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.     

Quantification of total mitochondrial DNA and the 4977-bp common deletion in Pearson's syndrome lymphoblasts using a fluorogenic 5'-nuclease (TaqMan) real-time polymerase chain reaction assay and plasmid external calibration standards

This study describes a multiplex real-time polymerase chain reaction (PCR) assay that quantifies total mitochondrial DNA (mtDNA(total)) and mtDNA bearing the 4977-base pair 'common deletion' (deltamtDNA4977) in lymphoblasts derived from an individual diagnosed with Pearson's syndrome. The method is unique in its use of plasmids as external quantification standards and its use of multiplex conditions. Standards are validated by comparison with purified mtDNA amplification curves and by the fact that curves are largely unaffected by nuclear DNA (nucDNA). Finally, slopes of standard curves and unknowns are shown to be similar to each other and to theoretical predictions. From these data, mtDNA(total) in these cells is calculated to be 3258 (+723/-592) copies per cell while deltamtDNA4977 averages 232 (+136/-86) copies per cell or 7% (+4.65/-2.81).     

Quantitative analysis of electrophoresis data: novel curve fitting methodology and its application to the determination of a protein-DNA binding constant.

A computer program, GelExplorer, which uses a new methodology for obtaining quantitative information about electrophoresis has been developed. It provides a straightforward, easy-to-use graphical interface, and includes a number of features which offer significant advantages over existing methods for quantitative gel analysis. The method uses curve fitting with a nonlinear least-squares optimization to deconvolute overlapping bands. Unlike most curve fitting approaches, the data is treated in two dimensions, fitting all the data across the entire width of the lane. This allows for accurate determination of the intensities of individual, overlapping bands, and in particular allows imperfectly shaped bands to be accurately modeled. Experiments described in this paper demonstrate empirically that the Lorentzian lineshape reproduces the contours of an individual gel band and provides a better model than the Gaussian function for curve fitting of electrophoresis bands. Results from several fitting applications are presented and a discussion of the sources and magnitudes of uncertainties in the results is included. Finally, the method is applied to the quantitative analysis of a hydroxyl radical footprint titration experiment to obtain the free energy of binding of the lambda repressor protein to the OR1 operator DNA sequence.     

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.     

Group testing to annihilate pairs applied to DNA cross-hybridization elimination using SYBR Green I.

A group testing (or pooling) method for DNA strands that identifies at least one strand in a pair of cross-hybridized oligonucleotides is given. This pooling method can be extended to any population of objects where certain pairs together produce an observable function or signal. Pairs of objects may work together to produce an undesirable result or a detrimental function. If just a single element of such a pair is identified and eliminated, then the undesirable function of that pair is destroyed. In particular, the ability to ensure that a set DNA probes do not yield undesired cross-hybridizations is important when these probes and/or their complements are used in the production of a hybridization signal that is intended to convey information. Here we report a "proof of principle" method, similar to those used to screen DNA libraries, that screens pools of probes for unwanted cross-hybridization events and identifies the offending probes. In the reported experiment, a cross-hybridized duplex in a pool of probes is detected by using the fluorescent dye SYBR Green I. This dye is known to produce greater fluorescence when bound to duplex DNA as opposed to single-stranded DNA. The method described here is sensitive, fast, and simple.     

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.