Databases and tools for constructing signal transduction networks in cancer

DOKI: Domain knowledge-driven inference method for reverse-engineering transcriptional regulatory relationships among genes in cancer

Efficient reverse-engineering methods are important for identifying transcriptional regulatory relationships among genes in cancer. These methods are becoming increasingly useful in this era where huge volumes of data are generated through the use of high-throughput technologies such as next-generation sequencing technologies and microarrays. However, it is important to improve current methods because of complications involved in modelling complex biological systems. In this paper, we present a novel approach, Domain Knowledge-driven Inference (DOKI), for identification of transcriptional regulatory relationships among genes, given a biological context such as cancer. Combining data normalization, the use of a probability distribution function and Kullback-Leibler Divergence, DOKI incorporates a domain knowledge-driven criterion to make determinations of the existence of regulatory relationships between given transcription factors and given specific gene targets. Characteristics of DOKI enable it to adequately handle complexities inherent in data, and accurately unearth linear and higher-order dependent relationships among genes. DOKI performed equally well with one established high-performing method and better than three other high-performing methods on relatively small data sets. However, it remarkably outperformed these methods on larger data sets to demonstrate its utility. Furthermore, we demonstrate the relevance of such inference algorithms for identifying novel relationships among genes in breast cancer, as some of the consensus results representing novel relationships were confirmed in previously published experimental results. Thus, DOKI will facilitate current efforts to gain etiological insights and help uncover new targeted therapies for various diseases.

Systems biology: perspectives on multiscale modeling in research on endocrine-related cancers

Drawing on concepts from experimental biology, computer science, informatics, mathematics and statistics, systems biologists integrate data across diverse platforms and scales of time and space to create computational and mathematical models of the integrative, holistic functions of living systems. Endocrine-related cancers are well suited to study from a systems perspective because of the signaling complexities arising from the roles of growth factors, hormones and their receptors as critical regulators of cancer cell biology and from the interactions among cancer cells, normal cells and signaling molecules in the tumor microenvironment. Moreover, growth factors, hormones and their receptors are often effective targets for therapeutic intervention, such as estrogen biosynthesis, estrogen receptors or HER2 in breast cancer and androgen receptors in prostate cancer. Given the complexity underlying the molecular control networks in these cancers, a simple, intuitive understanding of how endocrine-related cancers respond to therapeutic protocols has proved incomplete and unsatisfactory. Systems biology offers an alternative paradigm for understanding these cancers and their treatment. To correctly interpret the results of systems-based studies requires some knowledge of how in silico models are built, and how they are used to describe a system and to predict the effects of perturbations on system function. In this review, we provide a general perspective on the field of cancer systems biology, and we explore some of the advantages, limitations and pitfalls associated with using predictive multiscale modeling to study endocrine-related cancers.