This does not necessarily correspond linearly to the mass of rhamnolipids
secreted. The rhamnolipids secreted by P. aeruginosa can have variable composition (reviewed in ) and rhamnolipids exist both Nirogacestat chemical structure in mono- and di-L-rhamnose forms. Methods such as thin layer chromatography, to distinguish the mono-L-rhamnose from di-L-rhamnose rhamnolipids, or mass spectrometry  allow more precise measurements. These analyses could be used to complement reconstructed time series and help further characterize the regulation of rhamnolipids, which are important virulence factors for P. aeruginosa [9, 10]. In the long term, unveiling the molecular mechanisms regulating the timing and quantity of rhamnolipid secretion can lead to the rational development of new therapies that specifically target virulent secretions to fight P.
aeruginosa infection. Cell density in bacterial and other cell populations is often monitored by optical density at 600 nm (OD600), in spite of its ISRIB ic50 inherent noisiness and limited dynamic range. For this reason, we chose to apply our method to time series of OD600. We envision that any other high-resolution time series data should be useable for aligning curves, including fluorescence or bioluminescence. The only requirement is that the calculated time delays and inoculum dilution must have a linear relationship for the range of inoculum concentrations used (Figures 2 and 5). The alignment method we used was an algorithm developed specifically for our purpose (code supplied as supporting material). Nevertheless, any other algorithm that aligns sets of growth curves and that determines concomitant time delays can in principle be used. We also tested our analysis by aligning the growth curves visually. Although the visual alignment gave acceptable results (not shown), an automated method using an unsupervised yet robust algorithm such as the one provided here is preferable for speed and consistency (manual alignment is possible through Dapagliflozin Additional File 5). The method introduced
here can potentially be applied to many other experimental problems that have exponentially growing cultures and where the integration of online and offline measurements is desired. Besides the growth of P. aeruginosa and its rhamnolipid secretion, another example is indole production by altruistic bacteria . Indole was found to be important for antibiotic resistance of bacterial populations, but the secreted quantities must be assessed through offline measurements. Growth curve synchronization could be used to quantify the timing and quantity of indole production and help further elucidate the population dynamics. Our method could also be ABT-263 solubility dmso extended to include other online measurements such as pH quantification by color change of pH indicators (e.g. phenol red).