Consortium overview

Application of systems biology and high-throughput genomics to solve a concrete clinical problem, i.e. to personalize medication is a formidable challenge. This requires integration of many different forms of expertise and complex analytical methods that are applied to diverse materials such as human cells and knockout mice. Some of the patient groups are hard to find (e.g. monozygous twins) or need to be examined more than once. Finally, the results need to be validated in clinical studies to see if treatment response can be predicted. Since there is limited experiences of analysing data of such diversity and complexity the integrated development and application of new computational, bioinformatics and statistical solutions is required.

In order to build MLM all the high-throughput experiments must be performed on the same individuals. Therefore the clinical materials are obtained in one clinical research centre, The Center for Individualized Medication (CiMED), Linköping University Hospital, Sweden. CiMED integrates clinical, experimental and bioinformatic researchers in a laboratory equipped for both low- and high-throughput studies. Thus, clinical, experimental and theoretical hypotheses can be interactively formed and tested in the same environment. CiMED is headed by a clinician, professor Mikael Benson, who coordinates this project. Obtaining rare patient materials is simplified by patient registries, such as the Swedish Twin Registry (figure 2). High-throughput experiments are also performed by Cenix Bioscience, a biotech SME with expertise in RNA interference, as well as at Oslo University (WP5 and WP3, respectively)

A team of leading international experts has been assembled to analyse the data. Professor Michael A. Langston and his group of post docs and PhD students at the Department of Electrical Engineering and Computer Science at the University of Tennessee were the first to harness fixed-parameter tractability in order to develop pioneering clique-centric methods that find modules of putatively co-regulated genes in gene expression data (Abu-Khzam et al. Algorithmica 2006), link these modules to genetic variation using quantitative trait loci (Chesler et al. Nat Genet 2005), and synthesize from these methods novel topological differential analysis tools (Voy et al. PLoS Comput Biol 2006). ML is also an expert on combinatorial algorithms for the integration and analysis of biological data of large scale and wide diversity (Kirova et al. CAMDA 2006; Zhang et al. Supercomputing 2005). This work is facilitated by state-of-the-art supercomputers at the nearby Oak Ridge National Laboratory, where ML is a Collaborating Scientist in the Systems Genetics Group within the Biosciences Division (WP2).

Functional annotation of modules is done in WP3 by Professor Eivind Hovig and associates using their PubGene co-citation literature network (Jenssen et al. Nat Genet 2001). An advantage of PubGene is that modules are not restricted by canonical gene interaction pathways that have been described in healthy cells. It may therefore be particularly suitable to functionally annotate disease modules that may have gene interactions that differ from those in healthy cells. Another advantage is that PubGene is combined with other data sources to provide cell-specific and multi-layer network information. Additionally PubGenes' algorithms can be modified in a customized fashion to adjust to the context specific needs of the complex diseases to be analyzed clinically. EH is an expert in fields including the bioinformatics of biomedical text mining, microarray analysis, and annotation based comparisons of DNA features. EH has both a wetlab group within cancer and gene silencing, and a bioinformatics group.

Figure 2. Analysis of selected patient groups, such as monozygous twins, under controlled conditions reduces the complexity of the project. The photo shows a nurse at the Unit for Clinical Systems Biology obtaining blood samples from twins for in vitro allergen-challenge of CD4 + lymphocytes.

An important challenge in the project is that high-throughput technologies generate data where the number of variables greatly exceeds the number of observation. This requires the development and application of new statistical methods, which will be undertaken in WP 4 by Professor David J. Balding and Dr Lachlan J.M. Coin of the Centre for Biostatistics at Imperial College London. DJB has a PhD in applied probability from Oxford, and since graduating has worked to apply mathematical, computational and statistical methods to solve problems in biology and medicine, particularly in population, evolutionary and medical genetics. Recently he has been active in developing and applying novel statistical methods for the analysis of genetic association data, particularly for genome-wide association studies (Sladek et al. Nature 2007) These methods have tackled problems of confounding by population structure, exploiting haplotype clustering to strengthen signals of association, and simultaneous analysis of large numbers of genetic variants (Balding et al. Nat Rev Genet 2006). LJMC has a PhD in Bioinformatics from the Wellcome Trust Sanger Institute and Cambridge University, and has worked in comparative genomics, phylogenetics, microarray analysis, identification of copy-number variants and haplotype clustering methods based on Hidden Markov Models (Coin et al. PNAS 2003, Futreal PA et al. Nat Rev Cancer 2004).

The role of disease-associated genes identified through the methods described above, will be confirmed experimentally using high-throughput RNAi (HT-RNAi). Cenix BioScience GmbH, represented by Drs. Christophe Echeverri (founder, CEO/CSO) and Birte Sönnichsen (COO) (WP5) were the first to pioneer high throughput applications of RNAi (HT-RNAi), carrying out the first comprehensive genome-wide RNAi screen (Sönnichsen et al, Nature 2005) for genes involved in early embryogenesis of the nematode worm C. elegans, where RNAi was first described in 1998 by Fire and Mello's Nobel Prize-winning work (Fire et al, Nature 1998). Since 2001, Cenix has focused entirely on further developing and applying the power of genome-scale HT-RNAi with high content, multi-parametric assays using automated microscopy in a wide range of human and rodent cultured cell models. As such, Cenix has since established itself as a global leader in exploiting this technology in collaboration with both academic and pharmaceutical research groups, to advance a wide range of basic research and applied disease fields including oncology as well as metabolic, infectious and cardiovascular diseases (e.g. Sachse et al. Oncogene 2004). Along the way, Cenix has also led the way in defining the combination of computational and automated laboratory analysis infrastructures required to drive these advanced functional genomics applications (Sachse et al. Methods Enzymol 2005; Echeverri et al. Nat Rev Genet 2006 and Nat Methods, 2006). CE has a PhD in cell biology from the University of Massachussetts (Worcester, MA), and his postdoctoral work at the European Molecular Biology Laboratory (EMBL, Heidelberg, Germany) pioneering the genome-scale use of RNAi formed the basis for founding Cenix. BS has a PhD in cell biology from the University of Göttingen (Göttingen, Germany), and joined Cenix as one of its first senior scientists following her postdoctoral work at Cancer Research UK (formerly, the ICRF) in London and the EMBL, (Heidelberg, Germany).