Sauer Group, Metabolism, Systems Biology, Modelling, Bistability

Tutors:

Dimitris Christodoulou ()
Markus Basan ()

Duration: 3-6 months

Background

Growth rates are not only an important determinant of bacterial fitness, but also one of their central physiological characteristics. To sustain growth, metabolic and transcriptional networks operate in an intertwined fashion, where transcription factors alter enzyme abundance (transcriptional regulation) and altered metabolite concentrations modulate the activity of enzymes (allosteric regulation). The current conviction in microbiology is that growth rates of a particular strain are determined solely by growth conditions, like for instance carbon/nitrogen availability or temperature. Hence, after some lag time, a bacterial culture transferred to certain growth conditions should grow at a unique growth rate defined by these conditions.

Surprisingly, we recently identified cases, in which E. coli exhibits significantly different growth rates in the same conditions dependent on the previous growth condition, potentially unsettling fundamental paradigm of microbiology. Preliminary results indicate that these divergent growth rates constitute a stable, history-dependent steady state (hysteresis), as cells remain at these growth rates for more than 10 generations. So far, we have experimentally verified this phenomenon for growth of E. coli on several unrelated carbon combinations. In these cases, cells grew initially only in one carbon source and were then supplemented by the other. The cultures reached distinct growth states dependent on their initial carbon source with growth rates differing by more than 20%. We have indications of this phenomenon for other conditions and speculate that it originates from suboptimal or even competing regulation of overlapping regulatory layers (transcriptional and allosteric). Preliminary metabolite data appears to support this hypothesis. The proposed project is closely linked to ongoing endeavors in our lab to establish a kinetic, multi-scale computational model, combining both allosteric and transcriptional regulation and to uncover the molecular origin of lag phases and long periods of non-growth during medium shifts. Therefore, in the proposed project, we also want to identify and quantify tradeoffs stemming from overlapping regulatory layers and from competing objectives like the minimization of lag time and maximization of growth rate on carbon combinations.

Your project

This project is planned to be ~75% experimental work and ~25% computational analysis and uses cutting-edge techniques in both parts, namely high throughput metabolomics techniques, (dynamic) 13C labelling, Flux Balance Analysis, dynamic gene expression measurements and validation of hypotheses using genetic constructs.

The goals of this project entail i) establishing the generality and prevalence pattern of the hysteresis phenomenon, ii) uncovering the underlying molecular mechanisms, iii) investigating links between this phenomenon and other objectives (e.g. minimization of lag time), iv) constructing and testing a quantitative and predictive theoretical model. To this end, you will perform high-throughput physiology screening, you will measure central metabolite pools and physiological parameters using state-of-the-art techniques (LC – MS / MS, HPLC, micro-cultivation devices) and learn how to handle quantitative and dynamic datasets in order to extract novel biological insights. You will learn to make use of programming tools in order to statistically evaluate different hypotheses and also implement and use kinetic models in order to combine and understand the available data. Finally, you will develop hypotheses regarding underlying mechanisms and test these ideas experimentally by combining genetics and ‘omics’ technologies.

Interested? Send us an email (see above) or drop by for a cup of coffee (HPT D73).