Project acronym 3CBIOTECH
Project Cold Carbon Catabolism of Microbial Communities underprinning a Sustainable Bioenergy and Biorefinery Economy
Researcher (PI) Gavin James Collins
Host Institution (HI) NATIONAL UNIVERSITY OF IRELAND GALWAY
Country Ireland
Call Details Starting Grant (StG), LS9, ERC-2010-StG_20091118
Summary The applicant will collaborate with Irish, European and U.S.-based colleagues to develop a sustainable biorefinery and bioenergy industry in Ireland and Europe. The focus of this ERC Starting Grant will be the application of classical microbiological, physiological and real-time polymerase chain reaction (PCR)-based assays, to qualitatively and quantitatively characterize microbial communities underpinning novel and innovative, low-temperature, anaerobic waste (and other biomass) conversion technologies, including municipal wastewater treatment and, demonstration- and full-scale biorefinery applications.
Anaerobic digestion (AD) is a naturally-occurring process, which is widely applied for the conversion of waste to methane-containing biogas. Low-temperature (<20 degrees C) AD has been applied by the applicant as a cost-effective alternative to mesophilic (c. 35C) AD for the treatment of several waste categories. However, the microbiology of low-temperature AD is poorly understood. The applicant will work with microbial consortia isolated from anaerobic bioreactors, which have been operated for long-term experiments (>3.5 years), and include organic acid-oxidizing, hydrogen-producing syntrophic microbes and hydrogen-consuming methanogens. A major focus of the project will be the ecophysiology of psychrotolerant and psychrophilic methanogens already identified and cultivated by the applicant. The project will also investigate the role(s) of poorly-understood Crenarchaeota populations and homoacetogenic bacteria, in complex consortia. The host organization is a leading player in the microbiology of waste-to-energy applications. The applicant will train a team of scientists in all aspects of the microbiology and bioengineering of biomass conversion systems.
Summary
The applicant will collaborate with Irish, European and U.S.-based colleagues to develop a sustainable biorefinery and bioenergy industry in Ireland and Europe. The focus of this ERC Starting Grant will be the application of classical microbiological, physiological and real-time polymerase chain reaction (PCR)-based assays, to qualitatively and quantitatively characterize microbial communities underpinning novel and innovative, low-temperature, anaerobic waste (and other biomass) conversion technologies, including municipal wastewater treatment and, demonstration- and full-scale biorefinery applications.
Anaerobic digestion (AD) is a naturally-occurring process, which is widely applied for the conversion of waste to methane-containing biogas. Low-temperature (<20 degrees C) AD has been applied by the applicant as a cost-effective alternative to mesophilic (c. 35C) AD for the treatment of several waste categories. However, the microbiology of low-temperature AD is poorly understood. The applicant will work with microbial consortia isolated from anaerobic bioreactors, which have been operated for long-term experiments (>3.5 years), and include organic acid-oxidizing, hydrogen-producing syntrophic microbes and hydrogen-consuming methanogens. A major focus of the project will be the ecophysiology of psychrotolerant and psychrophilic methanogens already identified and cultivated by the applicant. The project will also investigate the role(s) of poorly-understood Crenarchaeota populations and homoacetogenic bacteria, in complex consortia. The host organization is a leading player in the microbiology of waste-to-energy applications. The applicant will train a team of scientists in all aspects of the microbiology and bioengineering of biomass conversion systems.
Max ERC Funding
1 499 797 €
Duration
Start date: 2011-05-01, End date: 2016-04-30
Project acronym 3D-OA-HISTO
Project Development of 3D Histopathological Grading of Osteoarthritis
Researcher (PI) Simo Jaakko Saarakkala
Host Institution (HI) OULUN YLIOPISTO
Country Finland
Call Details Starting Grant (StG), LS7, ERC-2013-StG
Summary "Background: Osteoarthritis (OA) is a common musculoskeletal disease occurring worldwide. Despite extensive research, etiology of OA is still poorly understood. Histopathological grading (HPG) of 2D tissue sections is the gold standard reference method for determination of OA stage. However, traditional 2D-HPG is destructive and based only on subjective visual evaluation. These limitations induce bias to clinical in vitro OA diagnostics and basic research that both rely strongly on HPG.
Objectives: 1) To establish and validate the very first 3D-HPG of OA based on cutting-edge nano/micro-CT (Computed Tomography) technologies in vitro; 2) To use the established method to clarify the beginning phases of OA; and 3) To validate 3D-HPG of OA for in vivo use.
Methods: Several hundreds of human osteochondral samples from patients undergoing total knee arthroplasty will be collected. The samples will be imaged in vitro with nano/micro-CT and clinical high-end extremity CT devices using specific contrast-agents to quantify tissue constituents and structure in 3D in large volume. From this information, a novel 3D-HPG is developed with statistical classification algorithms. Finally, the developed novel 3D-HPG of OA will be applied clinically in vivo.
Significance: This is the very first study to establish 3D-HPG of OA pathology in vitro and in vivo. Furthermore, the developed technique hugely improves the understanding of the beginning phases of OA. Ultimately, the study will contribute for improving OA patients’ quality of life by slowing the disease progression, and for providing powerful tools to develop new OA therapies."
Summary
"Background: Osteoarthritis (OA) is a common musculoskeletal disease occurring worldwide. Despite extensive research, etiology of OA is still poorly understood. Histopathological grading (HPG) of 2D tissue sections is the gold standard reference method for determination of OA stage. However, traditional 2D-HPG is destructive and based only on subjective visual evaluation. These limitations induce bias to clinical in vitro OA diagnostics and basic research that both rely strongly on HPG.
Objectives: 1) To establish and validate the very first 3D-HPG of OA based on cutting-edge nano/micro-CT (Computed Tomography) technologies in vitro; 2) To use the established method to clarify the beginning phases of OA; and 3) To validate 3D-HPG of OA for in vivo use.
Methods: Several hundreds of human osteochondral samples from patients undergoing total knee arthroplasty will be collected. The samples will be imaged in vitro with nano/micro-CT and clinical high-end extremity CT devices using specific contrast-agents to quantify tissue constituents and structure in 3D in large volume. From this information, a novel 3D-HPG is developed with statistical classification algorithms. Finally, the developed novel 3D-HPG of OA will be applied clinically in vivo.
Significance: This is the very first study to establish 3D-HPG of OA pathology in vitro and in vivo. Furthermore, the developed technique hugely improves the understanding of the beginning phases of OA. Ultimately, the study will contribute for improving OA patients’ quality of life by slowing the disease progression, and for providing powerful tools to develop new OA therapies."
Max ERC Funding
1 500 000 €
Duration
Start date: 2014-02-01, End date: 2019-01-31
Project acronym A-DIET
Project Metabolomics based biomarkers of dietary intake- new tools for nutrition research
Researcher (PI) Lorraine Brennan
Host Institution (HI) UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN
Country Ireland
Call Details Consolidator Grant (CoG), LS7, ERC-2014-CoG
Summary In todays advanced technological world, we can track the exact movement of individuals, analyse their genetic makeup and predict predisposition to certain diseases. However, we are unable to accurately assess an individual’s dietary intake. This is without a doubt one of the main stumbling blocks in assessing the link between diet and disease/health. The present proposal (A-DIET) will address this issue with the overarching objective to develop novel strategies for assessment of dietary intake.
Using approaches to (1) identify biomarkers of specific foods (2) classify people into dietary patterns (nutritypes) and (3) develop a tool for integration of dietary and biomarker data, A-DIET has the potential to dramatically enhance our ability to accurately assess dietary intake. The ultimate output from A-DIET will be a dietary assessment tool which can be used to obtain an accurate assessment of dietary intake by combining dietary and biomarker data which in turn will allow investigations into relationships between diet, health and disease. New biomarkers of specific foods will be identified and validated using intervention studies and metabolomic analyses. Methods will be developed to classify individuals into dietary patterns based on biomarker/metabolomic profiles thus demonstrating the novel concept of nutritypes. Strategies for integration of dietary and biomarker data will be developed and translated into a tool that will be made available to the wider scientific community.
Advances made in A-DIET will enable nutrition epidemiologist’s to properly examine the relationship between diet and disease and develop clear public health messages with regard to diet and health. Additionally results from A-DIET will allow researchers to accurately assess people’s diet and implement health promotion strategies and enable dieticians in a clinical environment to assess compliance to therapeutic diets such as adherence to a high fibre diet or a gluten free diet.
Summary
In todays advanced technological world, we can track the exact movement of individuals, analyse their genetic makeup and predict predisposition to certain diseases. However, we are unable to accurately assess an individual’s dietary intake. This is without a doubt one of the main stumbling blocks in assessing the link between diet and disease/health. The present proposal (A-DIET) will address this issue with the overarching objective to develop novel strategies for assessment of dietary intake.
Using approaches to (1) identify biomarkers of specific foods (2) classify people into dietary patterns (nutritypes) and (3) develop a tool for integration of dietary and biomarker data, A-DIET has the potential to dramatically enhance our ability to accurately assess dietary intake. The ultimate output from A-DIET will be a dietary assessment tool which can be used to obtain an accurate assessment of dietary intake by combining dietary and biomarker data which in turn will allow investigations into relationships between diet, health and disease. New biomarkers of specific foods will be identified and validated using intervention studies and metabolomic analyses. Methods will be developed to classify individuals into dietary patterns based on biomarker/metabolomic profiles thus demonstrating the novel concept of nutritypes. Strategies for integration of dietary and biomarker data will be developed and translated into a tool that will be made available to the wider scientific community.
Advances made in A-DIET will enable nutrition epidemiologist’s to properly examine the relationship between diet and disease and develop clear public health messages with regard to diet and health. Additionally results from A-DIET will allow researchers to accurately assess people’s diet and implement health promotion strategies and enable dieticians in a clinical environment to assess compliance to therapeutic diets such as adherence to a high fibre diet or a gluten free diet.
Max ERC Funding
1 995 548 €
Duration
Start date: 2015-08-01, End date: 2020-07-31
Project acronym ABIONYS
Project Artificial Enzyme Modules as Tools in a Tailor-made Biosynthesis
Researcher (PI) Jan DESKA
Host Institution (HI) AALTO KORKEAKOULUSAATIO SR
Country Finland
Call Details Consolidator Grant (CoG), PE5, ERC-2019-COG
Summary In order to tackle some of the prime societal challenges of this century, science has to urgently provide effective tools addressing the redesign of chemical value chains through the exploitation of novel, bio-based raw materials, and the discovery and implementation of more resource-efficient production platforms. Nature will inevitably play a pivotal role in the imminent transformation of industrial strategies, and the recent bioeconomy approaches can only be regarded as initial step towards a sustainable future. Operating at the interface between chemistry and life sciences, my ABIONYS will fundamentally challenge the widely held distinction separating chemical from biosynthesis, and will deliver the first proof-of-concept where abiotic reactions act as productive puzzle pieces in biosynthetic arrangements. On the basis of our previous ground-breaking discoveries on artificial enzyme functions, I will create a significantly extended toolbox of biocatalysis modules by applying protein-based interpretations of synthetically crucial but non-natural reactions i.e. transformations that are in no way biosynthetically encoded in living organisms. My research will exploit these tools in multi-enzyme cascades for the preparation of complex organic target structures, not only to highlight the great synthetic potential of these approaches, but also to lay the groundwork for in vivo implementations. Eventually, the knowledge gathered from enzyme discovery and cascade design will enable to create an unprecedented class of bioproduction systems, where the genetic incorporation of artificial enzyme functions into recombinant microbial host organisms will yield tailor-made cellular factories. Combining classical organic synthesis strategies with the power of modern biotechnology, ABIONYS is going to transform the way we synthesize complex and functional building blocks by allowing us to encode organic chemistry thinking into living production platforms.
Summary
In order to tackle some of the prime societal challenges of this century, science has to urgently provide effective tools addressing the redesign of chemical value chains through the exploitation of novel, bio-based raw materials, and the discovery and implementation of more resource-efficient production platforms. Nature will inevitably play a pivotal role in the imminent transformation of industrial strategies, and the recent bioeconomy approaches can only be regarded as initial step towards a sustainable future. Operating at the interface between chemistry and life sciences, my ABIONYS will fundamentally challenge the widely held distinction separating chemical from biosynthesis, and will deliver the first proof-of-concept where abiotic reactions act as productive puzzle pieces in biosynthetic arrangements. On the basis of our previous ground-breaking discoveries on artificial enzyme functions, I will create a significantly extended toolbox of biocatalysis modules by applying protein-based interpretations of synthetically crucial but non-natural reactions i.e. transformations that are in no way biosynthetically encoded in living organisms. My research will exploit these tools in multi-enzyme cascades for the preparation of complex organic target structures, not only to highlight the great synthetic potential of these approaches, but also to lay the groundwork for in vivo implementations. Eventually, the knowledge gathered from enzyme discovery and cascade design will enable to create an unprecedented class of bioproduction systems, where the genetic incorporation of artificial enzyme functions into recombinant microbial host organisms will yield tailor-made cellular factories. Combining classical organic synthesis strategies with the power of modern biotechnology, ABIONYS is going to transform the way we synthesize complex and functional building blocks by allowing us to encode organic chemistry thinking into living production platforms.
Max ERC Funding
1 995 707 €
Duration
Start date: 2020-11-01, End date: 2025-10-31
Project acronym Active-DNA
Project Computationally Active DNA Nanostructures
Researcher (PI) Damien WOODS
Host Institution (HI) NATIONAL UNIVERSITY OF IRELAND MAYNOOTH
Country Ireland
Call Details Consolidator Grant (CoG), PE6, ERC-2017-COG
Summary During the 20th century computer technology evolved from bulky, slow, special purpose mechanical engines to the now ubiquitous silicon chips and software that are one of the pinnacles of human ingenuity. The goal of the field of molecular programming is to take the next leap and build a new generation of matter-based computers using DNA, RNA and proteins. This will be accomplished by computer scientists, physicists and chemists designing molecules to execute ``wet'' nanoscale programs in test tubes. The workflow includes proposing theoretical models, mathematically proving their computational properties, physical modelling and implementation in the wet-lab.
The past decade has seen remarkable progress at building static 2D and 3D DNA nanostructures. However, unlike biological macromolecules and complexes that are built via specified self-assembly pathways, that execute robotic-like movements, and that undergo evolution, the activity of human-engineered nanostructures is severely limited. We will need sophisticated algorithmic ideas to build structures that rival active living systems. Active-DNA, aims to address this challenge by achieving a number of objectives on computation, DNA-based self-assembly and molecular robotics. Active-DNA research work will range from defining models and proving theorems that characterise the computational and expressive capabilities of such active programmable materials to experimental work implementing active DNA nanostructures in the wet-lab.
Summary
During the 20th century computer technology evolved from bulky, slow, special purpose mechanical engines to the now ubiquitous silicon chips and software that are one of the pinnacles of human ingenuity. The goal of the field of molecular programming is to take the next leap and build a new generation of matter-based computers using DNA, RNA and proteins. This will be accomplished by computer scientists, physicists and chemists designing molecules to execute ``wet'' nanoscale programs in test tubes. The workflow includes proposing theoretical models, mathematically proving their computational properties, physical modelling and implementation in the wet-lab.
The past decade has seen remarkable progress at building static 2D and 3D DNA nanostructures. However, unlike biological macromolecules and complexes that are built via specified self-assembly pathways, that execute robotic-like movements, and that undergo evolution, the activity of human-engineered nanostructures is severely limited. We will need sophisticated algorithmic ideas to build structures that rival active living systems. Active-DNA, aims to address this challenge by achieving a number of objectives on computation, DNA-based self-assembly and molecular robotics. Active-DNA research work will range from defining models and proving theorems that characterise the computational and expressive capabilities of such active programmable materials to experimental work implementing active DNA nanostructures in the wet-lab.
Max ERC Funding
2 349 603 €
Duration
Start date: 2018-11-01, End date: 2023-10-31
Project acronym ADHESWITCHES
Project Adhesion switches in cancer and development: from in vivo to synthetic biology
Researcher (PI) Mari Johanna Ivaska
Host Institution (HI) TURUN YLIOPISTO
Country Finland
Call Details Consolidator Grant (CoG), LS3, ERC-2013-CoG
Summary Integrins are transmembrane cell adhesion receptors controlling cell proliferation and migration. Our objective is to gain fundamentally novel mechanistic insight into the emerging new roles of integrins in cancer and to generate a road map of integrin dependent pathways critical in mammary gland development and integrin signalling thus opening new targets for therapeutic interventions. We will combine an in vivo based translational approach with cell and molecular biological studies aiming to identify entirely novel concepts in integrin function using cutting edge techniques and synthetic-biology tools.
The specific objectives are:
1) Integrin inactivation in branching morphogenesis and cancer invasion. Integrins regulate mammary gland development and cancer invasion but the role of integrin inactivating proteins in these processes is currently completely unknown. We will investigate this using genetically modified mice, ex-vivo organoid models and human tissues with the aim to identify beneficial combinational treatments against cancer invasion.
2) Endosomal adhesomes – cross-talk between integrin activity and integrin “inside-in signaling”. We hypothesize that endocytosed active integrins engage in specialized endosomal signaling that governs cell survival especially in cancer. RNAi cell arrays, super-resolution STED imaging and endosomal proteomics will be used to investigate integrin signaling in endosomes.
3) Spatio-temporal co-ordination of adhesion and endocytosis. Several cytosolic proteins compete for integrin binding to regulate activation, endocytosis and recycling. Photoactivatable protein-traps and predefined matrix micropatterns will be employed to mechanistically dissect the spatio-temporal dynamics and hierarchy of their recruitment.
We will employ innovative and unconventional techniques to address three major unanswered questions in the field and significantly advance our understanding of integrin function in development and cancer.
Summary
Integrins are transmembrane cell adhesion receptors controlling cell proliferation and migration. Our objective is to gain fundamentally novel mechanistic insight into the emerging new roles of integrins in cancer and to generate a road map of integrin dependent pathways critical in mammary gland development and integrin signalling thus opening new targets for therapeutic interventions. We will combine an in vivo based translational approach with cell and molecular biological studies aiming to identify entirely novel concepts in integrin function using cutting edge techniques and synthetic-biology tools.
The specific objectives are:
1) Integrin inactivation in branching morphogenesis and cancer invasion. Integrins regulate mammary gland development and cancer invasion but the role of integrin inactivating proteins in these processes is currently completely unknown. We will investigate this using genetically modified mice, ex-vivo organoid models and human tissues with the aim to identify beneficial combinational treatments against cancer invasion.
2) Endosomal adhesomes – cross-talk between integrin activity and integrin “inside-in signaling”. We hypothesize that endocytosed active integrins engage in specialized endosomal signaling that governs cell survival especially in cancer. RNAi cell arrays, super-resolution STED imaging and endosomal proteomics will be used to investigate integrin signaling in endosomes.
3) Spatio-temporal co-ordination of adhesion and endocytosis. Several cytosolic proteins compete for integrin binding to regulate activation, endocytosis and recycling. Photoactivatable protein-traps and predefined matrix micropatterns will be employed to mechanistically dissect the spatio-temporal dynamics and hierarchy of their recruitment.
We will employ innovative and unconventional techniques to address three major unanswered questions in the field and significantly advance our understanding of integrin function in development and cancer.
Max ERC Funding
1 887 910 €
Duration
Start date: 2014-05-01, End date: 2019-04-30
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 AGELESS
Project Comparative genomics / ‘wildlife’ transcriptomics uncovers the mechanisms of halted ageing in mammals
Researcher (PI) Emma Teeling
Host Institution (HI) UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN
Country Ireland
Call Details Starting Grant (StG), LS2, ERC-2012-StG_20111109
Summary "Ageing is the gradual and irreversible breakdown of living systems associated with the advancement of time, which leads to an increase in vulnerability and eventual mortality. Despite recent advances in ageing research, the intrinsic complexity of the ageing process has prevented a full understanding of this process, therefore, ageing remains a grand challenge in contemporary biology. In AGELESS, we will tackle this challenge by uncovering the molecular mechanisms of halted ageing in a unique model system, the bats. Bats are the longest-lived mammals relative to their body size, and defy the ‘rate-of-living’ theories as they use twice as much the energy as other species of considerable size, but live far longer. This suggests that bats have some underlying mechanisms that may explain their exceptional longevity. In AGELESS, we will identify the molecular mechanisms that enable mammals to achieve extraordinary longevity, using state-of-the-art comparative genomic methodologies focused on bats. We will identify, using population transcriptomics and telomere/mtDNA genomics, the molecular changes that occur in an ageing wild population of bats to uncover how bats ‘age’ so slowly compared with other mammals. In silico whole genome analyses, field based ageing transcriptomic data, mtDNA and telomeric studies will be integrated and analysed using a networks approach, to ascertain how these systems interact to halt ageing. For the first time, we will be able to utilize the diversity seen within nature to identify key molecular targets and regions that regulate and control ageing in mammals. AGELESS will provide a deeper understanding of the causal mechanisms of ageing, potentially uncovering the crucial molecular pathways that can be modified to halt, alleviate and perhaps even reverse this process in man."
Summary
"Ageing is the gradual and irreversible breakdown of living systems associated with the advancement of time, which leads to an increase in vulnerability and eventual mortality. Despite recent advances in ageing research, the intrinsic complexity of the ageing process has prevented a full understanding of this process, therefore, ageing remains a grand challenge in contemporary biology. In AGELESS, we will tackle this challenge by uncovering the molecular mechanisms of halted ageing in a unique model system, the bats. Bats are the longest-lived mammals relative to their body size, and defy the ‘rate-of-living’ theories as they use twice as much the energy as other species of considerable size, but live far longer. This suggests that bats have some underlying mechanisms that may explain their exceptional longevity. In AGELESS, we will identify the molecular mechanisms that enable mammals to achieve extraordinary longevity, using state-of-the-art comparative genomic methodologies focused on bats. We will identify, using population transcriptomics and telomere/mtDNA genomics, the molecular changes that occur in an ageing wild population of bats to uncover how bats ‘age’ so slowly compared with other mammals. In silico whole genome analyses, field based ageing transcriptomic data, mtDNA and telomeric studies will be integrated and analysed using a networks approach, to ascertain how these systems interact to halt ageing. For the first time, we will be able to utilize the diversity seen within nature to identify key molecular targets and regions that regulate and control ageing in mammals. AGELESS will provide a deeper understanding of the causal mechanisms of ageing, potentially uncovering the crucial molecular pathways that can be modified to halt, alleviate and perhaps even reverse this process in man."
Max ERC Funding
1 499 768 €
Duration
Start date: 2013-01-01, End date: 2017-12-31
Project acronym AI-DEMON
Project Artificial intelligence design of molecular nano-magnets and molecular qubits
Researcher (PI) Alessandro LUNGHI
Host Institution (HI) THE PROVOST, FELLOWS, FOUNDATION SCHOLARS & THE OTHER MEMBERS OF BOARD OF THE COLLEGE OF THE HOLY & UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN
Country Ireland
Call Details Starting Grant (StG), PE4, ERC-2020-STG
Summary As technologies based on semiconductors and ferromagnets are reaching their limits in computational and memory-storage capabilities, new technologies based on spin are emerging as alternative. Magnetic molecules represent the ultimate small-scale magnetic unit that can be synthesized and processed into a device for spintronics and quantum computing applications but their use is confined to very low temperatures. The grand challenge of this proposal is to design magnetic molecules with long spin lifetime at ambient temperature by tuning the main microscopic interaction responsible for spin relaxation: the spin-phonon coupling. AI-DEMON will address this challenge by developing a novel first-principles and machine-learning computational framework able to cover all the essential aspects of the design of new coordination compounds with tailored properties. AI-DEMON has three main objectives, each one representing a major contribution to the field: i) I will unveil the mechanism of spin-phonon relaxation in magnetic molecules by developing a quantitative first-principles spin relaxation theory, ii) I will efficiently explore the chemical space of magnetic coordination compounds by developing a universal machine-learning model able to predict vibrational and magnetic properties, and iii) I will design molecular prototypes with tailored magnetic and vibrational properties by developing generative machine-learning methods. Preliminary results on spin relaxation theory and machine-learning applied to magnetic properties show great promise and set the cornerstone of the project. The use of novel methodologies, such as machine learning and first-principles spin dynamics, represent a strong disruption in the current approach to theoretical modelling and discovery of new magnetic molecules and will propel the field into a new and modern era. Significant impact beyond the field of molecular magnetism, e.g. bio-inorganic chemistry and solid-state qubits, can also be anticipated.
Summary
As technologies based on semiconductors and ferromagnets are reaching their limits in computational and memory-storage capabilities, new technologies based on spin are emerging as alternative. Magnetic molecules represent the ultimate small-scale magnetic unit that can be synthesized and processed into a device for spintronics and quantum computing applications but their use is confined to very low temperatures. The grand challenge of this proposal is to design magnetic molecules with long spin lifetime at ambient temperature by tuning the main microscopic interaction responsible for spin relaxation: the spin-phonon coupling. AI-DEMON will address this challenge by developing a novel first-principles and machine-learning computational framework able to cover all the essential aspects of the design of new coordination compounds with tailored properties. AI-DEMON has three main objectives, each one representing a major contribution to the field: i) I will unveil the mechanism of spin-phonon relaxation in magnetic molecules by developing a quantitative first-principles spin relaxation theory, ii) I will efficiently explore the chemical space of magnetic coordination compounds by developing a universal machine-learning model able to predict vibrational and magnetic properties, and iii) I will design molecular prototypes with tailored magnetic and vibrational properties by developing generative machine-learning methods. Preliminary results on spin relaxation theory and machine-learning applied to magnetic properties show great promise and set the cornerstone of the project. The use of novel methodologies, such as machine learning and first-principles spin dynamics, represent a strong disruption in the current approach to theoretical modelling and discovery of new magnetic molecules and will propel the field into a new and modern era. Significant impact beyond the field of molecular magnetism, e.g. bio-inorganic chemistry and solid-state qubits, can also be anticipated.
Max ERC Funding
1 499 786 €
Duration
Start date: 2021-01-01, End date: 2025-12-31
Project acronym AI-PREVENT
Project A nationwide artificial intelligence risk assessment for primary prevention of cardiometabolic diseases
Researcher (PI) Andrea Ganna
Host Institution (HI) HELSINGIN YLIOPISTO
Country Finland
Call Details Starting Grant (StG), LS7, ERC-2020-STG
Summary Diabetes, stroke and coronary artery disease (cardiometabolic diseases) are the leading cause of death in Europe. Given that effective pharmacological and lifestyle interventions are available, it is important to identify high risk individuals at an early stage. Traditionally, this is done using clinical prediction models. However, the established models have substantial limitations: they are often used by doctors only when an underlying disease is already suspected, they are not developed on updated nationally-representative data and they require time-consuming clinical measurements. Thus, a substantial part of the population is not provided with risk assessment. I propose to revolutionize the existing approaches to primary prevention by providing risk assessment of cardiometabolic diseases before an individual even steps into the doctor’s office for a visit. To this end my project has three main objectives:
1) Development of artificial intelligence (AI) approaches to model health trajectories based on nationwide registry data on medications, diagnoses, familial risk and socio-demographic information to obtain accurate risk estimates for cardiometabolic disease. I will integrate high quality data from selected countries that have long traditions of registry data (Finland and Sweden, over 7.5 million individuals).
2) To identify health trajectories that maximize the clinical utility of genetic scores by integrating genetic and registry-based data on > 1 million people to identify subgroups of individuals for whom genetic information might improve risk prediction.
3) Validation of AI and genetic-based risk assessment as first-stage screening via a clinical study in 2800 individuals.
My project leverages the latest developments in AI and high-quality data of unprecedented scale to deliver a paradigm shift with important public health consequences by potentially changing the way cardiometabolic disease risk is assessed.
Summary
Diabetes, stroke and coronary artery disease (cardiometabolic diseases) are the leading cause of death in Europe. Given that effective pharmacological and lifestyle interventions are available, it is important to identify high risk individuals at an early stage. Traditionally, this is done using clinical prediction models. However, the established models have substantial limitations: they are often used by doctors only when an underlying disease is already suspected, they are not developed on updated nationally-representative data and they require time-consuming clinical measurements. Thus, a substantial part of the population is not provided with risk assessment. I propose to revolutionize the existing approaches to primary prevention by providing risk assessment of cardiometabolic diseases before an individual even steps into the doctor’s office for a visit. To this end my project has three main objectives:
1) Development of artificial intelligence (AI) approaches to model health trajectories based on nationwide registry data on medications, diagnoses, familial risk and socio-demographic information to obtain accurate risk estimates for cardiometabolic disease. I will integrate high quality data from selected countries that have long traditions of registry data (Finland and Sweden, over 7.5 million individuals).
2) To identify health trajectories that maximize the clinical utility of genetic scores by integrating genetic and registry-based data on > 1 million people to identify subgroups of individuals for whom genetic information might improve risk prediction.
3) Validation of AI and genetic-based risk assessment as first-stage screening via a clinical study in 2800 individuals.
My project leverages the latest developments in AI and high-quality data of unprecedented scale to deliver a paradigm shift with important public health consequences by potentially changing the way cardiometabolic disease risk is assessed.
Max ERC Funding
1 550 057 €
Duration
Start date: 2021-01-01, End date: 2025-12-31
