Post Doctoral Researcher
University of Cambridge
47 Bateman Street
Cambridge CB2 1LR
My main research interest is to gain a quantitative understanding of cellular decision making. All cells must cope with dynamic environments and respond accordingly in order to grow and survive. I am interested in how cells discriminate between different environmental states, integrate dynamic outputs from multiple networks, and maintain robustness of function across different intracellular states and growth conditions. There is a growing realisation that these questions must be tackled at the single cell level in order to distinguish between different regulatory models and understand their design principles.
In my present research, I use a bacterial system and a combination of theory and time-lapse microscopy experiments to study gene expression at the level of single cells. I study how specific networks are dynamically coupled to each other, thus allowing cells to integrate multiple environmental signals, and what are the functional roles of such integration in the context of different growing conditions.
I did a PhD in Peter Swain's lab at the University of Edinburgh, where I used mathematical modelling to gain insight into two simple, yet ubiquitous, sensing and transductions mechanisms: allosteric sensing and phosphorylation-dephosphorylation cycles. I studied the input-output dynamics of these mechanisms in terms of the fundamental constraints inherent in their design.
Bruno M.C. Martins, Arijit K. Das, Liliana Antunes, James C.W. Locke, Frequency doubling in the cyanobacterial circadian clock, Mol Syst Biol, 12:896 (2016).
Bruno M.C. Martins, James C.W. Locke, Microbial individuality: how single-cell heterogeneity enables population level strategies, Curr Opin Microbiol, 24:104-12 (2015).
Bruno M.C. Martins, Peter S. Swain, Ultrasensitivity in phosphorylation-dephosphorylation cycles with little substrate, PLoS Comput Biol, 9:e1003175 (2013).
Bruno M.C. Martins, Peter S. Swain, Trade-offs and constraints in allosteric sensing, PLoS Comput Biol, 7:e1002261 (2011).