The interplay of intrinsic and extrinsic bounded noises in genetic networks

After being considered as a nuisance to be filtered out, it became recently clear that biochemical noise plays a complex role, often fully functional, for a genetic network. The influence of intrinsic and extrinsic noises on genetic networks has intensively been investigated in last ten years, though contributions on the co-presence of both are sparse. Extrinsic noise is usually modeled as an unbounded white or colored gaussian stochastic process, even though realistic stochastic perturbations are clearly bounded. In this paper we consider Gillespie-like stochastic models of nonlinear networks, i.e. the intrinsic noise, where the model jump rates are affected by colored bounded extrinsic noises synthesized by a suitable biochemical state-dependent Langevin system. These systems are described by a master equation, and a simulation algorithm to analyze them is derived. This new modeling paradigm should enlarge the class of systems amenable at modeling. We investigated the influence of both amplitude and autocorrelation time of a extrinsic Sine-Wiener noise on: $(i)$ the Michaelis-Menten approximation of noisy enzymatic reactions, which we show to be applicable also in co-presence of both intrinsic and extrinsic noise, $(ii)$ a model of enzymatic futile cycle and $(iii)$ a genetic toggle switch. In $(ii)$ and $(iii)$ we show that the presence of a bounded extrinsic noise induces qualitative modifications in the probability densities of the involved chemicals, where new modes emerge, thus suggesting the possibile functional role of bounded noises

BTBR ob/ob mouse model of type 2 diabetes exhibits early loss of retinal function and retinal inflammation followed by late vascular changes

AIMS/HYPOTHESIS: Diabetic retinopathy is increasing in prevalence worldwide and is fast becoming a global epidemic and a leading cause of visual loss. Current therapies are limited, and the development of effective treatments for diabetic retinopathy requires a greater in-depth knowledge of disease progression and suitable modelling of diabetic retinopathy in animals. The aim of this study was to assess the early pathological changes in retinal morphology and neuronal, inflammatory and vascular features consistent with diabetic retinopathy in the ob/ob mouse model of type 2 diabetes, to investigate whether features similar to those in human diabetic retinopathy were present. METHODS: Male and female wild-type (+/+), heterozygous (+/−) and homozygous (−/−) BTBR ob/ob mice were examined at 6, 10, 15 and 20 weeks of age. Animals were weighed and blood glucose was measured. TUNEL and brain-specific homeobox/POU domain protein 3A (BRN3A) markers were used to examine retinal ganglion cells. We used immunostaining (collagen IV and platelet endothelial cell adhesion molecule [PECAM]/CD31), spectral domain optical coherence tomography and vitreous fluorophotometry to investigate vascular morphology and permeability. Oscillatory potential and photopic and scotopic electroretinograms helped to differentiate neuronal phenotypes. Concanavalin A leucostasis and immunostaining with glial fibrillary acidic protein (GFAP) and ionised calcium-binding adapter molecule 1 (IBA-1) identified differences in inflammatory status. Paraffin sections and transmission electron microscopy were used to reveal changes in the thickness and structure of the retinal layer. RESULTS: Following the development of obesity and hyperglycaemia (p < 0.001), early functional deficits (p < 0.001) and thinning of the inner retina (p < 0.001) were identified. Glial activation, leucostasis (p < 0.05) and a shift in microglia/macrophage phenotype were observed before microvascular degeneration (p < 0.05) and elevated vascular permeability occurred (p < 0.05). CONCLUSION/INTERPRETATION: The development of diabetic retinopathy in the ob/ob mouse represents a platform that will enable the development of new therapies, particularly for the early stages of disease

A Dominated Coupling From The Past algorithm for the stochastic simulation of networks of biochemical reactions

<p>Abstract</p> <p>Background</p> <p>In recent years, stochastic descriptions of biochemical reactions based on the Master Equation (ME) have become widespread. These are especially relevant for models involving gene regulation. Gillespie’s Stochastic Simulation Algorithm (SSA) is the most widely used method for the numerical evaluation of these models. The SSA produces exact samples from the distribution of the ME for finite times. However, if the stationary distribution is of interest, the SSA provides no information about convergence or how long the algorithm needs to be run to sample from the stationary distribution with given accuracy. </p> <p>Results</p> <p>We present a proof and numerical characterization of a Perfect Sampling algorithm for the ME of networks of biochemical reactions prevalent in gene regulation and enzymatic catalysis. Our algorithm combines the SSA with Dominated Coupling From The Past (DCFTP) techniques to provide guaranteed sampling from the stationary distribution. The resulting DCFTP-SSA is applicable to networks of reactions with uni-molecular stoichiometries and sub-linear, (anti-) monotone propensity functions. We showcase its applicability studying steady-state properties of stochastic regulatory networks of relevance in synthetic and systems biology.</p> <p>Conclusion</p> <p>The DCFTP-SSA provides an extension to Gillespie’s SSA with guaranteed sampling from the stationary solution of the ME for a broad class of stochastic biochemical networks.</p

Pre-treatment and real-time image guidance for a fixed-beam radiotherapy system.

Purpose: A radiotherapy system with a fixed treatment beam and a rotating patient positioning system could be smaller, more robust and more cost effective compared to conventional rotating gantry systems. However, patient rotation could cause anatomical deformation and compromise treatment delivery. In this work, we demonstrate an image-guided treatment workflow with a fixed beam prototype system that accounts for deformation during rotation to maintain dosimetric accuracy. Methods: The prototype system consists of an Elekta Synergy linac with the therapy beam orientated downward and a custom-built patient rotation system (PRS). A phantom that deforms with rotation was constructed and rotated within the PRS to quantify the performance of two image guidance techniques: motion compensated cone-beam CT (CBCT) for pre-treatment volumetric imaging and kilovoltage infraction monitoring (KIM) for real-time image guidance. The phantom was irradiated with a 3D conformal beam to evaluate the dosimetric accuracy of the workflow. Results: The motion compensated CBCT was used to verify pre-treatment position and the average calculated position was within -0.3 ± 1.1 mm of the phantom's ground truth position at 0°. KIM tracked the position of the target in real-time as the phantom was rotated and the average calculated position was within -0.2 ± 0.8 mm of the phantom's ground truth position. A 3D conformal treatment delivered on the prototype system with image guidance had a 3%/2 mm gamma pass rate of 96.3% compared to 98.6% delivered using a conventional rotating gantry linac. Conclusions: In this work, we have shown that image guidance can be used with fixed-beam treatment systems to measure and account for changes in target position in order to maintain dosimetric coverage during horizontal rotation. This treatment modality could provide a viable treatment option when there insufficient space for a conventional linear accelerator or where the cost is prohibitive

Global parameter estimation methods for stochastic biochemical systems

<p>Abstract</p> <p>Background</p> <p>The importance of stochasticity in cellular processes having low number of molecules has resulted in the development of stochastic models such as chemical master equation. As in other modelling frameworks, the accompanying rate constants are important for the end-applications like analyzing system properties (e.g. robustness) or predicting the effects of genetic perturbations. Prior knowledge of kinetic constants is usually limited and the model identification routine typically includes parameter estimation from experimental data. Although the subject of parameter estimation is well-established for deterministic models, it is not yet routine for the chemical master equation. In addition, recent advances in measurement technology have made the quantification of genetic substrates possible to single molecular levels. Thus, the purpose of this work is to develop practical and effective methods for estimating kinetic model parameters in the chemical master equation and other stochastic models from single cell and cell population experimental data.</p> <p>Results</p> <p>Three parameter estimation methods are proposed based on the maximum likelihood and density function distance, including probability and cumulative density functions. Since stochastic models such as chemical master equations are typically solved using a Monte Carlo approach in which only a finite number of Monte Carlo realizations are computationally practical, specific considerations are given to account for the effect of finite sampling in the histogram binning of the state density functions. Applications to three practical case studies showed that while maximum likelihood method can effectively handle low replicate measurements, the density function distance methods, particularly the cumulative density function distance estimation, are more robust in estimating the parameters with consistently higher accuracy, even for systems showing multimodality.</p> <p>Conclusions</p> <p>The parameter estimation methodologies described in this work have provided an effective and practical approach in the estimation of kinetic parameters of stochastic systems from either sparse or dense cell population data. Nevertheless, similar to kinetic parameter estimation in other modelling frameworks, not all parameters can be estimated accurately, which is a common problem arising from the lack of complete parameter identifiability from the available data.</p

Acceptability of a theory-based sedentary behaviour reduction intervention for older adults ('On Your Feet to Earn Your Seat').

Background: Adults aged 60 years and over spend most time sedentary and are the least physically active of all age groups. This early-phase study explored acceptability of a theory-based intervention to reduce sitting time and increase activity in older adults, as part of the intervention development process. Methods: An 8-week uncontrolled trial was run among two independent samples of UK adults aged 60–75 years. Sample 1, recruited from sheltered housing on the assumption that they were sedentary and insufficiently active, participated between December 2013 and March 2014. Sample 2, recruited through community and faith centres and a newsletter, on the basis of self-reported inactivity (<150 weekly minutes of moderate-to-vigorous activity) and sedentary behaviour (≥6 h mean daily sitting), participated between March and August 2014. Participants received a booklet offering 16 tips for displacing sitting with light-intensity activity and forming activity habits, and self-monitoring ‘tick-sheets’. At baseline, 4-week, and 8-week follow-ups, quantitative measures were taken of physical activity, sedentary behaviour, and habit. At 8 weeks, tick-sheets were collected and a semi-structured interview conducted. Acceptability was assessed for each sample separately, through attrition and adherence to tips, ANOVAs for behaviour and habit changes, and, for both samples combined, thematic analysis of interviews. Results: In Sample 1, 12 of 16 intervention recipients completed the study (25 % attrition), mean adherence was 40 % (per-tip range: 15–61 %), and there were no clear patterns of changes in sedentary or physical activity behaviour or habit. In Sample 2, 23 of 27 intervention recipients completed (15 % attrition), and mean adherence was 58 % (per-tip range: 39–82 %). Sample 2 decreased mean sitting time and sitting habit, and increased walking, moderate activity, and activity habit. Qualitative data indicated that both samples viewed the intervention positively, found the tips easy to follow, and reported health and wellbeing gains. Conclusions: Low attrition, moderate adherence, and favourability in both samples, and positive changes in Sample 2, indicate the intervention was acceptable. Higher attrition, lower adherence, and no apparent behavioural impact among Sample 1 could perhaps be attributable to seasonal influences. The intervention has been refined to address emergent acceptability problems. An exploratory controlled trial is underway

Phosphoenolpyruvate carboxylase dentified as a key enzyme in erythrocytic Plasmodium falciparum carbon metabolism

Phospoenolpyruvate carboxylase (PEPC) is absent from humans but encoded in thePlasmodium falciparum genome, suggesting that PEPC has a parasite-specific function. To investigate its importance in P. falciparum, we generated a pepc null mutant (D10Δpepc), which was only achievable when malate, a reduction product of oxaloacetate, was added to the growth medium. D10Δpepc had a severe growth defect in vitro, which was partially reversed by addition of malate or fumarate, suggesting that pepc may be essential in vivo. Targeted metabolomics using 13C-U-D-glucose and 13C-bicarbonate showed that the conversion of glycolytically-derived PEP into malate, fumarate, aspartate and citrate was abolished in D10Δpepc and that pentose phosphate pathway metabolites and glycerol 3-phosphate were present at increased levels. In contrast, metabolism of the carbon skeleton of 13C,15N-U-glutamine was similar in both parasite lines, although the flux was lower in D10Δpepc; it also confirmed the operation of a complete forward TCA cycle in the wild type parasite. Overall, these data confirm the CO2 fixing activity of PEPC and suggest that it provides metabolites essential for TCA cycle anaplerosis and the maintenance of cytosolic and mitochondrial redox balance. Moreover, these findings imply that PEPC may be an exploitable target for future drug discovery

Linear mapping approximation of gene regulatory networks with stochastic dynamics

The intractability of most stochastic models of gene regulatory networks (GRNs) limits their utility. Here, the authors present a linear-mapping approximation mapping models onto simpler ones, giving approximate but accurate analytic or semi- analytic solutions for a wide range of model GRNs

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-art tool demonstrates both accuracy and efficiency of the presented method