Modelling ion homeostasis in yeast

Gene interaction networks and models of cation homeostasis in Saccharomyces cerevisiae-TRANSLUCENT

Collaborators:

•     Joaquin Ariño, Autonomous University of
      Barcelona, Spain
•     Edda Klipp, Max-Panck-Institute
      Berlin, Germany
•     Jost Ludwig, University of Bonn, Germany
•     José Ramos, University of Cordoba, Spain
•     Paul van Heusden, University of Leiden,
      The Netherlands
•     Hana Sychrova, Academy of Science, Czech Republic

TRANSLUCENT is a SysMO project (http://www.sysmo.net/)

The yeast Saccharomyces cerevisiae is a remarkably versatile model system with a myriad of biotechnological applications that fulls all the criteria essential for a systems biology approach. In this organism, maintenance of cation homeostasis is an essential process that aects physiological parameters such as membrane potential, intracellular pH, cell volume and that directly inuences nutrient uptake and growth. Most key elements mediating uptake and eux of the major alkali cations (potassium and sodium) have been discovered and physiologically characterized during the last decade. However, a systems level understanding of the interplay and regulation of the biophysical and regulatory processes involved is still lacking.

We have developed a mathematical model describing the electrophysiological relationship between potassium concentration, membrane voltage, pH and cell volume. After several cycles of model driven experimentation and model improvement we could verify our model predictions. Most importantly, we showed that the current understanding of potassium control in yeast has to be revised and that proton uxes are the main regulators of potassium control. A further result of this project is a new mathematical algorithm for the inference of unmodelled subprocesses. This algorithm is a useful tool for the systematic extension and improvement of general dynamic models. Currently, we are extending the model to also incorporate metabolic processes. For that, we are developing new methods for the sampling of constrained metabolic fluxes from high dimensional solution spaces. In addition, we have implemented novel techniques for the automatic analysis of cellular growth curves under various environmental conditions.

Kahm, M., Navarrete, C., Llopis-Torregrosa, V., Herrera, R., Barreto, L., Yenush, L., Arinho, J., Ramos, J. and Kschischo, M. Potassium Starvation in Yeast: Mechanisms of Homeostasis Revealed by Mathematical Modeling. PLoS Comput Biol 8(6): e1002548. doi:10.1371/journal.pcbi.1002548 (2012).

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002548

Kahm, M., Hasenbrink, G., Lichtenberg-Frate , H., Ludwig, J. and Kschischo, M. Grofit: Fitting biological growth curves with R. Journal of Statistical Software, 33 (2010). 
http://www.jstatsoft.org/v33/i07.