Determining and interpreting correlations in lipidomic networks found in glioblastoma cells

Background: Intelligent and multitiered quantitative analysis of biological systems rapidly evolves to a key technique in studying biomolecular cancer aspects. Newly emerging advances in both measurement as well as bio-inspired computational techniques have facilitated the development of lipidomics technologies and offer an excellent opportunity to understand regulation at the molecular level in many diseases. Results: We present computational approaches to study the response of glioblastoma U87 cells to gene- and chemo-therapy. To identify distinct biomarkers and differences in therapeutic outcomes, we develop a novel technique based on graph-clustering. This technique facilitates the exploration and visualization of co-regulations in glioblastoma lipid profiling data. We investigate the changes in the correlation networks for different therapies and study the success of novel gene therapies targeting aggressive glioblastoma. Conclusions: The novel computational paradigm provides unique “fingerprints” by revealing the intricate interactions at the lipidome level in glioblastoma U87 cells with induced apoptosis (programmed cell death) and thus opens a new window to biomedical frontiers

Watermarking strategies for IP protection of micro-processor cores

L. Parrilla, E. Castillo, U. Meyer-Bäse, A. García, D. González, E. Todorovich, E. Boemo, A. Lloris, "Watermarking strategies for IP protection of micro-processor cores", Proceedings of SPIE 7703, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VIII, 77030L (2010). Copyright 2010 Society of Photo‑Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.Reuse-based design has emerged as one of the most important methodologies for integrated circuit design, with reusable Intellectual Property (IP) cores enabling the optimization of company resources due to reduced development time and costs. This is of special interest in the Field-Programmable Logic (FPL) domain, which mainly relies on automatic synthesis tools. However, this design methodology has brought to light the intellectual property protection (IPP) of those modules, with most forms of protection in the EDA industry being difficult to translate to this domain. However, IP core watermarking has emerged as a tool for IP core protection. Although watermarks may be inserted at different levels of the design flow, watermarking Hardware Description Language (HDL) descriptions has been proved to be a robust and secure option. In this paper, a new framework for the protection of μP cores is presented. The protection scheme is derived from the IPP@HDL procedure and it has been adapted to the singularities of μP cores, overcoming the problems for the digital signature extraction in such systems. Additionally, the feature of hardware activation has been introduced, allowing the distribution of μP cores in a "demo" mode and a later activation that can be easily performed by the customer executing a simple program. Application examples show that the additional hardware introduced for protection and/or activation has no effect over the performance, and showing an assumable area increase.This work was partially funded by project TEC2007-68074-C02-01/MIC (Plan Nacional I+D+I, Spain). CAD tools and supporting material were provided by Altera Corp. trough University Program agreements. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsors

Determining and interpreting correlations in lipidomic networks found in glioblastoma cells

Background: Intelligent and multitiered quantitative analysis of biological systems rapidly evolves to a key technique in studying biomolecular cancer aspects. Newly emerging advances in both measurement as well as bio-inspired computational techniques have facilitated the development of lipidomics technologies and offer an excellent opportunity to understand regulation at the molecular level in many diseases. Results: We present computational approaches to study the response of glioblastoma U87 cells to gene- and chemo-therapy. To identify distinct biomarkers and differences in therapeutic outcomes, we develop a novel technique based on graph-clustering. This technique facilitates the exploration and visualization of co-regulations in glioblastoma lipid profiling data. We investigate the changes in the correlation networks for different therapies and study the success of novel gene therapies targeting aggressive glioblastoma. Conclusions: The novel computational paradigm provides unique “fingerprints” by revealing the intricate interactions at the lipidome level in glioblastoma U87 cells with induced apoptosis (programmed cell death) and thus opens a new window to biomedical frontiers. Background Glioblastoma are highly invasive brain tumors. Th

Biomedical signal analysis: Contemporary methods and applications.

Biomedical signal analysis has become one of the most important visualization and interpretation methods in biology and medicine. Many new and powerful instruments for detecting, storing, transmitting, analyzing, and displaying images have been developed in recent years, allowing scientists and physicians to obtain quantitative measurements to support scientific hypotheses and medical diagnoses. This book offers an overview of a range of proven and new methods, discussing both theoretical and practical aspects of biomedical signal analysis and interpretation.After an introduction to the topic and a survey of several processing and imaging techniques, the book describes a broad range of methods, including continuous and discrete Fourier transforms, independent component analysis (ICA), dependent component analysis, neural networks, and fuzzy logic methods. The book then discusses applications of these theoretical tools to practical problems in everyday biosignal processing, considering such subjects as exploratory data analysis and low-frequency connectivity analysis in fMRI, MRI signal processing including lesion detection in breast MRI, dynamic cerebral contrast-enhanced perfusion MRI, skin lesion classification, and microscopic slice image processing and automatic labeling. Biomedical Signal Analysis can be used as a text or professional reference. Part I, on methods, forms a self-contained text, with exercises and other learning aids, for upper-level undergraduate or graduate-level students. Researchers or graduate students in systems biology, genomic signal processing, and computer-assisted radiology will find both parts I and II (on applications) a valuable handbook

Foundations of medical imaging and signal recording.

Computer processing and analysis of medical images, as well as experimental data analysis of physiological signals, have evolved since the late 1980s from a variety of directions, ranging from signal and imaging acquisition equipment to areas such as digital signal and image processing, computer vision, and pattern recognition. The most important physiological signals, such as electrocardiograms (ECG), electromyograms (EMG), electroencephalograms (EEG), and magnetoencephalograms (MEG), represent analog signals that are digitized for the purposes of storage and data analysis. The nature of medical images is very broad; it is as simple as an chest X-ray or as sophisticated as noninvasive brain imaging, such as functional magnetic resonance imaging (fMRI). While medical imaging is concerned with the interaction of all forms of radiation with tissue and the clinical extraction of relevant information, its analysis encompasses the measurement of anatomical and physiological parameters from images, image processing, and motion and change detection from image sequences. This chapter gives an overview of biological signal and image analysis, and describes the basic model for computer-aided systems as a commonbasis enabling the study of several problems of medical-imagingbased diagnostics

Auditory Neuron Models for Cochlea Implants

Auditory perception neurons, also called inner hair cells (IHCs), because of their physical shape, transform the mechanical movements of the basilar membrane into electrical impulses. The impulse coding of the IHC is the main information carrier in the auditory process and is the basis for improvements of cochlea implants as well as for low rate, high quality speech processing and compression. This paper compares biologically motivated models (Meddis, Cooke, Brachman-Payton) with a newly developed model which is transfer function oriented. The new model has only three reservoirs and the parameters can be controlled through five small ROM tables. We compare this model with the often used Meddis model in terms of accuracy, system parameter flexibility, and hardware effort in an FPGA implementation. Keywords: Inner Hair Cell (IHC), Cochlea Implants, Auditory Neurons, FPGA design 1. INTRODUCTION Cochlea implants (CIs) have been successfully used to treat deaf patients having defective ..

Uncertain gene regulatory networks simplified by gramian-based approach.

The complexity of gene regulatory networks described by coupled nonlinear differen- tial equations is often an obstacle for analysis purposes. They are prone to internal parametrical fluctuations making thus robustness a crucial property of these net- works to attenuate the effects of internal fluctuation. Therefore, the development of effective model reduction techniques for uncertain biological systems is of paramount importance in the field of systems biology. In this paper, we apply a Gramian-based approach for model reduction for gene regulatory networks based only on finding generalized Gramians and standard matrix transformations. The method is based on finding a generalized controllability and observability Gramian of the uncertain system and then based on a state transformation matrix a reduced-order representation. Under the assumption that the structured uncertainties are norm-bounded, we can prove that the reduced-order balanced system is also stable

Robust stability analysis and design under consideration of multiple feedback loops of the tryptophan regulatory network of Escherichia coli.

The tryptophan system present in Escherichia coli represents an important regulatory unit described by multiple feedback loops. The role of these feedback loops is crucial for the analysis of the dynamical behavior of the tryptophan synthesis. We analyze the robust stability of this system which models the dynamics of both fast state, such as transcription and synthesis of free operator, and slow state, such as translation and tryptophan synthesis under consideration of nonlinear uncertainties. In addition, we analyze the role of these feedback loops as key design components of this regulatory unit responsible for its physiological performance. The range of allowed parameter perturbations and the conditions that ensure the existence of asymptotically stable equilibria of the perturbed system are determined. We also analyze two important alternate regulatory designs for the tryptophan synthesis pathway and derive the stability conditions