Summary
A central goal of molecular medicine is to understand how genetics, diet, and environment interact to determine health. However, most complex diseases arise from slow, stochastic changes involving large numbers of genes, making it difficult to systematically develop preventative therapies. To study the early and mid-life origins of late-life diseases, we need new methods capable of measuring the high-dimensional dynamics of physiologic change during aging.
C. elegans is a small, fast-aging animal and a powerful model for asking fundamental questions about the conserved molecular origins of complex diseases. However, it is not yet feasible to systematically collect molecular and phenotypic time-series at the precision and scale needed to build quantitative dynamic models of aging. Recently, I developed an automated microscopy and image processing technology that allows life-long observation of large populations. In this proposal, we develop this prototype into an integrative platform combining transcriptomic profiling, in vivo biosensors, and new imaging technology. Collecting data at multiple spatial scales—molecules, cells, individuals, and populations—we can map the causal steps through which slow, stochastic molecular changes drive increases in disease risk. We will then apply this method at scale to characterize all known lifespan-altering interventions in C. elegans, including many being explored for clinical application.
Combining molecular genetics with theoretic approaches, we will build quantitative models of how complex diseases emerge from slow molecular-level changes, and make methodological progress toward rapid characterization of the determinants of age-associated diseases. This work will help isolate the physiologic changes whose disruption delays aging and reduces disease risk, including new targets for preventative therapies.
C. elegans is a small, fast-aging animal and a powerful model for asking fundamental questions about the conserved molecular origins of complex diseases. However, it is not yet feasible to systematically collect molecular and phenotypic time-series at the precision and scale needed to build quantitative dynamic models of aging. Recently, I developed an automated microscopy and image processing technology that allows life-long observation of large populations. In this proposal, we develop this prototype into an integrative platform combining transcriptomic profiling, in vivo biosensors, and new imaging technology. Collecting data at multiple spatial scales—molecules, cells, individuals, and populations—we can map the causal steps through which slow, stochastic molecular changes drive increases in disease risk. We will then apply this method at scale to characterize all known lifespan-altering interventions in C. elegans, including many being explored for clinical application.
Combining molecular genetics with theoretic approaches, we will build quantitative models of how complex diseases emerge from slow molecular-level changes, and make methodological progress toward rapid characterization of the determinants of age-associated diseases. This work will help isolate the physiologic changes whose disruption delays aging and reduces disease risk, including new targets for preventative therapies.
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| Web resources: | https://cordis.europa.eu/project/id/852201 |
| Start date: | 01-01-2020 |
| End date: | 30-06-2025 |
| Total budget - Public funding: | 1 499 981,00 Euro - 1 499 981,00 Euro |
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Original description
A central goal of molecular medicine is to understand how genetics, diet, and environment interact to determine health. However, most complex diseases arise from slow, stochastic changes involving large numbers of genes, making it difficult to systematically develop preventative therapies. To study the early and mid-life origins of late-life diseases, we need new methods capable of measuring the high-dimensional dynamics of physiologic change during aging.C. elegans is a small, fast-aging animal and a powerful model for asking fundamental questions about the conserved molecular origins of complex diseases. However, it is not yet feasible to systematically collect molecular and phenotypic time-series at the precision and scale needed to build quantitative dynamic models of aging. Recently, I developed an automated microscopy and image processing technology that allows life-long observation of large populations. In this proposal, we develop this prototype into an integrative platform combining transcriptomic profiling, in vivo biosensors, and new imaging technology. Collecting data at multiple spatial scales—molecules, cells, individuals, and populations—we can map the causal steps through which slow, stochastic molecular changes drive increases in disease risk. We will then apply this method at scale to characterize all known lifespan-altering interventions in C. elegans, including many being explored for clinical application.
Combining molecular genetics with theoretic approaches, we will build quantitative models of how complex diseases emerge from slow molecular-level changes, and make methodological progress toward rapid characterization of the determinants of age-associated diseases. This work will help isolate the physiologic changes whose disruption delays aging and reduces disease risk, including new targets for preventative therapies.
Status
SIGNEDCall topic
ERC-2019-STGUpdate Date
27-04-2024
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