Training of yeast cell dynamics.

In both industrial fermenters and in their natural habitats, microorganisms often experience an inhomogeneous and fluctuating environment. In this paper we mimicked one aspect of this nonideal behaviour by imposing a low and oscillating extracellular glucose concentration on nonoscillating suspensions of yeast cells. The extracellular dynamics changed the intracellular dynamics--which was monitored through NADH fluorescence--from steady to equally dynamic; the latter followed the extracellular dynamics at the frequency of glucose pulsing. Interestingly, the amplitude of the oscillation of the NADH fluorescence increased with time. This increase in amplitude was sensitive to inhibition of protein synthesis, and was due to a change in the cells rather than in the medium; the cell population was 'trained' to respond to the extracellular dynamics. To examine the mechanism behind this 'training', we subjected the cells to a low and constant extracellular glucose concentration. Seventy-five minutes of adaptation to a low and constant glucose concentration induced the same increase of the amplitude of the forced NADH oscillations as did the train of glucose pulses. Furthermore, 75 min of adaptation to a low (oscillating or continuous) glucose concentration decreased the K(M) of the glucose transporter from 26 mm to 3.5 mm. When subsequently the apparent K(M) was increased by addition of maltose, the amplitude of the forced oscillations dropped to its original value. This demonstrated that the increased affinity of glucose transport was essential for the training of the cells' dynamics.

Yeast glycolytic oscillations that are not controlled by a single oscillophore: a new definition of oscillophore strength.

Biochemical oscillations, such as glycolytic oscillations, are often believed to be caused by a single so-called 'oscillophore'. The main characteristics of yeast glycolytic oscillations, such as frequency and amplitude, are however controlled by several enzymes. In this paper, we develop a method to quantify to which extent any enzyme determines the occurrence of oscillations. Principles extrapolated from metabolic control analysis are applied to calculate the control exerted by individual enzymes on the real and imaginary parts of the eigenvalues of the Jacobian matrix. We propose that the control exerted by an enzyme on the real part of the smallest eigenvalue, in terms of absolute value, quantifies to which extent that enzyme contributes to the emergence of instability. Likewise the control exerted by an enzyme on the imaginary part of complex eigenvalues may serve to quantify the extent to which that enzyme contributes to the tendency of the system to oscillate. The method was applied both to a core model and to a realistic model of yeast glycolytic oscillations. Both the control over stability and the control over oscillatory tendency were distributed among several enzymes, of which glucose transport, pyruvate decarboxylase and ATP utilization were the most important. The distributions of control were different for stability and oscillatory tendency, showing that control of instability does not imply control of oscillatory tendency nor vice versa. The control coefficients summed up to 1, suggesting the existence of a new summation theorem. These results constitute proof that glycolytic oscillations in yeast are not caused by a single oscillophore and provide a new, subtle, definition for the oscillophore strength of an enzyme.

Control analysis for autonomously oscillating biochemical networks.

It has hitherto not been possible to analyze the control of oscillatory dynamic cellular processes in other than qualitative ways. The control coefficients, used in metabolic control analyses of steady states, cannot be applied directly to dynamic systems. We here illustrate a way out of this limitation that uses Fourier transforms to convert the time domain into the stationary frequency domain, and then analyses the control of limit cycle oscillations. In addition to the already known summation theorems for frequency and amplitude, we reveal summation theorems that apply to the control of average value, waveform, and phase differences of the oscillations. The approach is made fully operational in an analysis of yeast glycolytic oscillations. It follows an experimental approach, sampling from the model output and using discrete Fourier transforms of this data set. It quantifies the control of various aspects of the oscillations by the external glucose concentration and by various internal molecular processes. We show that the control of various oscillatory properties is distributed over the system enzymes in ways that differ among those properties. The models that are described in this paper can be accessed on http://jjj.biochem.sun.ac.za.