Predicting growth through biophysical measurements and context-dependent mathematical modelling

Supervisors: Peter Swain, Ian Stansfield

Project description:

In this project, the PhD student will use single-cell, systems biology approaches to understand energy homeostasis in microbes, a central factor in growth and responses to stress. Understanding how microbes maintain energy homeostasis while responding to environmental challenge is key to two of our most important industrial and medical challenges: optimising biotechnological expression of proteins and overcoming antibiotic resistance in microbial pathogens.

In both cases we need to understand and predict how growth rates respond to an imposed change in demand for cellular resources and energy caused by either the introduction of a recombinant gene or, in the case of antibiotic responses, by the chemical challenges and the corresponding resistance induced in response to a drug.  Biotechnology, for instance, uses gene expression to produce vaccines, pharmaceuticals and chemical feedstocks, but the resulting physiological challenge for a host organism is considerable because protein synthesis is one of the most energetically demanding processes in the cell, and production yields are often correspondingly compromised. In antimicrobial therapy, resistance to antibiotics, now identified as one of the greatest dangers to human health, frequently depends on the activity of ATP-dependent drug efflux pumps. If we are to reduce such resistance, we must understand the relationship between these molecular mechanisms and energy homeostasis.

The PhD student will combine biophysical, single-cell measurements of cellular energy levels with mathematical modelling to develop a “whole cell” model to predict growth rates. Measurements of pH and mitochondrial potential using specialized fluorescent proteins, of membrane potential using voltage-sensitive dyes, and of ATP levels using reporters based on fluorescence resonance energy transfer will be made in budding yeast at different growth rates in microfluidic devices. These biophysical measurements will be combined to estimate proton motive force, and all will be incorporated into a mathematical model that builds on earlier work from both our laboratories.  The model will focus on the costs of translation and of maintaining the plasma membrane potential, the two most energetically demanding processes for exponentially growing microbes.

Using the model, the student will both test strategies for increasing yields for heterologous proteins and determine the effects of over-expression of drug-efflux pumps on growth rate. The model predictions will be investigated experimentally with the synthesis of GFP, a heterologous protein, and with different regimens of applications of anti-fungals, such as sodium azide, and of mitochondrial uncouplers: 2,4 dinitrophenol, mammalian UCP1 and UCP2, and weak acids, such as acetic acid.

 

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