Unraveling the Inner Workings of Cells via Statistical Inference

Presenter: Andrea De Martino
Wednesday, May 25th, 2022 17:00
Location: SmartData@Covivio

Intracellular metabolic activity (e.g. reaction fluxes and metabolite levels) is largely inaccessible to experiments. I will describe recent work aimed at constructing generative models of cellular metabolism through statistical inference from empirical data. After motivating why this problem is worth the effort and clarifying the technical difficulties to be overcome, I will focus on (i) the existence of a testable hard bound relating fitness to (inferred) heterogeneity in all cell types, and (ii) the possibility to identify `objective functions’ of metabolic activity. If time permits, I will finally present some results on the reconstruction of inter-cellular interactions in populations of cancer cells from high-resolution pH micronvironment data obtained via ratiometric nanofibers. 

Biography: I am a Senior scientist (Primo ricercatore) at CNR (Rome) and a Research fellow at the Italian Institute for Genomic Medicine (Candiolo), currently on leave at Politecnico di Torino. I am interested in the physics/biology interface across multiple scales, from single cells to populations and ecosystems. My favorite questions concern (a) how efficiently living systems process environmental cues, and (b) how coordinated multi-cellular and population-level behaviour arises from single-cell physiology and gene expression. My preferred model systems are bacteria and RNA regulatory networks. My toolbox is that of statistical mechanics and information theory (mainly solvable mathematical models and statistical inference).

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