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Steve Oliver

Functional genomics and systems biology

We aim to gain an integrated view of how a simple eukaryotic cell, the baker’s yeast, Saccharomyces cerevisiae, works. Yeast was the first eukaryote to have its genome completely sequenced and we are determining how its 6,000 genes interact to allow it to grow, divide, develop, and respond to environmental changes . This integrative view of yeast should provide an important 'navigational aid' to guide our studies of the more complex genomes of humans, crop plants, and farm animals.

Our work involves both experiments with living cells and the construction of mathematical models of pathways and networks for use in computer simulations that generate predictions that we can test in vivo. These predictions include how genes interact in metabolism, which is essential to engineering novel pathways in yeast using synthetic biology.  They also enable us to automate the process of generating hypotheses, designing and executing experiments, and evaluating data. This “Robot Scientist” approach enabled the discovery of novel scientific knowledge by a machine, without human intervention.

The models and experimental systems we use with yeast sometimes lead us in unexpected directions, such as predicting the impact of gene copy number variation in cancer, constructing network models to identify genes important in Alzheimer’s Disease, or using yeast ‘surrogates’ to screen for drugs against parasitic diseases .

Lab members: Ayca Cankorur Cetinkaya, Lu Cao, Joao Dias, Duygu Dikicioglu, Midori Harris, Andy Hesketh, Kim Rutherford, Trevor Sawyer, Yingzhi (Fiona) Tang, Valerie Wood, Nianshu Zhang

 

Visit web site at Cambridge Systems Biology Centre

 

Key publications:

1. FidanerIB, Cankorur-CetinkayaA, DikiciogluD, KirdarB, Ali AT, Oliver SG (2016) CLUSTERnGO: A user-defined non-linear modelling platform for two-stage clustering of time-series data. Bioinformatics 32, 388-397.

2. Binder BJ, Sundstrom JF, Gardner JM, Jiranek V, Oliver SG (2015) Quantifying two-dimensional filamentous and invasive growth spatial patterns in yeast colonies. PLoS Comp Biol 11: e1004070. (15 pages)

3. RutherfordKM, HarrisMA, LockA, OliverSG, WoodV (2014) Canto: An online tool for community literature curation. Bioinformatics 30, 1791-1792.

4. Bilsland E, Sparkes A, Williams K, Moss HJ, de Clare M, Pir P, Rowland J, Aubrey W, Pateman R, Young M, Carrington M, King RD, Oliver SG (2013) Yeast-based automated high-throughput screens to identify anti-parasitic lead compounds. Open Biol. 3: 120158. (13 pages.)

5. Pir P, Gutteridge A, Wu J, Rash B, Kell DB, Zhang N, Oliver SG (2012) The genetic control of growth rate: a systems biology study in yeast. BMC Systems Biology 6: 4 (14 pages).