Abstract
BackgroundMolecular networks are the basis of biological processes. Such networks can be decomposed into smaller modules, also known as network motifs. These motifs show interesting dynamical behaviors, in which co-operativity effects between the motif components play a critical role in human diseases. We have developed a motif-searching algorithm, which is able to identify common motif types from the cancer networks and signal transduction networks (STNs). Some of the network motifs are interconnected which can be merged together and form more complex structures, the so-called coupled motif structures (CMS). These structures exhibit mixed dynamical behavior, which may lead biological organisms to perform specific functions.ResultsIn this study, we integrate transcription factors (TFs), microRNAs (miRNAs), miRNA targets and network motifs information to build the cancer-related TF-miRNA-motif networks (TMMN). This allows us to examine the role of network motifs in cancer formation at different levels of regulation, i.e. transcription initiation (TF → miRNA), gene-gene interaction (CMS), and post-transcriptional regulation (miRNA → target genes). Among the cancer networks and STNs we considered, it is found that there is a substantial amount of crosstalking through motif interconnections, in particular, the crosstalk between prostate cancer network and PI3K-Akt STN.ConclusionsTo validate the role of network motifs in cancer formation, several examples are presented which demonstrated the effectiveness of the present approach. A web-based platform has been set up which can be accessed at: http://ppi.bioinfo.asia.edu.tw/pathway/. It is very likely that our results can supply very specific CMS missing information for certain cancer types, it is an indispensable tool for cancer biology research.
Highlights
Molecular networks are the basis of biological processes
The present study addresses the following issues; (i) collect highly confident regulatory relations from cancer networks and signal transduction networks (STNs), (ii) analyze the abundance of five common types of network motifs, (iii) merge interconnected motif types to form coupled motif structures (CMS), (iv) perform gene set enrichment analysis for CMS, (v) construct TF-miRNA-motif networks (TMMN), (vi) perform text mining to validate the motif results, and (vii) quantify crosstalking between cancer networks and STNs
Our results suggested that the number of bi-fans and single-input motif (SIM) motifs outnumber other motif types
Summary
Molecular networks are the basis of biological processes. Such networks can be decomposed into smaller modules, known as network motifs. Examples of local structures are: auto-regulation loop (ARL, either catalytic or repression), feedback loop (FBL), feed-forward loop (FFL, either coherent or incoherent), bi-fan and single-input motif (SIM) [4,5,6] These five network motifs are responsible for a large portion of molecular adjustments when the host is subjected to changes in the external environment (e.g. temperature, chemical concentrations), cell differentiation, development, and signal transduction [7]. Such network motifs are known to have interesting dynamical properties. Identifying different network motif types is the first step towards a better understanding of network biology at a system level
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