Introduction to a Biological Systems Science

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

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

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)

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

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

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

The Triform algorithm: improved sensitivity and specificity in ChIP-Seq peak finding

Background: Chromatin immunoprecipitation combined with high-throughput sequencing (ChIP-Seq) is the most frequently used method to identify the binding sites of transcription factors. Active binding sites can be seen as peaks in enrichment profiles when the sequencing reads are mapped to a reference genome. However, the profiles are normally noisy, making it challenging to identify all significantly enriched regions in a reliable way and with an acceptable false discovery rate. Results: We present the Triform algorithm, an improved approach to automatic peak finding in ChIP-Seq enrichment profiles for transcription factors. The method uses model-free statistics to identify peak-like distributions of sequencing reads, taking advantage of improved peak definition in combination with known characteristics of ChIP-Seq data. Conclusions: Triform outperforms several existing methods in the identification of representative peak profiles in curated benchmark data sets. We also show that Triform in many cases is able to identify peaks that are more consistent with biological function, compared with other methods. Finally, we show that Triform can be used to generate novel information on transcription factor binding in repeat regions, which represents a particular challenge in many ChIP-Seq experiments. The Triform algorithm has been implemented in R, and is available via http://tare.medisin.ntnu.no/triform. Keywords: ChIP-Seq, Peak finding, Benchmark, Repeat