Informatics approaches for identifying biologic relationships in time‐series data

Abstract

A vital goal of the genomic era is to identify biologic relationships between genes and gene products and to understand how these relationships influence phenotypes. Time course data contain a vast amount of causal and mechanistic information about complex systems, but experimental and informatics challenges must be overcome to produce and extract this information from biologic systems. Mathematical modeling and bioinformatics methods are being developed in anticipation of experiments involving the coordinated measurement of cellular and molecular quantities at various spatial and temporal scales. Experimental methods that probe at the nanoscale will facilitate the exploration of biologic systems at the single-cell and single-molecule level, but will also introduce special challenges for mathematical modeling because events at nanoscale concentrations are subject to the influence of intrinsic noise. This review addresses the progress, challenges, and frontiers in the field of time-series informatics. The ultimate goal of time-series informatics is to move beyond descriptive relationships and toward predictive models of emergent, or systemic, behaviors of biologic systems as a whole.

Ultrasharp nonlinear photothermal and photoacoustic resonances and holes beyond the spectral limit

“…Plasmonic gold nanoparticles have demonstrated their potential as PT/PA molecular contrast agents because of their ultrahigh near-infrared absorption, which is much greater than that of conventional dyes. They convert of absorbed energy into heat, acoustic waves, and nanobubbles effectively and swiftly, and their plasmon resonances can be tuned to a desired spectral range by varying the nanoparticle size, shape, and composition 36 – 41 . However, because of their relatively broad plasmonic bands of 80 to 200 nm, the multiplexing capacity of these nanoparticles to target simultaneously several disease-associated markers is typically limited to two distinct nonoverlapping colours 34 , 35 .…”

Grammatical Immune System Evolution for Reverse Engineering Nonlinear Dynamic Bayesian Models

“…The goal of systems biology is to understand the network of interacting genes, proteins, and biochemical reactions that regulate systemic properties of an organism. A realistic biological network, rather than a static graph, should contain nodes that produce time-varying input/output and edges that represent fl ux through the system [1]. For many biological pathways there is a lack of accurate mathematical models capable of capturing causal dependencies and mechanistic information contained in kinetic data.…”

Data Integration of High-Throughput Proteomic and Transcriptomic Data based on Public Database Knowledge

“…The sampling rate should in theory be i) adjusted to the scale in which the biomarker variation is expected to occur, ii) adjusted to the speed in which changes are occurring in certain time spans, if known in advance, and iii) higher than 2 K [K + log(N )], with K denoting regulatory inputs per gene and N denoting the number of biomarkers. (McKinney, 2009) As the number of regulatory inputs per gene is typically unknown, the presented approach uses interpolation between measurement time points. Nevertheless a careful interpretation of the results gained by the presented data integration method is required because high noise levels in the system might cause deviation from smooth profiles.…”