Dynamic gene expression data is generally available as microarray samples that are collected over a time course or under multiple conditions. As techniques in dynamic network analysis continue to advance, tools that can visualize these complex networks will become increasingly important to understanding the systematic rewiring of the transcriptional regulatory circuitry that controls cell behavior.ĭynamic network analysis begins with data collection and the creation of a series of gene-gene interactions (networks) from the data (Figure 1). In addition, recent algorithmic advancements allow these time-varying networks to be reverse engineered from a time series of molecular profiles. Recently, biologists have attained a deeper knowledge of the functional and regulatory underpinnings of complex biological processes by studying dynamic gene-gene relationships. Representing these dynamic interactions with a single static network limits the biological insights that can be derived from analysis. However, biological processes such as development and disease progression evolve over time and react to changing environments. Traditionally, gene networks have been analyzed as static entities. Understanding these network relationships between genes can lead to greater insight into cellular processes, such as the cell cycle or disease progressions.
In particular, gene expression measurements can be used to infer network relationships between genes in a cell, potentially uncovering important interactions that perturb the cellular state. The rapid development of high-throughput technology and increasing amounts of biological data promises greater insight into the complex interactions that govern cellular function.
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