Species translation challenge (STC)

Rodent studies have proved indispensable as models of human diseases and have undoubtedly helped to unravel molecular mechanisms. The biomedical field has generally worked under the assumption that biological processes in mice or rats can correspond to biological processes in humans under analogous conditions. Yet few studies have addressed the limitation in which biological events observed in rodents can be translated to humans. Providing some answers to this fundamental question will be invaluable to the biomedical research community as rodent models will continue to be a central tool in biomedical research.

Intra-Species Protein Phosphorylation Prediction

While the main aim of the set of sub-challenges was focused on translation between species, the intra-species protein phosphorylation sub-challenge focused on translation between gene expression data and the corresponding phosphoprotein data in rat.

The gene expression data were collected using the Affymetrix® microarray platform and were a standard data type in the field with mature analysis techniques. On the other hand, transcriptomics data have known limitations. The systems biology field is therefore, moving to proteomics in order to provide a more comprehensive view of cellular dynamics. The expression level of protein phosphorylation is one type of proteomic data that can augment gene expression data in order to provide a more complete view of cellular signaling.

Inter-Species Protein Phosphorylation Prediction

For more than two decades, transcriptomics microarray gene expression profiles have been widely used in research to investigate genome-wide perturbations of the biological systems under various conditions, e.g. chemical, disease, environmental, mutation. However, the observed gene expression regulation is already the result of a cascade of upstream signalling events going from the cell membrane to the nucleus. Therefore, acquiring phosphoproteomic data can provide a comprehensive view of cellular dynamics. In terms of comparability and translatability between species, the field has acknowledged that there can be difficulties in discerning clear relationships between the genes of two species, sometimes even for genes that appear to be orthologous. Although very similar in sequence, genes may diverge in function or take on new roles. Such features can limit simple translation of the functional roles of similar genes from one species to another. Therefore the inter-species protein phosphorylation prediction sub-challenge was posed at the protein level to assess the translatability of phosphorylation status of different proteins that cover a range of diverse cellular processes.

Inter-Species Pathway Perturbation Prediction

The biomedical field has generally worked under the assumption that biological processes in mice or rats will correspond to biological processes in humans under analogous conditions. However, comparisons of transcriptomic data have pointed to the difficulties of performing a translation based uniquely on the expression level of orthologous genes. The difficulty could lie at several levels. For example, orthologous genes may have diverged in function and play different roles in the cell. One potential method to improve translation is to take a pathway-centric view and consider whether similar pathways in rat and human are activated under analogous conditions. The inter-species pathway perturbation sub-challenge aimed at aiming the translatability at the pathway level.

Species Specific Network Inference

Systems biology emphasizes the study of relationships and connectivity between the components of a complex system. As such, pathway diagrams are the primary representation of complex biological systems, and the construction of accurate and complete pathway maps is an on-going challenge in the field.

The two main approaches that have been taken to build pathway maps are knowledge-driven and data-driven. The knowledge-driven approach uses a priori data, often curated from the literature, to define entities (nodes) and connections (edges) that can be assembled into network diagrams. In contrast, the data-driven approach seeks to infer the connection based on inference from large dataset using methods such as regression analysis and Bayesian probabilistic models.

Combining disparate data types in pathway maps is a useful way of synthesizing such diverse knowledge into a consistent and unified view of a complex biological system. In addition, knowledge-driven approaches are often used to construct the scaffold network that can be augmented and refined using data-driven approaches.