Viral population estimation using pyrosequencing

The diversity of virus populations within single infected hosts presents a major difficulty for the natural immune response as well as for vaccine design and antiviral drug therapy. Recently developed pyrophosphate based sequencing technologies (pyrosequencing) can be used for quantifying this diversity by ultra-deep sequencing of virus samples. We present computational methods for the analysis of such sequence data and apply these techniques to pyrosequencing data obtained from HIV populations within patients harboring drug resistant virus strains. Our main result is the estimation of the population structure of the sample from the pyrosequencing reads. This inference is based on a statistical approach to error correction, followed by a combinatorial algorithm for constructing a minimal set of haplotypes that explain the data. Using this set of explaining haplotypes, we apply a statistical model to infer the frequencies of the haplotypes in the population via an EM algorithm. We demonstrate that pyrosequencing reads allow for effective population reconstruction by extensive simulations and by comparison to 165 sequences obtained directly from clonal sequencing of four independent, diverse HIV populations. Thus, pyrosequencing can be used for cost-effective estimation of the structure of virus populations, promising new insights into viral evolutionary dynamics and disease control strategies.Comment: 23 pages, 13 figure

Supportive interventions to improve physiological and psychological health outcomes among patients undergoing cystectomy: A systematic review

Background Our understanding of effective perioperative supportive interventions for patients undergoing cystectomy procedures and how these may affect short and long-term health outcomes is limited. Methods Randomised controlled trials involving any non-surgical, perioperative interventions designed to support or improve the patient experience for patients undergoing cystectomy procedures were reviewed. Comparison groups included those exposed to usual clinical care or standard procedure. Studies were excluded if they involved surgical procedure only, involved bowel preparation only or involved an alternative therapy such as aromatherapy. Any short and long-term outcomes reflecting the patient experience or related urological health outcomes were considered. Results 19 articles (representing 15 individual studies) were included for review. Heterogeneity in interventions and outcomes across studies meant meta-analyses were not possible. Participants were all patients with bladder cancer and interventions were delivered over different stages of the perioperative period. The overall quality of evidence and reporting was low and outcomes were predominantly measured in the short-term. However, the findings show potential for exercise therapy, pharmaceuticals, ERAS protocols, psychological/educational programmes, chewing gum and nutrition to benefit a broad range of physiological and psychological health outcomes. Conclusions Supportive interventions to date have taken many different forms with a range of potentially meaningful physiological and psychological health outcomes for cystectomy patients. Questions remain as to what magnitude of short-term health improvements would lead to clinically relevant changes in the overall patient experience of surgery and long-term recovery

Cell killing and resistance in pre-operative breast cancer chemotherapy

<p>Abstract</p> <p>Background</p> <p>Despite the recent development of technologies giving detailed images of tumours <it>in vivo</it>, direct or indirect ways to measure how many cells are actually killed by a treatment or are resistant to it are still beyond our reach.</p> <p>Methods</p> <p>We designed a simple model of tumour progression during treatment, based on descriptions of the key phenomena of proliferation, quiescence, cell killing and resistance, and giving as output the macroscopically measurable tumour volume and growth fraction. The model was applied to a database of the time course of volumes of breast cancer in patients undergoing pre-operative chemotherapy, for which the initial estimate of proliferating cells by the measure of the percentage of Ki67-positive cells was available.</p> <p>Results</p> <p>The analysis recognises different patterns of response to treatment. In one subgroup of patients the fitting implied drug resistance. In another subgroup there was a shift to higher sensitivity during the therapy. In the subgroup of patients where killing of cycling cells had the highest score, the drugs showed variable efficacy against quiescent cells.</p> <p>Conclusion</p> <p>The approach was feasible, providing items of information not otherwise available. Additional data, particularly sequential Ki67 measures, could be added to the system, potentially reducing uncertainty in estimates of parameter values.</p

Graphical Approach to Model Reduction for Nonlinear Biochemical Networks

Model reduction is a central challenge to the development and analysis of multiscale physiology models. Advances in model reduction are needed not only for computational feasibility but also for obtaining conceptual insights from complex systems. Here, we introduce an intuitive graphical approach to model reduction based on phase plane analysis. Timescale separation is identified by the degree of hysteresis observed in phase-loops, which guides a “concentration-clamp” procedure for estimating explicit algebraic relationships between species equilibrating on fast timescales. The primary advantages of this approach over Jacobian-based timescale decomposition are that: 1) it incorporates nonlinear system dynamics, and 2) it can be easily visualized, even directly from experimental data. We tested this graphical model reduction approach using a 25-variable model of cardiac β1-adrenergic signaling, obtaining 6- and 4-variable reduced models that retain good predictive capabilities even in response to new perturbations. These 6 signaling species appear to be optimal “kinetic biomarkers” of the overall β1-adrenergic pathway. The 6-variable reduced model is well suited for integration into multiscale models of heart function, and more generally, this graphical model reduction approach is readily applicable to a variety of other complex biological systems

Adaptive Service Deployment using In-Network Mediation

Serendipitous peer discovery is important for emerging Internet applications, particularly in dynamic environments (e.g., the IoT, ubiquitous and fog domains) where a large number of resources operate different services in any one locality and resource availability varies unpredictably over time. The current approach is to select services at design time based on offered providers and their reputation. This obviously has its limitations, particularly in terms of scalability and adaptivity, let alone the challenges of crossing vendor and operator divides. This work demonstrates how an application is better able to dynamically adapt to unforeseen environmental changes through in-network mediation of service requests. In our model, application developers express their service needs using intents. These are mapped to appropriate service providers with explicit consideration of the intermediate network. We design a general architecture and associated algorithms to realise intent formulation and processing for mapping application intents to service providers. Our results demonstrate the feasibility of adopting in-network mediation to enable adaptive application deployment using declarative intents