Research Groups
Introduction
Blundell Brindle Broadhurst Brown Carrington Dupree Evan  Head of Dept. Farndale Gay Grace Griffin Hendrich Hesketh Higgins Hollfelder Hong Howe Hyvonen Jackson S Jackson T Jackson R Laue Leadlay Lester Lilley Lipkow	Livesey Luisi Lummis Mata McCafferty Metcalfe Mishima Miska Monie Mott Murzina Nietlispach Owen Oliver Packman Pellegrini Robinson Salmond Scadden Silva Smith A Smith C Standart Thomas Timmers Tolkovsky Welch Zhang
Past members

Functional Genomics and Systems Biology

Research Grouping: Functional genomics, systems biology and genetic medicine

We are trying to gain a fully integrated view of the workings of a simple eukaryotic cell – the brewing and baking yeast, Saccharomyces cerevisiae.

Larger image in new window Strategy to generate reciprocal translocations in yeast (Delneri et al. Nature 422, 68-72, 2004)

This yeast was the first eukaryotic organism to have its genome completely sequenced and we aim to determine how the 6,000 or so genes in the yeast genome interact to allow this simple eukaryotic cell to grow, divide, develop, and respond to environmental changes. If this fully integrative, view of the yeast cell can be obtained, it should provide an important 'navigational aid' to guide our studies of more complex genomes, such as those of humans, crop plants, and farm animals.

We are carrying out functional genomic experiments at four levels of analysis: the genome, transcriptome, proteome, and metabolome. These represent, respectively, the complete complement of genes, mRNA molecules, proteins, and metabolites present in the yeast cell. The genome can be taken as a constant in most of our experiments. However, we are able to exquisitely manipulate the yeast genome in the laboratory. We are exploiting this ability in order to: delete all members of a particular gene family from the genome; eliminate all non-essential genes and create a 'minimalist' genome; and place essential yeast genes under the control of conditional promoters to enable their use in the functional analysis of human genes via transcomplementation.

Larger image in new window Tests in different media show that gene interactions are context-dependent   (Harrison et al. PNAS 104, 2307-2312, 2007)

In contrast to the genome, the transcriptome, proteome and metabolome are 'context-dependent'; i.e. they change according to the physiological or developmental state of the cells. Thus, these levels of analysis are particularly useful in uncovering function. This is especially true of the proteome and metabolome since proteins and metabolites are functional effectors within the cell, whereas mRNAs are simply agents of information transfer. However, the transcriptome can be readily studied using hybridisation-array technology. Analyses of the proteome and metabolome, on the other hand, are more challenging. They require a variety of separation techniques and advanced analytical procedures (e.g. mass spectrometry, NMR and IR spectroscopy). We are exploiting all of these techniques in our studies on yeast, and also developing (in collaboration with computer scientists and bioinformaticians) novel computational methods for the storage, analysis, and representation of functional genomic data.

In our Systems Biology work, we are taking both a ‘top-down’ and a ‘bottom-up’ approach to modelling the yeast cell. In the early stages of this process, we are using Metabolic Control Analysis (MCA) as a theoretical framework.

Larger image in new window Integration of proteome and metabolic control to show carbon and nitrogen metabolic fluxes at the enzyme level  (Castrillo et al. J. Biol. 6:4, 2007)

MCA is a conceptual and mathematical formalism that models the relative contributions of individual effectors in a pathway to both the flux through the pathway and the concentrations of individual intermediates within it. In our whole-cell analyses, flux equates to growth rate. In the top-down approach, we are identifying genes encoding proteins with high flux control coefficients (HFC genes) and using them to build a coarse-grained model of the eukaryotic cell, as exemplified by yeast. In the bottom-up approach, individual sub-systems are modelled in detail and this requires that ‘natural’ biological systems be identified and the degree to which they are (or can be) isolated from the rest of yeast’s networks must be determined. To do this, we employ flux-balance/flux-coupling analyses, combined with both genetics and metabolomics, to define metabolic and other systems.

Lab members
Annette Alcasabas, Greg Amoutzias, Prachi Balyan, Elizabeth Bilsland, Juan Castrillo, Duygu Dikicioglu, Michaela Freeland, Konstantinos Gkargkas, Melanie Gulston, Alex Gutteridge, Andy Hesketh, Hayley Leverett, Balázs Papp, Pnar Pir, Jenny (Zhenzhen) Quan, Trevor Sawyer, Nianshu Zhang

References