10h : Eugenio Cinquemani (Inria): Power spectral analysis for the optimal design of gene reporter systems
An established technique for the monitoring of gene expression dynamics is the use of fluorescent reporter proteins. Synthesized in response to promoter activation of the gene of interest, fluorescent proteins provide a visible readout of gene expression that can be quantified over time both at an ensemble population and at a single-cell level. Fluorescent reporter system response to promoter activation can be described as standard transcription-translation reaction networks, taking the form of stochastic models for single cells and deterministic models for population averages. The kinetic rate constants of these systems constitute design parameters for the experimenter that shape the response to promoter activation.
In this talk, reporter systems are analyzed from a signal processing viewpoint. The power spectral transfer function for stochastic (single-cell) response models is developed and compared with the frequency response of corresponding deterministic (population) models. Both response models are shown to be equivalent to a linear filter, with a noise component for single-cell response coming from intrinsic noise of the gene expression process. These results are next used to explore the design of the kinetic rate constants. Assuming additive measurement noise on the observed fluorescent levels, design guidelines are established to optimize information content of the reporter output in spite of measurement noise and, for single-cell monitoring, of intrinsic noise. A final discussion of the results points out fundamental differences between gene expression monitoring in populations and in single cells.
Keywords: Moment equations, frequency response, gene expression, intrinsic noise
11h : Loïc Chalmandrier (UGA, LIPhy): Calibrating process-based biodiversity models with functional traits
Process-based models are seldom used to identify the mechanisms that structure species-rich biological communities. One reason is that species demography and interactions are often too difficult to estimate in situ or experimentally. Here, I will show how to use functional trait data and biodiversity data instead to infer species demography and interactions. I will present case studies demonstrating the value of this approach to model abiotic filtering and competition in plant communities and present the future directions of my research on that topic.