Programmability of Chemical Reaction Networks

Motivated by the intriguing complexity of biochemical circuitry within individual cells we study Stochastic Chemical Reaction Networks (SCRNs), a formal model that considers a set of chemical reactions acting on a finite number of molecules in a well-stirred solution according to standard chemical kinetics equations. SCRNs have been widely used for describing naturally occurring (bio)chemical systems, and with the advent of synthetic biology they become a promising language for the design of artificial biochemical circuits. Our interest here is the computational power of SCRNs and how they relate to more conventional models of computation. We survey known connections and give new connections between SCRNs and Boolean Logic Circuits, Vector Addition Systems, Petri Nets, Gate Implementability, Primitive Recursive Functions, Register Machines, Fractran, and Turing Machines. A theme to these investigations is the thin line between decidable and undecidable questions about SCRN behavior

Bayesian inference of biochemical kinetic parameters using the linear noise approximation

Background Fluorescent and luminescent gene reporters allow us to dynamically quantify changes in molecular species concentration over time on the single cell level. The mathematical modeling of their interaction through multivariate dynamical models requires the deveopment of effective statistical methods to calibrate such models against available data. Given the prevalence of stochasticity and noise in biochemical systems inference for stochastic models is of special interest. In this paper we present a simple and computationally efficient algorithm for the estimation of biochemical kinetic parameters from gene reporter data. Results We use the linear noise approximation to model biochemical reactions through a stochastic dynamic model which essentially approximates a diffusion model by an ordinary differential equation model with an appropriately defined noise process. An explicit formula for the likelihood function can be derived allowing for computationally efficient parameter estimation. The proposed algorithm is embedded in a Bayesian framework and inference is performed using Markov chain Monte Carlo. Conclusion The major advantage of the method is that in contrast to the more established diffusion approximation based methods the computationally costly methods of data augmentation are not necessary. Our approach also allows for unobserved variables and measurement error. The application of the method to both simulated and experimental data shows that the proposed methodology provides a useful alternative to diffusion approximation based methods

A summer heat wave decreases the immunocompetence of the mesograzer, Idotea baltica

Extreme events associated with global change will impose increasing stress on coastal organisms. How strong biological interactions such as the host–parasite arms-race are modulated by environmental change is largely unknown. The immune system of invertebrates, in particular phagocytosis and phenoloxidase activity response are key defence mechanisms against parasites, yet they may be sensitive to environmental perturbations. We here simulated an extreme event that mimicked the European heat wave in 2003 to investigate the effect of environmental change on the immunocompetence of the mesograzer Idotea baltica. Unlike earlier studies, our experiment aimed at simulation of the natural situation as closely as possible by using long acclimation, a slow increase in temperature and a natural community setting including the animals’ providence with natural food sources (Zostera marina and Fucus vesiculosus). Our results demonstrate that a simulated heat wave results in decreased immunocompetence of the mesograzer Idotea baltica, in particular a drop of phagocytosis by 50%. This suggests that global change has the potential to significantly affect host–parasite interactions

Syntactic Markovian Bisimulation for Chemical Reaction Networks

In chemical reaction networks (CRNs) with stochastic semantics based on continuous-time Markov chains (CTMCs), the typically large populations of species cause combinatorially large state spaces. This makes the analysis very difficult in practice and represents the major bottleneck for the applicability of minimization techniques based, for instance, on lumpability. In this paper we present syntactic Markovian bisimulation (SMB), a notion of bisimulation developed in the Larsen-Skou style of probabilistic bisimulation, defined over the structure of a CRN rather than over its underlying CTMC. SMB identifies a lumpable partition of the CTMC state space a priori, in the sense that it is an equivalence relation over species implying that two CTMC states are lumpable when they are invariant with respect to the total population of species within the same equivalence class. We develop an efficient partition-refinement algorithm which computes the largest SMB of a CRN in polynomial time in the number of species and reactions. We also provide an algorithm for obtaining a quotient network from an SMB that induces the lumped CTMC directly, thus avoiding the generation of the state space of the original CRN altogether. In practice, we show that SMB allows significant reductions in a number of models from the literature. Finally, we study SMB with respect to the deterministic semantics of CRNs based on ordinary differential equations (ODEs), where each equation gives the time-course evolution of the concentration of a species. SMB implies forward CRN bisimulation, a recently developed behavioral notion of equivalence for the ODE semantics, in an analogous sense: it yields a smaller ODE system that keeps track of the sums of the solutions for equivalent species.Comment: Extended version (with proofs), of the corresponding paper published at KimFest 2017 (http://kimfest.cs.aau.dk/

Systemic Risk and Default Clustering for Large Financial Systems

As it is known in the finance risk and macroeconomics literature, risk-sharing in large portfolios may increase the probability of creation of default clusters and of systemic risk. We review recent developments on mathematical and computational tools for the quantification of such phenomena. Limiting analysis such as law of large numbers and central limit theorems allow to approximate the distribution in large systems and study quantities such as the loss distribution in large portfolios. Large deviations analysis allow us to study the tail of the loss distribution and to identify pathways to default clustering. Sensitivity analysis allows to understand the most likely ways in which different effects, such as contagion and systematic risks, combine to lead to large default rates. Such results could give useful insights into how to optimally safeguard against such events.Comment: in Large Deviations and Asymptotic Methods in Finance, (Editors: P. Friz, J. Gatheral, A. Gulisashvili, A. Jacqier, J. Teichmann) , Springer Proceedings in Mathematics and Statistics, Vol. 110 2015

Skittle: A 2-Dimensional Genome Visualization Tool

<p>Abstract</p> <p>Background</p> <p>It is increasingly evident that there are multiple and overlapping patterns within the genome, and that these patterns contain different types of information - regarding both genome function and genome history. In order to discover additional genomic patterns which may have biological significance, novel strategies are required. To partially address this need, we introduce a new data visualization tool entitled Skittle.</p> <p>Results</p> <p>This program first creates a 2-dimensional nucleotide display by assigning four colors to the four nucleotides, and then text-wraps to a user adjustable width. This nucleotide display is accompanied by a "repeat map" which comprehensively displays all local repeating units, based upon analysis of all possible local alignments. Skittle includes a smooth-zooming interface which allows the user to analyze genomic patterns at any scale.</p> <p>Skittle is especially useful in identifying and analyzing tandem repeats, including repeats not normally detectable by other methods. However, Skittle is also more generally useful for analysis of any genomic data, allowing users to correlate published annotations and observable visual patterns, and allowing for sequence and construct quality control.</p> <p>Conclusions</p> <p>Preliminary observations using Skittle reveal intriguing genomic patterns not otherwise obvious, including structured variations inside tandem repeats. The striking visual patterns revealed by Skittle appear to be useful for hypothesis development, and have already led the authors to theorize that imperfect tandem repeats could act as information carriers, and may form tertiary structures within the interphase nucleus.</p

Generation of a large volume of clinically relevant nanometre-sized ultra-high-molecular-weight polyethylene wear particles for cell culture studies.

It has recently been shown that the wear of ultra-high-molecular-weight polyethylene in hip and knee prostheses leads to the generation of nanometre-sized particles, in addition to micron-sized particles. The biological activity of nanometre-sized ultra-high-molecular-weight polyethylene wear particles has not, however, previously been studied due to difficulties in generating sufficient volumes of nanometre-sized ultra-high-molecular-weight polyethylene wear particles suitable for cell culture studies. In this study, wear simulation methods were investigated to generate a large volume of endotoxin-free clinically relevant nanometre-sized ultra-high-molecular-weight polyethylene wear particles. Both single-station and six-station multidirectional pin-on-plate wear simulators were used to generate ultra-high-molecular-weight polyethylene wear particles under sterile and non-sterile conditions. Microbial contamination and endotoxin levels in the lubricants were determined. The results indicated that microbial contamination was absent and endotoxin levels were low and within acceptable limits for the pharmaceutical industry, when a six-station pin-on-plate wear simulator was used to generate ultra-high-molecular-weight polyethylene wear particles in a non-sterile environment. Different pore-sized polycarbonate filters were investigated to isolate nanometre-sized ultra-high-molecular-weight polyethylene wear particles from the wear test lubricants. The use of the filter sequence of 10, 1, 0.1, 0.1 and 0.015 µm pore sizes allowed successful isolation of ultra-high-molecular-weight polyethylene wear particles with a size range of < 100 nm, which was suitable for cell culture studies

Physicians' attitudes about obesity and their associations with competency and specialty: A cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Physicians frequently report negative attitudes about obesity which is thought to affect patient care. However, little is known about how attitudes toward treating obese patients are formed. We conducted a cross-sectional survey of physicians in order to better characterize their attitudes and explore the relationships among attitudes, perceived competency in obesity care, including report of weight loss in patients, and other key physician, training, and practice characteristics.</p> <p>Methods</p> <p>We surveyed all 399 physicians from internal medicine, pediatrics, and psychiatry specialties at one institution regarding obesity care attitudes, competency, including physician report of percent of their patients who lose weight. We performed a factor analysis on the attitude items and used hierarchical regression analysis to explore the degree to which competency, reported weight loss, physician, training and practice characteristics explained the variance in each attitude factor.</p> <p>Results</p> <p>The overall response rate was 63%. More than 40% of physicians had a negative reaction towards obese patients, 56% felt qualified to treat obesity, and 46% felt successful in this realm. The factor analysis revealed 4 factors–<it>Physician Discomfort/Bias, Physician Success/Self Efficacy, Positive Outcome Expectancy</it>, and <it>Negative Outcome Expectancy</it>. Competency and reported percent of patients who lose weight were most strongly associated with the <it>Physician Success/Self Efficacy </it>attitude factor. Greater skill in patient assessment was associated with less <it>Physician Discomfort/Bias</it>. Training characteristics were associated with outcome expectancies with newer physicians reporting more positive treatment expectancies. Pediatric faculty was more positive and psychiatry faculty less negative in their treatment expectancies than internal medicine faculty.</p> <p>Conclusion</p> <p>Physician attitudes towards obesity are associated with competency, specialty, and years since postgraduate training. Further study is necessary to determine the direction of influence and to explore the impact of these attitudes on patient care.</p

Aerosolized Delivery of Antifungal Agents

Pulmonary infections caused by Aspergillus species are associated with significant morbidity and mortality in immunocompromised patients. Although the treatment of pulmonary fungal infections requires the use of systemic agents, aerosolized delivery is an attractive option in prevention because the drug can concentrate locally at the site of infection with minimal systemic exposure. Current clinical evidence for the use of aerosolized delivery in preventing fungal infections is limited to amphotericin B products, although itraconazole, voriconazole, and caspofungin are under investigation. Based on conflicting results from clinical trials that evaluated various amphotericin B formulations, the routine use of aerosolized delivery cannot be recommended. Further research with well-designed clinical trials is necessary to elucidate the therapeutic role and risks associated with aerosolized delivery of antifungal agents. This article provides an overview of aerosolized delivery systems, the intrapulmonary pharmacokinetic properties of aerosolized antifungal agents, and key findings from clinical studies