Model-driven Analysis of Gene Expression Economy – MAGEEc

Despite the large amount of available data, the precise biophysical principles governing the interdependence between cell growth, gene expression and the allocation of cellular resources remain still unexplained.

This proposal aims at understanding the regulation of the complex cellular economy and the interplay with physiology by new theoretical approaches and model-driven data analyses (omics level), placing itself between phenomenological descriptions and extremely detailed mechanistic models.

MAGEEc will establish a multi-scale and comprehensive framework built on nonequilibrium approaches to quantify the usage of cellular resources at both the level of transcription and translation, and throughly investigate the role of each potential bottleneck in the protein synthesis process. Importantly, this will be connected to a description of ribosomes and RNA polymerase turnover (biosynthesis and degradation). The outcomes will provide a more refined interpretation of our current understanding of growth laws, and provide new quantitative relationships that will be tested with either experiments or available datasets.

We will also vary the weight and load of the different bottlenecks of gene expression, to unveil how resources are re-allocated to respond to different perturbations, how synthesis yields and growth are affected. We will mainly focus on sub-lethal doses of antibiotics and gene dosage (via heterologous protein synthesis). We will explore the role of RNP granules in spatiotemporally regulating the tradeoffs of the translation machinery as a result of an induced stress.

All steps of MAGEEc will be informed by experimental data, with dedicated measures and data available in the literature. We will compile and re-analyse raw data from the literature from many different experimental techniques and that are related to gene expression (synthesis rates, degradation,…) and cellular physiology (growth rates, volume,...), in different conditions. Datasets will be crucial to validate the models or infer parameters by model-driven analyses. We also expect to design a new experimental protocol to accurately measure ribosome abundances and distinguish the amount of active and inactive ribosome in different experimental conditions.