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Jeroen S. van Zon

Publications and source records attributed to Jeroen S. van Zon.

5 recordsLinked to original sources
arXiv2020

Functional modules from variable genes: Leveraging percolation to analyze noisy, high-dimensional data

While measurement advances now allow extensive surveys of gene activity (large numbers of genes across many samples), interpretation of these data is often confounded by noise -- expression counts can differ strongly across samples due to variation of both biological and experimental origin. Complimentary to perturbation approaches, we extract functionally related groups of genes by analyzing the standing variation within a sampled population. To distinguish biologically meaningful patterns from uninterpretable noise, we focus on correlated variation and develop a novel density-based clustering approach that takes advantage of a percolation transition generically arising in random, uncorrelated data. We apply our approach to two contrasting RNA sequencing data sets that sample individual variation -- across single cells of fission yeast and whole animals of C. elegans worms -- and demonstrate robust applicability and versatility in revealing correlated gene clusters of diverse biological origin, including cell cycle phase, development/reproduction, tissue-specific functions, and feeding history. Our technique exploits generic features of noisy high-dimensional data and is applicable, beyond gene expression, to feature-rich data that sample population-level variability in the presence of noise.

q-bio.GN↗
arXiv2007

An allosteric model of KaiC phosphorylation

In a recent series of ground-breaking experiments, Nakajima et al. [Science 308, 414-415 (2005)] showed that the three cyanobacterial clock proteins KaiA, KaiB, and KaiC are sufficient in vitro to generate circadian phosphorylation of KaiC. Here, we present a mathematical model of the Kai system. At its heart is the assumption that KaiC can exist in two conformational states, one favoring phosphorylation and the other dephosphorylation. Each individual KaiC hexamer then has a propensity to be phosphorylated in a cyclic manner. To generate macroscopic oscillations, however, the phosphorylation cycles of the different hexamers must be synchronized. We propose a novel synchronisation mechanism based on differential affinity: KaiA stimulates KaiC phosphorylation, but the limited supply of KaiA dimers binds preferentially to those KaiC hexamers that are falling behind in the oscillation. KaiB sequesters KaiA and stabilizes the dephosphorylating KaiC state. We show that our model can reproduce a wide range of published data, including the observed insensitivity of the oscillation period to variations in temperature, and that it makes nontrivial predictions about the effects of varying the concentrations of the Kai proteins.

q-bio.MN↗
arXiv2006

Diffusion of transcription factors can drastically enhance the noise in gene expression

We study by simulation the effect of the diffusive motion of repressor molecules on the noise in mRNA and protein levels in the case of a repressed gene. We find that spatial fluctuations due to diffusion can drastically enhance the noise in gene expression. For a fixed repressor strength, the noise due to diffusion can be minimized by increasing the number of repressors or by decreasing the rate of the open complex formation. We also show that the effect of spatial fluctuations can be well described by a two-step kinetic scheme, where formation of an encounter complex by diffusion and the subsequent association reaction are treated separately. Our results also emphasize that power spectra are a highly useful tool for studying the propagation of noise through the different stages of gene expression.

q-bio.MN↗
arXiv2004

Green's Function Reaction Dynamics: a new approach to simulate biochemical networks at the particle level and in time and space

Biochemical networks are the analog computers of life. They allow living cells to control a large number of biological processes, such as gene expression and cell signalling. In biochemical networks, the concentrations of the components are often low. This means that the discrete nature of the reactants and the stochastic character of their interactions have to be taken into account. Moreover, the spatial distribution of the components can be of crucial importance. However, the current numerical techniques for simulating biochemical networks either ignore the particulate nature of matter or treat the spatial fluctuations in a mean-field manner. We have developed a new technique, called Green's Function Reaction Dynamics (GFRD), that makes it possible to simulate biochemical networks at the particle level and in both time and space. In this scheme, a maximum time step is chosen such that only single particles or pairs of particles have to be considered. For these particles, the Smoluchowski equation can be solved analytically using Green's functions. The main idea of GFRD is to exploit the exact solution of the Smoluchoswki equation to set up an event-driven algorithm. This allows GFRD to make large jumps in time when the particles are far apart from each other. Here, we apply the technique to a simple model of gene expression. The simulations reveal that the scheme is highly efficient. Under biologically relevant conditions, GFRD is up to six orders of magnitude faster than conventional particle-based techniques for simulating biochemical networks in time and space.

q-bio.MN↗