LLM3D: a log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression data

All cellular processes are regulated by condition-specific and time-dependent interactions between transcription factors and their target genes. While in simple organisms, e.g. bacteria and yeast, a large amount of experimental data is available to support functional transcription regulatory interactions, in mammalian systems reconstruction of gene regulatory networks still heavily depends on the accurate prediction of transcription factor binding sites. Here, we present a new method, log-linear modeling of 3D contingency tables (LLM3D), to predict functional transcription factor binding sites. LLM3D combines gene expression data, gene ontology annotation and computationally predicted transcription factor binding sites in a single statistical analysis, and offers a methodological improvement over existing enrichment-based methods. We show that LLM3D successfully identifies novel transcriptional regulators of the yeast metabolic cycle, and correctly predicts key regulators of mouse embryonic stem cell self-renewal more accurately than existing enrichment-based methods. Moreover, in a clinically relevant in vivo injury model of mammalian neurons, LLM3D identified peroxisome proliferator-activated receptor γ (PPARγ) as a neuron-intrinsic transcriptional regulator of regenerative axon growth. In conclusion, LLM3D provides a significant improvement over existing methods in predicting functional transcription regulatory interactions in the absence of experimental transcription factor binding data

Flexible model-based joint probabilistic clustering of binary and continuous inputs and its application to genetic regulation and cancer

Clustering is used widely in ‘omics’ studies and is often tackled with standard methods such as hierarchical clustering or k-means which are limited to a single data type. In addition, these methods are further limited by having to select a cut-off point at specific level of dendrogram- a tree diagram or needing a pre-defined number of clusters respectively. The increasing need for integration of multiple data sets leads to a requirement for clustering methods applicable to mixed data types, where the straightforward application of standard methods is not necessarily the best approach. A particularly common problem involves clustering entities characterized by a mixture of binary data, for example, presence or absence of mutations, binding, motifs, and/or epigenetic marks and continuous data, for example, gene expression, protein abundance and/or metabolite levels. In this work, we presented a generic method based on a probabilistic model for clustering this mixture of data types, and illustrate its application to genetic regulation and the clustering of cancer samples. It uses penalized maximum likelihood (ML) estimation of mixture model parameters using information criteria (model selection objective function) and meta-heuristic searches for optimum clusters. Compatibility of several information criteria with our model-based joint clustering was tested, including the well-known Akaike Information Criterion (AIC) and its empirically determined derivatives (AICλ), Bayesian Information Criterion (BIC) and its derivative (CAIC), and Hannan-Quinn Criterion (HQC). We have experimentally shown with simulated data that AIC and AIC (λ=2.5) worked well with our method. We show that the resulting clusters lead to useful hypotheses: in the case of genetic regulation these concern regulation of groups of genes by specific sets of transcription factors and in the case of cancer samples combinations of gene mutations are related to patterns of gene expression. The clusters have potential mechanistic significance and in the latter case are significantly linked to survival

Dissecting Transcriptional Regulatory Networks with Systems Biology Approaches

In the past decade, technologies such as the DNA microarray and ChIP-on-chip have generated a large amount of high-throughput data for biologists. Although these data has provided us systems-level information about gene regulation, a major challenge in systems biology is to derive methodologies that will infer the underlying dynamics and mechanisms of gene regulation. This thesis research is focused on understanding these mechanisms of transcriptional regulation using systems biology approaches. Transcription regulatory networks play an important role in mediating external stimuli and coordinating responses to changing environments. Different methods that infer regulatory interactions directly from microarray data have been developed in the recent past. However, the implicit assumption in these methods that the transcription factor (TF) mRNA expression can be used as a proxy of its activity at protein level is not always correct, due to post-transcriptional and post-translational modifications of TFs. In this study, a method named iARACNe was developed. It uses the inferred TF activities to estimate the regulatory activity between TFs and their targets. The study demonstrated that the accuracy of the inferred networks using this method was greatly improved. Two additional methods, OmniMiner and coEDGi, which allow a better understanding of the physical interactions between TFs and target genes, were developed in this thesis research. OmniMiner detects and predicts the potential binding sites for the TFs of interest, while coEDGi enables identification of common enhancers upstream of co-regulated genes. Compared to other approaches which only allow isolated analyses, the systems biology approaches developed in this research provide an opportunity for biologists to study transcriptional regulations from both functional genomics and regulatory sequence perspectives simultaneously