Describing the complexity of systems: multi-variable "set complexity" and the information basis of systems biology

Context dependence is central to the description of complexity. Keying on the pairwise definition of "set complexity" we use an information theory approach to formulate general measures of systems complexity. We examine the properties of multi-variable dependency starting with the concept of interaction information. We then present a new measure for unbiased detection of multi-variable dependency, "differential interaction information." This quantity for two variables reduces to the pairwise "set complexity" previously proposed as a context-dependent measure of information in biological systems. We generalize it here to an arbitrary number of variables. Critical limiting properties of the "differential interaction information" are key to the generalization. This measure extends previous ideas about biological information and provides a more sophisticated basis for study of complexity. The properties of "differential interaction information" also suggest new approaches to data analysis. Given a data set of system measurements differential interaction information can provide a measure of collective dependence, which can be represented in hypergraphs describing complex system interaction patterns. We investigate this kind of analysis using simulated data sets. The conjoining of a generalized set complexity measure, multi-variable dependency analysis, and hypergraphs is our central result. While our focus is on complex biological systems, our results are applicable to any complex system.Comment: 44 pages, 12 figures; made revisions after peer revie

Comparison of reproducibility, accuracy, sensitivity, and specificity of miRNA quantification platforms

Given the increasing interest in their use as disease biomarkers, the establishment of reproducible, accurate, sensitive, and specific platforms for microRNA (miRNA) quantification in biofluids is of high priority. We compare four platforms for these characteristics: small RNA sequencing (RNA-seq), FirePlex, EdgeSeq, and nCounter. For a pool of synthetic miRNAs, coefficients of variation for technical replicates are lower for EdgeSeq (6.9%) and RNA-seq (8.2%) than for FirePlex (22.4%); nCounter replicates are not performed. Receiver operating characteristic analysis for distinguishing present versus absent miRNAs shows small RNA-seq (area under curve 0.99) is superior to EdgeSeq (0.97), nCounter (0.94), and FirePlex (0.81). Expected differences in expression of placenta-associated miRNAs in plasma from pregnant and non-pregnant women are observed with RNA-seq and EdgeSeq, but not FirePlex or nCounter. These results indicate that differences in performance among miRNA profiling platforms impact ability to detect biological differences among samples and thus their relative utility for research and clinical use

Mammalian cells in culture actively export specific microRNAs

The discovery of microRNAs (miRNAs) as a new class of regulators of gene expression has triggered an explosion of research, but has left many unanswered questions about how this regulation works and how it is integrated with other regulatory mechanisms. A number of miRNAs have been found to be present in blood plasma and other body fluids of humans and mice in surprisingly high concentrations. This observation was unexpected in two respects: first, the fact that these molecules are present at all outside the cell at significant concentrations; and second, that these molecules appear to be stable outside of the cell. In light of this it has been suggested that the biological function of miRNAs may also extend outside of the cell and mediate cell-cell communication^[1-5]^. Such a system would be expected to export specific miRNAs from cells in response to specific biological stimuli. We report here that after serum deprivation several human cell lines tested do export a spectrum of miRNAs into the culture medium. The export response is substantial and prompt. The exported miRNAs are found both within and outside of microvesicles and exosomes. We have identified some candidate protein components of this system outside the cell, and found one exported protein that plays a role in protecting miRNA from degradation. Our results point to a hitherto unrecognized and uncharacterized miRNA trafficking system in mammalian cells that may involve cell-cell communication

Thermal stress cycling of GaAs solar cells

A thermal cycling experiment was performed on GaAs solar cells to establish the electrical and structural integrity of these cells under the temperature conditions of a simulated low-Earth orbit of 3-year duration. Thirty single junction GaAs cells were obtained and tests were performed to establish the beginning-of-life characteristics of these cells. The tests consisted of cell I-V power output curves, from which were obtained short-circuit current, open circuit voltage, fill factor, and cell efficiency, and optical micrographs, spectral response, and ion microprobe mass analysis (IMMA) depth profiles on both the front surfaces and the front metallic contacts of the cells. Following 5,000 thermal cycles, the performance of the cells was reexamined in addition to any factors which might contribute to performance degradation. It is established that, after 5,000 thermal cycles, the cells retain their power output with no loss of structural integrity or change in physical appearance

Networks from gene expression time series: characterization of correlation patterns

This paper describes characteristic features of networks reconstructed from gene expression time series data. Several null models are considered in order to discriminate between informations embedded in the network that are related to real data, and features that are due to the method used for network reconstruction (time correlation).Comment: 10 pages, 3 BMP figures, 1 Table. To appear in Int. J. Bif. Chaos, July 2007, Volume 17, Issue

Reconstruction of Network Evolutionary History from Extant Network Topology and Duplication History

Genome-wide protein-protein interaction (PPI) data are readily available thanks to recent breakthroughs in biotechnology. However, PPI networks of extant organisms are only snapshots of the network evolution. How to infer the whole evolution history becomes a challenging problem in computational biology. In this paper, we present a likelihood-based approach to inferring network evolution history from the topology of PPI networks and the duplication relationship among the paralogs. Simulations show that our approach outperforms the existing ones in terms of the accuracy of reconstruction. Moreover, the growth parameters of several real PPI networks estimated by our method are more consistent with the ones predicted in literature.Comment: 15 pages, 5 figures, submitted to ISBRA 201