Extraction of transcription regulatory signals from genome-wide DNA–protein interaction data

Deciphering gene regulatory network architecture amounts to the identification of the regulators, conditions in which they act, genes they regulate, cis-acting motifs they bind, expression profiles they dictate and more complex relationships between alternative regulatory partnerships and alternative regulatory motifs that give rise to sub-modalities of expression profiles. The ‘location data’ in yeast is a comprehensive resource that provides transcription factor–DNA interaction information in vivo. Here, we provide two contributions: first, we developed means to assess the extent of noise in the location data, and consequently for extracting signals from it. Second, we couple signal extraction with better characterization of the genetic network architecture. We apply two methods for the detection of combinatorial associations between transcription factors (TFs), the integration of which provides a global map of combinatorial regulatory interactions. We discover the capacity of regulatory motifs and TF partnerships to dictate fine-tuned expression patterns of subsets of genes, which are clearly distinct from those displayed by most genes assigned to the same TF. Our findings provide carefully prioritized, high-quality assignments between regulators and regulated genes and as such should prove useful for experimental and computational biologists alike

Velocity fluctuations of population fronts propagating into metastable states

The position of propagating population fronts fluctuates because of the discreteness of the individuals and stochastic character of processes of birth, death and migration. Here we consider a Markov model of a population front propagating into a metastable state, and focus on the weak noise limit. For typical, small fluctuations the front motion is diffusive, and we calculate the front diffusion coefficient. We also determine the probability distribution of rare, large fluctuations of the front position and, for a given average front velocity, find the most likely population density profile of the front. Implications of the theory for population extinction risk are briefly considered.Comment: 8 pages, 3 figure

Quark Solitons from Effective Action of QCD

We derive an effective low energy action for QCD in 4 dimensions. The low energy dynamics is described by chiral fields transforming non-trivially under both color and flavor. We use the method of anomaly integration from the QCD action. The solitons of the theory have the quantum numbers of quarks. They are expected to be the constituent quarks of hadrons. In two dimensions our result is exact, namely the bosonic gauged action of WZW.Comment: 19 pages (phyzzx macropackage) WIS-93/110/Nov-PH, TAUP 2117-93. Some sign changes, one sentence added following eq(3.9), and one ref. [23] adde

Constituent Quarks as Solitons

We exhibit soliton solutions of QCD in two dimensions that have the quantum numbers of quarks. They exist only for quarks heavier than the dimensional gauge coupling, and have infinite energy, corresponding to the presence of a string carrying the non-singlet color flux off to spatial infinity. The quark solitons also disappear at finite temperature, as the temperature-dependent effective quark mass is reduced in the approach to the quark/hadron phase transition.Comment: 14 page

Estimating Cell Depth from Somatic Mutations

The depth of a cell of a multicellular organism is the number of cell divisions it underwent since the zygote, and knowing this basic cell property would help address fundamental problems in several areas of biology. At present, the depths of the vast majority of human and mouse cell types are unknown. Here, we show a method for estimating the depth of a cell by analyzing somatic mutations in its microsatellites, and provide to our knowledge for the first time reliable depth estimates for several cells types in mice. According to our estimates, the average depth of oocytes is 29, consistent with previous estimates. The average depth of B cells ranges from 34 to 79, linearly related to the mouse age, suggesting a rate of one cell division per day. In contrast, various types of adult stem cells underwent on average fewer cell divisions, supporting the notion that adult stem cells are relatively quiescent. Our method for depth estimation opens a window for revealing tissue turnover rates in animals, including humans, which has important implications for our knowledge of the body under physiological and pathological conditions