SUMMARY Autoinduction (AI), the response to self-produced chemical signals, is widespread in the bacterial world. This process controls vastly different target functions, such as luminescence, nutrient acquisition, and biofilm formation, in different ways and integrates additional environmental and physiological cues. This diversity raises questions about unifying principles that underlie all AI systems. Here, we suggest that such core principles exist. We argue that the general purpose of AI systems is the homeostatic control of costly cooperative behaviors, including, but not limited to, secreted public goods. First, costly behaviors require preassessment of their efficiency by cheaper AI signals, which we encapsulate in a hybrid “push-pull” model. The “push” factors cell density, diffusion, and spatial clustering determine when a behavior becomes effective. The relative importance of each factor depends on each species' individual ecological context and life history. In turn, “pull” factors, often stress cues that reduce the activation threshold, determine the cellular demand for the target behavior. Second, control is homeostatic because AI systems, either themselves or through accessory mechanisms, not only initiate but also maintain the efficiency of target behaviors. Third, AI-controlled behaviors, even seemingly noncooperative ones, are generally cooperative in nature, when interpreted in the appropriate ecological context. The escape of individual cells from biofilms, for example, may be viewed as an altruistic behavior that increases the fitness of the resident population by reducing starvation stress. The framework proposed here helps appropriately categorize AI-controlled behaviors and allows for a deeper understanding of their ecological and evolutionary functions.

Biofilms are microbial communities encased in a layer of extracellular polymeric substances (EPS). The EPS matrix provides several functional purposes for the biofilm, such as protecting bacteria from environmental stresses, and providing mechanical stability. Quorum sensing is a cell-cell communication mechanism used by several bacterial taxa to coordinate gene expression and behaviour in groups, based on population densities.

Autoinducer signals enable coordinated behaviour of bacterial populations, a phenomenon originally described as quorum sensing. Autoinducer systems are often controlled by environmental substances as nutrients or secondary metabolites (signals) from neighbouring organisms. In cell aggregates and biofilms gradients of signals and environmental substances emerge. Mathematical modelling is used to analyse the functioning of the system. We find that the autoinducer regulation network generates spatially heterogeneous behaviour, up to a kind of multicellularity-like division of work, especially under nutrient-controlled conditions. A hybrid push/pull concept is proposed to explain the ecological function. The analysis allows to explain hitherto seemingly contradicting experimental findings.

Populations of genetically identical Sinorhizobium fredii NGR234 cells differ significantly in their expression profiles of autoinducer (AI)-dependent and AI-independent genes. Promoter fusions of the NGR234 AI synthase genes traI and ngrI showed high levels of phenotypic heterogeneity during growth in TY medium on a single-cell level. However, adding very high concentrations of N -(3-oxooctanoyl-)- l -homoserine lactone resulted in a more homogeneous expression profile. Similarly, the lack of internally synthesized AIs in the background of the NGR234-Δ traI or the NGR234-Δ ngrI mutant resulted in a highly homogenous expression of the corresponding promoter fusions in the population. Expression studies with reporter fusions of the promoter regions of the quorum-quenching genes dlhR and qsdR1 and the type IV pilus gene cluster located on pNGR234 b suggested that factors other than AI molecules affect NGR234 phenotypic heterogeneity. Further studies with root exudates and developing Arabidopsis thaliana seedlings provide the first evidence that plant root exudates have strong effects on the heterogeneity of AI synthase and quorum-quenching genes in NGR234. Therefore, plant-released octopine appears to play a key role in modulation of heterogeneous gene expression.

Automated identification and quantification of algae in microscopic images is a tool that allows high taxonomic resolution with reasonable technical efforts. However, in samples containing various non-algal objects, this is still not a satisfactorily solved problem. We show that autofluorescence information improves discrimination of algae from non-algal objects as well as phycoerythrin (PE) containing algae from others. We analyse the stability of the autofluorescence to estimate its constraints. Cold and dark storage of glutaraldehyde fixed samples maintains autofluorescence sufficiently for 3 weeks. Under repeated excitations, chlorophyll a (Chl a) or PE autofluorescence show an exponential decrease followed by an intermediate maximum. A peak also occurs in emission wavelength ranges without chlorophyll and PE fluorescence. The unspecific autofluorescence causing the peaks is at least partly identical with the blue–green fluorescence (BGF) in plant cells. BGF interferes with identification of algae, thus correction of pigment autofluorescence with such unspecific fluorescence allows a more reliable algal discrimination procedure. A classification scheme for discrimination of Chl a and PE-containing algae shows a high performance in a test with natural samples. Integration of fluorescence and bright-field image information provides a powerful tool for phytoplankton analysis in complex samples.

Diabetes mellitus is a group of metabolic diseases with increased blood glucose concentration as the main symptom. This can be caused by a relative or a total lack of insulin which is produced by the β-cells in the pancreatic islets of Langerhans. Recent experimental results indicate the relevance of the β-cell cycle for the development of diabetes mellitus. This paper introduces a mathematical model that connects the dynamics of glucose and insulin concentration with the β-cell cycle. The interplay of glucose, insulin, and β-cell cycle is described with a system of ordinary differential equations. The model and its development will be presented as well as its mathematical analysis. The latter investigates the steady states of the model and their stability. Our model shows the connection of glucose and insulin concentrations to the β-cell cycle. In this way the important role of glucose as regulator of the cell cycle and the capability of the β-cell mass to adapt to metabolic demands can be presented. Simulations of the model correspond to the qualitative behavior of the glucose-insulin regulatory system showed in biological experiments. This work focus on modeling the physiological situation of the glucose-insulin regulatory system with a detailed consideration of the β-cell cycle. Furthermore, the presented model allows the simulation of pathological scenarios. Modification of different parameters results in simulation of either type 1 or type 2 diabetes.