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Jost, D.

Publications and source records attributed to Jost, D..

3 recordsLinked to original sources

How epigenome drives chromatin folding and dynamics, insights from efficient coarse-grained models of chromosomes

The 3D organization of chromosome is crucial for regulating gene expression and cell function. Many experimental and polymer modeling efforts are dedicated to deciphering the mechanistic principles behind chromosome folding. Chromosomes are long and densely packed - topologically constrained - polymers. The main challenge is therefore to develop adequate models and simulation methods to investigate properly the multi spatio-temporal scales of such macromolecules. Here, we discussed a generic strategy to develop efficient coarse-grained models for self-avoiding polymers on a lattice. Accounting accurately for the polymer entanglement length and the volumic density, we show that our simulation scheme not only captures the steady-state structural and dynamical properties of the system but also tracks the same dynamics at different coarse-graining. This strategy allows a strong power-law gain in numerical efficiency and offers a systematic way to define reliable coarse-grained null models for chromosomes and to go beyond the current limitations by studying long chromosomes during an extended time period with good statistics. We use our formalism to investigate in details the time evolution of the 3D organization of chromosome 3R (20 Mbp) in drosophila during one cell cycle (20 hours). We show that a combination of our coarse-graining strategy with a one-parameter block copolymer model integrating epigenomic-driven interactions quantitatively reproduce experimental data at the chromosome-scale and predict that chromatin motion is very dynamic during the cell cycle.

biophysics

In vivo, chromatin is a fluctuating polymer chain at equilibrium constrained by internal friction

Chromosome mechanical properties determine DNA folding and dynamics, and underlie all major nuclear functions. Here we combine modeling and real-time motion tracking experiments to infer the physical parameters describing chromatin fibers. In vitro, motion of nucleosome arrays can be accurately modeled by assuming a Kuhn length of 35-55 nm. In vivo, the amplitude of chromosome fluctuations is drastically reduced, and depends on transcription. Transcription activation increases chromatin dynamics only if it involves gene relocalization, while global transcriptional inhibition augments the fluctuations, yet without relocalization. Chromatin fiber motion is accounted for by a model of equilibrium fluctuations of a polymer chain, in which random contacts along the chromosome contour induce an excess of internal friction. Simulations that reproduce chromosome conformation capture and imaging data corroborate this hypothesis. This model unravels the transient nature of chromosome contacts, characterized by a life time of [~]2 seconds and a free energy of formation of [~]1 kBT.

genetics

Assigning Function To Natural Allelic Variation Via Dynamic Modeling Of Gene Network Induction

More and more natural DNA variants are being linked to physiological traits. Yet, understanding what differences they make on molecular regulations remains challenging. Important properties of gene regulatory networks can be captured by computational models. If model parameters can be personalized according to the genotype, their variation may then reveal how DNA variants operate in the network. Here, we combined experiments and computations to visualize natural alleles of the yeast GAL3 gene in a space of model parameters describing the galactose response network. Alleles altering the activation of Gal3p by galactose were discriminated from those affecting its activity (production/degradation or efficiency of the activated protein). The approach allowed us to correctly predict that a non-synonymous SNP would change the binding affinity of Gal3p with the Gal80p transcriptional repressor. Our results illustrate how personalizing gene regulatory models can be used for the mechanistic interpretation of genetic variants.

systems biology