In this article , we present a polymer model of the chromosome that can be parameterized to quantitatively reproduce contact probabilities measured in high-throughput chromosome conformation capture  (Hi-C) or genome architecture mapping  (GAM) experiments. Specifically, our Gaussian effective model (GEM) introduces harmonic potentials to represent interactions detected during such experiments. As a central property, we derive an exact relation between the couplings of these potentials and the resulting contact probabilities . This relation is used here to solve the inverse problem of constructing a GEM which best reproduces the contact probabilities measured experimentally. For that purpose, we present a minimization scheme that searches for the GEM that has contact probabilities as close as possible to the experimental ones. We apply this method to several data sets generated from experiments using the Hi-C or GAM techniques. To illustrate potential applications of our method , we show how the reconstructed couplings can be used to investigate the URLanizationby Brownian Dynamics .
It is widely believed that the folding of the chromosome in the nucleus has a major effect on genetic expression. For example co-regulated genes in several species have been shown to colocalize in space despite being far away on the DNA sequence. In this manuscript , we present a new method to model the three-dimensional structure of the chromosome in live cells, based on DNA-DNA interactions measured in high-throughput chromosome conformation capture experiments (Hi-C) and genome architecture mapping experiments (GAM) . Our approach incorporates a polymer model, and directly uses the contact probabilities measured in Hi-C and GAM experiments rather than estimates of average distances between genomic loci. Specifically, we model the chromosome as a Gaussian polymer with harmonic interactions and extract the coupling coefficients best reproducing the experimental contact probabilities. In contrast to existing methods, we give an exact expression of the contact probabilities at thermodynamic equilibrium. The Gaussian effective model (GEM) reconstructed with our method reproduces experimental contacts with high accuracy. We also show how Brownian Dynamics simulations of our reconstructed GEM can be used to study URLanization, and possibly give some clue about its dynamics .
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In this article
It is widely believed that the folding of the chromosome in the nucleus has a major effect on genetic expression. For example co-regulated genes in several species have been shown to colocalize in space despite being far away on the DNA sequence. In this manuscript
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that can be parameterized to quantitatively reproduce contact probabilities
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experiments. Specifically, our Gaussian effective model (GEM) introduces harmonic potentials to represent interactions detected during such experiments. As a central property, we derive an exact relation between the couplings of these potentials and the resulting contact probabilities
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This relation is used here to solve the inverse problem of constructing a GEM which best reproduces the
Our approach incorporates a polymer model, and directly uses the
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measured experimentally. For that purpose, we present a minimization scheme that searches for the GEM that has contact probabilities as close as possible to the experimental ones. We apply this method to several data sets generated from experiments using the
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or GAM techniques. To illustrate potential applications of our method , we show how the reconstructed couplings
and GAM experiments rather than estimates of average distances between genomic loci. Specifically, we model the chromosome as a Gaussian polymer with harmonic interactions and extract the coupling coefficients best reproducing the experimental contact probabilities. In contrast to existing methods, we give an exact expression of the contact probabilities at thermodynamic equilibrium. The Gaussian effective model (GEM) reconstructed with our method reproduces experimental contacts with high accuracy. We also show how Brownian Dynamics simulations of our reconstructed GEM
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investigate the URLanizationby Brownian Dynamics
study URLanization, and possibly give some clue about its dynamics