Engineered control of cellular circuits
Developing light-controlled proteins to study spatiotemporal dynamics of signaling in active neuron subpopulations during learning, aiming to inform therapies for brain disorders.
Projectdetails
Introduction
Protein signaling in cells is precisely coordinated in space and time. Molecular chemogenetics, optogenetics, and biosensors have generated a scientific revolution enabling the spatiotemporal codes of protein signaling in single cells.
Challenges in Multicellular Environments
However, it is a great challenge to study protein dynamics in a physiological multicellular environment due to:
- The extensive variability in protein signaling within individual cells.
- The sparsity of driver cells responsible for a specific physiological process.
Development of Technologies
To build causal relationships between proteins and multi-cellular behavior, we will develop broadly applicable technologies by engineering proteins that enable the control of target proteins with light, exclusively in the relevant driver cell subpopulations.
Applications in Biological Fields
These approaches can be used in any biological field in which protein signaling is critical for multi-cellular behavior. Here, we will focus on three different stages of a challenging neurobiology process.
Neurobiology Focus
Upon sensory experience, for example, by learning a new task, only the subsets of neurons within a corresponding brain region switch to the active state. It is largely unknown how proteins that are activated in these sparsely activated neuronal circuits operate in space and time.
Expected Outcomes
Our technologies will enlighten the spatiotemporal dynamics of proteins in active neuron subpopulations responding to certain learning tasks in mice. Understanding such learning neuronal circuit responses at the molecular level will pave the way to develop new therapeutic approaches for brain disorders including epilepsy, depression, and autism spectrum disorders.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 1.494.669 |
Totale projectbegroting | € 1.494.669 |
Tijdlijn
Startdatum | 1-5-2023 |
Einddatum | 30-4-2028 |
Subsidiejaar | 2023 |
Partners & Locaties
Projectpartners
- KAROLINSKA INSTITUTETpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
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Engineering synthetic mechanotransduction through nucleocytoplasmic transportThis project aims to engineer synthetic mechanotransduction in cells to control gene expression through mechanical signals, enhancing our understanding of cell behavior in response to tissue mechanics. | ERC Advanced... | € 2.499.875 | 2023 | Details |
Designing Allosteric Protein Switches by In Vivo Directed Evolution and Computational InferenceDaVinci-Switches aims to revolutionize switchable protein engineering by combining synthetic biology and machine learning to create light- and drug-inducible proteins for regenerative medicine applications. | ERC Starting... | € 1.619.687 | 2022 | Details |
From single cells to microbial consortia: bridging the gaps between synthetic circuit design and emerging dynamics of heterogeneous populationsThe project aims to develop mathematical methods to control synthetic gene circuits in microbial populations, enhancing functionality and bioproduction of challenging proteins through population dynamics. | ERC Starting... | € 1.497.790 | 2023 | Details |
Protein function regulation through inserts for response to biological, chemical and physical signals
This project aims to develop a modular platform for engineering proteins to sense and respond to diverse signals, enhancing their functionality for innovative biomedical applications.
Shedding light on three-dimensional gene regulation
This project aims to elucidate gene expression regulation during differentiation using an ultra-fast optogenetic system and high-resolution genomic tools to study 3D chromatin interactions.
Engineering synthetic mechanotransduction through nucleocytoplasmic transport
This project aims to engineer synthetic mechanotransduction in cells to control gene expression through mechanical signals, enhancing our understanding of cell behavior in response to tissue mechanics.
Designing Allosteric Protein Switches by In Vivo Directed Evolution and Computational Inference
DaVinci-Switches aims to revolutionize switchable protein engineering by combining synthetic biology and machine learning to create light- and drug-inducible proteins for regenerative medicine applications.
From single cells to microbial consortia: bridging the gaps between synthetic circuit design and emerging dynamics of heterogeneous populations
The project aims to develop mathematical methods to control synthetic gene circuits in microbial populations, enhancing functionality and bioproduction of challenging proteins through population dynamics.