Dynamics of transcription driven by the tetA promoter, one event at a time, in live Escherichia coli cells.

In Escherichia coli, tetracycline prevents translation. When subject to tetracycline, E. coli express TetA to pump it out by a mechanism that is sensitive, while fairly independent of cellular metabolism. We constructed a target gene, PtetA-mRFP1-96BS, with a 96 MS2-GFP binding site array in a single-copy BAC vector, whose expression is controlled by the tetA promoter. We measured the in vivo kinetics of production of individual RNA molecules of the target gene as a function of inducer concentration and temperature. From the distributions of intervals between transcription events, we find that RNA production by PtetA is a sub-Poissonian process. Next, we infer the number and duration of the prominent sequential steps in transcription initiation by maximum likelihood estimation. Under full induction and at optimal temperature, we observe three major steps. We find that the kinetics of RNA production under the control of PtetA, including number and duration of the steps, varies with induction strength and temperature. The results are supported by a set of logical pairwise Kolmogorov-Smirnov tests. We conclude that the expression of TetA is controlled by a sequential mechanism that is robust, whereas sensitive to external signals.

Cell segmentation by multi-resolution analysis and maximum likelihood estimation (MAMLE).

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.

Asymmetric disposal of individual protein aggregates in Escherichia coli, one aggregate at a time.

Escherichia coli cells employ an asymmetric strategy at division, segregating unwanted substances to older poles, which has been associated with aging in these organisms. The kinetics of this process is still poorly understood. Using the MS2 coat protein fused to green fluorescent protein (GFP) and a reporter construct with multiple MS2 binding sites, we tracked individual RNA-MS2-GFP complexes in E. coli cells from the time when they were produced. Analyses of the kinetics and brightness of the spots showed that these spots appear in the midcell region, are composed of a single RNA-MS2-GFP complex, and reach a pole before another target RNA is formed, typically remaining there thereafter. The choice of pole is probabilistic and heavily biased toward one pole, similar to what was observed by previous studies regarding protein aggregates. Additionally, this mechanism was found to act independently on each disposed molecule. Finally, while the RNA-MS2-GFP complexes were disposed of, the MS2-GFP tagging molecules alone were not. We conclude that this asymmetric mechanism to segregate damage at the expense of aging individuals acts probabilistically on individual molecules and is capable of the accurate classification of molecules for disposal.

Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images.

BACKGROUND: Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed. RESULTS: To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies. CONCLUSIONS: These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.

Extradural haematoma in children: surgical experiences and prospective analysis of 170 cases.

AIM: We present our experiences in the management of extradural haematoma in children which involved an aggressive diagnostic approach, prompt surgical evacuation results in an excellent outcome. MATERIAL AND METHODS: 170 EDH patients who underwent surgery in our department from January 2006 to July 2010 included in this prospective study. Each patient evaluated in term of age, sex, mode of injury, localization of haematoma, clinical presentation, CT findings, operative measures and outcome. RESULTS: Out of 170 cases 72%, (n=122) were boys and 28 %,( n=48) were girls. The boys to girls ratio was 2.54: 1. Age ranged from 1.8 to 18 years with a mean age of 9.49 years. Most of the victims are in the 11-18 years age group 44% ( n=74). The most common mode of injury was fall 41%, (n = 70) followed by Road traffic Accident (RTA) 32%, (n = 54) .The most common clinical presentation was altered sensorium 61 %, (n= 104), followed by headache and/or vomiting 56 %, (n = 96). The mortality rate was 8%. CONCLUSION: EDH is recognized as one of the most rewarding neurosurgical emergencies. It must be diagnosed in the early period of the trauma and evacuated early to prevent potential mortality and morbidity. Different factors affect the outcome of extradural haematoma Surgery including age of the patient, presence of cranial fractures, associated brain lesions and pre-operative neurological condition of patient, duration of time interval between onset of coma and surgical intervention. Better prognosis is seen in patients less than 10 years of age.

Probabilistic RNA partitioning generates transient increases in the normalized variance of RNA numbers in synchronized populations of Escherichia coli.

We explore the effects of probabilistic RNA partitioning during cell division on the normalized variance of RNA numbers across generations of bacterial populations. We first characterize these effects in model cell populations, where gene expression is modeled as a delayed stochastic process, as a function of the synchrony in cell division, the rate of division, and the RNA degradation rate. We further explore the additional variance that arises if the partitioning is biased. Next, in Escherichia coli cells expressing RNA tagged with MS2d-GFP, we measured the normalized variance of RNA numbers across several generations, with cell divisions synchronized by heat shock. We show that synchronized cell populations exhibit transient increases in normalized variance following cell divisions, as predicted by the model, which are not observed in unsynchronized populations. We conclude that errors in partitioning of RNA molecules generate diversity between the offspring of individual bacteria and thus constitute a form of reproductive bet-hedging.

Diversity of temporal correlations between genes in models of noisy and noiseless gene networks.

Gene regulatory networks (GRNs) are parallel information processing systems, binding past events to future actions. Since cell types stably remain in restricted subsets of the possible states of the GRN, they are likely the dynamical attractors of the GRN. These attractors differ in which genes are active and in the amount of information propagating within the network. Using mutual information (I) as a measure of information propagation between genes in a GRN, modeled as finite-sized Random Boolean Networks (RBN), we study how the dynamical regime of the GRN affects I within attractors (I(A)). The spectra of I(A) of individual RBNs are found to be scattered and diverse, and distributions of I(A) of ensembles are non-trivial and change shape with mean connectivity. Mean and diversity of I(A) values maximize in the chaotic near-critical regime, whereas ordered near-critical networks are the best at retaining the distinctiveness of each attractor's I(A) with noise. The results suggest that selection likely favors near-critical GRNs as these both maximize mean and diversity of I(A), and are the most robust to noise. We find similar I(A) distributions in delayed stochastic models of GRNs. For a particular stochastic GRN, we show that both mean and variance of I(A) have local maxima as its connectivity and noise levels are varied, suggesting that the conclusions for the Boolean network models may be generalizable to more realistic models of GRNs.

Information propagation within the Genetic Network of Saccharomyces cerevisiae.

BACKGROUND: A gene network's capacity to process information, so as to bind past events to future actions, depends on its structure and logic. From previous and new microarray measurements in Saccharomyces cerevisiae following gene deletions and overexpressions, we identify a core gene regulatory network (GRN) of functional interactions between 328 genes and the transfer functions of each gene. Inferred connections are verified by gene enrichment. RESULTS: We find that this core network has a generalized clustering coefficient that is much higher than chance. The inferred Boolean transfer functions have a mean p-bias of 0.41, and thus similar amounts of activation and repression interactions. However, the distribution of p-biases differs significantly from what is expected by chance that, along with the high mean connectivity, is found to cause the core GRN of S. cerevisiae's to have an overall sensitivity similar to critical Boolean networks. In agreement, we find that the amount of information propagated between nodes in finite time series is much higher in the inferred core GRN of S. cerevisiae than what is expected by chance. CONCLUSIONS: We suggest that S. cerevisiae is likely to have evolved a core GRN with enhanced information propagation among its genes.

Reconstruction and validation of RefRec: a global model for the yeast molecular interaction network.

Molecular interaction networks establish all cell biological processes. The networks are under intensive research that is facilitated by new high-throughput measurement techniques for the detection, quantification, and characterization of molecules and their physical interactions. For the common model organism yeast Saccharomyces cerevisiae, public databases store a significant part of the accumulated information and, on the way to better understanding of the cellular processes, there is a need to integrate this information into a consistent reconstruction of the molecular interaction network. This work presents and validates RefRec, the most comprehensive molecular interaction network reconstruction currently available for yeast. The reconstruction integrates protein synthesis pathways, a metabolic network, and a protein-protein interaction network from major biological databases. The core of the reconstruction is based on a reference object approach in which genes, transcripts, and proteins are identified using their primary sequences. This enables their unambiguous identification and non-redundant integration. The obtained total number of different molecular species and their connecting interactions is approximately 67,000. In order to demonstrate the capacity of RefRec for functional predictions, it was used for simulating the gene knockout damage propagation in the molecular interaction network in approximately 590,000 experimentally validated mutant strains. Based on the simulation results, a statistical classifier was subsequently able to correctly predict the viability of most of the strains. The results also showed that the usage of different types of molecular species in the reconstruction is important for accurate phenotype prediction. In general, the findings demonstrate the benefits of global reconstructions of molecular interaction networks. With all the molecular species and their physical interactions explicitly modeled, our reconstruction is able to serve as a valuable resource in additional analyses involving objects from multiple molecular -omes. For that purpose, RefRec is freely available in the Systems Biology Markup Language format.