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.     

Evaluation of bull fertility in dairy and beef cattle using cow field data

A successful outcome to a given service is a combination of both male and female fertility. Despite this, most national evaluations for fertility are generally confined to female fertility with evaluations for male fertility commonly undertaken by individual breeding organisations and generally not made public. The objective of this study was to define a pertinent male fertility trait for seasonal calving production systems, and to develop a multiple regression mixed model that may be used to evaluate male fertility at a national level. The data included in the study after editing consisted of 361,412 artificial inseminations from 206,683 cow-lactations (134,911 cows) in 2,843 commercial dairy and beef herds. Fixed effects associated with whether a successful pregnancy ensued (pregnant = 1) or not (pregnant = 0) from a given service were year by month of service, day of the week, days since calving, cow parity, level of calving difficulty experienced, whether or not the previous calving was associated with perinatal mortality, and age of the service bull at the date of insemination. Non-additive genetic effects such as heterosis and recombination loss as well as inbreeding level of the service bull, dam or mating were not associated with a successful pregnancy; there was no difference in pregnancy rate between fresh or frozen semen. Random effects included in the model were the additive genetic effect of the cow, as well as a within lactation and across lactation permanent environmental effect of the cow; pedigree group effects based on cow breed were also included via the relationship matrix. Temporal differences in the AI technician and service bull were also included as random effects. A difference in five percentage units in male fertility was evident between the average effects of different dairy and beef breeds. The correlation between raw pregnancy rates for bulls with more than 100 services (n = 431) and service bull solutions from the mixed model analysis was 0.66. The correlation between the raw pregnancy rates of 288 technicians with more than 100 services and their respective solutions from the mixed model was 0.35. These low to moderate correlations suggest considerable re-ranking among both service bulls and technicians and suggest possibly a benefit of using a statistical model to better estimate the performance of both service bulls and technicians.     

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.     

Phosphorylation of rat liver mitochondrial carnitine palmitoyltransferase-I: effect on the kinetic properties of the enzyme

Hepatic carnitine palmitoyltransferase-I (CPT-IL) isolated from mitochondrial outer membranes obtained in the presence of protein phosphatase inhibitors is readily recognized by phosphoamino acid antibodies. Mass spectrometric analysis of CPT-IL tryptic digests revealed the presence of three phosphopeptides including one with a protein kinase CKII (CKII) consensus site. Incubation of dephosphorylated outer membranes with protein kinases and [gamma-32P]ATP resulted in radiolabeling of CPT-I only by CKII. Using mass spectrometry, only one region of phosphorylation was detected in CPT-I isolated from CKII-treated mitochondria. The sequence of the peptide and position of phosphorylated amino acids have been determined unequivocally as FpSSPETDpSHRFGK (residues 740-752). Furthermore, incubation of dephosphorylated outer membranes with CKII and unlabeled ATP led to increased catalytic activity and rendered malonyl-CoA inhibition of CPT-I from competitive to uncompetitive. These observations identify a new mechanism for regulation of hepatic CPT-I by phosphorylation.     

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.