An overview of bioinformatics methods for modeling biological pathways in yeast

Abstract

The advent of high-throughput genomics techniques, along with the completion of genome sequencing projects, identification of protein-protein interactions and reconstruction of genome-scale pathways, has accelerated the development of systems biology research in the yeast organism Saccharomyces cerevisiae In particular, discovery of biological pathways in yeast has become an important forefront in systems biology, which aims to understand the interactions among molecules within a cell leading to certain cellular processes in response to a specific environment. While the existing theoretical and experimental approaches enable the investigation of well-known pathways involved in metabolism, gene regulation and signal transduction, bioinformatics methods offer new insights into computational modeling of biological pathways. A wide range of computational approaches has been proposed in the past for reconstructing biological pathways from high-throughput datasets. Here we review selected bioinformatics approaches for modeling biological pathways inS. cerevisiae, including metabolic pathways, gene-regulatory pathways and signaling pathways. We start with reviewing the research on biological pathways followed by discussing key biological databases. In addition, several representative computational approaches for modeling biological pathways in yeast are discussed.

Keywords: Saccharomyces cerevisiae; gene regulatory network; metabolic pathway; signaling regulation.

Figure 1

Interrelationship among signaling pathway, gene regulatory network and metabolic pathway in a cell. Cell starts to recognize and receive the signals by activating the membrane receptors, responding to the stimulus of the changes in the outside environment. The receptors can help transmit the signal outside the cell membrane into the signaling pathway inside the cell, activating a series of biochemical reactions. Take the MAPK signaling pathway in yeast (Top-right) from KEGG [2] as example, the signaling pathway is initialized by the stimuli of peptide mating pheromones, which are Mat-alpha and MatA, then the receptor Ste2 or Ste3 connects the peptide mating pheromone and activates the MAPK signal transduction cascades. The signaling cascade will finally produce the protein kinases that can enter the nucleus and activate the gene transcription by binding the transcription factors onto promoters of DNA. In the example of cell cycle yeast pathway (Bottom-left), the Cln3-Cdc28 protein kinases activate the transcription factors SBF and MNF, which regulate the Cln1/2 gene expression. The gene regulatory network can control the gene expression levels of mRNA by activating the transcription factors, and further translate the mRNA into proteins. Specific proteins, called enzymes, will participate in the metabolic pathway and catalyze a series of biochemical reactions by converting substrates into products, in which the product of one reaction becomes the substrate of next reactions. In the Glycolysis/Gluconeogenesis pathway (Bottom-right), experiments [3] showed the regulators identified in cell cycle also regulated the metabolic enzymes to catalyze the cellular metabolism. (A colour version of this figure is available online at: http://bfg.oxfordjournals.org)

Figure 2

Flowchart for pathway modeling using computational approaches. The bioinformatics methods for pathway modeling starts with the hypothesis of pathway construction which can be derived from experiments or theory. Then computational methods can be performed on the experiment data (e.g. Microarray data, RNASeq data) and knowledge information (e.g. Pathway information, functional annotation) to model the biological pathways. The predicted pathways can be refined by evaluating each model with experiments and hypothesis. (A colour version of this figure is available online at: http://bfg.oxfordjournals.org)

Figure 3

Clustering algorithms. We discussed two typical clustering algorithms applied in yeast pathway modeling. The clustering methods are classified into two categories: partition-based clustering (Top) and hierarchical-based clustering (Bottom). (A colour version of this figure is available online at: http://bfg.oxfordjournals.org)