Project acronym AXONSURVIVAL
Project Axon survival: the role of protein synthesis
Researcher (PI) Christine Elizabeth Holt
Host Institution (HI) THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE
Call Details Advanced Grant (AdG), LS5, ERC-2012-ADG_20120314
Summary Neurons make long-distance connections with synaptic targets via axons. These axons survive throughout the lifetime of an organism, often many years in mammals, yet how axons are maintained is not fully understood. Recently, we provided in vivo evidence that local mRNA translation in mature axons is required for their maintenance. This new finding, along with in vitro work from other groups, indicates that promoting axonal protein synthesis is a key mechanism by which trophic factors act to prevent axon degeneration. Here we propose a program of research to investigate the importance of ribosomal proteins (RPs) in axon maintenance and degeneration. The rationale for this is fourfold. First, recent genome-wide studies of axonal transcriptomes have revealed that protein synthesis (including RP mRNAs) is the highest functional category in several neuronal types. Second, some RPs have evolved extra-ribosomal functions that include signalling, such as 67LR which acts both as a cell surface receptor for laminin and as a RP. Third, mutations in different RPs in vertebrates cause unexpectedly specific defects, such as the loss of optic axons. Fourth, preliminary results show that RP mRNAs are translated in optic axons in response to trophic factors. Collectively these findings lead us to propose that locally synthesized RPs play a role in axon maintenance through either ribosomal or extra-ribosomal function. To pursue this proposal, we will perform unbiased screens and functional assays using an array of experimental approaches and animal models. By gaining an understanding of how local RP synthesis contributes to axon survival, our studies have the potential to provide novel insights into how components conventionally associated with a housekeeping role (translation) are linked to axon degeneration. Our findings could provide new directions for developing therapeutic tools for neurodegenerative disorders and may have an impact on more diverse areas of biology and disease.
Summary
Neurons make long-distance connections with synaptic targets via axons. These axons survive throughout the lifetime of an organism, often many years in mammals, yet how axons are maintained is not fully understood. Recently, we provided in vivo evidence that local mRNA translation in mature axons is required for their maintenance. This new finding, along with in vitro work from other groups, indicates that promoting axonal protein synthesis is a key mechanism by which trophic factors act to prevent axon degeneration. Here we propose a program of research to investigate the importance of ribosomal proteins (RPs) in axon maintenance and degeneration. The rationale for this is fourfold. First, recent genome-wide studies of axonal transcriptomes have revealed that protein synthesis (including RP mRNAs) is the highest functional category in several neuronal types. Second, some RPs have evolved extra-ribosomal functions that include signalling, such as 67LR which acts both as a cell surface receptor for laminin and as a RP. Third, mutations in different RPs in vertebrates cause unexpectedly specific defects, such as the loss of optic axons. Fourth, preliminary results show that RP mRNAs are translated in optic axons in response to trophic factors. Collectively these findings lead us to propose that locally synthesized RPs play a role in axon maintenance through either ribosomal or extra-ribosomal function. To pursue this proposal, we will perform unbiased screens and functional assays using an array of experimental approaches and animal models. By gaining an understanding of how local RP synthesis contributes to axon survival, our studies have the potential to provide novel insights into how components conventionally associated with a housekeeping role (translation) are linked to axon degeneration. Our findings could provide new directions for developing therapeutic tools for neurodegenerative disorders and may have an impact on more diverse areas of biology and disease.
Max ERC Funding
2 426 573 €
Duration
Start date: 2013-03-01, End date: 2018-09-30
Project acronym BALDWINIAN_BEETLES
Project "The origin of the fittest: canalization, plasticity and selection as a consequence of provisioning during development"
Researcher (PI) Rebecca Kilner
Host Institution (HI) THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE
Call Details Starting Grant (StG), LS8, ERC-2012-StG_20111109
Summary "A major outstanding challenge for evolutionary biology is to explain how novel adaptations arise. We propose to test whether developmental plasticity initiates evolutionary change in morphological, behavioural and social traits, using laboratory experiments, fieldwork and comparative analyses.
Using burying beetles Nicrophorus spp as our model experimental system, we shall:
1) Test whether variation in parental provisioning during development induces correlated phenotypic change in adult body size and a suite of life history traits; whether these phenotypic changes can be genetically accommodated under experimental evolution (the Baldwin Effect); and whether changes induced by experimental evolution mimic natural variation in adult body size and life history strategy among Nicrophorus species;
2) Test whether parental provisioning has a canalizing effect on the developmental environment, potentially storing up cryptic genetic variation which might then be released as random new phenotypes, if offspring are exposed to a new developmental environment;
3) Investigate whether developmental trade-offs, induced by under-provisioning from parents, provide the first step towards the evolution of a novel interspecific mutualism. Is a second species recruited in adulthood to carry out the function of a structure that was under-nourished during development?
4) Using comparative analyses of data from the literature on insects, frogs, birds and mammals, we shall test whether the evolution of parental provisioning in a given lineage is positively correlated with the number of species in the lineage.
Our proposal is original in focusing on developmental plasticity induced by variation in parental provisioning. Given the diverse and numerous species that provision their young, including several whose genomes have now been sequenced, this potentially opens up a rich new area for future work on the developmental mechanisms underlying evolutionary innovations."
Summary
"A major outstanding challenge for evolutionary biology is to explain how novel adaptations arise. We propose to test whether developmental plasticity initiates evolutionary change in morphological, behavioural and social traits, using laboratory experiments, fieldwork and comparative analyses.
Using burying beetles Nicrophorus spp as our model experimental system, we shall:
1) Test whether variation in parental provisioning during development induces correlated phenotypic change in adult body size and a suite of life history traits; whether these phenotypic changes can be genetically accommodated under experimental evolution (the Baldwin Effect); and whether changes induced by experimental evolution mimic natural variation in adult body size and life history strategy among Nicrophorus species;
2) Test whether parental provisioning has a canalizing effect on the developmental environment, potentially storing up cryptic genetic variation which might then be released as random new phenotypes, if offspring are exposed to a new developmental environment;
3) Investigate whether developmental trade-offs, induced by under-provisioning from parents, provide the first step towards the evolution of a novel interspecific mutualism. Is a second species recruited in adulthood to carry out the function of a structure that was under-nourished during development?
4) Using comparative analyses of data from the literature on insects, frogs, birds and mammals, we shall test whether the evolution of parental provisioning in a given lineage is positively correlated with the number of species in the lineage.
Our proposal is original in focusing on developmental plasticity induced by variation in parental provisioning. Given the diverse and numerous species that provision their young, including several whose genomes have now been sequenced, this potentially opens up a rich new area for future work on the developmental mechanisms underlying evolutionary innovations."
Max ERC Funding
1 499 914 €
Duration
Start date: 2012-11-01, End date: 2017-10-31
Project acronym BARCODE
Project The use of genetic profiling to guide prostate cancer targeted screening and cancer care
Researcher (PI) Rosalind Anne Eeles
Host Institution (HI) THE INSTITUTE OF CANCER RESEARCH: ROYAL CANCER HOSPITAL
Call Details Advanced Grant (AdG), LS7, ERC-2013-ADG
Summary "Prostate cancer is the commonest solid cancer in men in the European Community. There is evidence for genetic predisposition to the development of prostate cancer and our group has found the largest number of such genetic variants described to date worldwide. The next challenge is to harness these discoveries to advance the clinical care of populations and prostate cancer patients to improve screening and target treatments. This proposal, BARCODE, aims to be ground-breaking in this area. BARCODE has two components (1) to profile a population in England using the current 77 genetic variant profile and compare screening outcomes with those from population based screening studies to determine if genetics can target screening more effectively in this disease by identifying prostate cancer that more often needs treatment and (2) genetically profiling men with prostate cancer in the uro-oncology clinic for a panel of genes which predict for worse outcome so that these men can be offered more intensive staging and treatment within clinical trials. This will use next generation sequencing technology using a barcoding system which we have developed to speed up throughput and reduce costs. The PI will spend 35% of her time on this project and she will not charge for her time spent on this grant as she is funded by The University of London UK. The research team at The Institute Of Cancer Research, London, UK is a multidisciplinary team which leads the field of genetic predisposition to prostate cancer and its clinical application and so is well placed to deliver on this research. This application will have a dramatic impact on other researchers as it is ground –breaking and state of the art in its application of genetic findings to public health and cancer care. It will therefore influence the work being undertaken in both these areas to integrate genetic profiling and gene panel analysis into population screening and cancer care respectively."
Summary
"Prostate cancer is the commonest solid cancer in men in the European Community. There is evidence for genetic predisposition to the development of prostate cancer and our group has found the largest number of such genetic variants described to date worldwide. The next challenge is to harness these discoveries to advance the clinical care of populations and prostate cancer patients to improve screening and target treatments. This proposal, BARCODE, aims to be ground-breaking in this area. BARCODE has two components (1) to profile a population in England using the current 77 genetic variant profile and compare screening outcomes with those from population based screening studies to determine if genetics can target screening more effectively in this disease by identifying prostate cancer that more often needs treatment and (2) genetically profiling men with prostate cancer in the uro-oncology clinic for a panel of genes which predict for worse outcome so that these men can be offered more intensive staging and treatment within clinical trials. This will use next generation sequencing technology using a barcoding system which we have developed to speed up throughput and reduce costs. The PI will spend 35% of her time on this project and she will not charge for her time spent on this grant as she is funded by The University of London UK. The research team at The Institute Of Cancer Research, London, UK is a multidisciplinary team which leads the field of genetic predisposition to prostate cancer and its clinical application and so is well placed to deliver on this research. This application will have a dramatic impact on other researchers as it is ground –breaking and state of the art in its application of genetic findings to public health and cancer care. It will therefore influence the work being undertaken in both these areas to integrate genetic profiling and gene panel analysis into population screening and cancer care respectively."
Max ERC Funding
2 499 123 €
Duration
Start date: 2014-10-01, End date: 2019-09-30
Project acronym BARRIERS
Project The evolution of barriers to gene exchange
Researcher (PI) Roger BUTLIN
Host Institution (HI) THE UNIVERSITY OF SHEFFIELD
Call Details Advanced Grant (AdG), LS8, ERC-2015-AdG
Summary Speciation is a central process in evolution that involves the origin of barriers to gene flow between populations. Species are typically isolated by several barriers and assembly of multiple barriers separating the same populations seems to be critical to the evolution of strong reproductive isolation. Barriers resulting from direct selection can become coincident through a process of coupling while reinforcement can add barrier traits that are not under direct selection. In the presence of gene flow, these processes are opposed by recombination. While recent research using the latest sequencing technologies has provided much increased knowledge of patterns of differentiation and the genetic basis of local adaptation, it has so far added little to understanding of the coupling and reinforcement processes.
In this project, I will focus on the accumulation of barriers to gene exchange and the processes underlying increasing reproductive isolation. I will use the power of natural contact zones, combined with novel manipulative experiments, to separate the processes that underlie patterns of differentiation and introgression. The Littorina saxatilis model system allows me to do this with both local replication and a contrast between distinct spatial contexts on a larger geographic scale. I will use modelling to determine how processes interact and to investigate the conditions most likely to promote coupling and reinforcement. Overall, the project will provide major new insights into the speciation process, particularly revealing the requirements for progress towards complete reproductive isolation.
Summary
Speciation is a central process in evolution that involves the origin of barriers to gene flow between populations. Species are typically isolated by several barriers and assembly of multiple barriers separating the same populations seems to be critical to the evolution of strong reproductive isolation. Barriers resulting from direct selection can become coincident through a process of coupling while reinforcement can add barrier traits that are not under direct selection. In the presence of gene flow, these processes are opposed by recombination. While recent research using the latest sequencing technologies has provided much increased knowledge of patterns of differentiation and the genetic basis of local adaptation, it has so far added little to understanding of the coupling and reinforcement processes.
In this project, I will focus on the accumulation of barriers to gene exchange and the processes underlying increasing reproductive isolation. I will use the power of natural contact zones, combined with novel manipulative experiments, to separate the processes that underlie patterns of differentiation and introgression. The Littorina saxatilis model system allows me to do this with both local replication and a contrast between distinct spatial contexts on a larger geographic scale. I will use modelling to determine how processes interact and to investigate the conditions most likely to promote coupling and reinforcement. Overall, the project will provide major new insights into the speciation process, particularly revealing the requirements for progress towards complete reproductive isolation.
Max ERC Funding
2 499 927 €
Duration
Start date: 2016-09-01, End date: 2021-08-31
Project acronym BAYES-KNOWLEDGE
Project Effective Bayesian Modelling with Knowledge before Data
Researcher (PI) Norman Fenton
Host Institution (HI) QUEEN MARY UNIVERSITY OF LONDON
Call Details Advanced Grant (AdG), PE6, ERC-2013-ADG
Summary This project aims to improve evidence-based decision-making. What makes it radical is that it plans to do this in situations (common for critical risk assessment problems) where there is little or even no data, and hence where traditional statistics cannot be used. To address this problem Bayesian analysis, which enables domain experts to supplement observed data with subjective probabilities, is normally used. As real-world problems typically involve multiple uncertain variables, Bayesian analysis is extended using a technique called Bayesian networks (BNs). But, despite many great benefits, BNs have been under-exploited, especially in areas where they offer the greatest potential for improvements (law, medicine and systems engineering). This is mainly because of widespread resistance to relying on subjective knowledge. To address this problem much current research assumes sufficient data are available to make the expert’s input minimal or even redundant; with such data it may be possible to ‘learn’ the underlying BN model. But this approach offers nothing when there is limited or no data. Even when ‘big’ data are available the resulting models may be superficially objective but fundamentally flawed as they fail to capture the underlying causal structure that only expert knowledge can provide.
Our solution is to develop a method to systemize the way expert driven causal BN models can be built and used effectively either in the absence of data or as a means of determining what future data is really required. The method involves a new way of framing problems and extensions to BN theory, notation and tools. Working with relevant domain experts, along with cognitive psychologists, our methods will be developed and tested experimentally on real-world critical decision-problems in medicine, law, forensics, and transport. As the work complements current data-driven approaches, it will lead to improved BN modelling both when there is extensive data as well as none.
Summary
This project aims to improve evidence-based decision-making. What makes it radical is that it plans to do this in situations (common for critical risk assessment problems) where there is little or even no data, and hence where traditional statistics cannot be used. To address this problem Bayesian analysis, which enables domain experts to supplement observed data with subjective probabilities, is normally used. As real-world problems typically involve multiple uncertain variables, Bayesian analysis is extended using a technique called Bayesian networks (BNs). But, despite many great benefits, BNs have been under-exploited, especially in areas where they offer the greatest potential for improvements (law, medicine and systems engineering). This is mainly because of widespread resistance to relying on subjective knowledge. To address this problem much current research assumes sufficient data are available to make the expert’s input minimal or even redundant; with such data it may be possible to ‘learn’ the underlying BN model. But this approach offers nothing when there is limited or no data. Even when ‘big’ data are available the resulting models may be superficially objective but fundamentally flawed as they fail to capture the underlying causal structure that only expert knowledge can provide.
Our solution is to develop a method to systemize the way expert driven causal BN models can be built and used effectively either in the absence of data or as a means of determining what future data is really required. The method involves a new way of framing problems and extensions to BN theory, notation and tools. Working with relevant domain experts, along with cognitive psychologists, our methods will be developed and tested experimentally on real-world critical decision-problems in medicine, law, forensics, and transport. As the work complements current data-driven approaches, it will lead to improved BN modelling both when there is extensive data as well as none.
Max ERC Funding
1 572 562 €
Duration
Start date: 2014-04-01, End date: 2018-03-31
Project acronym BCELLMECHANICS
Project Regulation of antibody responses by B cell mechanical activity
Researcher (PI) Pavel Tolar
Host Institution (HI) IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE
Call Details Consolidator Grant (CoG), LS6, ERC-2014-CoG
Summary The production of antibodies against pathogens is an effective mechanism of protection against a wide range of infections. However, some pathogens evade antibody responses by rapidly changing their composition. Designing vaccines that elicit antibody responses against invariant parts of the pathogen is a rational strategy to combat existing and emerging pathogens. Production of antibodies is initiated by binding of B cell receptors (BCRs) to foreign antigens presented on the surfaces of antigen presenting cells. This binding induces B cell signalling and internalisation of the antigens for presentation to helper T cells. Although it is known that T cell help controls B cell expansion and differentiation into antibody-secreting and memory B cells, how the strength of antigen binding to the BCR regulates antigen internalisation remains poorly understood. As a result, the response and the affinity maturation of individual B cell clones are difficult to predict, posing a problem for the design of next-generation vaccines. My aim is to develop an understanding of the cellular mechanisms that underlie critical B cell activation steps. My laboratory has recently described that B cells use mechanical forces to extract antigens from antigen presenting cells. We hypothesise that application of mechanical forces tests BCR binding strength and thereby regulates B cell clonal selection during antibody affinity maturation and responses to pathogen evasion. We propose to test this hypothesis by (1) determining the magnitude and timing of the forces generated by B cells, and (2) determining the role of the mechanical properties of BCR-antigen bonds in affinity maturation and (3) in the development of broadly neutralising antibodies. We expect that the results of these studies will contribute to our understanding of the mechanisms that regulate the antibody repertoire in response to infections and have practical implications for the development of vaccines.
Summary
The production of antibodies against pathogens is an effective mechanism of protection against a wide range of infections. However, some pathogens evade antibody responses by rapidly changing their composition. Designing vaccines that elicit antibody responses against invariant parts of the pathogen is a rational strategy to combat existing and emerging pathogens. Production of antibodies is initiated by binding of B cell receptors (BCRs) to foreign antigens presented on the surfaces of antigen presenting cells. This binding induces B cell signalling and internalisation of the antigens for presentation to helper T cells. Although it is known that T cell help controls B cell expansion and differentiation into antibody-secreting and memory B cells, how the strength of antigen binding to the BCR regulates antigen internalisation remains poorly understood. As a result, the response and the affinity maturation of individual B cell clones are difficult to predict, posing a problem for the design of next-generation vaccines. My aim is to develop an understanding of the cellular mechanisms that underlie critical B cell activation steps. My laboratory has recently described that B cells use mechanical forces to extract antigens from antigen presenting cells. We hypothesise that application of mechanical forces tests BCR binding strength and thereby regulates B cell clonal selection during antibody affinity maturation and responses to pathogen evasion. We propose to test this hypothesis by (1) determining the magnitude and timing of the forces generated by B cells, and (2) determining the role of the mechanical properties of BCR-antigen bonds in affinity maturation and (3) in the development of broadly neutralising antibodies. We expect that the results of these studies will contribute to our understanding of the mechanisms that regulate the antibody repertoire in response to infections and have practical implications for the development of vaccines.
Max ERC Funding
1 999 386 €
Duration
Start date: 2015-09-01, End date: 2020-08-31
Project acronym BeeDanceGap
Project Honeybee communication: animal social learning at the height of social complexity
Researcher (PI) Ellouise Leadbeater
Host Institution (HI) ROYAL HOLLOWAY AND BEDFORD NEW COLLEGE
Call Details Starting Grant (StG), LS8, ERC-2014-STG
Summary Learning from others is fundamental to ecological success across the animal kingdom, but a key theme to emerge from recent research is that individuals respond differently to social information. Understanding this diversity is an imposing challenge, because it is hard to replicate the overwhelming complexity of free-living groups within controlled laboratory conditions. Yet here I propose that one of the most complex social models that we know of— the sophisticated eusocial societies of honeybees— offer unrivaled and yet unrecognized potential to study social information flow through a natural group. The honeybee “dance language” is one of the most celebrated communication systems in the animal world, and central to a powerful information network that drives our most high-profile pollinator to food, but bee colonies are uniquely tractable for two reasons. Firstly, next-generation transcriptomics could allow us to delve deep into this complexity at the molecular level, on a scale that is simply not available in vertebrate social systems. I propose to track information flow through a natural group using brain gene expression profiles, to understand how dances elicit learning in the bee brain. Secondly, although bee foraging ranges are vast and diverse, social learning takes place in one centralized location (the hive). The social sciences now offer powerful new tools to analyze social networks, and I will use a cutting-edge network-based modelling approach to understand how the importance of social learning mechanisms shifts with ecology. In the face of global pollinator decline, understanding the contribution of foraging drivers to colony success has never been more pressing, but the importance of the dance language reaches far beyond food security concerns. This research integrates proximate and ultimate perspectives to produce a comprehensive, multi-disciplinary program; a high-risk, high-gain journey into new territory for understanding animal communication.
Summary
Learning from others is fundamental to ecological success across the animal kingdom, but a key theme to emerge from recent research is that individuals respond differently to social information. Understanding this diversity is an imposing challenge, because it is hard to replicate the overwhelming complexity of free-living groups within controlled laboratory conditions. Yet here I propose that one of the most complex social models that we know of— the sophisticated eusocial societies of honeybees— offer unrivaled and yet unrecognized potential to study social information flow through a natural group. The honeybee “dance language” is one of the most celebrated communication systems in the animal world, and central to a powerful information network that drives our most high-profile pollinator to food, but bee colonies are uniquely tractable for two reasons. Firstly, next-generation transcriptomics could allow us to delve deep into this complexity at the molecular level, on a scale that is simply not available in vertebrate social systems. I propose to track information flow through a natural group using brain gene expression profiles, to understand how dances elicit learning in the bee brain. Secondly, although bee foraging ranges are vast and diverse, social learning takes place in one centralized location (the hive). The social sciences now offer powerful new tools to analyze social networks, and I will use a cutting-edge network-based modelling approach to understand how the importance of social learning mechanisms shifts with ecology. In the face of global pollinator decline, understanding the contribution of foraging drivers to colony success has never been more pressing, but the importance of the dance language reaches far beyond food security concerns. This research integrates proximate and ultimate perspectives to produce a comprehensive, multi-disciplinary program; a high-risk, high-gain journey into new territory for understanding animal communication.
Max ERC Funding
1 422 010 €
Duration
Start date: 2016-02-01, End date: 2021-01-31
Project acronym BEEHIVE
Project Bridging the Evolution and Epidemiology of HIV in Europe
Researcher (PI) Christopher Fraser
Host Institution (HI) THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Call Details Advanced Grant (AdG), LS2, ERC-2013-ADG
Summary The aim of the BEEHIVE project is to generate novel insight into HIV biology, evolution and epidemiology, leveraging next-generation high-throughput sequencing and bioinformatics to produce and analyse whole-genomes of viruses from approximately 3,000 European HIV-1 infected patients. These patients have known dates of infection spread over the last 25 years, good clinical follow up, and a wide range of clinical prognostic indicators and outcomes. The primary objective is to discover the viral genetic determinants of severity of infection and set-point viral load. This primary objective is high-risk & blue-skies: there is ample indirect evidence of polymorphisms that alter virulence, but they have never been identified, and it is not known how easy they are to discover. However, the project is also high-reward: it could lead to a substantial shift in the understanding of HIV disease.
Technologically, the BEEHIVE project will deliver new approaches for undertaking whole genome association studies on RNA viruses, including delivering an innovative high-throughput bioinformatics pipeline for handling genetically diverse viral quasi-species data (with viral diversity both within and between infected patients).
The project also includes secondary and tertiary objectives that address critical open questions in HIV epidemiology and evolution. The secondary objective is to use viral genetic sequences allied to mathematical epidemic models to better understand the resurgent European epidemic amongst high-risk groups, especially men who have sex with men. The aim will not just be to establish who is at risk of infection, which is known from conventional epidemiological approaches, but also to characterise the risk factors for onwards transmission of the virus. Tertiary objectives involve understanding the relationship between the genetic diversity within viral samples, indicative of on-going evolution or dual infections, to clinical outcomes.
Summary
The aim of the BEEHIVE project is to generate novel insight into HIV biology, evolution and epidemiology, leveraging next-generation high-throughput sequencing and bioinformatics to produce and analyse whole-genomes of viruses from approximately 3,000 European HIV-1 infected patients. These patients have known dates of infection spread over the last 25 years, good clinical follow up, and a wide range of clinical prognostic indicators and outcomes. The primary objective is to discover the viral genetic determinants of severity of infection and set-point viral load. This primary objective is high-risk & blue-skies: there is ample indirect evidence of polymorphisms that alter virulence, but they have never been identified, and it is not known how easy they are to discover. However, the project is also high-reward: it could lead to a substantial shift in the understanding of HIV disease.
Technologically, the BEEHIVE project will deliver new approaches for undertaking whole genome association studies on RNA viruses, including delivering an innovative high-throughput bioinformatics pipeline for handling genetically diverse viral quasi-species data (with viral diversity both within and between infected patients).
The project also includes secondary and tertiary objectives that address critical open questions in HIV epidemiology and evolution. The secondary objective is to use viral genetic sequences allied to mathematical epidemic models to better understand the resurgent European epidemic amongst high-risk groups, especially men who have sex with men. The aim will not just be to establish who is at risk of infection, which is known from conventional epidemiological approaches, but also to characterise the risk factors for onwards transmission of the virus. Tertiary objectives involve understanding the relationship between the genetic diversity within viral samples, indicative of on-going evolution or dual infections, to clinical outcomes.
Max ERC Funding
2 499 739 €
Duration
Start date: 2014-04-01, End date: 2019-03-31
Project acronym BIG_IDEA
Project Building an Integrated Genetic Infectious Disease Epidemiology Approach
Researcher (PI) Francois Balloux
Host Institution (HI) UNIVERSITY COLLEGE LONDON
Call Details Starting Grant (StG), LS8, ERC-2010-StG_20091118
Summary Epidemiology and public health planning will increasingly rely on the analysis of genetic sequence data. The recent swine-derived influenza A/H1N1 pandemic may represent a tipping point in this trend, as it is arguably the first time when multiple strains of a human pathogen have been sequenced essentially in real time from the very beginning of its spread. However, the full potential of genetic information cannot be fully exploited to infer the spread of epidemics due to the lack of statistical methodologies capable of reconstructing transmission routes from genetic data structured both in time and space. To address this urgent need, we propose to develop a methodological framework for the reconstruction of the spatiotemporal dynamics of disease outbreaks and epidemics based on genetic sequence data. Rather than reconstructing most recent common ancestors as in phylogenetics, we will directly infer the most likely ancestries among the sampled isolates. This represents an entirely novel paradigm and allows for the development of statistically coherent and powerful inference software within a Bayesian framework. The methodological framework will be developed in parallel with the analysis of real genetic/genomic data from important human pathogens. We will in particular focus on the 2009 A/H1N1 pandemic influenza, methicilin-resistant Staphylococcus aureus clones (MRSAs), Batrachochytrium dendrobatidis, a fungus currently devastating amphibian populations worldwide. The tools we are proposing to develop are likely to impact radically on the field of infectious disease epidemiology and affect the way infectious emerging pathogens are monitored by biologists and public health professionals.
Summary
Epidemiology and public health planning will increasingly rely on the analysis of genetic sequence data. The recent swine-derived influenza A/H1N1 pandemic may represent a tipping point in this trend, as it is arguably the first time when multiple strains of a human pathogen have been sequenced essentially in real time from the very beginning of its spread. However, the full potential of genetic information cannot be fully exploited to infer the spread of epidemics due to the lack of statistical methodologies capable of reconstructing transmission routes from genetic data structured both in time and space. To address this urgent need, we propose to develop a methodological framework for the reconstruction of the spatiotemporal dynamics of disease outbreaks and epidemics based on genetic sequence data. Rather than reconstructing most recent common ancestors as in phylogenetics, we will directly infer the most likely ancestries among the sampled isolates. This represents an entirely novel paradigm and allows for the development of statistically coherent and powerful inference software within a Bayesian framework. The methodological framework will be developed in parallel with the analysis of real genetic/genomic data from important human pathogens. We will in particular focus on the 2009 A/H1N1 pandemic influenza, methicilin-resistant Staphylococcus aureus clones (MRSAs), Batrachochytrium dendrobatidis, a fungus currently devastating amphibian populations worldwide. The tools we are proposing to develop are likely to impact radically on the field of infectious disease epidemiology and affect the way infectious emerging pathogens are monitored by biologists and public health professionals.
Max ERC Funding
1 483 080 €
Duration
Start date: 2010-11-01, End date: 2015-10-31
Project acronym BIGBAYES
Project Rich, Structured and Efficient Learning of Big Bayesian Models
Researcher (PI) Yee Whye Teh
Host Institution (HI) THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Call Details Consolidator Grant (CoG), PE6, ERC-2013-CoG
Summary As datasets grow ever larger in scale, complexity and variety, there is an increasing need for powerful machine learning and statistical techniques that are capable of learning from such data. Bayesian nonparametrics is a promising approach to data analysis that is increasingly popular in machine learning and statistics. Bayesian nonparametric models are highly flexible models with infinite-dimensional parameter spaces that can be used to directly parameterise and learn about functions, densities, conditional distributions etc, and have been successfully applied to regression, survival analysis, language modelling, time series analysis, and visual scene analysis among others. However, to successfully use Bayesian nonparametric models to analyse the high-dimensional and structured datasets now commonly encountered in the age of Big Data, we will have to overcome a number of challenges. Namely, we need to develop Bayesian nonparametric models that can learn rich representations from structured data, and we need computational methodologies that can scale effectively to the large and complex models of the future. We will ground our developments in relevant applications, particularly to natural language processing (learning distributed representations for language modelling and compositional semantics) and genetics (modelling genetic variations arising from population, genealogical and spatial structures).
Summary
As datasets grow ever larger in scale, complexity and variety, there is an increasing need for powerful machine learning and statistical techniques that are capable of learning from such data. Bayesian nonparametrics is a promising approach to data analysis that is increasingly popular in machine learning and statistics. Bayesian nonparametric models are highly flexible models with infinite-dimensional parameter spaces that can be used to directly parameterise and learn about functions, densities, conditional distributions etc, and have been successfully applied to regression, survival analysis, language modelling, time series analysis, and visual scene analysis among others. However, to successfully use Bayesian nonparametric models to analyse the high-dimensional and structured datasets now commonly encountered in the age of Big Data, we will have to overcome a number of challenges. Namely, we need to develop Bayesian nonparametric models that can learn rich representations from structured data, and we need computational methodologies that can scale effectively to the large and complex models of the future. We will ground our developments in relevant applications, particularly to natural language processing (learning distributed representations for language modelling and compositional semantics) and genetics (modelling genetic variations arising from population, genealogical and spatial structures).
Max ERC Funding
1 918 092 €
Duration
Start date: 2014-05-01, End date: 2019-04-30
