Principles of biomolecular networks
Date du début: 1 févr. 2014,
Date de fin: 31 janv. 2019
Cells process information using biochemical circuits of interacting proteins and genes. We wish to define principles guiding the design of those circuits. The interplay between variability and robustness is of key interest to us. Bio-molecular processes are stochastic, environmental conditions fluctuate, and sequence polymorphisms are abundant. How is variability buffered to maintain reproducible outcomes? Can variability enhance computational abilities? What is the impact of variability on bio-molecular circuit design? We will explore those fundamental questions in three contexts:Source of variability in Gene expression: We previously examined the mechanistic basis of expression variability, defining promoter structures associated with low vs. high variability. We will now address the more challenging question: what evolutionary pressures shape the expression program? On the network level, we will define mutual effects of selection for increased expression and for optimal growth. On the metabolic level, we will define which aspect of the expression process is limiting and the genomic consequences of this limitation.Role of expression variability in Nutrient homeostasis: We recently reported that repression of high affinity transporter in rich nutrient (the ‘dual-transporter’ motif) enables advanced preparation to nutrient depletion. We will now validate an additional predicted property of this motif: cells become committed to the starvation program, escaping it due to expression noise only. To this end, we will introduce a novel method for modulating expression noise while maintaining mean abundance.Buffering variability in Embryonic patterning: Buffering fluctuations is essential in embryonic patterning. We previously established that the embryonic DV axis of Drosophila is robustly patterned through the newly defined shuttling mechanism. We will quantify the ability of this system to buffer size variations (scaling), and reveal the underlying scaling mechanism.
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