Quantifying massively parallel microbial growth with spatially mediated interactions.

Quantitative understanding of microbial growth is an essential prerequisite for successful control of pathogens as well as various biotechnology applications. Even though the growth of cell populations has been extensively studied, microbial growth remains poorly characterised at the spatial level. Indeed, even isogenic populations growing at different locations on solid growth medium typically show significant location-dependent variability in growth. Here we show that this variability can be attributed to the initial physiological states of the populations, the interplay between populations interacting with their local environment and the diffusion of nutrients and energy sources coupling the environments. We further show how the causes of this variability change throughout the growth of a population. We use a dual approach, first applying machine learning regression models to discover that location dominates growth variability at specific times, and, in parallel, developing explicit population growth models to describe this spatial effect. In particular, treating nutrient and energy source concentration as a latent variable allows us to develop a mechanistic resource consumer model that captures growth variability across the shared environment. As a consequence, we are able to determine intrinsic growth parameters for each local population, removing confounders common to location-dependent variability in growth. Importantly, our explicit low-parametric model for the environment paves the way for massively parallel experimentation with configurable spatial niches for testing specific eco-evolutionary hypotheses.

Drug-induced resistance evolution necessitates less aggressive treatment.

Increasing body of experimental evidence suggests that anticancer and antimicrobial therapies may themselves promote the acquisition of drug resistance by increasing mutability. The successful control of evolving populations requires that such biological costs of control are identified, quantified and included to the evolutionarily informed treatment protocol. Here we identify, characterise and exploit a trade-off between decreasing the target population size and generating a surplus of treatment-induced rescue mutations. We show that the probability of cure is maximized at an intermediate dosage, below the drug concentration yielding maximal population decay, suggesting that treatment outcomes may in some cases be substantially improved by less aggressive treatment strategies. We also provide a general analytical relationship that implicitly links growth rate, pharmacodynamics and dose-dependent mutation rate to an optimal control law. Our results highlight the important, but often neglected, role of fundamental eco-evolutionary costs of control. These costs can often lead to situations, where decreasing the cumulative drug dosage may be preferable even when the objective of the treatment is elimination, and not containment. Taken together, our results thus add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and motivate further experimental and clinical investigation of the mutagenicity and other hidden collateral costs of therapies.

Understanding cellular growth strategies via optimal control.

Evolutionary prediction and control are increasingly interesting research topics that are expanding to new areas of application. Unravelling and anticipating successful adaptations to different selection pressures becomes crucial when steering rapidly evolving cancer or microbial populations towards a chosen target. Here we introduce and apply a rich theoretical framework of optimal control to understand adaptive use of traits, which in turn allows eco-evolutionarily informed population control. Using adaptive metabolism and microbial experimental evolution as a case study, we show how demographic stochasticity alone can lead to lag time evolution, which appears as an emergent property in our model. We further show that the cycle length used in serial transfer experiments has practical importance as it may cause unintentional selection for specific growth strategies and lag times. Finally, we show how frequency-dependent selection can be incorporated to the state-dependent optimal control framework allowing the modelling of complex eco-evolutionary dynamics. Our study demonstrates the utility of optimal control theory in elucidating organismal adaptations and the intrinsic decision making of cellular communities with high adaptive potential.

Effects of polarization in the presence and absence of biocides on biofilms in a simulated paper machine water.

The antifouling potential of electric polarization combined and not combined with biocides was studied in nonsaline warm water with high organic content. Deinococcus geothermalis is a bacterium known for forming colored biofilms in paper machines and for its persistence against cleaning and chemical treatments. When D. geothermalis biofilms grown for 24 h in simulated paper machine water were exposed to cathodic or cathodically weighted pulsed polarization at least 60% (P < 0.05) of the biofilms were removed from stainless steel (AISI 316L). Biofilm removal by 25 ppm (effective substances 5-25 ppm) of oxidizing biocides (bromochloro-5,5-dimethylhydantoin, 2,2-dibromo-2-cyanoacetamide, peracetic acid) increased to 70% when combined with cathodically weighted pulsed polarization. Using a novel instrument that allows real-time detection of reactive oxygen species (ROS) we showed that the polarization program effective in antifouling generated ROS in a pulsed manner on the steel surface. We thus suggest that the observed added value of oxidative biocides combined with polarization depended on ROS. This suggestion was supported by the finding that a reductive biocide, methylene bisthiocyanate, counteracted the antifouling effect of polarization.

Destruction of PAHS from soil by using pressurized hot water extraction coupled with supercritical water oxidation.

Chemical processes utilizing water both as extraction solvent and reaction medium are promising "Green Chemistry" alternatives to conventional techniques. Equipment for on-line coupled hot water extraction and supercritical water oxidation was constructed to extract polyaromatic hydrocarbons and toluene from sea sand followed by oxidation using hydrogen peroxide. The effectiveness of the technique is based on the physico-chemical properties of heated and pressurized water. Extraction efficiency increased with temperature and time; the best results were obtained at 300 degrees C with 40 min extraction time. In the oxidation stage, conversion of the PAHs increased with reaction time and oxidant concentration and the best conversion (97.0-99.9%, depending on the compound) was obtained at 425 degrees C with 43 s reaction time. Benzaldehyde and benzoic acid were the most abundant reaction intermediates in the oxidation process. In addition, phenol, p-cresol, and benzyl alcohol were found as intermediates. The intermediates originated mainly from toluene, which was present in much greater concentration than PAHs in the reaction medium.