The Condition-Dependent Transcriptional Network in Escherichia coli

A primer to frequent itemset mining for bioinformatics

Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences.

General organisational principles of the transcriptional regulation system: a tree or a circle?

Recent advances of systemic approaches to gene expression and cellular metabolism provide unforeseen opportunities for relating and integrating extensive datasets describing the transcriptional regulation system as a whole. However, due to the multifaceted nature of the phenomenon, these datasets often contain logically distinct types of information determined by underlying approach and adopted methodology of data analysis. Consequently, to integrate the datasets comprising information on the states of chromatin structure, transcriptional regulatory network and cellular metabolism, a novel methodology enabling interconversion of logically distinct types of information is required. Here we provide a holistic conceptual framework for analysis of global transcriptional regulation as a system coordinated by structural coupling between the transcription machinery and DNA topology, acting as interdependent sensors and determinants of metabolic functions. In this operationally closed system any transition in physiological state represents an emergent property determined by shifts in structural coupling, whereas genetic regulation acts as a genuine device converting one logical type of information into the other.

Integration of ‘omics’ data: does it lead to new insights into host–microbe interactions?

The interaction between both beneficial and pathogenic microbes and their host has been the subject of many studies. Although the field of systems biology is rapidly evolving, the use of a systems biology approach by means of high-throughput techniques to study host–microbe interactions is just beginning to be explored. In this review, we discuss some of the most recently developed high-throughput ‘omics’ techniques and their use in the context of host–microbe interaction. Moreover, we highlight studies combining several techniques that are pioneering the integration of ‘omics’ data related to host–microbe interactions. Finally, we list the major challenges ahead for successful systems biology research on host–microbe interactions.