associated with alternative forwards simulations. Point mutation creates a lot of species with only one member, which is not observed in reality. The phylogenetic trees it generates consequently appear unrealistic having many passing mutations counted as novel species. The other two mechanisms are random fission and peripheral isolate speciation where new species arrive as a small founding population. This approach is promising but the coalescence simulations cannot be used for these modes of speciation. This is extremely restrictive and prevents detailed studies. We will develop novel modification of coalescence to make it suitable for investigating the random fission and peripheral isolate modes of speciation. Speciation in nature is a gradual process but all three existing mechanisms assume a sudden speciation event. We propose a novel speciation mechanism where each individual has a simple genome including two genes. This solves the problem of passing mutations in a different way; they exist but would never be defined as a distinct species because the passing mutation would only influence one of the two genes. This mechanism of speciation is gradual because time passes between the mutation of the first gene and mutation of the second gene. It is mutation of the second gene that completes the speciation process. We will fully investigate this mode of speciation and the phylogenetic trees it generates. We have a number of exciting applications for these novel methods, each of which requires a different spatially explicit structure in the model. A two dimensional spatially explicit version of the model is suitable for comparison with empirical phylogenies from collaborator Stephen Hubbell. We have access to a further dataset collected across a rainfall gradient. This will give us the opportunity to test a version of the model that includes habitat heterogeneity. A network of distinct communities is an appropriate spatial structure for many applications including archipelagos and disease dynamics. For example, our collaborator Luke Harmon has zooplankton data collected from fresh water lakes, where a comparison with neutral models would be insightful. A medical application also exists regarding bioflms. These are adhesive matrices and infections that are untreatable with antibiotics. Recent research has shown an extreme rate of diversification in these biofilms. A test using a three dimensional neutral model would be insightful research in understanding within biofilm competition.

The shift from hunting and gathering to an agricultural way of life was one of the most profound events in the history of our species and one which continues to impact our existence today. Understanding this process is key to understanding the origins and rise of human civilization. Despite decades of study, however, fundamental questions regarding why, where and how it occurred remain largely unanswered. Such a fundamental change in human existence could not have been possible without the domestication of selected animals and plants. The dog is crucial in this story since it was not only the first ever domestic animal, but also the only animal to be domesticated by hunter-gatherers several thousand years before the appearance of farmers. The bones and teeth of early domestic dogs and their wild wolf ancestors hold important clues to our understanding of how, where and when humans and wild animals began the relationship we still depend upon today. These remains have been recovered from as early as 15,000 years ago in numerous archaeological sites across Eurasia suggesting that dogs were either domesticated independently on several occasions across the Old World, or that dogs were domesticated just once and subsequently spreading with late Stone Age hunter gatherers across the Eurasian continent and into North America. There are also those who suggest that wolves were involved in an earlier, failed domestication experiment by Ice Age Palaeolithic hunters about 32,000 years ago. Despite the fact that we generally know the timing and locations of the domestication of all the other farmyard animals, we still know very little for certain about the origins of our most iconic domestic animal. New scientific techniques that include the combination of genetics and statistical analyses of the shapes of ancient bones and teeth are beginning to provide unique insights into the biology of the domestication process itself, as well as new ways of tracking the spread of humans and their domestic animals around the globe. By employing these techniques we will be able to observe the variation that existed in early wolf populations at different levels of biological organization, identify diagnostic signatures that pinpoint which ancestral wolf populations were involved in early dog domestication, reveal the shape (and possibly the genetic) signatures specifically linked to the domestication process and track those signatures through time and space. We have used this combined approach successfully in our previous research enabling us to definitively unravel the complex story of pig domestication in both Europe and the Far East. We have shown that pigs were domesticated multiple times and in multiple places across Eurasia, and the fine-scale resolution of the data we have generated has also allowed us to reveal the migration routes pigs took with early farmers across Europe and into the Pacific. By applying this successful research model to ancient dogs and wolves, we will gain much deeper insight into the fundamental questions that still surround the story of dog domestication.

Growth factors are molecules within our body that participate in many physiological process that are key during development as they control stem cell function. These molecules thus have the potential to drive the regeneration of tissues in a broad range of medical conditions, including in musculoskeletal (bone repair), haematological (bone marrow transplantation) and cardiovascular (infarction, heart attack) diseases. Growth factors are currently produced commercially and are used regularly in clinical applications. However, they are very power cell signalling molecules and dose is critical as balance between effect and safety has to be considered. To date the use of growth factors in regenerative medicine has been only partially successful and even controversial. The growth factors are rapidly broken down and cleared by the body. This makes prolonged delivery (as is required to effect repair) a problem and typically higher than wanted doses are administered to get around this. While their help in regeneration is undoubted, collateral side effects can be catastrophic e.g. tumour formation. We have developed new technology that directly addresses these concerns as it uses materials (that can be topically implanted) to deliver low, but effective, growth factor doses; this programme is about the safe use of growth factors in clinical applications. This will not only reduce risks for patients who currently receive growth factor treatments, but will open up therapies that can include co-transplantation with stem cells to a wider range of patients as doctors would not have to keep these therapies back for cases of most pressing need. This increased use would minimise costs as growth factors are very expensive and reduced dose would save money per treatment. Our approach is unique and this programme grant will allow us to enhance the UK's world leading position through innovative bioengineering. We know that stem cells have huge regenerative potential and that growth factors provide exquisite stem cell control - both are currently untapped. We will engineer new materials to enable growth factor technology, and critically stem cell technologies, where traditional approaches are falling very short of the mark.

Synthetic Biology (SynBio) is an emerging engineering discipline with an ambitious goal: empowering scientists with the ability to programme new functions into cells, just like we would do with computers. Despite a thriving community and notable successes, however, writing "functioning algorithms" for cells remains extremely time-consuming. This is a roadblock towards the engineering of mammalian cells, an area uniquely positioned to develop potentially groundbreaking therapeutic applications. This translates into high development costs that, in turn, are limiting the pace at which Synthetic Biology progresses towards applications. Model-Based System Engineering (MBSE) is the answer the engineering community found to similar problems and is widely used to streamline manufacturing. In this framework, mathematical models are used to screen candidate designs via simulations and bring to testing only the most promising solutions. Despite being an engineering discipline, SynBio lacks a MBSE framework. This is largely due to three connected issues: (a) the scarcity of accurate mathematical models of parts (e.g. promoters) in the first place. Such a shortage (b) makes it difficult to "reverse engineer" the connection between the DNA sequence and the kinetics of the transcribed mRNA (e.g. promoter sequence and leakiness of expresion). This means that (c) the inverse "re-design" problem, i.e. finding the optimal DNA sequence of a part, cannot be solved, let alone automatically. With this fellowship, I aim at filling this gap and develop a "Model-Based Biosystem Engineering" (MBBE) framework to automate the Design-Build-Test-Learn (DBTL) cycle in Synthetic Biology. Given their role in cell and gene therapy, with my team, we will focus on synthetic promoters for mammalian cells. Prompted by the recent successes and challenges of CAR T cells -immune cells engineered to kill cancer cells, we will use the framework to engineer a hypoxia-inducible promoter that optimises a set of criteria we will determine and prioritise with our collaborator Prof. Chen at UCLA. We will first focus on the development of the MBBE framework; to this aim we will tackle the three issues mentioned above by: (a) developing a high-throughput microfluidic device that allows to infer, with minimum experimental efforts (via Optimal Experimental Design), reliable mathematical models of hundreds of variants of a promoter, (b) using these results to automatically learn/predict gene expression dynamics from promoter sequence via machine learning and (c) combining this prediction scheme with computational optimisation to identify and refine promoter sequences so that they satisfy given specifications and maximise pre-determined objectives. To develop a hypoxia-inducible promoter, we will start from an initial pool of 600 sequences -designed to cover a fraction of the design space as big as possible, and we will iterate twice over our automatic DBTL loop to finally obtain promoter(s) that can be used to overcome the current limitations of CAR T cells. Besides automating the DBTL cycle, the approach I propose has three main benefits: it allows to obtain, and publicly share, reliable models (1) faster -as we will use Optimal Experimental Design methods to minimise experimental efforts, (2) cost-effectively -as microfluidics drastically reduces the use of reagents and automation renders human intervention unnecessary; (3) in a reproducible way -as all the data and the steps in the inference are tracked and immediately made publicly available.

Ecologists have long puzzled over the mechanisms that maintain biological diversity - that permit so many natural species to live alongside others with which they compete for resources. Many biologists would maintain that species coexist by exploiting their environment in different ways, and patterns of abundance and rarity reflect the distinct roles played by different species in the community. This view was supported by Charles Darwin, who wrote in 1859 `When we look at the plants and bushes clothing an entangled bank, we are tempted to attribute their proportional numbers and kinds to what we call chance. But how false a view is this!' However, a recent theory by Stephen P. Hubbell asserts the opposite: that individuals in ecological communities behave as if they were exactly the same, and that a species is only `rare' or `common' because of purely random, chance events. This theory has been remarkably successful at describing the patterns of rarity and abundance in many natural systems. However, not even Hubbell himself would claim that the `assumption of ecological equivalence' that underpins his theory is a faithful description of how species interact. The question is: why do natural systems behave as if their constituents are ecologically equivalent, when this is not true? This project will address this question by studying models where individuals of different species are not ecologically equivalent. While similar models have been studied before, they are so complicated that they cannot be solved mathematically, which makes it difficult to compare them to data. By contrast, Hubbell's model is relatively simple, and its properties can be studied in great detail. However, by studying models that are only slightly different from Hubbell's, we can use the exact solution to Hubbell's model to calculate how the predictions of our models differ from his. This method of piggy-backing on an exactly solvable model to study an unsolvable one is a well-established practice in applied mathematics. We shall investigate how differences in competitive ability and in species' preferences for different habitat contribute to patterns of biodiversity. This will show us how strong these processes would need to be to produce noticeably different predictions from Hubbell's model. We shall use our models to analyse data for highly diverse systems such as tropical forests and coral reefs. In this way, we shall measure and distinguish between the different processes that make these systems so diverse.