Network inference and network response identification: moving genome-scale data to the next level of biological discovery

Abstract

The escalating amount of genome-scale data demands a pragmatic stance from the research community. How can we utilize this deluge of information to better understand biology, cure diseases, or engage cells in bioremediation or biomaterial production for various purposes? A research pipeline moving new sequence, expression and binding data towards practical end goals seems to be necessary. While most individual researchers are not motivated by such well-articulated pragmatic end goals, the scientific community has already self-organized itself to successfully convert genomic data into fundamentally new biological knowledge and practical applications. Here we review two important steps in this workflow: network inference and network response identification, applied to transcriptional regulatory networks. Among network inference methods, we concentrate on relevance networks due to their conceptual simplicity. We classify and discuss network response identification approaches as either data-centric or network-centric. Finally, we conclude with an outlook on what is still missing from these approaches and what may be ahead on the road to biological discovery.

Network reconstruction reveals new links between aging and calorie restriction in yeast

“…Network inference is a difficult but necessary intermediate step in the systems biology workflow that converts genomescale data into biological discovery and practical applications (Veiga et al, 2010). Once constructed and validated, the molecular interaction network facilitates testable hypothesis generation by summarizing known interactions among the genes of interest.…”

Non-coding RNAs and complex distributed genetic networks

“…The understanding of such networks is still limited despite a few decades of studies by biologists, chemists, physicists and mathematicians [1][2][3][4][5]. The main activity in this area has long been focused on the interplay of mRNAs and proteins.…”

Localized mRNA translation and protein association

“…This approach also forms a basis for analysis of genetic networks in ensembles of cells with intercellular communication. The kinetic models of this category are abundant (see, e.g., recent reviews, focused on stochastic effects [1], oscillations [2], ncRNAs [3], and complex genetic networks [4]). …”