Daniel Hurley - Network Systems Biology

A general language for networks

Wed, 16 Mar 2016 00:06:04 +1100

Network models have great potential to uncover complex interactions and pathways not visible by other methods, but network modelling is still an ad hoc activity, with no general theory to relate different approaches, In Depth: watch a web presentation of this work nor principles for determining what a network tells us about the biology that created it. Progress in data-driven ‘network biology’ has been hampered by the lack of a general language for integrating different network methods from different domains of knowledge.

To overcome these obstacles, I am developing a grammar of network methods, an empirical grammar of data manipulation extended into graph theory. The grammar is influenced by the statistical graphics work of Wilkinson¹¹ See The Grammar of Graphics and Wickham²² See A Layered Grammar of Graphics and Tidy Data, and allows us to describe the structure of different network methods (like the syntax in a human grammar) and relate them directly back to the biological data used to create the network, in order to uncover biological meaning (semantics). Using simple graph elements and a declarative syntax, the grammar is able to describe existing network modelling in biology using a common set of concepts, focused particularly on network inference from genomic, proteomic and metabolomic data.

The grammar of network methods is a new research direction stemming from work started during my PhD, where I integrated diverse mathematical and statistical methods of reverse-engineering transcriptomic networks³³ The initial computational framework was written in MATLAB, and applied these methods to generate and validate experimental hypotheses in transformed cell lines⁴⁴ Applications in melanoma prognosis and endothelial cell biology. The grammar develops this work by describing these existing analysis methods using a common language, and showing clearly their similarities and differences.

Specifically, my current research implements the grammar in two programming languages common in systems and computational biology (R and Python), and uses it to answer two major open questions in computational biology: the unification of ‘knowledge-based’ and ‘data-driven’ network methods, and the effect of ensemble network methods (the ‘wisdom of crowds’ effect).

Completing this work will transform network modelling in biology, from an ad hoc activity where different methods exist in isolation from one another, into a principled, evidence-driven activity with heuristics for which methods are fruitful on which data for which outcomes.

Featured publications

Hurley, D. G., Cursons, J., Wang, Y. K., Budden, D. M., Print, C. G., & Crampin, E. J. (2014). NAIL, a software toolset for inferring, analyzing and visualizing regulatory networks. Bioinformatics, 31(2), 277–278. doi:10.1093/bioinformatics/btu612

Hurley, D., Araki, H., Tamada, Y., Dunmore, B., Sanders, D., Humphreys, S., … Print, C. G. (2012). Gene network inference and visualization tools for biologists: application to new human transcriptome datasets. Nucleic Acids Research, 40(6), 2377–2398. doi:10.1093/nar/gkr902

Applied network modelling

Wed, 16 Mar 2016 00:06:04 +1100

Data-driven network models can be applied to answer specific questions in experimental biology using genomic, transcriptomic and proteomic data. Collaborating with experimental researchers, I have developed network modelling approaches to guide experimental research in drug development¹¹ With colleagues at the Auckland Cancer Society Research Centre here and here, in signalling pathways in human skin²² With colleagues at the Auckland Bioengineering Institute here and here, and in neurochemistry³³ With colleagues at the Centre for Brain Research here.

Featured publications

Cursons J., Gao J., (Joint first authorship). Hurley D.G., Dunbar P.R., Jacobs M.D., Crampin E.J. Regulation of ERK-MAPK signaling in human epidermis. BMC Syst Biol. 2015;9(1):41.

Hunter, F. W., Jaiswal, J. K., Hurley, D. G., Liyanage, H. D., McManaway, S. P., Gu, Y., others. (2014). The flavoprotein FOXRED2 reductively activates nitro-chloromethylbenzindolines and other hypoxia-targeting prodrugs. Biochemical Pharmacology, 89(2), 224–235.

Jansson, D., Rustenhoven, J., Feng, S., Hurley, D., Oldfield, R. L., Bergin, P. S., … Dragunow, M. (2014). A role for human brain pericytes in neuroinflammation. Journal of Neuroinflammation, 11(1), 104.

Wang, J., Guise, C. P., Hsu, H.-L., Hurley, D., Wilson, W. R., & Patterson, A. V. (2013). Identification of reductases capable of metabolic activation of hypoxia targeting prodrug SN30000 and hypoxia marker EF5. In Cancer Research (Vol. 73, pp. 2111–2111)

Research reproducibility

Fri, 26 Jun 2015 23:06:04 +1000

Computational methods should be among the most reproducible methods in the life sciences; First Look: watch a web presentation of this work the environment and protocol for a computational method can be specified to a much greater degree, and with much greater precision, than a typical laboratory method. Unfortunately, many published computational results remain hard to reproduce, and methods are often not specified in sufficient detail to replicate or re-implement. I have proposed techniques for building reference environments¹¹ Little bootstrapped open-source virtual environments for reproducing a single computational result. enabling researchers to reproduce computational results precisely, with minimal effort, and implemented these for a range of results in computational biology²² Lots of published examples here.

My current research in this theme extends this approach and set of tools to work across all major languages (R/Python/Java/MATLAB/Fortran/C) and platforms in systems and computational biology. The method produces reference environments across three different reproducibility platforms: as a virtual machine, as a container, and as a cloud environment, all from a single set of configuration scripts. Reference environments integrate readily with other reproducible research technologies, and can contain any type of language or computation.

The reference environment approach How do reference environments relate to other types of ‘reproducible research’? I discuss that here explicitly separates the core scientific findings of a piece of computational research from the software implementation in which they are embedded, and allows readers and reviewers to choose the most appropriate implementation type for their situation. Reference environments “But why not just use <currently popular reproducibility tool/website>?” I discuss that here also have many applications in more general reproducibility; providing standard environments for testing or benchmarking, or in teaching to accelerate students’ hands-on experience with tools and languages.

Watch a web presentation of this work here

Featured publications

Hurley, D. G., Budden, D. M., & Crampin, E. J. (2014). Virtual Reference Environments: a simple way to make research reproducible. Briefings in Bioinformatics, bbu043. doi:10.1093/bib/bbu043