The outbreak of the Ebola virus in recent years has resulted in numerous research initiatives to seek new solutions to contain the virus. A number of approaches that have been investigated include new vaccines to boost the immune system. An alternative post-exposure treatment is presented in this paper. The proposed approach for clearing the Ebola virus can be developed through a microfluidic attenuator, which contains the engineered bacteria that traps Ebola flowing through the blood onto its membrane. The paper presents the analysis of the chemical binding force between the virus and a genetically engineered bacterium considering the opposing forces acting on the attachment point, including hydrodynamic tension and drag force. To test the efficacy of the technique, simulations of bacterial motility within a confined area to trap the virus were performed. More than 60 percent of the displaced virus could be collected within 15 minutes. While the proposed approach currently focuses on in vitro environments for trapping the virus, the system can be further developed into a future treatment system whereby blood can be cycled out of the body into a microfluidic device that contains the engineered bacteria to trap viruses.

MOTIVATION: Present research on gene expression using live cell imaging and fluorescent proteins or tagged RNA requires accurate automated methods of quantification of these molecules from the images. Here, we propose a novel automated method for classifying pixel intensities of fluorescent spots to RNA numbers. RESULTS: The method relies on a new model of intensity distributions of tagged RNAs, for which we estimated parameter values in maximum likelihood sense from measurement data, and constructed a maximum a posteriori classifier to estimate RNA numbers in fluorescent RNA spots. We applied the method to estimate the number of tagged RNAs in individual live Escherichia coli cells containing a gene coding for an RNA with MS2-GFP binding sites. We tested the method using two constructs, coding for either 96 or 48 binding sites, and obtained similar distributions of RNA numbers, showing that the method is adaptive. We further show that the results agree with a method that uses time series data and with quantitative polymerase chain reaction measurements. Lastly, using simulated data, we show that the method is accurate in realistic parameter ranges. This method should, in general, be applicable to live single-cell measurements of low-copy number fluorescence-tagged molecules. AVAILABILITY AND IMPLEMENTATION: MATLAB extensions written in C for parameter estimation and finding decision boundaries are available under Mozilla public license at http://www.cs.tut.fi/%7ehakkin22/estrna/ CONTACT: [email protected].

BACKGROUND: Cell imaging is becoming an indispensable tool for cell and molecular biology research. However, most processes studied are stochastic in nature, and require the observation of many cells and events. Ideally, extraction of information from these images ought to rely on automatic methods. Here, we propose a novel segmentation method, MAMLE, for detecting cells within dense clusters. METHODS: MAMLE executes cell segmentation in two stages. The first relies on state of the art filtering technique, edge detection in multi-resolution with morphological operator and threshold decomposition for adaptive thresholding. From this result, a correction procedure is applied that exploits maximum likelihood estimate as an objective function. Also, it acquires morphological features from the initial segmentation for constructing the likelihood parameter, after which the final segmentation is obtained. CONCLUSIONS: We performed an empirical evaluation that includes sample images from different imaging modalities and diverse cell types. The new method attained very high (above 90%) cell segmentation accuracy in all cases. Finally, its accuracy was compared to several existing methods, and in all tests, MAMLE outperformed them in segmentation accuracy.

MOTIVATION: Production and degradation of RNA and proteins are stochastic processes, difficulting the distinction between spurious fluctuations in their numbers and changes in the dynamics of a genetic circuit. An accurate method of change detection is key to analyze plasticity and robustness of stochastic genetic circuits. RESULTS: We use automatic change point detection methods to detect non-spurious changes in the dynamics of delayed stochastic models of gene networks at run time. We test the methods in detecting changes in mean and noise of protein numbers, and in the switching frequency of a genetic switch. We also detect changes, following genes' silencing, in the dynamics of a model of the core gene regulatory network of Saccharomyces cerevisiae with 328 genes. Finally, from images, we determine when RNA molecules tagged with fluorescent proteins are first produced in Escherichia coli. Provided prior knowledge on the time scale of the changes, the methods detect them accurately and are robust to fluctuations in protein and RNA levels. AVAILABILITY: Simulator: www.cs.tut.fi/~sanchesr/SGN/SGNSim.html CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.