Closing date: 01/09/2016.
Faculty / Organisational unit: Biology, Medicine & Health.
School / Directorate: School of Medical Sciences.
Division: Developmental Biology & Medicine.
Employment type: Fixed Term.
Duration: As soon as possible until 4 March 2019 (further extension subject to funding).
Location: Oxford Road, Manchester.
Salary: Grade 6, £30,738 to £37,768. Grade 7, £38,896 to £47,801 per annum.
Hours per week: Full time.
The Faculty of Biology, Medicine and Health are inviting applications for a Research Associate or Research Fellow. Appointments will be made based on how extensively you meet the criteria.
Understanding gene network heterogeneity in development.
Cell fate choice and proportioning are typically considered to be ordered, robust and reproducible. However, noise and stochasticity can lead to heterogeneous gene network activity. Consequently, it has been proposed that gene networks may be ‘wired’ to buffer these fluctuations. Alternatively, heterogeneity may be functionally important to prime cells or increase the spectrum of differentiation capabilities. Addressing these questions represents one of the greatest challenges in Developmental and Stem Cell Biology. However, to date it has been impossible to follow entire gene network behaviour in individual cells, or to follow their temporal changes in activity in individual cells as cells commit to differentiation along different linages. Single cell gene expression analysis, together with novel computational reconstruction of gene network dynamics provides this opportunity.
This work builds upon our recent finding (Chattwood et al, eLife 2014) that the interplay between dynamic heterogeneity in Ras-GTPase activity and nutritional status is required for normal lineage priming and robust running of an ultradian cell fate oscillator. We are seeking an enthusiastic and outstanding postdoctoral researcher to join a multidisciplinary team led by Professor Chris Thompson. You will use single cell RNA sequencing to identify groups of heterogeneously expressed genes within normal populations of cells. Computational approaches will be used to identify putative genes involved in lineage priming and cell fate choice. In addition, the role played by these genes will be tested in the lab through the analysis of gene knockout strains, live cell imaging and molecular genetics.
Extensive experience of using either computational approaches