genomics meets physiology

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• what do all these multiomic datasets and single-cell atlases mean in light of cell physiology?

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• can we use physiological models and experiments to improve our understanding of single-cell multiomic data?

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• can we use single-cell multiomic data to improve our understanding of cell physiological models and experiments?

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bioelectricity, cellular excitability and electrogenesis

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how do cells and organisms use electricity for rapid signalling across evolution, development and in disease?

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​evolution of nervous systems and excitable cells

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• how did cooperating specialised cell types evolve from unicellular, multifunctional ancestors as the structure and behavior of organisms increased in complexity?

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we are studying the progressive division of labor of brain cells and the emergence of their physiological dependencies.

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systems biology of brain dysfunction

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we use brain dysfunction as a model to study how genotypes map onto complex phenotypes

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• why different causes lead to similar phenotypes?

• why similar causes lead to heterogeneous phenotypes?

• why and how mutations increase the risk of developing brain disorders?

• why are some environments good and some bad, given a genetic background?

• understanding neurodegeneration across scales: human evolution, longevity, lifespan, and healthspan.

• understanding disease risk in terms of developmental constraints, evolutionary mismatch, and maladaptive processes.

 

we investigate these problems at molecular network, genomic regulatory, and cellular bases. 

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systems biology of myelin breakdown and repair

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myelination is a multicellular process co-opted during nervous system evolution as a useful solution enabling neuronal function in large and complex organisms. Its developmental dynamics is somehow special in humans, and its breakdown in aging is associated with brain dysfunction in multiple disorders.

 

we are interested in investigating these processes at multiple biological scales

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​computational single-cell biology​

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we develop computational tools for integrative analysis of single-cell genomic measurements using machine learning and network theory

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we are applying these tools to produce integrative data resources that are helpful in testing our ideas about the mechanisms of brain function and dysfunction -- and their evolutionary history 

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