Nicolae Radu Zabet

Models of transcription factor binding : Sensitivity of activation functions to model assumptions

We present three models of how transcription factors (TFs) bind to their specific binding sites on the DNA: a model based on statistical physics, a Markov-chain model and a computational simulation. Comparison of these models suggests that the effect of non-specific binding can be significant. We also investigate possible mechanisms for cooperativity. The simulation model suggests that direct interactions between TFs are unlikely to be the main source of cooperativity between specific binding sites, because such interactions tend to lead to the formation of clusters on the DNA with undesirable side-effects.

Computational limits to binary genes

We analyse the trade-off between the speed with which a gene can propagate information, the noise of its output and its metabolic cost. Our main finding is that for any given level of metabolic cost there is an optimal trade-off between noise and processing speed. Any system with a non-vanishing leak expression rate is suboptimal, i.e. it will exhibit higher noise and/or slower speed than leak-free systems with the same metabolic cost. We also show that there is an optimal Hill coefficient h which minimizes noise and metabolic cost at fixed speeds, and an optimal threshold K which minimizes noise.

Homotypic clusters of transcription factor binding sites: A model system for understanding the physical mechanics of gene expression

The organization of binding sites in cis-regulatory elements (CREs) can influence gene expression through a combination of physical mechanisms, ranging from direct interactions between TF molecules to DNA looping and transient chromatin interactions. The study of simple and common building blocks in promoters and other CREs allows us to dissect how all of these mechanisms work together. Many adjacent TF binding sites for the same TF species form homotypic clusters, and these CRE architecture building blocks serve as a prime candidate for understanding interacting transcriptional mechanisms. Homotypic clusters are prevalent in both bacterial and eukaryotic genomes, and are present in both promoters as well as more distal enhancer/silencer elements. Here, we review previous theoretical and experimental studies that show how the complexity (number of binding sites) and spatial organization (distance between sites and overall distance from transcription start sites) of homotypic clusters influence gene expression. In particular, we describe how homotypic clusters modulate the temporal dynamics of TF binding, a mechanism that can affect gene expression, but which has not yet been sufficiently characterized. We propose further experiments on homotypic clusters that would be useful in developing mechanistic models of gene expression.

Estimating binding properties of transcription factors from genome-wide binding profiles

The binding of transcription factors (TFs) is essential for gene expression. One important characteristic is the actual occupancy of a putative binding site in the genome. In this study, we propose an analytical model to predict genomic occupancy that incorporates the preferred target sequence of a TF in the form of a position weight matrix (PWM), DNA accessibility data (in the case of eukaryotes), the number of TF molecules expected to be bound specifically to the DNA and a parameter that modulates the specificity of the TF. Given actual occupancy data in the form of ChIP-seq profiles, we backwards inferred copy number and specificity for five Drosophila TFs during early embryonic development: Bicoid, Caudal, Giant, Hunchback and Kruppel. Our results suggest that these TFs display thousands of molecules that are specifically bound to the DNA and that whilst Bicoid and Caudal display a higher specificity, the other three TFs (Giant, Hunchback and Kruppel) display lower specificity in their binding (despite having PWMs with higher information content). This study gives further weight to earlier investigations into TF copy numbers that suggest a significant proportion of molecules are not bound specifically to the DNA.

DNA sequence properties that predict susceptibility to epiallelic switching

Transgenerationally heritable epialleles are defined by the stable propagation of alternative transcriptional states through mitotic and meiotic cell cycles. Given that the propagation of DNA methylation at CpG sites, mediated in Arabidopsis by MET1, plays a central role in epigenetic inheritance, we examined genomewide DNA methylation in partial and complete loss‐of‐function met1 mutants. We interpreted the data in relation to transgenerational epiallelic stability, which allowed us to classify chromosomal targets of epigenetic regulation into (i) single copy and methylated exclusively at CpGs, readily forming epialleles, and (ii) transposon‐derived, methylated at all cytosines, which may or may not form epialleles. We provide evidence that DNA sequence features such as density of CpGs and genomic repetitiveness of the loci predispose their susceptibility to epiallelic switching. The importance and predictive power of these genetic features were confirmed by analyses of common epialleles in natural Arabidopsis accessions, epigenetic recombinant inbred lines (epiRILs) and also verified in rice.

Physical constraints determine the logic of bacterial promoter architectures

Site-specific transcription factors (TFs) bind to their target sites on the DNA, where they regulate the rate at which genes are transcribed. Bacterial TFs undergo facilitated diffusion (a combination of 3D diffusion around and 1D random walk on the DNA) when searching for their target sites. Using computer simulations of this search process, we show that the organization of the binding sites, in conjunction with TF copy number and binding site affinity, plays an important role in determining not only the steady state of promoter occupancy, but also the order at which TFs bind. These effects can be captured by facilitated diffusion-based models, but not by standard thermodynamics. We show that the spacing of binding sites encodes complex logic, which can be derived from combinations of three basic building blocks: switches, barriers and clusters, whose response alone and in higher orders of organization we characterize in detail. Effective promoter organizations are commonly found in the E. coli genome and are highly conserved between strains. This will allow studies of gene regulation at a previously unprecedented level of detail, where our framework can create testable hypothesis of promoter logic.

Computational models for large-scale simulations of facilitated diffusion.

The binding of site-specific transcription factors to their genomic target sites is a key step in gene regulation. While the genome is huge, transcription factors belong to the least abundant protein classes in the cell. It is therefore fascinating how short the time frame is that they require to home in on their target sites. The underlying search mechanism is called facilitated diffusion and assumes a combination of three-dimensional diffusion in the space around the DNA combined with one-dimensional random walk on it. In this review, we present the current understanding of the facilitated diffusion mechanism and identify questions that lack a clear or detailed answer. One way to investigate these questions is through stochastic simulation and, in this manuscript, we support the idea that such simulations are able to address them. Finally, we review which biological parameters need to be included in such computational models in order to obtain a detailed representation of the actual process.

A comprehensive computational model of facilitated diffusion in prokaryotes

Motivation: Gene activity is mediated by site-specific transcription factors (TFs). Their binding to defined regions in the genome determines the rate at which their target genes are transcribed. Results: We present a comprehensive computational model of the search process of TF for their genomic target site(s). The computational model considers: the DNA sequence, various TF species and the interaction of the individual molecules with the DNA or between themselves. We also demonstrate a systematic approach how to parametrize the system using available experimental data. Contact: n.r.zabet@gen.cam.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Optimal parameter settings for information processing in gene regulatory networks.

Gene networks can often be interpreted as computational circuits. This article investigates the computational properties of gene regulatory networks defined in terms of the speed and the accuracy of the output of a gene network. It will be shown that there is no single optimal set of parameters, but instead, there is a trade-off between speed and accuracy. Using the trade-off it will also be shown how systems with various parameters can be ranked with respect to their computational efficiency. Numerical analysis suggests that the trade-off can be improved when the output gene is repressing itself, even though the accuracy or the speed of the auto-regulated system may be worse than the unregulated system.

The effects of transcription factor competition on gene regulation

Transcription factor (TF) molecules translocate by facilitated diffusion (a combination of 3D diffusion around and 1D random walk on the DNA). Despite the attention this mechanism received in the last 40 years, only a few studies investigated the influence of the cellular environment on the facilitated diffusion mechanism and, in particular, the influence of “other” DNA binding proteins competing with the TF molecules for DNA space. Molecular crowding on the DNA is likely to influence the association rate of TFs to their target site and the steady state occupancy of those sites, but it is still not clear how it influences the search in a genome-wide context, when the model includes biologically relevant parameters (such as: TF abundance, TF affinity for DNA and TF dynamics on the DNA). We performed stochastic simulations of TFs performing the facilitated diffusion mechanism, and considered various abundances of cognate and non-cognate TFs. We show that, for both obstacles that move on the DNA and obstacles that are fixed on the DNA, changes in search time are not statistically significant in case of biologically relevant crowding levels on the DNA. In the case of non-cognate proteins that slide on the DNA, molecular crowding on the DNA always leads to statistically significant lower levels of occupancy, which may confer a general mechanism to control gene activity levels globally. When the “other” molecules are immobile on the DNA, we found a completely different behavior, namely: the occupancy of the target site is always increased by higher molecular crowding on the DNA. Finally, we show that crowding on the DNA may increase transcriptional noise through increased variability of the occupancy time of the target sites.