Augmented Sparse Reconstruction of Protein Signaling Networks

The problem of reconstructing and identifying intracellular protein signaling and biochemical networks is of critical importance in biology today. We sought to develop a mathematical approach to this problem using, as a test case, one of the most well-studied and clinically important signaling networks in biology today, the epidermal growth factor receptor (EGFR) driven signaling cascade. More specifically, we suggest a method, augmented sparse reconstruction, for the identification of links among nodes of ordinary differential equation (ODE) networks from a small set of trajectories with different initial conditions. Our method builds a system of representation by using a collection of integrals of all given trajectories and by attenuating block of terms in the representation itself. The system of representation is then augmented with random vectors, and minimization of the 1-norm is used to find sparse representations for the dynamical interactions of each node. Augmentation by random vectors is crucial, since sparsity alone is not able to handle the large error-in-variables in the representation. Augmented sparse reconstruction allows to consider potentially very large spaces of models and it is able to detect with high accuracy the few relevant links among nodes, even when moderate noise is added to the measured trajectories. After showing the performance of our method on a model of the EGFR protein network, we sketch briefly the potential future therapeutic applications of this approach.Comment: 24 pages, 6 figure

Delay-Coordinates Embeddings as a Data Mining Tool for Denoising Speech Signals

In this paper we utilize techniques from the theory of non-linear dynamical systems to define a notion of embedding threshold estimators. More specifically we use delay-coordinates embeddings of sets of coefficients of the measured signal (in some chosen frame) as a data mining tool to separate structures that are likely to be generated by signals belonging to some predetermined data set. We describe a particular variation of the embedding threshold estimator implemented in a windowed Fourier frame, and we apply it to speech signals heavily corrupted with the addition of several types of white noise. Our experimental work seems to suggest that, after training on the data sets of interest,these estimators perform well for a variety of white noise processes and noise intensity levels. The method is compared, for the case of Gaussian white noise, to a block thresholding estimator

Wavelet maxima curves of surface latent heat flux associated with two recent Greek earthquakes

International audienceMulti sensor data available through remote sensing satellites provide information about changes in the state of the oceans, land and atmosphere. Recent studies have shown anomalous changes in oceans, land, atmospheric and ionospheric parameters prior to earthquakes events. This paper introduces an innovative data mining technique to identify precursory signals associated with earthquakes. The proposed methodology is a multi strategy approach which employs one dimensional wavelet transformations to identify singularities in the data, and an analysis of the continuity of the wavelet maxima in time and space to identify the singularities associated with earthquakes. The proposed methodology has been employed using Surface Latent Heat Flux (SLHF) data to study the earthquakes which occurred on 14 August 2003 and on 1 March 2004 in Greece. A single prominent SLHF anomaly has been found about two weeks prior to each of the earthquakes

Quotient Signal Decomposition and Order Estimation

In this paper we propose a method for blind signal decomposition that does not require the independence or stationarity of the sources. This method, that we consider a simple instance of non-linear projection pursuit, is based on the possibility of recovering the areas in the time-frequency where the original signals are isolated or almost isolated with the use of suitable quotients of linear combinations of the spectrograms of the mixtures.We then threshold such quotients according to the value of their imaginary part to prove that the method is theoretically sound under mild assumptions on the mixing matrix and the sources. We study one basic algorithm based on this method. Moreover we propose a practical measure of separation for the sources in a given time frequency representation.The algorithm has the important feature of estimating the number of sources with two measurements, it then requires n-2 additional measurements to provide a reconstruction of n sources. Experimental results show that the method works even when severalshifted version of the same source are mixed

Recurrent herpes simplex virus-1 infection induces hallmarks of neurodegeneration and cognitive deficits in mice

Herpes simplex virus type 1 (HSV-1) is a DNA neurotropic virus, usually establishing latent infections in the trigeminal ganglia followed by periodic reactivations. Although numerous findings suggested potential links between HSV-1 and Alzheimer's disease (AD), a causal relation has not been demonstrated yet. Hence, we set up a model of recurrent HSV-1 infection in mice undergoing repeated cycles of viral reactivation. By virological and molecular analyses we found: i) HSV-1 spreading and replication in different brain regions after thermal stress-induced virus reactivations; ii) accumulation of AD hallmarks including amyloid-\u3b2 protein, tau hyperphosphorylation, and neuroinflammation markers (astrogliosis, IL-1\u3b2 and IL-6). Remarkably, the progressive accumulation of AD molecular biomarkers in neocortex and hippocampus of HSV-1 infected mice, triggered by repeated virus reactivations, correlated with increasing cognitive deficits becoming irreversible after seven cycles of reactivation. Collectively, our findings provide evidence that mild and recurrent HSV-1 infections in the central nervous system produce an AD-like phenotype and suggest that they are a risk factor for AD

Characterizing global evolutions of complex systems via intermediate network representations

Recent developments in measurement techniques have enabled us to observe the time series of many components simultaneously. Thus, it is important to understand not only the dynamics of individual time series but also their interactions. Although there are many methods for analysing the interaction between two or more time series, there are very few methods that describe global changes of the interactions over time. Here, we propose an approach to visualise time evolution for the global changes of the interactions in complex systems. This approach consists of two steps. In the first step, we construct a meta-time series of networks. In the second step, we analyse and visualise this meta-time series by using distance and recurrence plots. Our two-step approach involving intermediate network representations elucidates the half-a-day periodicity of foreign exchange markets and a singular functional network in the brain related to perceptual alternations

Delay-Coordinates Embeddings as a Data Mining Tool for Denoising Speech Signals.

In this paper we utilize techniques from the theory of non-linear dynamical systems to define a notion of embedding estimators. More specifically we use delay-coordinates embeddings of sets of coe#cients of the measured signal (in some chosen frame) as a data mining tool to separate structures that are likely to be generated by signals belonging to some predetermined data set. We implement the embedding estimator in a windowed Fourier frame, and we apply it to speech signals heavily corrupted by the addition of several types of white noise. Our experimental work suggests that, after training on the data sets of interest, these estimators perform well for a variety of white noise processes and noise intensity levels