Oscillation patterns in negative feedback loops

Organisms are equipped with regulatory systems that display a variety of dynamical behaviours ranging from simple stable steady states, to switching and multistability, to oscillations. Earlier work has shown that oscillations in protein concentrations or gene expression levels are related to the presence of at least one negative feedback loop in the regulatory network. Here we study the dynamics of a very general class of negative feedback loops. Our main result is that in these systems the sequence of maxima and minima of the concentrations is uniquely determined by the topology of the loop and the activating/repressing nature of the interaction between pairs of variables. This allows us to devise an algorithm to reconstruct the topology of oscillating negative feedback loops from their time series; this method applies even when some variables are missing from the data set, or if the time series shows transients, like damped oscillations. We illustrate the relevance and the limits of validity of our method with three examples: p53-Mdm2 oscillations, circadian gene expression in cyanobacteria, and cyclic binding of cofactors at the estrogen-sensitive pS2 promoter.Comment: 10 pages, 8 figure

Growth, profits and technological choice: The case of the Lancashire cotton textile industry

Using Lancashire textile industry company case studies and financial records, mainly from the period just before the First World War, the processes of growth and decline are re-examined. These are considered by reference to the nature of Lancashire entrepreneurship and the impact on technological choice. Capital accumulation, associated wealth distributions and the character of Lancashire business organisation were sybiotically linked to the success of the industry before 1914. However, the legacy of that accumulation in later decades, chronic overcapacity, formed a barrier to reconstruction and enhanced the preciptious decline of a once great industry

Non-equilibrium stationary state of a two-temperature spin chain

A kinetic one-dimensional Ising model is coupled to two heat baths, such that spins at even (odd) lattice sites experience a temperature $T_{e}$ ($$). Spin flips occur with Glauber-type rates generalised to the case of two temperatures. Driven by the temperature differential, the spin chain settles into a non-equilibrium steady state which corresponds to the stationary solution of a master equation. We construct a perturbation expansion of this master equation in terms of the temperature difference and compute explicitly the first two corrections to the equilibrium Boltzmann distribution. The key result is the emergence of additional spin operators in the steady state, increasing in spatial range and order of spin products. We comment on the violation of detailed balance and entropy production in the steady state.Comment: 11 pages, 1 figure, Revte

Ex post aggregate real rates of return in Canada: 1947-1976

Cover title"June 1981."Series statement handwritten on cover"This is a draft of a study to be published by the Economic Council of Canada. It is made available to participants in the Rates of Return Conference on the strict understanding that it is not to be circulated or quoted without permission from the Economic Council of Canada."Includes bibliographical references (p. 77-80

Novel chimerized IgA CD20 antibodies : Improving neutrophil activation against CD20-positive malignancies

ABSTRACT Current combination therapies elicit high response rates in B cell malignancies, often using CD20 antibodies as the backbone of therapy. However, many patients eventually relapse or develop progressive disease. Therefore, novel CD20 antibodies combining multiple effector mechanisms were generated. To study whether neutrophil-mediated destruction of B cell malignancies can be added to the arsenal of effector mechanisms, we chimerized a panel of five previously described murine CD20 antibodies to the human IgG1, IgA1 and IgA2 isotype. Of this panel, we assessed in vitro antibody-dependent cell-mediated cytotoxicity (ADCC), complement-dependent cytotoxicity (CDC) and direct cell death induction capacity and studied the efficacy in two different in vivo mouse models. IgA antibodies outperformed IgG1 antibodies in neutrophil-mediated killing in vitro, both against CD20-expressing cell lines and primary patient material. In these assays, we observed loss of CD19 with both IgA and IgG antibodies. Therefore, we established a novel method to improve the assessment of B-cell depletion by CD20 antibodies by including CD24 as a stable cell marker. Subsequently, we demonstrated that only IgA antibodies were able to reduce B cell numbers in this context. Additionally, IgA antibodies showed efficacy in both an intraperitoneal tumor model with EL4 cells expressing huCD20 and in an adoptive transfer model with huCD20-expressing B cells. Taken together, we show that IgA, like IgG, can induce ADCC and CDC, but additionally triggers neutrophils to kill (malignant) B cells. We conclude that antibodies of the IgA isotype offer an attractive repertoire of effector mechanisms for the treatment of CD20-expressing malignancies.Peer reviewe

Integrate and Fire Neural Networks, Piecewise Contractive Maps and Limit Cycles

We study the global dynamics of integrate and fire neural networks composed of an arbitrary number of identical neurons interacting by inhibition and excitation. We prove that if the interactions are strong enough, then the support of the stable asymptotic dynamics consists of limit cycles. We also find sufficient conditions for the synchronization of networks containing excitatory neurons. The proofs are based on the analysis of the equivalent dynamics of a piecewise continuous Poincar\'e map associated to the system. We show that for strong interactions the Poincar\'e map is piecewise contractive. Using this contraction property, we prove that there exist a countable number of limit cycles attracting all the orbits dropping into the stable subset of the phase space. This result applies not only to the Poincar\'e map under study, but also to a wide class of general n-dimensional piecewise contractive maps.Comment: 46 pages. In this version we added many comments suggested by the referees all along the paper, we changed the introduction and the section containing the conclusions. The final version will appear in Journal of Mathematical Biology of SPRINGER and will be available at http://www.springerlink.com/content/0303-681

Systematic Evaluation of Normalization Methods for Glycomics Data Based on Performance of Network Inference

Glycomics measurements, like all other high-throughput technologies, are subject to technical variation due to fluctuations in the experimental conditions. The removal of this non-biological signal from the data is referred to as normalization. Contrary to other omics data types, a systematic evaluation of normalization options for glycomics data has not been published so far. In this paper, we assess the quality of different normalization strategies for glycomics data with an innovative approach. It has been shown previously that Gaussian Graphical Models (GGMs) inferred from glycomics data are able to identify enzymatic steps in the glycan synthesis pathways in a data-driven fashion. Based on this finding, here, we quantify the quality of a given normalization method according to how well a GGM inferred from the respective normalized data reconstructs known synthesis reactions in the glycosylation pathway. The method therefore exploits a biological measure of goodness. We analyzed 23 different normalization combinations applied to six large-scale glycomics cohorts across three experimental platforms: Liquid Chromatography – ElectroSpray Ionization-Mass Spectrometry (LC-ESI-MS), Ultra High Performance Liquid Chromatography with Fluorescence Detection (UHPLC-FLD), and Matrix Assisted Laser Desorption Ionization – Furier Transform Ion Cyclotron Resonance – Mass Spectrometry (MALDI-FTICR-MS). Based on our results, we recommend normalizing glycan data using the ‘Probabilistic Quotient’ method followed by log-transformation, irrespective of the measurement platform. This recommendation is further supported by an additional analysis, where we ranked normalization methods based on their statistical associations with age, a factor known to associate with glycomics measurements

Associative memory storing an extensive number of patterns based on a network of oscillators with distributed natural frequencies in the presence of external white noise

We study associative memory based on temporal coding in which successful retrieval is realized as an entrainment in a network of simple phase oscillators with distributed natural frequencies under the influence of white noise. The memory patterns are assumed to be given by uniformly distributed random numbers on $[0,2\pi)$ so that the patterns encode the phase differences of the oscillators. To derive the macroscopic order parameter equations for the network with an extensive number of stored patterns, we introduce the effective transfer function by assuming the fixed-point equation of the form of the TAP equation, which describes the time-averaged output as a function of the effective time-averaged local field. Properties of the networks associated with synchronization phenomena for a discrete symmetric natural frequency distribution with three frequency components are studied based on the order parameter equations, and are shown to be in good agreement with the results of numerical simulations. Two types of retrieval states are found to occur with respect to the degree of synchronization, when the size of the width of the natural frequency distribution is changed.Comment: published in Phys. Rev.