Telling time with an intrinsically noisy clock

Intracellular transmission of information via chemical and transcriptional networks is thwarted by a physical limitation: the finite copy number of the constituent chemical species introduces unavoidable intrinsic noise. Here we provide a method for solving for the complete probabilistic description of intrinsically noisy oscillatory driving. We derive and numerically verify a number of simple scaling laws. Unlike in the case of measuring a static quantity, response to an oscillatory driving can exhibit a resonant frequency which maximizes information transmission. Further, we show that the optimal regulatory design is dependent on the biophysical constraints (i.e., the allowed copy number and response time). The resulting phase diagram illustrates under what conditions threshold regulation outperforms linear regulation.Comment: 10 pages, 5 figure

Statistics of correlated percolation in a bacterial community

Signal propagation over long distances is a ubiquitous feature of multicellular communities, but cell-to-cell variability can cause propagation to be highly heterogeneous. Simple models of signal propagation in heterogenous media, such as percolation theory, can potentially provide a quantitative understanding of these processes, but it is unclear whether these simple models properly capture the complexities of multicellular systems. We recently discovered that in biofilms of the bacterium Bacillus subtilis, the propagation of an electrical signal is statistically consistent with percolation theory, and yet it is reasonable to suspect that key features of this system go beyond the simple assumptions of basic percolation theory. Indeed, we find here that the probability for a cell to signal is not independent from other cells as assumed in percolation theory, but instead is correlated with its nearby neighbors. We develop a mechanistic model, in which correlated signaling emerges from cell division, phenotypic inheritance, and cell displacement, that reproduces the experimentally observed correlations. We find that the correlations do not significantly affect the spatial statistics, which we rationalize using a renormalization argument. Moreover, the fraction of signaling cells is not constant in space, as assumed in percolation theory, but instead varies within and across biofilms. We find that this feature lowers the fraction of signaling cells at which one observes the characteristic power-law statistics of cluster sizes, consistent with our experimental results. We validate the model using a mutant biofilm whose signaling probability decays along the propagation direction. Our results reveal key statistical features of a correlated signaling process in a multicellular community. More broadly, our results identify extensions to percolation theory that do or do not alter its predictions and may be more appropriate for biological systems.P50 GM085764 - NIGMS NIH HHS; Howard Hughes Medical Institute; R01 GM121888 - NIGMS NIH HHSPublished versio

Serially-regulated biological networks fully realize a constrained set of functions

We show that biological networks with serial regulation (each node regulated by at most one other node) are constrained to {\it direct functionality}, in which the sign of the effect of an environmental input on a target species depends only on the direct path from the input to the target, even when there is a feedback loop allowing for multiple interaction pathways. Using a stochastic model for a set of small transcriptional regulatory networks that have been studied experimentally, we further find that all networks can achieve all functions permitted by this constraint under reasonable settings of biochemical parameters. This underscores the functional versatility of the networks.Comment: 9 pages, 3 figure

Retrospective correction of involuntary microscopic head movement using highly accelerated fat image navigators (3D FatNavs) at 7T

Purpose: The goal of the present study was to use a three- dimensional (3D) gradient echo volume in combination with a fat-selective excitation as a 3D motion navigator (3D FatNav) for retrospective correction of microscopic head motion during high-resolution 3D structural scans of extended duration. The fat excitation leads to a 3D image that is itself sparse, allowing high parallel imaging acceleration factors – with the additional advantage of a minimal disturbance of the water signal used for the host sequence. Methods: A 3D FatNav was inserted into two structural proto- cols: an inversion-prepared gradient echo at 0.33 0.33 1.00 mm resolution and a turbo spin echo at 600 mm isotropic resolution. Results: Motion estimation was possible with high precision, allowing retrospective motion correction to yield clear improvements in image quality, especially in the conspicuity of very small blood vessels. Conclusion: The highly accelerated 3D FatNav allowed motion correction with noticeable improvements in image quality, even for head motion which was small compared with the voxel dimensions of the host sequence

Quantifying evolvability in small biological networks

We introduce a quantitative measure of the capacity of a small biological network to evolve. We apply our measure to a stochastic description of the experimental setup of Guet et al. (Science 296:1466, 2002), treating chemical inducers as functional inputs to biochemical networks and the expression of a reporter gene as the functional output. We take an information-theoretic approach, allowing the system to set parameters that optimize signal processing ability, thus enumerating each network's highest-fidelity functions. We find that all networks studied are highly evolvable by our measure, meaning that change in function has little dependence on change in parameters. Moreover, we find that each network's functions are connected by paths in the parameter space along which information is not significantly lowered, meaning a network may continuously change its functionality without losing it along the way. This property further underscores the evolvability of the networks.Comment: 8 pages, 3 figure

Incidental MALT Type Lymphoma Exhibiting Prominent Plasma Cell Differentiation Associated with Hashimoto’s Thyroiditis. A Two Case Report

We present here two cases of incidental extranodal marginal zone B-cell lymphoma of mucosa-associated lymphoid tissue (MALT lymphoma) showing prominent plasma cell differentiation associated with Hashimoto’s thyroiditis (HT). Histological examination demonstrated that both lesions exhibited HT including lymphoplasmacytic infiltration with the formation of germinal centers, destruction of the normal thyroid follicular architecture, Hürthle cell changes, and squamous metaplasia. The dominant tumor nodules of both cases contained large, well-circumscribed but unencapsulated aggregation of mature plasma cells and scattered centrocyte-like cells (CCL-cells). Both lesions contained a few lymphoepithelial lesions. Moreover, immunohistochemical study demonstrated that plasma cells and CCL-cells of these two lesions contained monotypic intracytoplasmic kappa light chain. Other small B-cell lymphomas, plasmacytoma and plasmablastic lymphoma were excluded using stains for CD5, CD10, CD23, CD43, CD56. Cyclin D1, human herpes virus type-8

Stochastic pump effect and geometric phases in dissipative and stochastic systems

The success of Berry phases in quantum mechanics stimulated the study of similar phenomena in other areas of physics, including the theory of living cell locomotion and motion of patterns in nonlinear media. More recently, geometric phases have been applied to systems operating in a strongly stochastic environment, such as molecular motors. We discuss such geometric effects in purely classical dissipative stochastic systems and their role in the theory of the stochastic pump effect (SPE).Comment: Review. 35 pages. J. Phys. A: Math, Theor. (in press

Regularity Properties and Pathologies of Position-Space Renormalization-Group Transformations

We reconsider the conceptual foundations of the renormalization-group (RG) formalism, and prove some rigorous theorems on the regularity properties and possible pathologies of the RG map. Regarding regularity, we show that the RG map, defined on a suitable space of interactions (= formal Hamiltonians), is always single-valued and Lipschitz continuous on its domain of definition. This rules out a recently proposed scenario for the RG description of first-order phase transitions. On the pathological side, we make rigorous some arguments of Griffiths, Pearce and Israel, and prove in several cases that the renormalized measure is not a Gibbs measure for any reasonable interaction. This means that the RG map is ill-defined, and that the conventional RG description of first-order phase transitions is not universally valid. For decimation or Kadanoff transformations applied to the Ising model in dimension $d \ge 3$, these pathologies occur in a full neighborhood $\{ \beta > \beta_0 ,\, |h| < \epsilon(\beta) \}$ of the low-temperature part of the first-order phase-transition surface. For block-averaging transformations applied to the Ising model in dimension $d \ge 2$, the pathologies occur at low temperatures for arbitrary magnetic-field strength. Pathologies may also occur in the critical region for Ising models in dimension $d \ge 4$. We discuss in detail the distinction between Gibbsian and non-Gibbsian measures, and give a rather complete catalogue of the known examples. Finally, we discuss the heuristic and numerical evidence on RG pathologies in the light of our rigorous theorems.Comment: 273 pages including 14 figures, Postscript, See also ftp.scri.fsu.edu:hep-lat/papers/9210/9210032.ps.

B7-H4 gene polymorphisms are associated with sporadic breast cancer in a Chinese Han population

© 2009 Zhang et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Information transmission in genetic regulatory networks: a review

Genetic regulatory networks enable cells to respond to the changes in internal and external conditions by dynamically coordinating their gene expression profiles. Our ability to make quantitative measurements in these biochemical circuits has deepened our understanding of what kinds of computations genetic regulatory networks can perform and with what reliability. These advances have motivated researchers to look for connections between the architecture and function of genetic regulatory networks. Transmitting information between network's inputs and its outputs has been proposed as one such possible measure of function, relevant in certain biological contexts. Here we summarize recent developments in the application of information theory to gene regulatory networks. We first review basic concepts in information theory necessary to understand recent work. We then discuss the functional complexity of gene regulation which arrises from the molecular nature of the regulatory interactions. We end by reviewing some experiments supporting the view that genetic networks responsible for early development of multicellular organisms might be maximizing transmitted 'positional' information.Comment: Submitted to J Phys: Condens Matter, 31 page