Faculty of Medicine,Dentistry and Health Sciences Department of Pathology

Inouye Laboratory - Medical Systems Biology

Contact: Dr Michael Inouye
Phone: +61 3 9035 8659
Fax: +61 3 8344 4004
Email: minouye@unimelb.edu.au

Mike grew up in the Seattle area before beginning undergraduate study in 1999 at the University of Washington, where he later graduated with BSc's in biochemistry and economics.  During this time, he was also introduced to computational genomics as the initial draft Human Genome was being finished, spending several years doing part-time research in gene finding and protein structure prediction.  He continued studying biochemistry as a graduate student at UCLA but elected to return to genomics when he moved to the Wellcome Trust Sanger Institute (Cambridge, UK) in 2005.  While at WTSI, Mike completed his PhD with Prof Leena Peltonen (WTSI) and Prof Gert-Jan van Ommen (Leiden University) and was heavily involved in the first wave of genome-wide association studies, especially the statistical methods thereof.  He also led large-scale efforts for the integrative analysis of molecular systems, identifying a gene co-expression network underlying metabolic traits.  In 2010, Mike came to the Walter and Eliza Hall Institute in Melbourne on an NHMRC postdoctoral fellowship to continue pursuing interests in genomics and systems biology.  In 2012, he joined the faculty at the University of Melbourne as a joint appointment between the Department of Pathology and the Department of Microbiology and Immunology.

Outside of research, Mike plays soccer, brews/drinks beer, hangs out in cafes and goes to (indie) movies.


Interested Postdocs and PhD Students

The group is always looking for talented postdocs and PhD/honours students.  If, in reading about the group and browsing our publications, you are interested please do get in touch.

The group is primarily a 'dry' laboratory (i.e. computational, no reagents or chemicals). We draw on many fields, which in practice means each member brings a unique mixture of skills and all are encouraged to work together. We have flexible working hours and are quite goal-oriented; when/where research is done is less important than, say, developing a new method to solve a problem or uncovering a disease gene. Mike is committed to training independent multi-disciplinary researchers who want to use genomics/systems biology/bioinformatics/biostatistics to alleviate disease.

NOTE: The project list below is only an incomplete snapshot.  We also encourage young researchers to develop ideas and projects of their own (how else will one become a scientist!), so we are happy to consider any research plans.


Research Interests

“Think of biology as a system for managing information.”
                                                                                                -- Paul Nurse, PRS

Overall aim: To use genomic and molecular profiling technologies to uncover the genetic basis of complex disease, understand pathogenesis at a systems-level, and build predictive models for patient stratification and precision medicine.

In the last decade, technological advances have driven the study of biology towards the statistical and computational sciences. We are now able to differentiate and quantify biomolecules at levels previously unimaginable, allowing us to study their interactions and relationships to health and disease in an unbiased, systems-level manner.

A corollary of this transformation is that sophisticated quantitative models can now be used to tease out underlying biological insights and pathogeneses of molecular systems. In our view, an "organism" can be thought of as a system whose components are derived from its genome(s) and which interact with each other and the environment in a spatial and temporal manner.  We think of these components (e.g. RNAs, proteins, mobilized DNA) as operating as part of networks with the other elements (e.g. metabolites, sunlight, micro-organisms). We therefore apply and develop concepts in graph theory, bioinformatics, epidemiology and biostatistics to understand how networks interact and what role they play in human diseases and traits.

Figure 1: Increasing complexity of systems profiling

Our research spans a broad range of diseases and organisms but it is primarily focused on cardiovascular/metabolic and autoimmune disease in humans (we also work in bacteria and mouse models). We are highly collaborative and have strong links with researchers around Melbourne, Australia, UK and Europe as well as the USA.


Research Projects

Molecular networks for cardiovascular and metabolic disease
Hundreds of genomic loci associated with cardiovascular and metabolic disease (CMD) and corresponding risk factors have been discovered and widely replicated, and, while these loci offer tantalizing clues into the pathogenesis of CMD, the downstream functional effects of causal variants and candidate genes need to be investigated empirically. As part of a worldwide network of collaborators, we aim to develop and apply novel approaches to characterize the genetic basis of gene and metabolic networks, to detect genetic and network interactions with CMD, to infer causality for those interactions, and to design powerful replication and meta-analysis strategies.

Building predictive genetic models
One of the goals of medical genetics is to accurately quantify the risk of disease given a genetic profile. With advances in genome-wide genotyping and whole genome sequencing of large-scale disease and control cohorts, there have been recent efforts to assess the performance of genetic prediction. We have previously explored the utility of lasso-penalized regression models for this purpose, with promising findings for autoimmune diseases like celiac disease and type 1 diabetes. For those diseases and traits with sufficient quantities of genetic variance, we aim to build models of genetic variation (SNPs, CNVs, micro-insertions/deletions, etc) which predict disease, trait or outcome at a level relevant for clinical screening and to refine these models so that particular genetic sub-groups at high risk can be identified.

Gene flow in microbial networks and the human microbiome
Together with Dr Kathryn Holt (UoM Microbiology & Immunology), we are investigating how genetic elements (including drug resistance genes, virulence factors, etc) move within and between bacterial species. This includes the development of novel approaches and computational tools to perform microbial genome surveillance with high-throughput DNA sequencing and to model networks of genetic elements across species. A second collaboration with Dr Holt, which also includes researchers from TICHR and UQ, is investigating the interactions between bacterial community structure (the 'microbiome'), viruses, and host genetics in the pathogenesis of childhood asthma.

Understanding immune regulation through gene networks
Many immune cell phenotypes (differentiation, isotype switching, etc) are regulated by gene networks and their dysfunction can have major implications for autoimmune disease. Various so-called master regulatory transcription factors have been identified but how they operate and what their direct/indirect interacting partners are remains largely unknown for many cell types. We are collaborating with researchers at WEHI to perform and analyse high-throughput RNA sequencing and other whole genome profiles (ChIPseq, etc) to address the question of how the immune system is regulated at the genomic level.

Genetics of rheumatic heart disease in Indigenous Australians
Rheumatic heart disease (RHD) is a disease of long-term cardiac valvular damage resulting from host infection with Group A Streptococcus and host autoimmune response. It is almost eradicated from prosperous populations but maintains a prevalence of ~2% in some impoverished populations, including Indigenous Australian communities. The pathogenesis of RHD is not well understood, however previous studies have suggested that there may be substantial genetic susceptibility. We are collaborating with the Menzies School of Health Research, TICHR and others to perform a genomic screen and fine-mapping for loci associated with RHD.

Supercomputers for complex analysis of genetic data
Current statistical approaches have only scratched the surface of what is possible with the large-scale genomic data now available for many disease studies. The use of supercomputers can open new avenues of investigation to researchers, however there remains a gap between those with the genetic/statistical expertise and the computational expertise needed to fully realize the potential of supercomputing. We are collaborating with researchers at UoM (MEGA), NICTA, and IBM to develop analytical and computational approaches for large-scale genomic and ancillary data which leverage massively-parallel, distributed memory supercomputers and to apply these methods to provide new insights into the genetic and environmental causes, aetiology and biology of breast cancer, colorectal cancer and other diseases.


Software


Group Members


Funding


Recent Publications

*indicates joint first authors

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