An HIV feedback resistor: auto-regulatory circuit deactivator and noise buffer.

Animal viruses (e.g., lentiviruses and herpesviruses) use transcriptional positive feedback (i.e., transactivation) to regulate their gene expression. But positive-feedback circuits are inherently unstable when turned off, which presents a particular dilemma for latent viruses that lack transcriptional repressor motifs. Here we show that a dissipative feedback resistor, composed of enzymatic interconversion of the transactivator, converts transactivation circuits into excitable systems that generate transient pulses of expression, which decay to zero. We use HIV-1 as a model system and analyze single-cell expression kinetics to explore whether the HIV-1 transactivator of transcription (Tat) uses a resistor to shut off transactivation. The Tat feedback circuit was found to lack bi-stability and Tat self-cooperativity but exhibited a pulse of activity upon transactivation, all in agreement with the feedback resistor model. Guided by a mathematical model, biochemical and genetic perturbation of the suspected Tat feedback resistor altered the circuit's stability and reduced susceptibility to molecular noise, in agreement with model predictions. We propose that the feedback resistor is a necessary, but possibly not sufficient, condition for turning off noisy transactivation circuits lacking a repressor motif (e.g., HIV-1 Tat). Feedback resistors may be a paradigm for examining other auto-regulatory circuits and may inform upon how viral latency is established, maintained, and broken

SimpactCyan 1.0 : an open-source simulator for individual-based models in HIV epidemiology with R and Python interfaces

SimpactCyan is an open-source simulator for individual-based models in HIV epidemiology. Its core algorithm is written in C++ for computational efficiency, while the R and Python interfaces aim to make the tool accessible to the fast-growing community of R and Python users. Transmission, treatment and prevention of HIV infections in dynamic sexual networks are simulated by discrete events. A generic “intervention” event allows model parameters to be changed over time, and can be used to model medical and behavioural HIV prevention programmes. First, we describe a more efficient variant of the modified Next Reaction Method that drives our continuous-time simulator. Next, we outline key built-in features and assumptions of individual-based models formulated in SimpactCyan, and provide code snippets for how to formulate, execute and analyse models in SimpactCyan through its R and Python interfaces. Lastly, we give two examples of applications in HIV epidemiology: the first demonstrates how the software can be used to estimate the impact of progressive changes to the eligibility criteria for HIV treatment on HIV incidence. The second example illustrates the use of SimpactCyan as a data-generating tool for assessing the performance of a phylodynamic inference framework

Modeling viral infectious diseases and development of antiviral therapies using human induced pluripotent stem cell-derived systems

The recent biotechnology breakthrough of cell reprogramming and generation of induced pluripotent stem cells (iPSCs), which has revolutionized the approaches to study the mechanisms of human diseases and to test new drugs, can be exploited to generate patient-specific models for the investigation of host-pathogen interactions and to develop new antimicrobial and antiviral therapies. Applications of iPSC technology to the study of viral infections in humans have included in vitro modeling of viral infections of neural, liver, and cardiac cells; modeling of human genetic susceptibility to severe viral infectious diseases, such as encephalitis and severe influenza; genetic engineering and genome editing of patient-specific iPSC-derived cells to confer antiviral resistance

Hybrid statistical and mechanistic mathematical model guides mobile health intervention for chronic pain

Nearly a quarter of visits to the Emergency Department are for conditions that could have been managed via outpatient treatment; improvements that allow patients to quickly recognize and receive appropriate treatment are crucial. The growing popularity of mobile technology creates new opportunities for real-time adaptive medical intervention, and the simultaneous growth of big data sources allows for preparation of personalized recommendations. Here we focus on the reduction of chronic suffering in the sickle cell disease community. Sickle cell disease is a chronic blood disorder in which pain is the most frequent complication. There currently is no standard algorithm or analytical method for real-time adaptive treatment recommendations for pain. Furthermore, current state-of-the-art methods have difficulty in handling continuous-time decision optimization using big data. Facing these challenges, in this study we aim to develop new mathematical tools for incorporating mobile technology into personalized treatment plans for pain. We present a new hybrid model for the dynamics of subjective pain that consists of a dynamical systems approach using differential equations to predict future pain levels, as well as a statistical approach tying system parameters to patient data (both personal characteristics and medication response history). Pilot testing of our approach suggests that it has significant potential to predict pain dynamics given patients' reported pain levels and medication usages. With more abundant data, our hybrid approach should allow physicians to make personalized, data driven recommendations for treating chronic pain.Comment: 13 pages, 15 figures, 5 table

Rationale and Methodology of Reprogramming for Generation of Induced Pluripotent Stem Cells and Induced Neural Progenitor Cells.

Great progress has been made regarding the capabilities to modify somatic cell fate ever since the technology for generation of induced pluripotent stem cells (iPSCs) was discovered in 2006. Later, induced neural progenitor cells (iNPCs) were generated from mouse and human cells, bypassing some of the concerns and risks of using iPSCs in neuroscience applications. To overcome the limitation of viral vector induced reprogramming, bioactive small molecules (SM) have been explored to enhance the efficiency of reprogramming or even replace transcription factors (TFs), making the reprogrammed cells more amenable to clinical application. The chemical induced reprogramming process is a simple process from a technical perspective, but the choice of SM at each step is vital during the procedure. The mechanisms underlying cell transdifferentiation are still poorly understood, although, several experimental data and insights have indicated the rationale of cell reprogramming. The process begins with the forced expression of specific TFs or activation/inhibition of cell signaling pathways by bioactive chemicals in defined culture condition, which initiates the further reactivation of endogenous gene program and an optimal stoichiometric expression of the endogenous pluri- or multi-potency genes, and finally leads to the birth of reprogrammed cells such as iPSCs and iNPCs. In this review, we first outline the rationale and discuss the methodology of iPSCs and iNPCs in a stepwise manner; and then we also discuss the chemical-based reprogramming of iPSCs and iNPCs

HIV/AIDS Pandemic in Africa: Trends and Challenges

Three-quarters of the world’s AIDS population lives in Sub-Saharan Africa; most have no access to lifesaving drugs, testing facilities or even basic preventative health care. One of the major factors inhibiting medical professionals in Africa from treating this disease is the inability to access vast areas of the continent with adequately equipped medical facilities. To meet this need, Architecture for Humanity challenged the world’s architects and health care professionals to submit designs for a mobile HIV/AIDS health clinic. The pandemic is changing the demographic structure of Africa and wiping out life expectancy gains. Indeed, in many African countries, life expectancy is dropping from more than 60 years to around 45 years or even less. In this paper, we highlight the uniqueness of factors associated with HIV/AIDS pandemic in Africa and present its impact and challenges.HIV/AIDS, Africa

Stochastic multi-scale models of competition within heterogeneous cellular populations: simulation methods and mean-field analysis

We propose a modelling framework to analyse the stochastic behaviour of heterogeneous, multi-scale cellular populations. We illustrate our methodology with a particular example in which we study a population with an oxygen-regulated proliferation rate. Our formulation is based on an age-dependent stochastic process. Cells within the population are characterised by their age. The age-dependent (oxygen-regulated) birth rate is given by a stochastic model of oxygen-dependent cell cycle progression. We then formulate an age-dependent birth-and-death process, which dictates the time evolution of the cell population. The population is under a feedback loop which controls its steady state size: cells consume oxygen which in turns fuels cell proliferation. We show that our stochastic model of cell cycle progression allows for heterogeneity within the cell population induced by stochastic effects. Such heterogeneous behaviour is reflected in variations in the proliferation rate. Within this set-up, we have established three main results. First, we have shown that the age to the G1/S transition, which essentially determines the birth rate, exhibits a remarkably simple scaling behaviour. This allows for a huge simplification of our numerical methodology. A further result is the observation that heterogeneous populations undergo an internal process of quasi-neutral competition. Finally, we investigated the effects of cell-cycle-phase dependent therapies (such as radiation therapy) on heterogeneous populations. In particular, we have studied the case in which the population contains a quiescent sub-population. Our mean-field analysis and numerical simulations confirm that, if the survival fraction of the therapy is too high, rescue of the quiescent population occurs. This gives rise to emergence of resistance to therapy since the rescued population is less sensitive to therapy

On the application and generation of subsensory electrical nerve stimulation for the improvement of vibration perception in patients with HIV-related sensory neuropathy

This work investigates the application of Subsensory Electrical Noise Stimulation (SENS) to improve symptoms of HIV-related peripheral sensory neuropathy (HIVPN). HIV-PN occurs in roughly half of the 5 million people in South Africa with HIV. The disease has been shown to reduce quality of life and increase the risk of secondary ailments. Currently there is no treatment available. Previously, SENS has shown promise to improve tactile sensitivity in healthy populations and elderly individuals with peripheral neuropathic desensitisation. This work first establishes if SENS can improve the peripheral sensitivity of patients with HIV-PN, and secondly addresses practical aspects of using SENS in a therapeutic context. The vibrotactile sensitivity deficits of participants with HIV-PN and a matched control cohort is documented and analysed. It is found that HIV-PN participants have reduced sensitivity at all tested vibration frequencies (25 Hz, 50 Hz and 128 Hz), but especially at low frequencies. The interaction with vibration frequency indicates that HIV-PN may interact differently with different types of peripheral mechanoreceptors. SENS is then applied at four different amplitudes in an attempt to improve perception thresholds of the three vibration frequencies. SENS was shown to generally have a beneficial effect on 50 Hz vibration sensitivity for low SENS amplitudes. It had no effect, or a detrimental effect, at high SENS amplitudes, and also for 25 Hz and 128 Hz vibration frequencies. This work is also the first to document measures of pain with interventions of this type. No clear effects of SENS on sensations of pain were observed, which is a vital outcome if the therapy is to be developed further, since neuropathic pain is a frequent symptom of HIV-PN. The application of SENS as a practical therapy requires the accurate measurement of the participant’s electrical perception threshold, and a wearable device to apply the electrical signal on an ongoing basis. Research into the stability of electrical perception thresholds specifically aimed at subthreshold signals that would improve tactile sensitivity is presented. It was found that these thresholds vary wildly and correlated very little with possible explanatory variables, which introduces a new challenge for the development of SENS in future research. Currently there are no devices available to apply SENS in non-laboratory settings or for continuous use. The electronic design of a stimulator for using SENS as a wearable intervention is presented and characterised. The circuit is an efficient, low-power voltage to current converter that generates high voltages (120 V peak to peak) from a small, low-voltage rechargeable battery. The design and testing of control and instrumentation circuitry, as well as the addition of various safety and interface features is also documented. The battery life of the circuit is tested to operate for up to 33 hours and the circuit is tested to operate as expected in vivo. The results of this work demonstrate the potential viability of SENS as a therapy for HIV-PN, reveals the variability of electrical perception thresholds, explores the measures of pain for SENS interventions, and provides a complete and thoroughly tested design and implementation of an unparalleled electronic stimulator for nonlaboratory environments. The conclusions of this work form both a strong theoretical and practical basis for future SENS intervention research

The role of Computer Aided Process Engineering in physiology and clinical medicine

This paper discusses the potential role for Computer Aided Process Engineering (CAPE) in developing engineering analysis and design approaches to biological systems across multiple levels—cell signalling networks, gene, protein and metabolic networks, cellular systems, through to physiological systems. The 21st Century challenge in the Life Sciences is to bring together widely dispersed models and knowledge in order to enable a system-wide understanding of these complex systems. This systems level understanding should have broad clinical benefits. Computer Aided Process Engineering can bring systems approaches to (i) improving understanding of these complex chemical and physical (particularly molecular transport in complex flow regimes) interactions at multiple scales in living systems, (ii) analysis of these models to help to identify critical missing information and to explore the consequences on major output variables resulting from disturbances to the system, and (iii) ‘design’ potential interventions in in vivo systems which can have significant beneficial, or potentially harmful, effects which need to be understood. This paper develops these three themes drawing on recent projects at UCL. The first project has modeled the effects of blood flow on endothelial cells lining arteries, taking into account cell shape change resulting in changes in the cell skeleton which cause consequent chemical changes. A second is a project which is building an in silico model of the human liver, tieing together models from the molecular level to the liver. The composite model models glucose regulation in the liver and associated organs. Both projects involve molecular transport, chemical reactions, and complex multiscale systems, tackled by approaches from CAPE. Chemical Engineers solve multiple scale problems in manufacturing processes – from molecular scale through unit operations scale to plant-wide and enterprise wide systems – so have an appropriate skill set for tackling problems in physiology and clinical medicine, in collaboration with life and clinical scientists