Research

/Research
Research2019-07-03T21:37:17+00:00

Ongoing Research

The Peccoud Lab specializes in rationally designing and testing genetic constructs in high throughput. We employ a state-of-the-art data management infrastructure in order to develop and test models of complex gene networks.

Automated Prioritization and Design of Experiments

Collaborative Research: ABI Innovation: Automated Prioritization and Design of Experiments to Validate and Improve Mathematical Models of Molecular Regulatory Systems Source of Support: NSF Abstract: Complex networks of interacting molecules control all the physiological processes that occur in a living cell. It is impossible to deduce the functions of these networks using intuitive reasoning alone. Therefore, scientists construct mathematical [...]

Modeling DNA Manufacturing

EAGER: Modeling DNA Manufacturing Processes Using Extensible Attribute Grammars Source of Support: NSF Abstract: This EArly-concept Grant for Exploratory Research (EAGER) project supports the NSF mission of advancing the national health, prosperity and welfare by increasing the capabilities, accessibility and affordability of manufacturing services in support of America's leadership in the life sciences. The execution of many complex DNA [...]

Eukaryotic Cell Cycle Models

Stochastic Models of Cell Cycle Regulation in Eukaryotes Source of Support: NIH Abstract: The cell cycle is the process by which a growing cell replicates its genome and partitions the two copies of each chromosome to two daughter cells at division. It is of utmost importance to the perpetuation of life that these processes of replication (DNA synthesis) and [...]

Completed Research

Previous research projects of the Peccoud Lab.

Models in Systems Biology – Cell Cycle Control

Integrating Top-Down and Bottom-up Models in Systems Biology with Applications to Cell Cycle Control in Budding Yeast Source of Support: NIH Abstract: Two distinct approaches are being used to study complex cellular systems. The first approach automatically searches large datasets for correlations between genes and proteins and represents these as a graph with nodes and edges. The second approach painstakingly crafts detailed models that can be simulated by computer. These approaches have largely been developed separately until now. This project will meld these two approaches into a single framework, thereby allowing fast database searches to augment models that can be simulated. Specifically, the project will 1. Develop fast algorithms to search databases of molecular data to suggest extensions to models of cellular control systems 2. Develop new principles to test how well these extended models match experimental data and 3. Design experimental tests that can validate the predictions made by the first two steps. The project will validate [...]

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