On-line metabolic pathway analysis based on metabolic signal flow diagram

In this work, an integrated modeling approach based on a metabolic signal flow diagram and cellular energetics was used to model the metabolic pathway analysis for the cultivation of yeast on glucose. This approach enables us to make a clear analysis of the flow direction of the carbon fluxes in the metabolic pathways as well as of the degree of activation of a particular pathway for the synthesis of biomaterials for cell growth. The analyses demonstrate that the main metabolic pathways of Saccharomyces cerevisiae change significantly during batch culture. Carbon flow direction is toward glycolysis to satisfy the increase of requirement for precursors and energy. The enzymatic activation of TCA cycle seems to always be at normal level, which may result in the overflow of ethanol due to its limited capacity. The advantage of this approach is that it adopts both virtues of the metabolic signal flow diagram and the simple network analysis method, focusing on the investigation of the flow directions of carbon fluxes and the degree of activation of a particular pathway or reaction loop. All of the variables used in the model equations were determined on-line; the information obtained from the calculated metabolic coefficients may result in a better understanding of cell physiology and help to evaluate the state of the cell culture process.     

Detection of ultrasonic cavitation based on low-frequency analysis of acoustic signal

The acoustic cavitation phenomenon constitutes a potential hazard in ultrasound diagnostics and therapy so that early and effective detection of cavitation is of great interest. However, cavitation might even bring a higher risk especially when an echocontrast agent based on microbubbles is used. The major goal of the present work was to develop a cavitation detection method based on increased level of cavitation noise in the range of low frequencies (about 1 Hz). This method was applied in vitro using a model of body fluid containing a model echocontrast agent, such as 5% solution of lyophilized egg albumin, which was sonicated by ultrasound disintegrator. Ultrasound signal evokes cavitation in microbubble suspension accompanied by a certain level of cavitation acoustic noise. The level of noise voltage increased in the frequency range of 0.1 to 2 Hz in the presence of cavitation. Hence, this method makes it possible to determine the value of cavitation threshold. In addition, we examined how the cavitation threshold is affected by temperature and viscosity. It was found that the cavitation threshold decreased with growing temperature while it increased with growing viscosity.     

Gabor based analysis prior formulation for EEG signal reconstruction

This paper deals with the problem of tele-monitoring EEG signals. In EEG tele-monitoring system, the integral step is to compress the signals in computationally efficient manner so that they can be transmitted over a limited bandwidth. In such a situation a Compressed Sensing (CS) framework for compressing and recovering the signals is the most viable approach. Previously the well known synthesis prior formulation is used for reconstruction. For the first time in this work, we show that the lesser known analysis prior formulation is a more appropriate way to frame the reconstruction problem. We show that our method yields better results than the previous synthesis prior formulation.     

New light on bacterial carbonic anhydrases phylogeny based on the analysis of signal peptide sequences

Among protein families, carbonic anhydrases (CAs, EC 4.2.1.1) are metalloenzymes characterized by a common reaction mechanism in all life domains: the carbon dioxide hydration to bicarbonate and protons (CO₂+H₂O ? HCO₃^?+H⁺). Six genetically distinct CA families are known to date, the α-, β-, γ-, δ-, ζ- and η-CAs. The last CA class was recently discovered analyzing the amino acid sequences of CAs from Plasmodia. Bacteria encode for enzymes belonging to the α-, β-, and γ-CA classes and recently, phylogenetic analysis revealed an interesting relationship regarding the evolution of bacterial CA classes. This result evidenced that the three bacterial CA classes, in spite of the high level of the structural similarity, are evolutionarily distinct, but we noted that the primary structure of some β-CAs identified in the genome of Gram-negative bacteria present a pre-sequence of 18 or more amino acid residues at the N-terminal part. These observations and subsequent phylogenetic data presented here prompted us to propose that the β-CAs found in Gram-negative bacteria with a periplasmic space and characterized by the presence of a signal peptide might have a periplasmic localization and a role similar to that described previously for the α-CAs.     

Application of Petri net based analysis techniques to signal transduction pathways

Background  

Signal transduction pathways are usually modelled using classical quantitative methods, which are based on ordinary differential equations (ODEs). However, some difficulties are inherent in this approach. On the one hand, the kinetic parameters involved are often unknown and have to be estimated. With increasing size and complexity of signal transduction pathways, the estimation of missing kinetic data is not possible. On the other hand, ODEs based models do not support any explicit insights into possible (signal-) flows within the network. Moreover, a huge amount of qualitative data is available due to high-throughput techniques. In order to get information on the systems behaviour, qualitative analysis techniques have been developed. Applications of the known qualitative analysis methods concern mainly metabolic networks. Petri net theory provides a variety of established analysis techniques, which are also applicable to signal transduction models. In this context special properties have to be considered and new dedicated techniques have to be designed.     

Identification of pathway deregulation - gene expression based analysis of consistent signal transduction

Signaling pathways belong to a complex system of communication that governs cellular processes. They represent signal transduction from an extracellular stimulus via a receptor to intracellular mediators, as well as intracellular interactions. Perturbations in signaling cascade often lead to detrimental changes in cell function and cause many diseases, including cancer. Identification of deregulated pathways may advance the understanding of complex diseases and lead to improvement of therapeutic strategies. We propose Analysis of Consistent Signal Transduction (ACST), a novel method for analysis of signaling pathways. Our method incorporates information regarding pathway topology, as well as data on the position of every gene in each pathway. To preserve gene-gene interactions we use a subject-sampling permutation model to assess the significance of pathway perturbations. We applied our approach to nine independent datasets of global gene expression profiling. The results of ACST, as well as three other methods used to analyze signaling pathways, are presented in the context of biological significance and repeatability among similar, yet independent, datasets. We demonstrate the usefulness of using information of pathway structure as well as genes' functions in the analysis of signaling pathways. We also show that ACST leads to biologically meaningful results and high repeatability.     

WaveletQuant,an improved quantification software based on wavelet signal threshold de-noising for labeled quantitative proteomic analysis

Background  

Quantitative proteomics technologies have been developed to comprehensively identify and quantify proteins in two or more complex samples. Quantitative proteomics based on differential stable isotope labeling is one of the proteomics quantification technologies. Mass spectrometric data generated for peptide quantification are often noisy, and peak detection and definition require various smoothing filters to remove noise in order to achieve accurate peptide quantification. Many traditional smoothing filters, such as the moving average filter, Savitzky-Golay filter and Gaussian filter, have been used to reduce noise in MS peaks. However, limitations of these filtering approaches often result in inaccurate peptide quantification. Here we present the WaveletQuant program, based on wavelet theory, for better or alternative MS-based proteomic quantification.     

High-performance signal peptide prediction based on sequence alignment techniques

The accuracy of current signal peptide predictors is outstanding. The most successful predictors are based on neural networks and hidden Markov models, reaching a sensitivity of 99% and an accuracy of 95%. Here, we demonstrate that the popular BLASTP alignment tool can be tuned for signal peptide prediction reaching the same high level of prediction success. Alignment-based techniques provide additional benefits. In spite of high success rates signal peptide predictors yield false predictions. Simple sequences like polyvaline, for example, are predicted as signal peptides. The general architecture of learning systems makes it difficult to trace the cause of such problems. This kind of false predictions can be recognized or avoided altogether by using sequence comparison techniques. Based on these results we have implemented a public web service, called Signal-BLAST. Predictions returned by Signal-BLAST are transparent and easy to analyze. AVAILABILITY: Signal-BLAST is available online at http://sigpep.services.came.sbg.ac.at/signalblast.html.     

Analysis of ECG signal denoising method based on S-transform

Electrocardiogram (ECG), a noninvasive technique which is used generally as a primary diagnostic tool for cardiovascular diseases. A cleaned ECG signal provides necessary information about the electrophysiology of the heart diseases and ischemic changes that may occur. However in real situation, noise is often embedded with ECG signal during acquisition. In this paper, a novel ECG signal denoising technique is proposed using Stockwell transform (S-transform). This method is evaluated on several normal and abnormal ECG signals of MIT/BIH arrhythmia database, by artificially adding white Gaussian noises to visually inspected clean ECG recordings. The experimental results demonstrate that the proposed method shows the better signal to noise ratio (SNR), lower root mean square error (RMSE) and percent root mean square difference (PRD) compared to generally used ECG denoising method like wavelet transform.     

Method of predicting Splice Sites based on signal interactions

Background  

Predicting and proper ranking of canonical splice sites (SSs) is a challenging problem in bioinformatics and machine learning communities. Any progress in SSs recognition will lead to better understanding of splicing mechanism. We introduce several new approaches of combining a priori knowledge for improved SS detection. First, we design our new Bayesian SS sensor based on oligonucleotide counting. To further enhance prediction quality, we applied our new de novo motif detection tool MHMMotif to intronic ends and exons. We combine elements found with sensor information using Naive Bayesian Network, as implemented in our new tool SpliceScan.     

A model for signal detection based on an adaptive filter

A model for the detection of brief stimuli based on a change detection algorithm and a random walk traversed by the residuals generated by an adaptive filter is proposed. A linear relationship between mean RT and response proportion measures obtained in a simulation of the model was consistent with data obtained in psychophysical discrimination tasks using human observers. In this way the role of the sensory system as a detector of change in ambient stimulation could be incorporated into a signal detection model.     

Strategies for inhibiting multistage carcinogenesis based on signal transduction pathways

It is obvious that the simplest approach to cancer prevention is to avoid exposure to causative agents, whether they be tumor initiators, promoters, or agents that enhance the progression of cells to increasing degrees of malignancy. On the other hand, this simple approach will not always be feasible, either because the causative agent cannot be readily removed from the environment, the precise agent is not known with certainty, or individuals have already suffered significant exposure. It is necessary, therefore, to develop new strategies that can arrest or even reverse tumor development at various stages in the carcinogenic process. The long latency in tumor development, the multistage nature of the process, and the potential reversibility of some of these stages, offer reasons for optimism that this can be achieved. Advances in our understanding of the fundamental mechanisms by which environmental agents produce disturbances in growth control suggest very specific strategies. This paper provides examples of how recent knowledge in the areas of growth factors, growth factor receptors, protein kinases, signal transduction pathways, oncogenes and growth suppressor genes might lead to the development of such strategies. Major problems will include the development of agents which will specifically act on the target cells of interest without producing toxicity to other tissues, as well as better methods for identifying those individuals who are at risk of developing cancer and, therefore, warrant such therapy.     

ERG signal analysis using wavelet transform

The wavelet analysis is a powerful tool for analyzing and detecting features of signals characterized by time-dependent statistical properties, as biomedical signals. The identification and the analysis of the components of these signals in the time–frequency domain, give meaningful information about the physiological mechanisms that govern them. This article presents the results of the wavelet analysis applied to the a-wave component of the human electroretinogram. In order to deepen and improve our knowledge about the behavior of the early photoreceptoral response, including the possible activation of interactions and correlations among the photoreceptors, we have detected and identified the stable time–frequency components of the a-wave, using six representative values of luminance. The results indicate the occurrence of three frequencies lying in the range 20–200 Hz. The lowest one is attributed to the summed activities of the photoreceptors. The others are weaker and at low luminance one of them does not occur. We relate them to the response of the rods and the cones whose aggregate activities are non-linear and typically exhibit self-organization under selective stimuli. The identification of the stable frequency components and of their times of occurrence helps us to shine light about the complex mechanisms governing the a-wave. The present results are promising toward the assessment of more refined model concerning the photoreceptoral activities.     

Molecular analysis of auxin-specific signal transduction

The auxin-binding protein (ABP1) of maize has been purified, cloned and sequenced. Homologues have been found in a wide range of plants and at least seven ABP sequences from four different species are now known. We have developed a range of anti-ABP antibodies and these have been applied to analysis of the structure, localization and receptor function of ABP. ABP1 is a glycoprotein with two identical subunits of apparent M _r =22 kDa. The regions recognised by our five monoclonal antibodies (MAC 256–260) and by polyclonal antisera from our own and other laboratories have been specified by epitope mapping and fragmentation studies. All polyclonal anti-ABP sera recognise two or three dominant epitopes around the single glycosylation site. Two monoclonals (MAC 256, 259) are directed at the endoplasmic reticulum (ER) retention sequence KDEL at the C-terminus. Early biochemical data pointed to six amino acids likely to be involved in the auxin binding site. Inspection of the deduced sequence of ABP1 showed a hexapeptide (HRHSCE) containing five of these residues. Antibodies were raised against a polypeptide embracing this region and recognised ABP homologs in many species, suggesting that the region is highly conserved. This is confirmed by more recent information showing that the selected polypeptide contains the longest stretch of wholly conserved sequence in ABP1. Most strikingly, the antibodies show auxin agonist activity against protoplasts in three different electrophysiological systems-hyperpolarization of tobacco transmembrane potential; stimulation of outward ATP-dependent H⁺ current in maize; modulation of anion channels in tobacco. The biological activity of these antibodies indicates that the selected peptide does form a functionally important part of the auxin binding site and strongly supports a role for ABP1 as an auxin receptor. Although ABP contains a KDEL sequence and is located mainly in the ER lumen, the electrophysiological evidence shows clearly that some ABP must reach the outer face of the plasma membrane. One possible mechanism is suggested by our earlier demonstration that the ABP C-terminus recognised by MAC 256 undergoes an auxin-induced conformational change, masking the KDEL epitope and it is of interest that this C-terminal region appears to be important in auxin signalling [22]. So far we have been unable to detect the secretion of ABP into the medium of maize cell (bms) cultures reported by Jones and Herman [7]. However, recent silver enhanced immunogold studies on maize protoplasts have succeeded in visualizing ABP at the cell surface, as well as auxin-specific clustering of the signal induced within 30 minutes. The function of ABP in the ER, as well as the mechanisms of auxin signal transduction both at plasma membrane and gene levels remain to be elucidated.     

A novel distributed swarm control strategy based on coupled signal oscillators

The miniaturization of microrobots is accompanied by limitations of signaling, sensing and agility. Control of a swarm of simple microrobots has to cope with such constraints in a way which still guarantees the accomplishment of a task. A recently proposed communication method, which is based on the coupling of signal oscillators of individual agents [13], may provide a basis for a distributed control of a simulated swarm of simple microrobots (similar to I-Swarm microrobots) engaged in a cleaning scenario. This self-organized communication method was biologically inspired from males of chorusing insects which are known for the rapid synchronization of their acoustic signals in a chorus. Signal oscillator properties were used to generate waves of synchronized signaling (s-waves) among a swarm of agents. In a simulation of a cleaning scenario, agents on the dump initiated concentrically spreading s-waves by shortening their intrinsic signal period. Dirt-carrying agents localized the dump by heading against the wave front. After optimization of certain control parameters the properties of this distributed control strategy were investigated in different variants of a cleaning scenario. These include a second dump, obstacles, different agent densities, agent drop-out and a second signal oscillator.     

Targeting prostate cancer based on signal transduction and cell cycle pathways

Prostate cancer remains a leading cause of death in men despite increased capacity to diagnose at earlier stages. After prostate cancer has become hormone independent, which often occurs after hormonal ablation therapies, it is difficult to effectively treat. Prostate cancer may arise from mutations and dysregulation of various genes involved in regulation signal transduction (e.g., PTEN, Akt, etc) and the cell cycle (e.g., p53, p21Cip1, p27Kip1, Rb, etc.). This review focuses on the aberrant interactions of signal transduction and cell cycle genes products and how they can contribute to prostate cancer and alter therapeutic effectiveness.