arXiv subjects

Markus Nyberg

Publications and source records attributed to Markus Nyberg.

4 recordsLinked to original sources

arXiv2019

Modelling Protein Target-Search in Human Chromosomes

Several processes in the cell, such as gene regulation, start when key proteins recognise and bind to short DNA sequences. However, as these sequences can be hundreds of million times shorter than the genome, they are hard to find by simple diffusion: diffusion-limited association rates may underestimate $in~vitro$ measurements up to several orders of magnitude. Moreover, the rates increase if the DNA is coiled rather than straight. Here we model how this works $in~vivo$ in mammalian cells. We use chromatin-chromatin contact data from state-of-the-art Hi-C experiments to map the protein target-search onto a network problem. The nodes represent a DNA segment and the weight of the links is proportional to measured contact probabilities. We then put forward a master equation for the density of searching protein that allows us to calculate the association rates across the genome analytically. For segments where the rates are high, we find that they are enriched with active genes and have high RNA expression levels. This paper suggests that the DNA's 3D conformation is important for protein search times $in~vivo$ and offers a method to interpret protein-binding profiles in eukaryotes that cannot be explained by the DNA sequence itself.

q-bio.SC↗

arXiv2018

Persistence of Non-Markovian Gaussian Stationary Processes in Discrete Time

The persistence of a stochastic variable is the probability that it does not cross a given level during a fixed time interval. Although persistence is a simple concept to understand, it is in general hard to calculate. Here we consider zero mean Gaussian stationary processes in discrete time $n$. Few results are known for the persistence $P_0(n)$ in discrete time, except the large time behavior which is characterized by the nontrivial constant $θ$ through $P_0(n)\sim θ^n$. Using a modified version of the Independent Interval Approximation (IIA) that we developed before, we are able to calculate $P_0(n)$ analytically in $z$-transform space in terms of the autocorrelation function $A(n)$. If $A(n)\to0$ as $n\to\infty$, we extract $θ$ numerically, while if $A(n)=0$, for finite $n>N$, we find $θ$ exactly (within the IIA). We apply our results to three special cases: the nearest neighbor-correlated "first order moving average process" where $A(n)=0$ for $ n>1$, the double exponential-correlated "second order autoregressive process" where $A(n)=c_1λ_1^n+c_2λ_2^n$, and power law-correlated variables where $A(n)\sim n^{-μ}$. Apart from the power-law case when $μ<5$, we find excellent agreement with simulations.

cond-mat.stat-mech↗

arXiv2017

Zero-Crossing Statistics for Non-Markovian Time Series

In applications spaning from image analysis and speech recognition, to energy dissipation in turbulence and time-to failure of fatigued materials, researchers and engineers want to calculate how often a stochastic observable crosses a specific level, such as zero. At first glance this problem looks simple, but it is in fact theoretically very challenging. And therefore, few exact results exist. One exception is the celebrated Rice formula that gives the mean number of zero-crossings in a fixed time interval of a zero-mean Gaussian stationary processes. In this study we use the so-called Independent Interval Approximation to go beyond Rice's result and derive analytic expressions for all higher-order zero-crossing cumulants and moments. Our results agrees well with simulations for the non-Markovian autoregressive model.

cond-mat.stat-mech↗

arXiv2016

A simple method to calculate first-passage time densities of non-smooth processes

Numerous applications all the way from biology and physics to economics depend on the density of first crossings over a boundary. Motivated by the lack of analytical tools for computing first-passage time densities (FPTDs) for complex problems, we propose a new simple method based on the Independent Interval Approximation (IIA). We generalise previous formulations of the IIA to handle non-smooth processes, and derive a closed form expression for the FPTD in Laplace and $z$-transform space for arbitrary boundary and starting points in one dimension. We focus on Markov processes for which the IIA is exact. To apply our equations, we calculate the FPTD in two cases: the Ornstein-Uhlenbeck process and the discrete time Brownian walk. Our results are in good agreement with Langevin dynamics simulations.

cond-mat.stat-mech↗