Understanding regulation of metabolism through feasibility analysis.
Understanding cellular regulation of metabolism is a major challenge in systems biology. Thus far, the main assumption was that enzyme levels are key regulators in metabolic networks. However, regulation analysis recently showed that metabolism is rarely controlled via enzyme levels only, but through non-obvious combinations of hierarchical (gene and enzyme levels) and metabolic regulation (mass action and allosteric interaction). Quantitative analyses relating changes in metabolic fluxes to changes in transcript or protein levels have revealed a remarkable lack of understanding of the regulation of these networks. We study metabolic regulation via feasibility analysis (FA). Inspired by the constraint-based approach of Flux Balance Analysis, FA incorporates a model describing kinetic interactions between molecules. We enlarge the portfolio of objectives for the cell by defining three main physiologically relevant objectives for the cell: function, robustness and temporal responsiveness. We postulate that the cell assumes one or a combination of these objectives and search for enzyme levels necessary to achieve this. We call the subspace of feasible enzyme levels the feasible enzyme space. Once this space is constructed, we can study how different objectives may (if possible) be combined, or evaluate the conditions at which the cells are faced with a trade-off among those. We apply FA to the experimental scenario of long-term carbon limited chemostat cultivation of yeast cells, studying how metabolism evolves optimally. Cells employ a mixed strategy composed of increasing enzyme levels for glucose uptake and hexokinase and decreasing levels of the remaining enzymes. This trade-off renders the cells specialized in this low-carbon flux state to compete for the available glucose and get rid of over-overcapacity. Overall, we show that FA is a powerful tool for systems biologists to study regulation of metabolism, interpret experimental data and evaluate hypotheses.
The Delft Bioinformatics Lab, Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands. i.e.nikerel@tudelft.nl
Emrah
E
Nikerel
Jan
J
Berkhout
Fengyuan
F
Hu
Bas
B
Teusink
Marcel J T
MJ
Reinders
Dick
D
de Ridder
D000465:Algorithms; D002244:Carbon / Q000378:metabolism*; D019143:Evolution, Molecular; D025461:Feedback, Physiological; D015966:Gene Expression Regulation, Fungal; D005947:Glucose / Q000378:metabolism*; D006593:Hexokinase / Q000235:genetics* / Q000378:metabolism; D007700:Kinetics; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D011338:Probability Theory; D012441:Saccharomyces cerevisiae / Q000201:enzymology / Q000235:genetics*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics* / Q000378:metabolism; D049490:Systems Biology; D014158:Transcription, Genetic
D029701:Saccharomyces cerevisiae Proteins; D002244:Carbon; D006593:Hexokinase; D005947:Glucose