Uwe Sauer is Professor of Systems Biology at the ETH Zurich. His research interests focus on complex metabolic and regulation networks in bacteria and yeast. With more than 50 publications in the last 5 years, his lab has pioneered the development of quantitative experimental and computational methods for metabolic flux analyses. A particular focus is on higher throughput, mini-scale methods for metabolomics and fluxomics, and the integration of such data within computational models

Abstract

Condition-specific transcriptional control of metabolic fluxes and computational prediction

Sarah-Maria Fendt, Robert Schutz, Matthias Heinemann, Nicola Zamboni and Uwe Sauer, Institute of Molecular Systems Biology, ETH Zurich, Switzerland

Understanding how complex networks respond to genetic or environmental challenges is a key challenge for systems biology. Great strides have been made in our ability to monitor transcriptional and proteome responses, but it is not trivial to predict biological function and activity from such data. For metabolic networks, recent technological advances in mass spectrometry-based metabolomics and 13C-based flux analysis provide us with experimental methods to assess the networks functional state and its integrated output (1). Here we address which transcription factors actually control metabolic function in a given environment and whether such responses can be computationally predicted.

Using our mini-scale 13C-flux (2), I will first discuss large-scale regulation analyses in E. coli and B. subtilis. Systematic flux analysis of 120 transcription factor mutants in the yeast S. cerevisiae then reveal networks of active transcriptional regulation under 5 conditions. Using metabolomics and targeted proteomics for in depth analysis of key mutants then identify the precise molecular targets of these active regulation mechanisms. While many proteins are differentially expressed in deletions mutants and under different conditions, only relatively few of these expression changes actually cause flux alterations, as revealed by computational analyses of flux, proteome and metabolome data.

On the basis of these large in vivo flux data, we then ask whether there are generally valid principles that describe the distribution of flux under different conditions and how such metabolic networks respond to perturbations? Using the computational framework of flux balance analysis, we test two fundamentally different families of hypotheses: are cells optimized during evolution towards one or more objectives (3) or are their responses optimized towards minimal readjustments?

References

• Sauer, U. Mol. Sys. Biol. 2, 62-68 (2006).
• Fischer, E. & Sauer, U. Nat. Genet. 37, 636-640 (2005).
• R Schütz, L Küpfer & U Sauer. Mol. Sys. Biol.  3:119 (2007).

Keywords

Metabolic network, High-throughout biology, Computational prediction, Design principles