Abstract
Pulse experiments in continuous‐culture are a valuable tool in microbial physiology research. However, inferences become difficult when the cell response is followed by monitoring many biochemical variables or when several types of perturbations are compared. Moreover, there is no objective criterion to delimit the time‐window, so that the recorded responses will render valid inferences. Hence, we have investigated the capability of a multivariate approach to deal with complex data from a previously described series of pulse experiments. Data are concerned with 12 biochemical variables that were monitored when an anaerobic, steady‐continuous culture of E. coli O74K74 was disturbed by six types of pulses (glycerol, fumarate, acetate, crotonobetaine, hypersaline plus high‐glycerol basal medium and crotonobetaine plus hypersaline basal medium). Our analysis determined the instantaneous uptake rate for the pulsed metabolite (Dynamical Chemical‐Balances), reduced the multivariate observations to one response curve (Principal Component Analysis) and determined the optimal time‐window (Cluster Analysis). Finally, input‐output data were filtered (Orthogonal Signal Correction) while both blocks were mathematically connected (Partial Least‐squares Regression). This systematic approach allowed us to detect several relevant patterns not previously revealed: (i) Glycerol uptake rate did not follow a Michaelian kinetics but showed a biphasic dependence on glycerol concentration; noticeably, net uptake decreased 136‐fold despite the high availability of glycerol in the milieu. (ii) The structure of the bacterial response changed during time the glycerol‐disturbance lasted (2 h), hence analyses had to be limited to the early response (time from 0 to 5 min). (iii) By mathematically relating the input (glycerol uptake rate) with the output (12 biochemical responses) it was possible to identify which of the monitored variables were primary targets of the glycerol disturbance (namely: ATP, formate, acetyl‐CoA synthase, isocitrate dehydrogenase, and isocitrate lyase), which were secondarily responsive (ethanol) and those that were independent (acetate, carnitine, lactate, and NADH/NAD ratio). Identification was achieved even though all the analyzed variables were affected by the pulse. (iv) Some variables exhibited uncorrelated dynamics despite their close functional relationship (ATP and NADH/NAD ratio, ethanol and lactate; carnitine and the crotonobetaine hydratase complex; acetate and the enzymes phosphotransacetylase, acetyl‐CoA synthase and isocitrate lyase). The results are discussed in terms of E. coli transcriptional control, and it is concluded that glycerol pulse produces a stressing effect. The consequent activation of the polyamine‐dependent mechanisms involved in such stressing effect provides a unified explanation for how glycerol uptake is down‐regulated in the presence of high glycerol availability and how acetate can be produced without de novo biosynthesis. Biotechnol. Bioeng. 2009; 102: 910–922. © 2008 Wiley Periodicals, Inc.