A population‐based temporal logic gate for timing and recording chemical events

Engineered bacterial sensors have potential applications in human health monitoring, environmental chemical detection, and materials biosynthesis. While such bacterial devices have long been engineered to differentiate between combinations of inputs, their potential to process signal timing and duration has been overlooked. In this work, we present a two‐input temporal logic gate that can sense and record the order of the inputs, the timing between inputs, and the duration of input pulses. Our temporal logic gate design relies on unidirectional DNA recombination mediated by bacteriophage integrases to detect and encode sequences of input events. For an E. coli strain engineered to contain our temporal logic gate, we compare predictions of Markov model simulations with laboratory measurements of final population distributions for both step and pulse inputs. Although single cells were engineered to have digital outputs, stochastic noise created heterogeneous single‐cell responses that translated into analog population responses. Furthermore, when single‐cell genetic states were aggregated into population‐level distributions, these distributions contained unique information not encoded in individual cells. Thus, final differentiated sub‐populations could be used to deduce order, timing, and duration of transient chemical events.     

An innovative approach for testing bioinformatics programs using metamorphic testing

Background  

Recent advances in experimental and computational technologies have fueled the development of many sophisticated bioinformatics programs. The correctness of such programs is crucial as incorrectly computed results may lead to wrong biological conclusion or misguide downstream experimentation. Common software testing procedures involve executing the target program with a set of test inputs and then verifying the correctness of the test outputs. However, due to the complexity of many bioinformatics programs, it is often difficult to verify the correctness of the test outputs. Therefore our ability to perform systematic software testing is greatly hindered.     

A genetic ensemble approach for gene-gene interaction identification

Background  

It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging.     

Gene-gene interaction filtering with ensemble of filters

BACKGROUND: Complex diseases are commonly caused by multiple genes and their interactions with each other. Genome-wide association (GWA) studies provide us the opportunity to capture those disease associated genes and gene-gene interactions through panels of SNP markers. However, a proper filtering procedure is critical to reduce the search space prior to the computationally intensive gene-gene interaction identification step. In this study, we show that two commonly used SNP-SNP interaction filtering algorithms, ReliefF and tuned ReliefF (TuRF), are sensitive to the order of the samples in the dataset, giving rise to unstable and suboptimal results. However, we observe that the 'unstable' results from multiple runs of these algorithms can provide valuable information about the dataset. We therefore hypothesize that aggregating results from multiple runs of the algorithm may improve the filtering performance. RESULTS: We propose a simple and effective ensemble approach in which the results from multiple runs of an unstable filter are aggregated based on the general theory of ensemble learning. The ensemble versions of the ReliefF and TuRF algorithms, referred to as ReliefF-E and TuRF-E, are robust to sample order dependency and enable a more informative investigation of data characteristics. Using simulated and real datasets, we demonstrate that both the ensemble of ReliefF and the ensemble of TuRF can generate a much more stable SNP ranking than the original algorithms. Furthermore, the ensemble of TuRF achieved the highest success rate in comparison to many state-of-the-art algorithms as well as traditional χ2-test and odds ratio methods in terms of retaining gene-gene interactions.     

云南省米虾属一新种(十足目：匙指虾科)

报道了米虾属一新种-昆明米虾Caridina kunmingensis Wang et Liang,sp.nov.。昆明米虾与石林米虾Caridina shilinica Liang et Cai2000有相似之处,但前者额角较短,前两对步足腕节和螯短而粗,雄性第1腹肢内肢、雄附肢形态和结构均与后者不同。     

Protein inhibitors of serine proteinases

Serine proteinases and their natural protein inhibitors belong to the most intensively studied models of protein-protein recognition. Protein inhibitors do not form a single group but can be divided into about 20 different families. Global structures of proteins representing different inhibitor families are completely different and comprise alpha-helical proteins, beta-sheet proteins, alpha/beta-proteins and different folds of small disulfide-rich proteins. Three different types of inhibitors can be distinguished: canonical (standard mechanism) inhibitors, non-canonical inhibitors, and serpins. The canonical inhibitor binds to the enzyme through the exposed and convex binding loop, which is complementary to the active site of the enzyme. The mechanism of inhibition in this group is consistently very similar and resembles that of an ideal substrate. Non-canonical inhibitors, originating from blood sucking organisms, specifically block enzymes of the blood clotting cascade. The interaction is mediated through inhibitor N-terminus which binds to the proteinase forming a parallel beta-sheet. There are also extensive secondary interactions which provide an additional buried area and contribute significantly to the strength and specificity of recognition. Serpins are major proteinase inhibitors occurring in plasma. Similarly to canonical inhibitors, serpins interact with their target proteinases in a substrate-like manner. However, in the case of serpins, cleavage of a single peptide bond in a flexible and exposed binding loop leads to dramatic structural changes.     

Dissecting the thermodynamics of GAP-RhoA interactions

We describe a detailed study of the RhoA-binding epitope of the GAP domain of Graf, including the determination of the thermodynamic and kinetic parameters of the interaction of wild-type domain, and of its 15 single-site mutants, with cognate GTPases. We show that residues important for the structural integrity of the Arg-finger loop are critical for binding Rho and for the catalytic activity of GAP, but GTPase selectivity appears to be modulated by a much more subtle interplay of electrostatic and hydrophobic interactions involving residues on the periphery of the main interface. The eight residues targeted in this study are involved in three distinct patches on the surface, two of which appear to interact with highly conserved regions of the GTPase, while the third plays a role in GTPase selectivity.