Interactive analysis of systems biology molecular expression data

Mingwu Zhang,Xiang Zhang,David E Salt,John Burgner,Charles Buck,Michael D Kane,Qi Ouyang,Sunil Prabhakar,Alan Stephenson

doi:10.1186/1752-0509-2-23

Mingwu Zhang, Xiang Zhang + Show 7 more

Open Access

https://doi.org/10.1186/1752-0509-2-23

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Abstract

BackgroundSystems biology aims to understand biological systems on a comprehensive scale, such that the components that make up the whole are connected to one another and work through dependent interactions. Molecular correlations and comparative studies of molecular expression are crucial to establishing interdependent connections in systems biology. The existing software packages provide limited data mining capability. The user must first generate visualization data with a preferred data mining algorithm and then upload the resulting data into the visualization package for graphic visualization of molecular relations.ResultsPresented is a novel interactive visual data mining application, SysNet that provides an interactive environment for the analysis of high data volume molecular expression information of most any type from biological systems. It integrates interactive graphic visualization and statistical data mining into a single package. SysNet interactively presents intermolecular correlation information with circular and heatmap layouts. It is also applicable to comparative analysis of molecular expression data, such as time course data.ConclusionThe SysNet program has been utilized to analyze elemental profile changes in response to an increasing concentration of iron (Fe) in growth media (an ionomics dataset). This study case demonstrates that the SysNet software is an effective platform for interactive analysis of molecular expression information in systems biology.

Highlights

Systems biology aims to understand biological systems on a comprehensive scale, such that the components that make up the whole are connected to one another and work through dependent interactions
SysNet is the name of the system we have developed and it is able to: 1) interactively analyze intermolecular correlations using different statistical models, and 2) perform interactive comparative analysis of molecular expression data
Interactive analysis of molecular correlation – Measures of molecular correlation are descriptive statistics that represent the degree of relationship between two or more variables, but are not inferential statistical tests

Summary

Introduction

Systems biology aims to understand biological systems on a comprehensive scale, such that the components that make up the whole are connected to one another and work through dependent interactions. Technological advances allow collection of enormous quantities of data from all biological disciplines. These data provide key information about biomolecular functions, and raise new questions concerning the relationship of these molecules. A key goal in understanding (and predicting) biological behavior is represented by a relatively new discipline of systems biology that aims to provide a systems level understanding in which groups of component biomolecules and pathways are connected and operate interdependently [1]. Genomics provides the list of key components (genes) available for living systems whereas transcriptomics brings information about expression levels of individual genes in certain conditions via measurement of mRNA abundance. Cytomics provides the link from bio-molecules to cell function [7]

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