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      "title_narrative":["G-quadruplex biology in the human malaria parasite Plasmodium falciparum"],
      "description_narrative":["The proposed research concerns the most important human malaria parasite, Plasmodium falciparum. Malaria is one of the world's most debilitating infectious diseases, killing over half a million people every year and affecting several hundred million. Most of the deaths occur in young children in sub-Saharan Africa, but adults can also suffer from malaria throughout their lives, reducing quality of life and retarding economic development in endemic countries. The lack of an effective vaccine and the emergence of drug-resistant parasites mean that there is now an urgent need for research leading to a better understanding of the malaria parasite, and hence to new vaccine targets and treatment strategies for this disease.  The malaria parasite causes illness via the infection of red blood cells. It multiplies inside these cells and modifies their surfaces with proteins called PfEMP1s that bind to the walls of blood vessels. This is crucial for parasite survival as it removes infected cells from the circulating blood and protects them from passing through the spleen, which might recognize and destroy them. It also contributes to disease, with severe malaria being particularly associated with the accumulation of infected cells in vessels of the brain and placenta. It is therefore of great interest to malaria biologists to understand the mechanisms that control the expression of these adhesive PfEMP1 proteins. PfEMP1s are not expressed uniformly by all malaria parasites: instead, individual parasites regularly switch between different variants. This allows them to stay ahead of the immune system and sustain a chronic infection for months or even years. The parasites have a large, variable family of 'var' genes for different PfEMP1 proteins and they vary the expression of these genes by so-called 'epigenetic switching'. Furthermore, var genes recombine very readily to generate new variants, so each parasite strain - of which there are many hundreds circulating in endemic areas - has a unique repertoire of possible surface proteins. This is one reason why immunity to repeated malaria infections is slow to develop in humans: every new parasite strain looks different to the immune system, so people can be re-infected repeatedly throughout their lives.  Understanding and ultimately interfering with the expression, switching and recombination of var genes, and thus the variant expression of PfEMP1 proteins, could be a key to more effective immune control of malaria. Therefore, this research focuses on a new biological mechanism that the parasite may use for switching between var genes and for generating new variants. Our recent work has showed that an unusual DNA structure called a G-quadruplex that is concentrated around var genes seems to affect both these processes. To investigate this further, we now propose to map the G-quadruplexes throughout the Plasmodium genome, both in DNA and also in the messenger molecule, RNA. We will use a range of cutting-edge genome-wide technologies to do this, and will then check whether the distribution of the structures changes when the enzymes that unwind them are removed.  These studies will lead to a better understanding of the mechanisms underlying var gene dynamics, and may ultimately inform new strategies to combat malaria, since var genes - and the adhesive proteins that they encode - are central to malarial disease. The outcomes of the research will be published in open-access scientific journals and presented at international conferences. They will be communicated to the general public via summaries on appropriate websites and via science writing in magazines and/or online. Work such as this remains vital as long as the malaria parasite continues to cause an immense burden of human disease.","The Global Challenges Research Fund (GCRF) supports cutting-edge research to address challenges faced by developing countries. The fund addresses the UN sustainable development goals. It aims to maximise the impact of research and innovation to improve lives and opportunity in the developing world."],
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      "title_narrative":["A mathematical modeling framework for tuberculosis burden estimation and economic evaluation of pharmaceutical interventions"],
      "description_narrative":["Tuberculosis (TB) is a major cause of disease and death globally. In 2015, WHO estimated there were 9.6 million TB cases and 1.5 TB deaths. Nearly 500,000 of these cases were resistant to two or more of the main drugs used to treat TB. New drugs, and combinations of drugs, are being developed to treat tuberculosis, as are new vaccines that may protect against disease in adults.  Quantifying the burden of TB is fundamental to understanding its global epidemiology and for making appropriate resource allocation decisions. Most estimates of new TB case numbers each year rely strongly on the number of cases reported by countries in that year to WHO. Unfortunately, one in three TB cases are thought to go either undetected or unreported, so the number of cases reported underestimates the number of new cases. While one can correct for this, it is hard to know exactly how much to adjust the reported numbers. Some countries have good systems for recording causes of deaths, which can be used to estimate the number of deaths caused by TB. Increasingly, large and expensive prevalence surveys are being used to estimate the number of people with active disease in a population. These estimates are less subject to bias, but measure a different quantity. Little work has explored the best way of combining these three data sources.  A major goal of this work is to use mathematical transmission models for burden estimation and provide a unified framework for all data. These models yield the number of new cases, deaths, and also the prevalence of disease. They explicitly represent disease transmission and so introduce a dependence between the number of new cases in different years. These models involve parameters evidenced from previous epidemiological work, but must be calibrated to learn from data on TB reports, deaths and prevalence. Calibration means adjusting imperfectly known model parameters in order to match observed model outputs to the data. This process provides a model that may be used to make predictions about burden, but may also teach us something about the underlying processes. Many of the parameters concerning the epidemiology and disease course of TB are quite uncertain, and this uncertainty is rarely represented fully in models needing calibration, but will be done in this project using statistical techniques that also allow comparison of different models' performance.   TB burden estimation and calibration of transmission models are almost always carried out on a country-by-country basis. Many parameters describing disease progression are likely to be similar in different countries, even if their exact values differ for unknown reasons. Hierarchical modelling techniques allow such parameters to be correlated between countries. This can improve precision, particularly for countries with little data, as estimates can be informed by data from neighbouring countries. I will explore these techniques for the transmission model, and also in statistical modelling aiming to account for the observed patterns of drug-resistance. The transmission model will ultimately be extended to include different types of drug resistance.  As new treatments and vaccines emerge, those with responsibility for public health will want to understand the potential impact these new technologies can have in terms of gains in health, and changes in spending. Producing cost-effectiveness and budget impact evidence requires a model that includes transmission, in order to account for indirect benefits accrued by avoiding secondary cases. We will use our model to provide guidance to decision-makers seeking to maximise health gain with limited resources. We will also analyse sources of uncertainty in the model to identify future research that would have most value in increasing the precision of burden estimates and in reducing decision uncertainty around the introduction of new interventions.","The Global Challenges Research Fund (GCRF) supports cutting-edge research to address challenges faced by developing countries. The fund addresses the UN sustainable development goals. It aims to maximise the impact of research and innovation to improve lives and opportunity in the developing world."],
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