Project acronym activeFly
Project Circuit mechanisms of self-movement estimation during walking
Researcher (PI) M Eugenia CHIAPPE
Host Institution (HI) FUNDACAO D. ANNA SOMMER CHAMPALIMAUD E DR. CARLOS MONTEZ CHAMPALIMAUD
Call Details Starting Grant (StG), LS5, ERC-2017-STG
Summary The brain evolves, develops, and operates in the context of animal movements. As a consequence, fundamental brain functions such as spatial perception and motor control critically depend on the precise knowledge of the ongoing body motion. An accurate internal estimate of self-movement is thought to emerge from sensorimotor integration; nonetheless, which circuits perform this internal estimation, and exactly how motor-sensory coordination is implemented within these circuits are basic questions that remain to be poorly understood. There is growing evidence suggesting that, during locomotion, motor-related and visual signals interact at early stages of visual processing. In mammals, however, it is not clear what the function of this interaction is. Recently, we have shown that a population of Drosophila optic-flow processing neurons —neurons that are sensitive to self-generated visual flow, receives convergent visual and walking-related signals to form a faithful representation of the fly’s walking movements. Leveraging from these results, and combining quantitative analysis of behavior with physiology, optogenetics, and modelling, we propose to investigate circuit mechanisms of self-movement estimation during walking. We will:1) use cell specific manipulations to identify what cells are necessary to generate the motor-related activity in the population of visual neurons, 2) record from the identified neurons and correlate their activity with specific locomotor parameters, and 3) perturb the activity of different cell-types within the identified circuits to test their role in the dynamics of the visual neurons, and on the fly’s walking behavior. These experiments will establish unprecedented causal relationships among neural activity, the formation of an internal representation, and locomotor control. The identified sensorimotor principles will establish a framework that can be tested in other scenarios or animal systems with implications both in health and disease.
Summary
The brain evolves, develops, and operates in the context of animal movements. As a consequence, fundamental brain functions such as spatial perception and motor control critically depend on the precise knowledge of the ongoing body motion. An accurate internal estimate of self-movement is thought to emerge from sensorimotor integration; nonetheless, which circuits perform this internal estimation, and exactly how motor-sensory coordination is implemented within these circuits are basic questions that remain to be poorly understood. There is growing evidence suggesting that, during locomotion, motor-related and visual signals interact at early stages of visual processing. In mammals, however, it is not clear what the function of this interaction is. Recently, we have shown that a population of Drosophila optic-flow processing neurons —neurons that are sensitive to self-generated visual flow, receives convergent visual and walking-related signals to form a faithful representation of the fly’s walking movements. Leveraging from these results, and combining quantitative analysis of behavior with physiology, optogenetics, and modelling, we propose to investigate circuit mechanisms of self-movement estimation during walking. We will:1) use cell specific manipulations to identify what cells are necessary to generate the motor-related activity in the population of visual neurons, 2) record from the identified neurons and correlate their activity with specific locomotor parameters, and 3) perturb the activity of different cell-types within the identified circuits to test their role in the dynamics of the visual neurons, and on the fly’s walking behavior. These experiments will establish unprecedented causal relationships among neural activity, the formation of an internal representation, and locomotor control. The identified sensorimotor principles will establish a framework that can be tested in other scenarios or animal systems with implications both in health and disease.
Max ERC Funding
1 500 000 €
Duration
Start date: 2017-11-01, End date: 2022-10-31
Project acronym C.o.C.O.
Project Circuits of con-specific observation
Researcher (PI) Marta De Aragao Pacheco Moita
Host Institution (HI) FUNDACAO D. ANNA SOMMER CHAMPALIMAUD E DR. CARLOS MONTEZ CHAMPALIMAUD
Call Details Starting Grant (StG), LS5, ERC-2013-StG
Summary A great deal is known about the neural basis of associative fear learning. However, many animal species are able to use social cues to recognize threats, a defence mechanism that may be less costly than learning from self-experience. We have previously shown that rats perceive the cessation of movement-evoked sound as a signal of danger and its resumption as a signal of safety. To study transmission of fear between rats we assessed the behavior of an observer while witnessing a demonstrator rat display fear responses. With this paradigm we will take advantage of the accumulated knowledge on learned fear to investigate the neural mechanisms by which the social environment regulates defense behaviors. We will unravel the neural circuits involved in detecting the transition from movement-evoked sound to silence. Moreover, since observer rats previously exposed to shock display observational freezing, but naive observer rats do not, we will determine the mechanism by which prior experience contribute to observational freezing. To this end, we will focus on the amygdala, crucial for fear learning and expression, and its auditory inputs, combining immunohistochemistry, pharmacology and optogenetics. Finally, as the detection of and responses to threat are often inherently social, we will study these behaviors in the context of large groups of individuals. To circumvent the serious limitations in using large populations of rats, we will resort to a different model system. The fruit fly is the ideal model system, as it is both amenable to the search for the neural mechanism of behavior, while at the same time allowing the study of the behavior of large groups of individuals. We will develop behavioral tasks, where conditioned demonstrator flies signal danger to other naïve ones. These experiments unravel how the brain uses defense behaviors as signals of danger and how it contributes to defense mechanisms at the population level.
Summary
A great deal is known about the neural basis of associative fear learning. However, many animal species are able to use social cues to recognize threats, a defence mechanism that may be less costly than learning from self-experience. We have previously shown that rats perceive the cessation of movement-evoked sound as a signal of danger and its resumption as a signal of safety. To study transmission of fear between rats we assessed the behavior of an observer while witnessing a demonstrator rat display fear responses. With this paradigm we will take advantage of the accumulated knowledge on learned fear to investigate the neural mechanisms by which the social environment regulates defense behaviors. We will unravel the neural circuits involved in detecting the transition from movement-evoked sound to silence. Moreover, since observer rats previously exposed to shock display observational freezing, but naive observer rats do not, we will determine the mechanism by which prior experience contribute to observational freezing. To this end, we will focus on the amygdala, crucial for fear learning and expression, and its auditory inputs, combining immunohistochemistry, pharmacology and optogenetics. Finally, as the detection of and responses to threat are often inherently social, we will study these behaviors in the context of large groups of individuals. To circumvent the serious limitations in using large populations of rats, we will resort to a different model system. The fruit fly is the ideal model system, as it is both amenable to the search for the neural mechanism of behavior, while at the same time allowing the study of the behavior of large groups of individuals. We will develop behavioral tasks, where conditioned demonstrator flies signal danger to other naïve ones. These experiments unravel how the brain uses defense behaviors as signals of danger and how it contributes to defense mechanisms at the population level.
Max ERC Funding
1 412 376 €
Duration
Start date: 2013-12-01, End date: 2018-11-30
Project acronym DeepSPIN
Project Deep Learning for Structured Prediction in Natural Language Processing
Researcher (PI) André Filipe TORRES MARTINS
Host Institution (HI) INSTITUTO DE TELECOMUNICACOES
Call Details Starting Grant (StG), PE6, ERC-2017-STG
Summary Deep learning is revolutionizing the field of Natural Language Processing (NLP), with breakthroughs in machine translation, speech recognition, and question answering. New language interfaces (digital assistants, messenger apps, customer service bots) are emerging as the next technologies for seamless, multilingual communication among humans and machines.
From a machine learning perspective, many problems in NLP can be characterized as structured prediction: they involve predicting structurally rich and interdependent output variables. In spite of this, current neural NLP systems ignore the structural complexity of human language, relying on simplistic and error-prone greedy search procedures. This leads to serious mistakes in machine translation, such as words being dropped or named entities mistranslated. More broadly, neural networks are missing the key structural mechanisms for solving complex real-world tasks requiring deep reasoning.
This project attacks these fundamental problems by bringing together deep learning and structured prediction, with a highly disruptive and cross-disciplinary approach. First, I will endow neural networks with a "planning mechanism" to guide structural search, letting decoders learn the optimal order by which they should operate. This makes a bridge with reinforcement learning and combinatorial optimization. Second, I will develop new ways of automatically inducing latent structure inside the network, making it more expressive, scalable and interpretable. Synergies with probabilistic inference and sparse modeling techniques will be exploited. To complement these two innovations, I will investigate new ways of incorporating weak supervision to reduce the need for labeled data.
Three highly challenging applications will serve as testbeds: machine translation, quality estimation, and dependency parsing. To maximize technological impact, a collaboration is planned with a start-up company in the crowd-sourcing translation industry.
Summary
Deep learning is revolutionizing the field of Natural Language Processing (NLP), with breakthroughs in machine translation, speech recognition, and question answering. New language interfaces (digital assistants, messenger apps, customer service bots) are emerging as the next technologies for seamless, multilingual communication among humans and machines.
From a machine learning perspective, many problems in NLP can be characterized as structured prediction: they involve predicting structurally rich and interdependent output variables. In spite of this, current neural NLP systems ignore the structural complexity of human language, relying on simplistic and error-prone greedy search procedures. This leads to serious mistakes in machine translation, such as words being dropped or named entities mistranslated. More broadly, neural networks are missing the key structural mechanisms for solving complex real-world tasks requiring deep reasoning.
This project attacks these fundamental problems by bringing together deep learning and structured prediction, with a highly disruptive and cross-disciplinary approach. First, I will endow neural networks with a "planning mechanism" to guide structural search, letting decoders learn the optimal order by which they should operate. This makes a bridge with reinforcement learning and combinatorial optimization. Second, I will develop new ways of automatically inducing latent structure inside the network, making it more expressive, scalable and interpretable. Synergies with probabilistic inference and sparse modeling techniques will be exploited. To complement these two innovations, I will investigate new ways of incorporating weak supervision to reduce the need for labeled data.
Three highly challenging applications will serve as testbeds: machine translation, quality estimation, and dependency parsing. To maximize technological impact, a collaboration is planned with a start-up company in the crowd-sourcing translation industry.
Max ERC Funding
1 436 000 €
Duration
Start date: 2018-02-01, End date: 2023-01-31
Project acronym DYNEINOME
Project Cytoplasmic Dynein: Mechanisms of Regulation and Novel Interactors
Researcher (PI) Reto Gassmann
Host Institution (HI) INSTITUTO DE BIOLOGIA MOLECULAR E CELULAR-IBMC
Call Details Starting Grant (StG), LS3, ERC-2013-StG
Summary "The megadalton cytoplasmic dynein complex, whose motor subunit is encoded by a single gene, provides the major microtubule minus end-directed motility in cells and is essential for a wide range of processes, ranging from the transport of proteins, RNA, and membrane vesicles to nuclear migration and cell division. To achieve this stunning functional diversity, cytoplasmic dynein is subject to tight regulation by co-factors that modulate localization, interaction with cargo, and motor activity. At present, our knowledge of the underlying mechanisms remains limited. An overarching goal of this proposal is to gain an understanding of how interactions with diverse adaptor proteins regulate dynein function in space and time. We choose the nematode C. elegans as our model system, because it will enable us to study the biology of dynein regulation in the broad context of a metazoan organism. The nematode’s versatile genetic tools, its biochemical tractability, and the powerful molecular replacement technologies available, this makes for a uniquely attractive experimental system to address the mechanisms employed by dynein regulators through a combination of biochemical, proteomic, and cell biological assays. Specifically, we propose to use a biochemical reconstitution approach to obtain a detailed molecular picture of how dynein is targeted to the mitotic kinetochore; we will perform a forward genetic and proteomic screen to expand the so-far limited inventory of metazoan dynein interactors, whose functional characterization will shed light on known dynein-dependent processes and lead to novel unanticipated lines of research into dynein regulation; we will dissect the function and regulation of the most important dynein co-factor, the multi-subunit dynactin complex; and finally we will strive to establish a novel C. elegans model for human neurodegenerative disease, based on pathogenic point mutations in a dynactin subunit."
Summary
"The megadalton cytoplasmic dynein complex, whose motor subunit is encoded by a single gene, provides the major microtubule minus end-directed motility in cells and is essential for a wide range of processes, ranging from the transport of proteins, RNA, and membrane vesicles to nuclear migration and cell division. To achieve this stunning functional diversity, cytoplasmic dynein is subject to tight regulation by co-factors that modulate localization, interaction with cargo, and motor activity. At present, our knowledge of the underlying mechanisms remains limited. An overarching goal of this proposal is to gain an understanding of how interactions with diverse adaptor proteins regulate dynein function in space and time. We choose the nematode C. elegans as our model system, because it will enable us to study the biology of dynein regulation in the broad context of a metazoan organism. The nematode’s versatile genetic tools, its biochemical tractability, and the powerful molecular replacement technologies available, this makes for a uniquely attractive experimental system to address the mechanisms employed by dynein regulators through a combination of biochemical, proteomic, and cell biological assays. Specifically, we propose to use a biochemical reconstitution approach to obtain a detailed molecular picture of how dynein is targeted to the mitotic kinetochore; we will perform a forward genetic and proteomic screen to expand the so-far limited inventory of metazoan dynein interactors, whose functional characterization will shed light on known dynein-dependent processes and lead to novel unanticipated lines of research into dynein regulation; we will dissect the function and regulation of the most important dynein co-factor, the multi-subunit dynactin complex; and finally we will strive to establish a novel C. elegans model for human neurodegenerative disease, based on pathogenic point mutations in a dynactin subunit."
Max ERC Funding
1 367 466 €
Duration
Start date: 2014-03-01, End date: 2019-02-28
Project acronym FattyCyanos
Project Fatty acid incorporation and modification in cyanobacterial natural products
Researcher (PI) Pedro LEÃO
Host Institution (HI) CIIMAR - Centro Interdisciplinar de Investigação Marinha e Ambiental
Call Details Starting Grant (StG), PE5, ERC-2017-STG
Summary Known, but mostly novel natural products (NPs) are in high demand – these are used in drugs, cosmetics and agrochemicals and serve also as research tools to probe biological systems. NP structures inspire chemists to develop new syntheses, and NP biosynthetic enzymes add to the metabolic engineer’s toolbox. The advent of next generation DNA-sequencing has revealed a vastly rich pool of NP biosynthetic gene clusters (BGCs) among bacterial genomes, most of which with no corresponding NP. Hence, opportunities abound for the discovery of new chemistry and enzymology that has the potential to push the boundaries of chemical space and enzymatic reactivity. Still, we cannot reliably predict chemistry from BGCs with unusual organization or encoding unknown functionalities, and, for molecules of unorthodox architecture, it is difficult to anticipate how their BGCs are organized. It is the valuable, truly novel chemistry and biochemistry that lies on these unexplored connections, that we aim to reveal with this proposal. To achieve it, we will work with a chemically-talented group of organisms – cyanobacteria, and with a specific structural class – fatty acids (FAs) – that is metabolized in a quite peculiar fashion by these organisms, paving the way for NP and enzyme discovery. On one hand, we will exploit the unique FA metabolism of cyanobacteria to develop a feeding strategy that will quickly reveal unprecedented FA-incorporating NPs. On the other, we will scrutinize the intriguing biosynthesis of three unique classes of metabolites that we have isolated recently and that incorporate and modify FA-moieties. We will find the BGCs for these compounds and dissect the functionality involved in such puzzling modifications to uncover important underlying enzymatic chemistry. This proposal is a blend of discovery- and hypothesis-driven research at the NP chemistry/biosynthesis interface that draws on the experience of the PI’s work on different aspects of cyanobacterial NPs.
Summary
Known, but mostly novel natural products (NPs) are in high demand – these are used in drugs, cosmetics and agrochemicals and serve also as research tools to probe biological systems. NP structures inspire chemists to develop new syntheses, and NP biosynthetic enzymes add to the metabolic engineer’s toolbox. The advent of next generation DNA-sequencing has revealed a vastly rich pool of NP biosynthetic gene clusters (BGCs) among bacterial genomes, most of which with no corresponding NP. Hence, opportunities abound for the discovery of new chemistry and enzymology that has the potential to push the boundaries of chemical space and enzymatic reactivity. Still, we cannot reliably predict chemistry from BGCs with unusual organization or encoding unknown functionalities, and, for molecules of unorthodox architecture, it is difficult to anticipate how their BGCs are organized. It is the valuable, truly novel chemistry and biochemistry that lies on these unexplored connections, that we aim to reveal with this proposal. To achieve it, we will work with a chemically-talented group of organisms – cyanobacteria, and with a specific structural class – fatty acids (FAs) – that is metabolized in a quite peculiar fashion by these organisms, paving the way for NP and enzyme discovery. On one hand, we will exploit the unique FA metabolism of cyanobacteria to develop a feeding strategy that will quickly reveal unprecedented FA-incorporating NPs. On the other, we will scrutinize the intriguing biosynthesis of three unique classes of metabolites that we have isolated recently and that incorporate and modify FA-moieties. We will find the BGCs for these compounds and dissect the functionality involved in such puzzling modifications to uncover important underlying enzymatic chemistry. This proposal is a blend of discovery- and hypothesis-driven research at the NP chemistry/biosynthesis interface that draws on the experience of the PI’s work on different aspects of cyanobacterial NPs.
Max ERC Funding
1 462 938 €
Duration
Start date: 2018-01-01, End date: 2022-12-31
Project acronym FIELDS-KNOTS
Project Quantum fields and knot homologies
Researcher (PI) Piotr Sulkowski
Host Institution (HI) UNIWERSYTET WARSZAWSKI
Call Details Starting Grant (StG), PE2, ERC-2013-StG
Summary This project is concerned with fundamental problems arising at the interface of quantum field theory, knot theory, and the theory of random matrices. The main aim of the project is to understand two of the most profound phenomena in physics and mathematics, namely quantization and categorification, and to establish an explicit and rigorous framework where they come into play in an interrelated fashion. The project and its aims focus on the following areas:
- Knot homologies and superpolynomials. The aim of the project in this area is to determine homological knot invariants and to derive an explicit form of colored superpolynomials for a large class of knots and links.
- Super-A-polynomial. The aim of the project in this area is to develop a theory of the super-A-polynomial, to find an explicit form of the super-A-polynomial for a large class of knots, and to understand its properties.
- Three-dimensional supersymmetric N=2 theories. This project aims to find and understand dualities between theories in this class, in particular theories related to knots by 3d-3d duality, and to generalize this duality to the level of homological knot invariants.
- Topological recursion and quantization. The project aims to develop a quantization procedure based on the topological recursion, to demonstrate its consistency with knot-theoretic quantization of A-polynomials, and to generalize this quantization scheme to super-A-polynomials.
All these research areas are connected via remarkable dualities unraveled very recently by physicists and mathematicians. The project is interdisciplinary and aims to reach the above goals by taking advantage of these dualities, and through simultaneous and complementary development in quantum field theory, knot theory, and random matrix theory, in collaboration with renowned experts in each of those fields.
Summary
This project is concerned with fundamental problems arising at the interface of quantum field theory, knot theory, and the theory of random matrices. The main aim of the project is to understand two of the most profound phenomena in physics and mathematics, namely quantization and categorification, and to establish an explicit and rigorous framework where they come into play in an interrelated fashion. The project and its aims focus on the following areas:
- Knot homologies and superpolynomials. The aim of the project in this area is to determine homological knot invariants and to derive an explicit form of colored superpolynomials for a large class of knots and links.
- Super-A-polynomial. The aim of the project in this area is to develop a theory of the super-A-polynomial, to find an explicit form of the super-A-polynomial for a large class of knots, and to understand its properties.
- Three-dimensional supersymmetric N=2 theories. This project aims to find and understand dualities between theories in this class, in particular theories related to knots by 3d-3d duality, and to generalize this duality to the level of homological knot invariants.
- Topological recursion and quantization. The project aims to develop a quantization procedure based on the topological recursion, to demonstrate its consistency with knot-theoretic quantization of A-polynomials, and to generalize this quantization scheme to super-A-polynomials.
All these research areas are connected via remarkable dualities unraveled very recently by physicists and mathematicians. The project is interdisciplinary and aims to reach the above goals by taking advantage of these dualities, and through simultaneous and complementary development in quantum field theory, knot theory, and random matrix theory, in collaboration with renowned experts in each of those fields.
Max ERC Funding
1 345 080 €
Duration
Start date: 2013-12-01, End date: 2018-11-30
Project acronym IgYPurTech
Project IgY Technology: A Purification Platform using Ionic-Liquid-Based Aqueous Biphasic Systems
Researcher (PI) Mara Guadalupe Freire Martins
Host Institution (HI) UNIVERSIDADE DE AVEIRO
Call Details Starting Grant (StG), PE8, ERC-2013-StG
Summary With the emergence of antibiotic-resistant pathogens the development of antigen-specific antibodies for use in passive immunotherapy is, nowadays, a major concern in human society. Despite the most focused mammal antibodies, antibodies obtained from egg yolk of immunized hens, immunoglobulin Y (IgY), are an alternative option that can be obtained in higher titres by non-stressful and non-invasive methods. This large amount of available antibodies opens the door for a new kind of cheaper biopharmaceuticals. However, the production cost of high-quality IgY for large-scale applications remains higher than other drug therapies due to the lack of an efficient purification method. The search of new purification platforms is thus a vital demand to which liquid-liquid extraction using aqueous biphasic systems (ABS) could be the answer. Besides the conventional polymer-based systems, highly viscous and with a limited polarity/affinity range, a recent type of ABS composed of ionic liquids (ILs) may be employed. ILs are usually classified as “green solvents” due to their negligible vapour pressure. Yet, the major advantage of IL-based ABS relies on the possibility of tailoring their phases’ polarities aiming at extracting a target biomolecule. A proper manipulation of the system constituents and respective composition allows the pre-concentration, complete extraction, or purification of the most diverse biomolecules.
This research project addresses the development of a new technique for the extraction and purification of IgY from egg yolk using IL-based ABS. The proposed plan contemplates the optimization of purification systems at the laboratory scale and their use in countercurrent chromatography to achieve a simple, cost-effective and scalable process. The success of this project and its scalability to an industrial level certainly will allow the production of cheaper antibodies with a long-term impact in human healthcare.
Summary
With the emergence of antibiotic-resistant pathogens the development of antigen-specific antibodies for use in passive immunotherapy is, nowadays, a major concern in human society. Despite the most focused mammal antibodies, antibodies obtained from egg yolk of immunized hens, immunoglobulin Y (IgY), are an alternative option that can be obtained in higher titres by non-stressful and non-invasive methods. This large amount of available antibodies opens the door for a new kind of cheaper biopharmaceuticals. However, the production cost of high-quality IgY for large-scale applications remains higher than other drug therapies due to the lack of an efficient purification method. The search of new purification platforms is thus a vital demand to which liquid-liquid extraction using aqueous biphasic systems (ABS) could be the answer. Besides the conventional polymer-based systems, highly viscous and with a limited polarity/affinity range, a recent type of ABS composed of ionic liquids (ILs) may be employed. ILs are usually classified as “green solvents” due to their negligible vapour pressure. Yet, the major advantage of IL-based ABS relies on the possibility of tailoring their phases’ polarities aiming at extracting a target biomolecule. A proper manipulation of the system constituents and respective composition allows the pre-concentration, complete extraction, or purification of the most diverse biomolecules.
This research project addresses the development of a new technique for the extraction and purification of IgY from egg yolk using IL-based ABS. The proposed plan contemplates the optimization of purification systems at the laboratory scale and their use in countercurrent chromatography to achieve a simple, cost-effective and scalable process. The success of this project and its scalability to an industrial level certainly will allow the production of cheaper antibodies with a long-term impact in human healthcare.
Max ERC Funding
1 386 020 €
Duration
Start date: 2014-02-01, End date: 2019-01-31
Project acronym IMMOCAP
Project 'If immortality unveil…'– development of the novel types of energy storage systems with excellent long-term performance
Researcher (PI) Krzysztof FIC
Host Institution (HI) POLITECHNIKA POZNANSKA
Call Details Starting Grant (StG), PE8, ERC-2017-STG
Summary The major goal of the project is to develop a novel type of an electrochemical capacitor with high specific power (up to 5 kW/kg) and energy (up to 20 Wh/kg) preserved along at least 50 000 cycles. Thus, completion of the project will result in remarkable enhancement of specific energy, power and life time of modern electrochemical capacitors. Advanced electrochemical testing (galvanostatic cycling with constant power loads, electrochemical impedance spectroscopy, accelerated aging and kinetic tests) will be accompanied by materials design and detailed characterization. Moreover, the project aims at the implementation of novel concepts of the electrolytes and designing of new operando technique for capacitor characterization. All these efforts aim at the development of sustainable and efficient energy conversion and storage system.
Summary
The major goal of the project is to develop a novel type of an electrochemical capacitor with high specific power (up to 5 kW/kg) and energy (up to 20 Wh/kg) preserved along at least 50 000 cycles. Thus, completion of the project will result in remarkable enhancement of specific energy, power and life time of modern electrochemical capacitors. Advanced electrochemical testing (galvanostatic cycling with constant power loads, electrochemical impedance spectroscopy, accelerated aging and kinetic tests) will be accompanied by materials design and detailed characterization. Moreover, the project aims at the implementation of novel concepts of the electrolytes and designing of new operando technique for capacitor characterization. All these efforts aim at the development of sustainable and efficient energy conversion and storage system.
Max ERC Funding
1 385 000 €
Duration
Start date: 2017-10-01, End date: 2022-09-30
Project acronym StemCellHabitat
Project Metabolic and Timed Control of Stem Cell Fate in the Developing Animal
Researcher (PI) Catarina DE CERTIMA FERNANDES HOMEM
Host Institution (HI) UNIVERSIDADE NOVA DE LISBOA
Call Details Starting Grant (StG), LS3, ERC-2017-STG
Summary Stem cell (SC) proliferation during development requires tight spatial and temporal regulation to ensure correct cell number and right cell types are formed at the proper positions. Currently very little is known about how SCs are regulated during development. Specifically, it is unclear how SC waves of proliferation are regulated and how the fate of their progeny changes during development. In addition, it has recently become evident that metabolism provides additional complexity in cell fate regulation, highlighting the need for integrating metabolic information across physiological levels.
This project will answer the question of how the combination of metabolic state and temporal cues (animal developmental stage) regulate SC fate. I will use Drosophila melanogaster, an animal complex enough to be similar to higher eukaryotes and yet simple enough to dissect the mechanistic details of cell regulation and its impact on the organism. Drosophila neural stem cells, the neuroblasts (NB), are a fantastic model of temporally and metabolically regulated cells. NB lineage fate changes with time, directing the generation of a stereotypical set of neurons, after which they disappear. I have previously found that metabolism is an important regulator of NB cell cycle exit, which occurs in response to an increase in levels of oxidative phosphorylation.
Using a multidisciplinary approach combining genetics, cell type/age sorting, multi-omics analysis, fixed and 3D-live NB imaging and metabolite dynamics, I propose an integrative approach to investigate how NBs are regulated in the developing animal. First I will dissect the mechanisms by which metabolism regulates NB fate. Second, I will investigate how metabolism contributes to NB unlimited proliferation and brain tumors. Finally, we will address how temporal transcription factors and hormones dynamically affect cell fate decisions during development.
Summary
Stem cell (SC) proliferation during development requires tight spatial and temporal regulation to ensure correct cell number and right cell types are formed at the proper positions. Currently very little is known about how SCs are regulated during development. Specifically, it is unclear how SC waves of proliferation are regulated and how the fate of their progeny changes during development. In addition, it has recently become evident that metabolism provides additional complexity in cell fate regulation, highlighting the need for integrating metabolic information across physiological levels.
This project will answer the question of how the combination of metabolic state and temporal cues (animal developmental stage) regulate SC fate. I will use Drosophila melanogaster, an animal complex enough to be similar to higher eukaryotes and yet simple enough to dissect the mechanistic details of cell regulation and its impact on the organism. Drosophila neural stem cells, the neuroblasts (NB), are a fantastic model of temporally and metabolically regulated cells. NB lineage fate changes with time, directing the generation of a stereotypical set of neurons, after which they disappear. I have previously found that metabolism is an important regulator of NB cell cycle exit, which occurs in response to an increase in levels of oxidative phosphorylation.
Using a multidisciplinary approach combining genetics, cell type/age sorting, multi-omics analysis, fixed and 3D-live NB imaging and metabolite dynamics, I propose an integrative approach to investigate how NBs are regulated in the developing animal. First I will dissect the mechanisms by which metabolism regulates NB fate. Second, I will investigate how metabolism contributes to NB unlimited proliferation and brain tumors. Finally, we will address how temporal transcription factors and hormones dynamically affect cell fate decisions during development.
Max ERC Funding
1 697 493 €
Duration
Start date: 2018-02-01, End date: 2023-01-31
