Research

/Research
Research2019-04-17T17:40:28+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.

Stochastic Models of the Eukaryotic Cell Cycle

Source of Support: NIH Total Amount Requested: $2,251,374 (Peccoud’s Share $1,696,184) Period Covered: 05/01/15 to 04/30/19 Abstract: The cycle of cell growth, DNA synthesis, mitosis and cell division is the fundamental process by which cells (and all living organisms) grow, develop and reproduce. Hence, it is of crucial importance to science and human health to understand the molecular mechanisms that control these processes in eukaryotic cells. The control system is so complex that mathematical and computational methods are needed to reliably track the interactions of all the relevant genes, mRNAs, proteins, and multiprotein complexes. Deterministic models (ordinary differential equations) are adequate for understanding the average behavior of groups of cells, but to understand the far-from-average behavior of individual cells requires stochastic models that accurately account for noise stemming from small numbers of participating molecules within a single cell and from vagaries of the division process (i.e., unequal partitioning of molecular components between daughter cells). Accurately modeling the variable [...]

Integrating Top-Down and Bottom-up Models in Systems Biology with Applications to Cell Cycle Control in Budding Yeast

Source of Support: NIH  Amount Awarded: $2,090,926 (Peccoud’s Share $475,023) Period Covered: 05/01/11 to 04/30/17 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 this system by studying the mechanism of [...]

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