Project acronym AcetyLys
Project Unravelling the role of lysine acetylation in the regulation of glycolysis in cancer cells through the development of synthetic biology-based tools
Researcher (PI) Eyal Arbely
Host Institution (HI) BEN-GURION UNIVERSITY OF THE NEGEV
Country Israel
Call Details Starting Grant (StG), LS9, ERC-2015-STG
Summary Synthetic biology is an emerging discipline that offers powerful tools to control and manipulate fundamental processes in living matter. We propose to develop and apply such tools to modify the genetic code of cultured mammalian cells and bacteria with the aim to study the role of lysine acetylation in the regulation of metabolism and in cancer development. Thousands of lysine acetylation sites were recently discovered on non-histone proteins, suggesting that acetylation is a widespread and evolutionarily conserved post translational modification, similar in scope to phosphorylation and ubiquitination. Specifically, it has been found that most of the enzymes of metabolic processes—including glycolysis—are acetylated, implying that acetylation is key regulator of cellular metabolism in general and in glycolysis in particular. The regulation of metabolic pathways is of particular importance to cancer research, as misregulation of metabolic pathways, especially upregulation of glycolysis, is common to most transformed cells and is now considered a new hallmark of cancer. These data raise an immediate question: what is the role of acetylation in the regulation of glycolysis and in the metabolic reprogramming of cancer cells? While current methods rely on mutational analyses, we will genetically encode the incorporation of acetylated lysine and directly measure the functional role of each acetylation site in cancerous and non-cancerous cell lines. Using this methodology, we will study the structural and functional implications of all the acetylation sites in glycolytic enzymes. We will also decipher the mechanism by which acetylation is regulated by deacetylases and answer a long standing question – how 18 deacetylases recognise their substrates among thousands of acetylated proteins? The developed methodologies can be applied to a wide range of protein families known to be acetylated, thereby making this study relevant to diverse research fields.
Summary
Synthetic biology is an emerging discipline that offers powerful tools to control and manipulate fundamental processes in living matter. We propose to develop and apply such tools to modify the genetic code of cultured mammalian cells and bacteria with the aim to study the role of lysine acetylation in the regulation of metabolism and in cancer development. Thousands of lysine acetylation sites were recently discovered on non-histone proteins, suggesting that acetylation is a widespread and evolutionarily conserved post translational modification, similar in scope to phosphorylation and ubiquitination. Specifically, it has been found that most of the enzymes of metabolic processes—including glycolysis—are acetylated, implying that acetylation is key regulator of cellular metabolism in general and in glycolysis in particular. The regulation of metabolic pathways is of particular importance to cancer research, as misregulation of metabolic pathways, especially upregulation of glycolysis, is common to most transformed cells and is now considered a new hallmark of cancer. These data raise an immediate question: what is the role of acetylation in the regulation of glycolysis and in the metabolic reprogramming of cancer cells? While current methods rely on mutational analyses, we will genetically encode the incorporation of acetylated lysine and directly measure the functional role of each acetylation site in cancerous and non-cancerous cell lines. Using this methodology, we will study the structural and functional implications of all the acetylation sites in glycolytic enzymes. We will also decipher the mechanism by which acetylation is regulated by deacetylases and answer a long standing question – how 18 deacetylases recognise their substrates among thousands of acetylated proteins? The developed methodologies can be applied to a wide range of protein families known to be acetylated, thereby making this study relevant to diverse research fields.
Max ERC Funding
1 499 375 €
Duration
Start date: 2016-07-01, End date: 2021-06-30
Project acronym Age Asymmetry
Project Age-Selective Segregation of Organelles
Researcher (PI) Pekka Aleksi Katajisto
Host Institution (HI) HELSINGIN YLIOPISTO
Country Finland
Call Details Starting Grant (StG), LS3, ERC-2015-STG
Summary Our tissues are constantly renewed by stem cells. Over time, stem cells accumulate cellular damage that will compromise renewal and results in aging. As stem cells can divide asymmetrically, segregation of harmful factors to the differentiating daughter cell could be one possible mechanism for slowing damage accumulation in the stem cell. However, current evidence for such mechanisms comes mainly from analogous findings in yeast, and studies have concentrated only on few types of cellular damage.
I hypothesize that the chronological age of a subcellular component is a proxy for all the damage it has sustained. In order to secure regeneration, mammalian stem cells may therefore specifically sort old cellular material asymmetrically. To study this, I have developed a novel strategy and tools to address the age-selective segregation of any protein in stem cell division. Using this approach, I have already discovered that stem-like cells of the human mammary epithelium indeed apportion chronologically old mitochondria asymmetrically in cell division, and enrich old mitochondria to the differentiating daughter cell. We will investigate the mechanisms underlying this novel phenomenon, and its relevance for mammalian aging.
We will first identify how old and young mitochondria differ, and how stem cells recognize them to facilitate the asymmetric segregation. Next, we will analyze the extent of asymmetric age-selective segregation by targeting several other subcellular compartments in a stem cell division. Finally, we will determine whether the discovered age-selective segregation is a general property of stem cell in vivo, and it's functional relevance for maintenance of stem cells and tissue regeneration. Our discoveries may open new possibilities to target aging associated functional decline by induction of asymmetric age-selective organelle segregation.
Summary
Our tissues are constantly renewed by stem cells. Over time, stem cells accumulate cellular damage that will compromise renewal and results in aging. As stem cells can divide asymmetrically, segregation of harmful factors to the differentiating daughter cell could be one possible mechanism for slowing damage accumulation in the stem cell. However, current evidence for such mechanisms comes mainly from analogous findings in yeast, and studies have concentrated only on few types of cellular damage.
I hypothesize that the chronological age of a subcellular component is a proxy for all the damage it has sustained. In order to secure regeneration, mammalian stem cells may therefore specifically sort old cellular material asymmetrically. To study this, I have developed a novel strategy and tools to address the age-selective segregation of any protein in stem cell division. Using this approach, I have already discovered that stem-like cells of the human mammary epithelium indeed apportion chronologically old mitochondria asymmetrically in cell division, and enrich old mitochondria to the differentiating daughter cell. We will investigate the mechanisms underlying this novel phenomenon, and its relevance for mammalian aging.
We will first identify how old and young mitochondria differ, and how stem cells recognize them to facilitate the asymmetric segregation. Next, we will analyze the extent of asymmetric age-selective segregation by targeting several other subcellular compartments in a stem cell division. Finally, we will determine whether the discovered age-selective segregation is a general property of stem cell in vivo, and it's functional relevance for maintenance of stem cells and tissue regeneration. Our discoveries may open new possibilities to target aging associated functional decline by induction of asymmetric age-selective organelle segregation.
Max ERC Funding
1 500 000 €
Duration
Start date: 2016-05-01, End date: 2021-04-30
Project acronym ARCA
Project Analysis and Representation of Complex Activities in Videos
Researcher (PI) Juergen Gall
Host Institution (HI) RHEINISCHE FRIEDRICH-WILHELMS-UNIVERSITAT BONN
Country Germany
Call Details Starting Grant (StG), PE6, ERC-2015-STG
Summary The goal of the project is to automatically analyse human activities observed in videos. Any solution to this problem will allow the development of novel applications. It could be used to create short videos that summarize daily activities to support patients suffering from Alzheimer's disease. It could also be used for education, e.g., by providing a video analysis for a trainee in the hospital that shows if the tasks have been correctly executed.
The analysis of complex activities in videos, however, is very challenging since activities vary in temporal duration between minutes and hours, involve interactions with several objects that change their appearance and shape, e.g., food during cooking, and are composed of many sub-activities, which can happen at the same time or in various orders.
While the majority of recent works in action recognition focuses on developing better feature encoding techniques for classifying sub-activities in short video clips of a few seconds, this project moves forward and aims to develop a higher level representation of complex activities to overcome the limitations of current approaches. This includes the handling of large time variations and the ability to recognize and locate complex activities in videos. To this end, we aim to develop a unified model that provides detailed information about the activities and sub-activities in terms of time and spatial location, as well as involved pose motion, objects and their transformations.
Another aspect of the project is to learn a representation from videos that is not tied to a specific source of videos or limited to a specific application. Instead we aim to learn a representation that is invariant to a perspective change, e.g., from a third-person perspective to an egocentric perspective, and can be applied to various modalities like videos or depth data without the need of collecting massive training data for all modalities. In other words, we aim to learn the essence of activities.
Summary
The goal of the project is to automatically analyse human activities observed in videos. Any solution to this problem will allow the development of novel applications. It could be used to create short videos that summarize daily activities to support patients suffering from Alzheimer's disease. It could also be used for education, e.g., by providing a video analysis for a trainee in the hospital that shows if the tasks have been correctly executed.
The analysis of complex activities in videos, however, is very challenging since activities vary in temporal duration between minutes and hours, involve interactions with several objects that change their appearance and shape, e.g., food during cooking, and are composed of many sub-activities, which can happen at the same time or in various orders.
While the majority of recent works in action recognition focuses on developing better feature encoding techniques for classifying sub-activities in short video clips of a few seconds, this project moves forward and aims to develop a higher level representation of complex activities to overcome the limitations of current approaches. This includes the handling of large time variations and the ability to recognize and locate complex activities in videos. To this end, we aim to develop a unified model that provides detailed information about the activities and sub-activities in terms of time and spatial location, as well as involved pose motion, objects and their transformations.
Another aspect of the project is to learn a representation from videos that is not tied to a specific source of videos or limited to a specific application. Instead we aim to learn a representation that is invariant to a perspective change, e.g., from a third-person perspective to an egocentric perspective, and can be applied to various modalities like videos or depth data without the need of collecting massive training data for all modalities. In other words, we aim to learn the essence of activities.
Max ERC Funding
1 499 875 €
Duration
Start date: 2016-06-01, End date: 2021-05-31
Project acronym ARIADNE
Project ARgon ImAging DetectioN chambEr
Researcher (PI) Konstantinos Mavrokoridis
Host Institution (HI) THE UNIVERSITY OF LIVERPOOL
Country United Kingdom
Call Details Starting Grant (StG), PE2, ERC-2015-STG
Summary This proposal outlines a plan to combine Charge Couple Device (CCD) camera technologies with two-phase Liquid Argon Time Projection Chambers (LAr TPCs) utilising THick Gas Electron Multipliers (THGEMs) to evolve a next generation neutrino detector. This will be an entirely new readout option, and will open the prospect of revolutionary discoveries in fundamental particle physics. Furthermore, the Compton imaging power of this technology will be developed, which will have diverse applications in novel medical imaging techniques and detection of concealed nuclear materials.
Colossal LAr TPCs are the future for long-baseline-neutrino-oscillation physics around which the international neutrino community is rallying, with the common goal of discovering new physics beyond the Standard Model, which holds the key to our understanding of phenomena such as dark matter and the matter-antimatter asymmetry.
I have successfully provided a first demonstration of photographic capturing of muon tracks and single gammas interacting in the Liverpool 40 l LAr TPC using a CCD camera and THGEM. I propose an ambitious project of extensive research to mature this innovative LAr optical readout technology. I will construct a 650 l LAr TPC with integrated CCD/THGEM readout, capable of containing sufficient tracking information for full development and characterisation of this novel detector, with the goal of realising this game-changing technology in the planned future giant LAr TPCs. Camera readout can replace the current charge readout technology and associated scalability complications, and the excellent energy thresholds will enhance detector performance as well as extend research avenues to lower energy fundamental physics.
Also, I will explore the Compton imaging capability of LAr CCD/THGEM technology; the superiority of the energy threshold and spatial resolution of this system can offer significant advancement to medical imaging and the detection of concealed nuclear materials.
Summary
This proposal outlines a plan to combine Charge Couple Device (CCD) camera technologies with two-phase Liquid Argon Time Projection Chambers (LAr TPCs) utilising THick Gas Electron Multipliers (THGEMs) to evolve a next generation neutrino detector. This will be an entirely new readout option, and will open the prospect of revolutionary discoveries in fundamental particle physics. Furthermore, the Compton imaging power of this technology will be developed, which will have diverse applications in novel medical imaging techniques and detection of concealed nuclear materials.
Colossal LAr TPCs are the future for long-baseline-neutrino-oscillation physics around which the international neutrino community is rallying, with the common goal of discovering new physics beyond the Standard Model, which holds the key to our understanding of phenomena such as dark matter and the matter-antimatter asymmetry.
I have successfully provided a first demonstration of photographic capturing of muon tracks and single gammas interacting in the Liverpool 40 l LAr TPC using a CCD camera and THGEM. I propose an ambitious project of extensive research to mature this innovative LAr optical readout technology. I will construct a 650 l LAr TPC with integrated CCD/THGEM readout, capable of containing sufficient tracking information for full development and characterisation of this novel detector, with the goal of realising this game-changing technology in the planned future giant LAr TPCs. Camera readout can replace the current charge readout technology and associated scalability complications, and the excellent energy thresholds will enhance detector performance as well as extend research avenues to lower energy fundamental physics.
Also, I will explore the Compton imaging capability of LAr CCD/THGEM technology; the superiority of the energy threshold and spatial resolution of this system can offer significant advancement to medical imaging and the detection of concealed nuclear materials.
Max ERC Funding
1 837 911 €
Duration
Start date: 2016-03-01, End date: 2021-08-31
Project acronym AXIAL.EC
Project PRINCIPLES OF AXIAL POLARITY-DRIVEN VASCULAR PATTERNING
Researcher (PI) Claudio Franco
Host Institution (HI) INSTITUTO DE MEDICINA MOLECULAR JOAO LOBO ANTUNES
Country Portugal
Call Details Starting Grant (StG), LS4, ERC-2015-STG
Summary The formation of a functional patterned vascular network is essential for development, tissue growth and organ physiology. Several human vascular disorders arise from the mis-patterning of blood vessels, such as arteriovenous malformations, aneurysms and diabetic retinopathy. Although blood flow is recognised as a stimulus for vascular patterning, very little is known about the molecular mechanisms that regulate endothelial cell behaviour in response to flow and promote vascular patterning.
Recently, we uncovered that endothelial cells migrate extensively in the immature vascular network, and that endothelial cells polarise against the blood flow direction. Here, we put forward the hypothesis that vascular patterning is dependent on the polarisation and migration of endothelial cells against the flow direction, in a continuous flux of cells going from low-shear stress to high-shear stress regions. We will establish new reporter mouse lines to observe and manipulate endothelial polarity in vivo in order to investigate how polarisation and coordination of endothelial cells movements are orchestrated to generate vascular patterning. We will manipulate cell polarity using mouse models to understand the importance of cell polarisation in vascular patterning. Also, using a unique zebrafish line allowing analysis of endothelial cell polarity, we will perform a screen to identify novel regulators of vascular patterning. Finally, we will explore the hypothesis that defective flow-dependent endothelial polarisation underlies arteriovenous malformations using two genetic models.
This integrative approach, based on high-resolution imaging and unique experimental models, will provide a unifying model defining the cellular and molecular principles involved in vascular patterning. Given the physiological relevance of vascular patterning in health and disease, this research plan will set the basis for the development of novel clinical therapies targeting vascular disorders.
Summary
The formation of a functional patterned vascular network is essential for development, tissue growth and organ physiology. Several human vascular disorders arise from the mis-patterning of blood vessels, such as arteriovenous malformations, aneurysms and diabetic retinopathy. Although blood flow is recognised as a stimulus for vascular patterning, very little is known about the molecular mechanisms that regulate endothelial cell behaviour in response to flow and promote vascular patterning.
Recently, we uncovered that endothelial cells migrate extensively in the immature vascular network, and that endothelial cells polarise against the blood flow direction. Here, we put forward the hypothesis that vascular patterning is dependent on the polarisation and migration of endothelial cells against the flow direction, in a continuous flux of cells going from low-shear stress to high-shear stress regions. We will establish new reporter mouse lines to observe and manipulate endothelial polarity in vivo in order to investigate how polarisation and coordination of endothelial cells movements are orchestrated to generate vascular patterning. We will manipulate cell polarity using mouse models to understand the importance of cell polarisation in vascular patterning. Also, using a unique zebrafish line allowing analysis of endothelial cell polarity, we will perform a screen to identify novel regulators of vascular patterning. Finally, we will explore the hypothesis that defective flow-dependent endothelial polarisation underlies arteriovenous malformations using two genetic models.
This integrative approach, based on high-resolution imaging and unique experimental models, will provide a unifying model defining the cellular and molecular principles involved in vascular patterning. Given the physiological relevance of vascular patterning in health and disease, this research plan will set the basis for the development of novel clinical therapies targeting vascular disorders.
Max ERC Funding
1 618 750 €
Duration
Start date: 2016-09-01, End date: 2022-02-28
Project acronym BIGCODE
Project Learning from Big Code: Probabilistic Models, Analysis and Synthesis
Researcher (PI) Martin Vechev
Host Institution (HI) EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
Country Switzerland
Call Details Starting Grant (StG), PE6, ERC-2015-STG
Summary The goal of this proposal is to fundamentally change the way we build and reason about software. We aim to develop new kinds of statistical programming systems that provide probabilistically likely solutions to tasks that are difficult or impossible to solve with traditional approaches.
These statistical programming systems will be based on probabilistic models of massive codebases (also known as ``Big Code'') built via a combination of advanced programming languages and powerful machine learning and natural language processing techniques. To solve a particular challenge, a statistical programming system will query a probabilistic model, compute the most likely predictions, and present those to the developer.
Based on probabilistic models of ``Big Code'', we propose to investigate new statistical techniques in the context of three fundamental research directions: i) statistical program synthesis where we develop techniques that automatically synthesize and predict new programs, ii) statistical prediction of program properties where we develop new techniques that can predict important facts (e.g., types) about programs, and iii) statistical translation of programs where we investigate new techniques for statistical translation of programs (e.g., from one programming language to another, or to a natural language).
We believe the research direction outlined in this interdisciplinary proposal opens a new and exciting area of computer science. This area will combine sophisticated statistical learning and advanced programming language techniques for building the next-generation statistical programming systems.
We expect the results of this proposal to have an immediate impact upon millions of developers worldwide, triggering a paradigm shift in the way tomorrow's software is built, as well as a long-lasting impact on scientific fields such as machine learning, natural language processing, programming languages and software engineering.
Summary
The goal of this proposal is to fundamentally change the way we build and reason about software. We aim to develop new kinds of statistical programming systems that provide probabilistically likely solutions to tasks that are difficult or impossible to solve with traditional approaches.
These statistical programming systems will be based on probabilistic models of massive codebases (also known as ``Big Code'') built via a combination of advanced programming languages and powerful machine learning and natural language processing techniques. To solve a particular challenge, a statistical programming system will query a probabilistic model, compute the most likely predictions, and present those to the developer.
Based on probabilistic models of ``Big Code'', we propose to investigate new statistical techniques in the context of three fundamental research directions: i) statistical program synthesis where we develop techniques that automatically synthesize and predict new programs, ii) statistical prediction of program properties where we develop new techniques that can predict important facts (e.g., types) about programs, and iii) statistical translation of programs where we investigate new techniques for statistical translation of programs (e.g., from one programming language to another, or to a natural language).
We believe the research direction outlined in this interdisciplinary proposal opens a new and exciting area of computer science. This area will combine sophisticated statistical learning and advanced programming language techniques for building the next-generation statistical programming systems.
We expect the results of this proposal to have an immediate impact upon millions of developers worldwide, triggering a paradigm shift in the way tomorrow's software is built, as well as a long-lasting impact on scientific fields such as machine learning, natural language processing, programming languages and software engineering.
Max ERC Funding
1 500 000 €
Duration
Start date: 2016-04-01, End date: 2021-03-31
Project acronym BINDING FIBRES
Project Soluble dietary fibre: unraveling how weak bonds have a strong impact on function
Researcher (PI) Laura Nystroem
Host Institution (HI) EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
Country Switzerland
Call Details Starting Grant (StG), LS9, ERC-2015-STG
Summary Dietary fibres are recognized for their health promoting properties; nevertheless, many of the physicochemical mechanisms behind these effects remain poorly understood. While it is understood that dietary fibres can associate with small molecules influencing, both positively or negatively their absorption, the molecular mechanism, by which these associations take place, have yet to be elucidated We propose a study of the binding in soluble dietary fibres at a molecular level to establish binding constants for various fibres and nutritionally relevant ligands. The interactions between fibres and target compounds may be quite weak, but still have a major impact on the bioavailability. To gain insight to the binding mechanisms at a level of detail that has not earlier been achieved, we will apply novel combinations of analytical techniques (MS, NMR, EPR) and both natural as well as synthetic probes to elucidate the associations in these complexes from macromolecular to atomic level. Glucans, xyloglucans and galactomannans will serve as model soluble fibres, representative of real food systems, allowing us to determine their binding constants with nutritionally relevant micronutrients, such as monosaccharides, bile acids, and metals. Furthermore, we will examine supramolecular interactions between fibre strands to evaluate possible contribution of several fibre strands to the micronutrient associations. At the atomic level, we will use complementary spectroscopies to identify the functional groups and atoms involved in the bonds between fibres and the ligands. The proposal describes a unique approach to quantify binding of small molecules by dietary fibres, which can be translated to polysaccharide interactions with ligands in a broad range of biological systems and disciplines. The findings from this study may further allow us to predictably utilize fibres in functional foods, which can have far-reaching consequences in human nutrition, and thereby also public health.
Summary
Dietary fibres are recognized for their health promoting properties; nevertheless, many of the physicochemical mechanisms behind these effects remain poorly understood. While it is understood that dietary fibres can associate with small molecules influencing, both positively or negatively their absorption, the molecular mechanism, by which these associations take place, have yet to be elucidated We propose a study of the binding in soluble dietary fibres at a molecular level to establish binding constants for various fibres and nutritionally relevant ligands. The interactions between fibres and target compounds may be quite weak, but still have a major impact on the bioavailability. To gain insight to the binding mechanisms at a level of detail that has not earlier been achieved, we will apply novel combinations of analytical techniques (MS, NMR, EPR) and both natural as well as synthetic probes to elucidate the associations in these complexes from macromolecular to atomic level. Glucans, xyloglucans and galactomannans will serve as model soluble fibres, representative of real food systems, allowing us to determine their binding constants with nutritionally relevant micronutrients, such as monosaccharides, bile acids, and metals. Furthermore, we will examine supramolecular interactions between fibre strands to evaluate possible contribution of several fibre strands to the micronutrient associations. At the atomic level, we will use complementary spectroscopies to identify the functional groups and atoms involved in the bonds between fibres and the ligands. The proposal describes a unique approach to quantify binding of small molecules by dietary fibres, which can be translated to polysaccharide interactions with ligands in a broad range of biological systems and disciplines. The findings from this study may further allow us to predictably utilize fibres in functional foods, which can have far-reaching consequences in human nutrition, and thereby also public health.
Max ERC Funding
1 500 000 €
Duration
Start date: 2016-04-01, End date: 2022-03-31
Project acronym BroadSem
Project Induction of Broad-Coverage Semantic Parsers
Researcher (PI) Ivan Titov
Host Institution (HI) THE UNIVERSITY OF EDINBURGH
Country United Kingdom
Call Details Starting Grant (StG), PE6, ERC-2015-STG
Summary In the last one or two decades, language technology has achieved a number of important successes, for example, producing functional machine translation systems and beating humans in quiz games. The key bottleneck which prevents further progress in these and many other natural language processing (NLP) applications (e.g., text summarization, information retrieval, opinion mining, dialog and tutoring systems) is the lack of accurate methods for producing meaning representations of texts. Accurately predicting such meaning representations on an open domain with an automatic parser is a challenging and unsolved problem, primarily because of language variability and ambiguity. The reason for the unsatisfactory performance is reliance on supervised learning (learning from annotated resources), with the amounts of annotation required for accurate open-domain parsing exceeding what is practically feasible. Moreover, representations defined in these resources typically do not provide abstractions suitable for reasoning.
In this project, we will induce semantic representations from large amounts of unannotated data (i.e. text which has not been labeled by humans) while guided by information contained in human-annotated data and other forms of linguistic knowledge. This will allow us to scale our approach to many domains and across languages. We will specialize meaning representations for reasoning by modeling relations (e.g., facts) appearing across sentences in texts (document-level modeling), across different texts, and across texts and knowledge bases. Learning to predict this linked data is closely related to learning to reason, including learning the notions of semantic equivalence and entailment. We will jointly induce semantic parsers (e.g., log-linear feature-rich models) and reasoning models (latent factor models) relying on this data, thus, ensuring that the semantic representations are informative for applications requiring reasoning.
Summary
In the last one or two decades, language technology has achieved a number of important successes, for example, producing functional machine translation systems and beating humans in quiz games. The key bottleneck which prevents further progress in these and many other natural language processing (NLP) applications (e.g., text summarization, information retrieval, opinion mining, dialog and tutoring systems) is the lack of accurate methods for producing meaning representations of texts. Accurately predicting such meaning representations on an open domain with an automatic parser is a challenging and unsolved problem, primarily because of language variability and ambiguity. The reason for the unsatisfactory performance is reliance on supervised learning (learning from annotated resources), with the amounts of annotation required for accurate open-domain parsing exceeding what is practically feasible. Moreover, representations defined in these resources typically do not provide abstractions suitable for reasoning.
In this project, we will induce semantic representations from large amounts of unannotated data (i.e. text which has not been labeled by humans) while guided by information contained in human-annotated data and other forms of linguistic knowledge. This will allow us to scale our approach to many domains and across languages. We will specialize meaning representations for reasoning by modeling relations (e.g., facts) appearing across sentences in texts (document-level modeling), across different texts, and across texts and knowledge bases. Learning to predict this linked data is closely related to learning to reason, including learning the notions of semantic equivalence and entailment. We will jointly induce semantic parsers (e.g., log-linear feature-rich models) and reasoning models (latent factor models) relying on this data, thus, ensuring that the semantic representations are informative for applications requiring reasoning.
Max ERC Funding
1 457 185 €
Duration
Start date: 2016-05-01, End date: 2021-10-31
Project acronym BUNDLEFORCE
Project Unravelling the Mechanosensitivity of Actin Bundles in Filopodia
Researcher (PI) Antoine Guillaume Jegou
Host Institution (HI) CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Country France
Call Details Starting Grant (StG), LS1, ERC-2015-STG
Summary Eukaryotic cells constantly convert signals between biochemical energy and mechanical work to timely accomplish many key functions such as migration, division or development. Filopodia are essential finger-like structures that emerge at the cell front to orient the cell in response to its chemical and mechanical environment. Yet, the molecular interactions that make the filopodia mechanosensitive are not known. To tackle this challenge we propose unique biophysical in vitro and in vivo experiments of increasing complexity. Here we will focus on how the underlying actin filament bundle regulates filopodium growth and retraction cycles at the micrometer and seconds scales. These parallel actin filaments are mainly elongated at their barbed-end by formins and cross-linked by bundling proteins such as fascins.
We aim to:
1) Elucidate how formin and fascin functions are regulated by mechanics at the single filament level. We will investigate how formin partners and competitors present in filopodia affect formin processivity; how fascin affinity for the side of filaments is modified by filament tension and formin presence at the barbed-end.
2) Reconstitute filopodium-like actin bundles in vitro to understand how actin bundle size and fate are regulated down to the molecular scale. Using a unique experimental setup that combines microfluidics and optical tweezers, we will uncover for the first time actin bundles mechanosensitive capabilities, both in tension and compression.
3) Decipher in vivo the mechanics of actin bundles in filopodia, using fascins and formins with integrated fluorescent tension sensors.
This framework spanning from in vitro single filament to in vivo meso-scale actin networks will bring unprecedented insights into the role of actin bundles in filopodia mechanosensitivity.
Summary
Eukaryotic cells constantly convert signals between biochemical energy and mechanical work to timely accomplish many key functions such as migration, division or development. Filopodia are essential finger-like structures that emerge at the cell front to orient the cell in response to its chemical and mechanical environment. Yet, the molecular interactions that make the filopodia mechanosensitive are not known. To tackle this challenge we propose unique biophysical in vitro and in vivo experiments of increasing complexity. Here we will focus on how the underlying actin filament bundle regulates filopodium growth and retraction cycles at the micrometer and seconds scales. These parallel actin filaments are mainly elongated at their barbed-end by formins and cross-linked by bundling proteins such as fascins.
We aim to:
1) Elucidate how formin and fascin functions are regulated by mechanics at the single filament level. We will investigate how formin partners and competitors present in filopodia affect formin processivity; how fascin affinity for the side of filaments is modified by filament tension and formin presence at the barbed-end.
2) Reconstitute filopodium-like actin bundles in vitro to understand how actin bundle size and fate are regulated down to the molecular scale. Using a unique experimental setup that combines microfluidics and optical tweezers, we will uncover for the first time actin bundles mechanosensitive capabilities, both in tension and compression.
3) Decipher in vivo the mechanics of actin bundles in filopodia, using fascins and formins with integrated fluorescent tension sensors.
This framework spanning from in vitro single filament to in vivo meso-scale actin networks will bring unprecedented insights into the role of actin bundles in filopodia mechanosensitivity.
Max ERC Funding
1 499 190 €
Duration
Start date: 2016-03-01, End date: 2021-08-31
Project acronym BUNGEE-TOOLS
Project Building Next-Generation Computational Tools for High Resolution Neuroimaging Studies
Researcher (PI) Juan Eugenio Iglesias
Host Institution (HI) UNIVERSITY COLLEGE LONDON
Country United Kingdom
Call Details Starting Grant (StG), PE6, ERC-2015-STG
Summary Recent advances in magnetic resonance (MR) acquisition technology are providing us with images of the human brain of increasing detail and resolution. While these images hold promise to greatly increase our understanding of such a complex organ, the neuroimaging community relies on tools (e.g. SPM, FSL, FreeSurfer) which, being over a decade old, were designed to work at much lower resolutions. These tools do not consider brain substructures that are visible in present-day scans, and this inability to capitalize on the vast improvement of MR is hampering progress in the neuroimaging field.
In this ambitious project, which lies at the nexus of medical histology, neuroscience, biomedical imaging, computer vision and statistics, we propose to build a set of next-generation computational tools that will enable neuroimaging studies to take full advantage of the increased resolution of modern MR technology. The core of the tools will be an ultra-high resolution probabilistic atlas of the human brain, built upon multimodal data combining from histology and ex vivo MR. The resulting atlas will be used to analyze in vivo brain MR scans, which will require the development of Bayesian segmentation methods beyond the state of the art.
The developed tools, which will be made freely available to the scientific community, will enable the analysis of MR data at a superior level of structural detail, opening completely new opportunities of research in neuroscience. Therefore, we expect the tools to have a tremendous impact on the quest to understand the human brain (in health and in disease), and ultimately on public health and the economy.
Summary
Recent advances in magnetic resonance (MR) acquisition technology are providing us with images of the human brain of increasing detail and resolution. While these images hold promise to greatly increase our understanding of such a complex organ, the neuroimaging community relies on tools (e.g. SPM, FSL, FreeSurfer) which, being over a decade old, were designed to work at much lower resolutions. These tools do not consider brain substructures that are visible in present-day scans, and this inability to capitalize on the vast improvement of MR is hampering progress in the neuroimaging field.
In this ambitious project, which lies at the nexus of medical histology, neuroscience, biomedical imaging, computer vision and statistics, we propose to build a set of next-generation computational tools that will enable neuroimaging studies to take full advantage of the increased resolution of modern MR technology. The core of the tools will be an ultra-high resolution probabilistic atlas of the human brain, built upon multimodal data combining from histology and ex vivo MR. The resulting atlas will be used to analyze in vivo brain MR scans, which will require the development of Bayesian segmentation methods beyond the state of the art.
The developed tools, which will be made freely available to the scientific community, will enable the analysis of MR data at a superior level of structural detail, opening completely new opportunities of research in neuroscience. Therefore, we expect the tools to have a tremendous impact on the quest to understand the human brain (in health and in disease), and ultimately on public health and the economy.
Max ERC Funding
1 450 075 €
Duration
Start date: 2016-09-01, End date: 2021-08-31