Introduction to a Biological Systems Science

Biological systems analysis and biodynamic modelling of physiological and biological interrelationships in human body and mammal

Distributed Caching for Processing Raw Arrays

As applications continue to generate multi-dimensional data at exponentially increasing rates, fast analytics to extract meaningful results is becoming extremely important. The database community has developed array databases that alleviate this problem through a series of techniques. In-situ mechanisms provide direct access to raw data in the original format---without loading and partitioning. Parallel processing scales to the largest datasets. In-memory caching reduces latency when the same data are accessed across a workload of queries. However, we are not aware of any work on distributed caching of multi-dimensional raw arrays. In this paper, we introduce a distributed framework for cost-based caching of multi-dimensional arrays in native format. Given a set of files that contain portions of an array and an online query workload, the framework computes an effective caching plan in two stages. First, the plan identifies the cells to be cached locally from each of the input files by continuously refining an evolving R-tree index. In the second stage, an optimal assignment of cells to nodes that collocates dependent cells in order to minimize the overall data transfer is determined. We design cache eviction and placement heuristic algorithms that consider the historical query workload. A thorough experimental evaluation over two real datasets in three file formats confirms the superiority - by as much as two orders of magnitude - of the proposed framework over existing techniques in terms of cache overhead and workload execution time

Neurophysiology

Contains research objectives and reports on three research projects.National Aeronautics and Space Administration (Grant NsG-496)U.S. Air Force (Aeronautical Systems Division) under Contract AF33 (616)-7783The Teagle Foundation, Inc.National Institutes of Health (Grant MH-04737-03)National Institutes of Health (Grant NB-04897-01)National Science Foundation (Grant G-16526)Bell Telephone Laboratories, Inc

Neurophysiology

Contains research objectives and reports on one research project.U. S. Air Force Cambridge Research Laboratories under Contract AF19(628)-4147Bell Telephone Laboratories, Inc.National Institutes of Health (Grant MH-04737-04)National Science Foundation (Grant GP-2495)National Institutes of Health (Grant NB-04987-02)The Teagle Foundation, Inc.National Aeronautics and Space Administration (Grant NsG-496)U. S. Air Force (Aeronautical Systems Division) under Contract AF 33(615)-1747National Institutes of Health (Grant NB-04985-01

Negative Resistance in Brownian Transport

We prove that negative incremental resistance cannot occur on 1D spaces like the circle or the line; we construct an explicit two-dimensional model on the cylinder, and its collapse into a branched 1D backbone. We derive an accurate numerical method for solving our 2D model, and discuss the relevance of the model to biological ion channels.Comment: 3 separate figure

Neurophysiology

Contains research objectives and reports on nine research projects.The Teagle Foundation, Inc.U.S. Air Force (Aeronautical Systems Division) under Contract AF33(616)-7783Bell Telephone Laboratories, Inc.National Institutes of Health [Grant M-4235-(C1)]National Institutes of Health (Grant B-1865-(C3))National Institutes of Health (Grant MP-4737)National Institutes of Health (Grant B-2480(C1)

Expression of different survivin variants in gastric carcinomas: first clues to a role of survivin-2B in tumour progression

Survivin is a novel member of the inhibitor of apoptosis family and determines the susceptibility of tumour cells to pro-apoptotic stimuli. Recently, we identified two novel alternative splice variants of survivin, differing in their anti-apoptotic properties: whereas the anti-apoptotic potential of survivin-ΔEx3 is preserved, survivin-2B has lost its anti-apoptotic potential and may act as a naturally occurring antagonist of survivin. Because the in vivo expression of these alternative splice variants has not been explored so far, we analysed gastric carcinomas of different histological subtypes, grades and stages. Since no antibodies are currently available to determine the novel splice variants, quantitative reverse transcriptase polymerase chain reaction was performed, using RNA samples obtained from 30 different gastric carcinomas. Polymerase chain reactions products were quantified by densitometric evaluation. We found that all gastric carcinomas, irrespective of their histological types, grades or stages, express survivin-ΔEx3, survivin-2B and survivin, the latter being the dominant transcript. Comparing the disease stages I+II with III+IV, expression of survivin and survivin-ΔEx3 remained unchanged. In contrast, a significant (P=0.033) stage-dependent decrease in the expression of survivin-2B became evident. Our study demonstrates for the first time the expression of alternative splice variants in gastric carcinomas and provides a first clue to a role of survivin-2B in tumour progression

Neurophysiology

Contains research objectives.Bell Telephone Laboratories, Inc.The Teagle Foundation, Inc.National Institutes of Health (Grant NB-01865-05)National Institutes of Health (Grant MH-04737-02)U.S. Air Force (Aeronautical Systems Division) under Contract AF33(616)-778

Accurate Expression Profiling of Very Small Cell Populations

BACKGROUND: Expression profiling, the measurement of all transcripts of a cell or tissue type, is currently the most comprehensive method to describe their physiological states. Given that accurate profiling methods currently available require RNA amounts found in thousands to millions of cells, many fields of biology working with specialized cell types cannot use these techniques because available cell numbers are limited. Currently available alternative methods for expression profiling from nanograms of RNA or from very small cell populations lack a broad validation of results to provide accurate information about the measured transcripts. METHODS AND FINDINGS: We provide evidence that currently available methods for expression profiling of very small cell populations are prone to technical noise and therefore cannot be used efficiently as discovery tools. Furthermore, we present Pico Profiling, a new expression profiling method from as few as ten cells, and we show that this approach is as informative as standard techniques from thousands to millions of cells. The central component of Pico Profiling is Whole Transcriptome Amplification (WTA), which generates expression profiles that are highly comparable to those produced by others, at different times, by standard protocols or by Real-time PCR. We provide a complete workflow from RNA isolation to analysis of expression profiles. CONCLUSIONS: Pico Profiling, as presented here, allows generating an accurate expression profile from cell populations as small as ten cells