Utilizing stochastic model checking to analyze genetic circuits

pre-printWhen designing and analyzing genetic circuits, researchers are often interested in the probability of the system reaching a given state within a certain amount of time. Usually, this involves simulating the system to produce some time series data and analyzing this data to discern the state probabilities. However, as the complexity of models of genetic circuits grow, it becomes more difficult for researchers to reason about the different states by looking only at time series simulation results of the models. To address this problem, this paper employs the use of stochastic model checking, a method for determining the likelihood that certain events occur in a system, with continuous stochastic logic (CSL) properties to obtain similar results. This goal is accomplished by the introduction of a methodology for converting a genetic circuit model (GCM) into a continuous-time Markov chain (CTMC). This CTMC is analyzed using transient Markov chain analysis to determine the likelihood that the circuit satisfies a given CSL property in a finite amount of time. This paper illustrates a use of this methodology to determine the likelihood of failure in a genetic toggle switch and compares these results to stochastic simulation-based analysis of this same circuit. Our results show that this method results in a substantial speedup as compared with conventional simulation-based approaches

Efficient verification of hazard-freedom in gate-level timed asynchronous circuits

Journal ArticleAbstract-This paper presents an efficient method for verifying hazard-freedom in gate-level timed asynchronous circuits. Timed circuits are a class of asynchronous circuits that are optimized using explicit timing information. In asynchronous circuits, correct operation requires that there are no hazards in the circuit implementation. Therefore, when designing an asynchronous circuit, each internal node and output of the circuit must be verified for hazard-freedom to ensure correct operation. Current verification algorithms for timed circuits require an explicit state exploration that often results in state explosion for even modest-sized examples. The goal of this paper is to abstract the behavior of internal nodes and utilize this information to make a conservative determination of hazard-freedom for each node in the circuit. Experimental results indicate that this approach is substantially more efficient than existing timing verification tools. These results also indicate that this method scales well for large examples that could not be previously analyzed, in that it is capable of analyzing these circuits in less than a second. While this method is conservative in that some false hazards may be reported, our results indicate that their number is small

Approximation Techniques for Stochastic Analysis of Biological Systems

There has been an increasing demand for formal methods in the design process of safety-critical synthetic genetic circuits. Probabilistic model checking techniques have demonstrated significant potential in analyzing the intrinsic probabilistic behaviors of complex genetic circuit designs. However, its inability to scale limits its applicability in practice. This chapter addresses the scalability problem by presenting a state-space approximation method to remove unlikely states resulting in a reduced, finite state representation of the infinite-state continuous-time Markov chain that is amenable to probabilistic model checking. The proposed method is evaluated on a design of a genetic toggle switch. Comparisons with another state-of-the-art tool demonstrate both accuracy and efficiency of the presented method

Butterfly abundance is determined by food availability and is mediated by species traits

1. Understanding the drivers of population abundance across species and sites is crucial for effective conservation management. At present, we lack a framework for predicting which sites are likely to support abundant butterfly communities. 2. We address this problem by exploring the determinants of abundance among 1111 populations of butterflies in the UK, spanning 27 species on 54 sites. Our general hypothesis is that the availability of food resources is a strong predictor of population abundance both within and between species, but that the relationship varies systematically with species’ traits. 3. We found strong positive correlations between butterfly abundance and the availability of food resources. Our indices of host plant and nectar are both significant predictors of butterfly population density, but the relationship is strongest for host plants, which explain up to 36% of the inter-site variance in abundance for some species. 4. Among species, the host plant–abundance relationship is mediated by butterfly species traits. It is strongest among those species with narrow diet breadths, low mobility and habitat specialists. Abundance for species with generalist diet and habitat associations is uncorrelated with our host plant index. 5. The host plant–abundance relationship is more pronounced on sites with predominantly north-facing slopes, suggesting a role for microclimate in mediating resource availability. 6. Synthesis and applications. We have shown that simple measures can be used to help understand patterns in abundance at large spatial scales. For some butterfly species, population carrying capacity on occupied sites is predictable from information about the vegetation composition. These results suggest that targeted management to increase host plant availability will translate into higher carrying capacity. Among UK butterflies, the species that would benefit most from such intervention have recently experienced steep declines in both abundance and distribution. The host plant–abundance relationship we have identified is likely to be transferrable to other systems characterized by strong interspecific interactions across trophic levels. This raises the possibility that the quality of habitat patches for specialist species is estimable from rapid assessment of the host plant resource

Probabilistic Modeling of Space Shuttle Debris Impact

On Feb 1, 2003, the Shuttle Columbia was lost during its return to Earth. As a result of the conclusion that debris impact caused the damage to the left wing of the Columbia Space Shuttle Vehicle (SSV) during ascent, the Columbia Accident Investigation Board recommended that an assessment be performed of the debris environment experienced by the SSV during ascent. A flight rationale based on probabilistic assessment is used for the SSV return-to-flight. The assessment entails identifying all potential debris sources, their probable geometric and aerodynamic characteristics, and their potential for impacting and damaging critical Shuttle components. A probabilistic analysis tool, based on the SwRI-developed NESSUS probabilistic analysis software, predicts the probability of impact and damage to the space shuttle wing leading edge and thermal protection system components. Among other parameters, the likelihood of unacceptable damage depends on the time of release (Mach number of the orbiter) and the divot mass as well as the impact velocity and impact angle. A typical result is visualized in the figures below. Probability of impact and damage, as well as the sensitivities thereof with respect to the distribution assumptions, can be computed and visualized at each point on the orbiter or summarized per wing panel or tile zone

Spatial variations in snowpack chemistry and isotopic composition of NO3- along a nitrogen deposition gradient in West Greenland

Snowpack chemistry, nitrate stable isotopes and net deposition fluxes for the largest ice-free region in Greenland were investigated to determine whether there are spatial gradients from the ice sheet margin to the coast linked to a gradient in precipitation. Late-season snowpack was sampled in March 2011 at 8 locations within 3 lake catchments in each of 3 regions (ice sheet margin in the east, central area near Kelly Ville and the coastal zone to the west). At the coast, snowpack accumulation averaged 181 mm snow water equivalent (SWE), compared with 36 mm SWE by the ice sheet. Coastal snowpack showed significantly greater concentrations of marine salts (Na+, Cl−, other major cations), ammonium (regional means 1.4–2.7 µmol L−1), total and non-sea salt sulfate (total 1.8–7.7, non-sea salt 1.0–1.8 µmol L−1) than the two inland regions. Nitrate (1.5–2.4 µmol L−1) showed significantly lower concentrations at the coast. Despite lower concentrations, higher precipitation at the coast results in a strong deposition gradient for NO3− as well as NH4+ and non-sea salt sulfate (nss-SO42−) increasing from the inland regions to the coast (lowest at Kelly Ville 6, 4 and 3; highest at coast 9, 17 and 11 mol ha−1 yr−1 of NO3−, NH4+ and nss-SO42− respectively). The δ(15N) of snowpack NO3− shows a significant decrease from the ice sheet margin (−7.5 ‰) to the coast (−11.3 ‰). We attribute the spatial gradient of δ(15N) in SW Greenland to post-deposition processing rather than differing sources because of (1) the climatic gradient from ice sheet margin to coast, (2) within-catchment isotopic differences between terrestrial snowpack and lake-ice snowpack, and (3) similarities between fresh snow (rather than accumulated snowpack) at Kelly Ville and the coast. Hence the δ(15N) of coastal snowpack is most representative of snowfall in SW Greenland, but after deposition the effects of photolysis, volatilization and sublimation lead to enrichment of the remaining snowpack with the greatest effect in inland areas of low precipitation and high sublimation losses

Data from a Public–industry Partnership for Enhancing corn Nitrogen Research

Improving corn (Zeamays L.) N managementis pertinent to economic andenvironmental objectives. However, there are limited comprehensive data sources to develop and test N fertilizer decision aid tools across a wide geographic range of soil and weather scenarios. Therefore, a public-industry partnership was formed to conduct standardized corn N rate response field studies throughout the U.S. Midwest. This research was conducted using a standardized protocol at 49 site-years across eight states over the 2014–2016 growing seasons with many soil, plant, and weather related measurements. This note provides the data (found in supplemental files), outlines the data, summarizes key findings, and highlights the strengths and weakness for those who wish to use this dataset