Generalized binomial Tau-leap method for biochemical kinetics incorporating both delay and intrinsic noise

The delay stochastic simulation algorithm (DSSA) by Barrio et al. [Plos Comput. Biol.2, 117–E (2006)] was developed to simulate delayed processes in cell biology in the presence of intrinsic noise, that is, when there are small-to-moderate numbers of certain key molecules present in a chemical reaction system. These delayed processes can faithfully represent complex interactions and mechanisms that imply a number of spatiotemporal processes often not explicitly modeled such as transcription and translation, basic in the modeling of cell signaling pathways. However, for systems with widely varying reaction rate constants or large numbers of molecules, the simulation time steps of both the stochastic simulation algorithm (SSA) and the DSSA can become very small causing considerable computational overheads. In order to overcome the limit of small step sizes, various τ-leap strategies have been suggested for improving computational performance of the SSA. In this paper, we present a binomial τ- DSSA method that extends the τ-leap idea to the delay setting and avoids drawing insufficient numbers of reactions, a common shortcoming of existing binomial τ-leap methods that becomes evident when dealing with complex chemical interactions. The resulting inaccuracies are most evident in the delayed case, even when considering reaction products as potential reactants within the same time step in which they are produced. Moreover, we extend the framework to account for multicellular systems with different degrees of intercellular communication. We apply these ideas to two important genetic regulatory models, namely, the hes1 gene, implicated as a molecular clock, and a Her1/Her 7 model for coupled oscillating cells.

Stochastic simulation of chemical reactions in spatially complex media

Discrete stochastic simulations, via techniques such as the Stochastic Simulation Algorithm (SSA) are a powerful tool for understanding the dynamics of chemical kinetics when there are low numbers of certain molecular species. However, an important constraint is the assumption of well-mixedness and homogeneity. In this paper, we show how to use Monte Carlo simulations to estimate an anomalous diffusion parameter that encapsulates the crowdedness of the spatial environment. We then use this parameter to replace the rate constants of bimolecular reactions by a time-dependent power law to produce an SSA valid in cases where anomalous diffusion occurs or the system is not well-mixed (ASSA). Simulations then show that ASSA can successfully predict the temporal dynamics of chemical kinetics in a spatially constrained environment.

Stochastic models and simulation of ion channel dynamics

The behaviour of ion channels within cardiac and neuronal cells is intrinsically stochastic in nature. When the number of channels is small this stochastic noise is large and can have an impact on the dynamics of the system which is potentially an issue when modelling small neurons and drug block in cardiac cells. While exact methods correctly capture the stochastic dynamics of a system they are computationally expensive, restricting their inclusion into tissue level models and so approximations to exact methods are often used instead. The other issue in modelling ion channel dynamics is that the transition rates are voltage dependent, adding a level of complexity as the channel dynamics are coupled to the membrane potential. By assuming that such transition rates are constant over each time step, it is possible to derive a stochastic differential equation (SDE), in the same manner as for biochemical reaction networks, that describes the stochastic dynamics of ion channels. While such a model is more computationally efficient than exact methods we show that there are analytical problems with the resulting SDE as well as issues in using current numerical schemes to solve such an equation. We therefore make two contributions: develop a different model to describe the stochastic ion channel dynamics that analytically behaves in the correct manner and also discuss numerical methods that preserve the analytical properties of the model.

Stochastic chemical kinetics and the quasi-steady-state assumption : application to the stochastic simulation algorithm and chemical master equation

Recently the application of the quasi-steady-state approximation (QSSA) to the stochastic simulation algorithm (SSA) was suggested for the purpose of speeding up stochastic simulations of chemical systems that involve both relatively fast and slow chemical reactions [Rao and Arkin, J. Chem. Phys. 118, 4999 (2003)] and further work has led to the nested and slow-scale SSA. Improved numerical efficiency is obtained by respecting the vastly different time scales characterizing the system and then by advancing only the slow reactions exactly, based on a suitable approximation to the fast reactions. We considerably extend these works by applying the QSSA to numerical methods for the direct solution of the chemical master equation (CME) and, in particular, to the finite state projection algorithm [Munsky and Khammash, J. Chem. Phys. 124, 044104 (2006)], in conjunction with Krylov methods. In addition, we point out some important connections to the literature on the (deterministic) total QSSA (tQSSA) and place the stochastic analogue of the QSSA within the more general framework of aggregation of Markov processes. We demonstrate the new methods on four examples: Michaelis–Menten enzyme kinetics, double phosphorylation, the Goldbeter–Koshland switch, and the mitogen activated protein kinase cascade. Overall, we report dramatic improvements by applying the tQSSA to the CME solver.

Parallel implementation of stochastic simulation for large scale cellular processes

Experimental and theoretical studies have shown the importance of stochastic processes in genetic regulatory networks and cellular processes. Cellular networks and genetic circuits often involve small numbers of key proteins such as transcriptional factors and signaling proteins. In recent years stochastic models have been used successfully for studying noise in biological pathways, and stochastic modelling of biological systems has become a very important research field in computational biology. One of the challenge problems in this field is the reduction of the huge computing time in stochastic simulations. Based on the system of the mitogen-activated protein kinase cascade that is activated by epidermal growth factor, this work give a parallel implementation by using OpenMP and parallelism across the simulation. Special attention is paid to the independence of the generated random numbers in parallel computing, that is a key criterion for the success of stochastic simulations. Numerical results indicate that parallel computers can be used as an efficient tool for simulating the dynamics of large-scale genetic regulatory networks and cellular processes

Coupling hybrid-game strategies with particle swarm optimisation for multi-objective high lift systems design optimisation

This paper investigates the High Lift System (HLS) application of complex aerodynamic design problem using Particle Swarm Optimisation (PSO) coupled to Game strategies. Two types of optimization methods are used; the first method is a standard PSO based on Pareto dominance and the second method hybridises PSO with a well-known Nash Game strategies named Hybrid-PSO. These optimization techniques are coupled to a pre/post processor GiD providing unstructured meshes during the optimisation procedure and a transonic analysis software PUMI. The computational efficiency and quality design obtained by PSO and Hybrid-PSO are compared. The numerical results for the multi-objective HLS design optimisation clearly shows the benefits of hybridising a PSO with the Nash game and makes promising the above methodology for solving other more complex multi-physics optimisation problems in Aeronautics.

Modeling ion channel dynamics through reflected stochastic differential equations

Ion channels are membrane proteins that open and close at random and play a vital role in the electrical dynamics of excitable cells. The stochastic nature of the conformational changes these proteins undergo can be significant, however current stochastic modeling methodologies limit the ability to study such systems. Discrete-state Markov chain models are seen as the "gold standard," but are computationally intensive, restricting investigation of stochastic effects to the single-cell level. Continuous stochastic methods that use stochastic differential equations (SDEs) to model the system are more efficient but can lead to simulations that have no biological meaning. In this paper we show that modeling the behavior of ion channel dynamics by a reflected SDE ensures biologically realistic simulations, and we argue that this model follows from the continuous approximation of the discrete-state Markov chain model. Open channel and action potential statistics from simulations of ion channel dynamics using the reflected SDE are compared with those of a discrete-state Markov chain method. Results show that the reflected SDE simulations are in good agreement with the discrete-state approach. The reflected SDE model therefore provides a computationally efficient method to simulate ion channel dynamics while preserving the distributional properties of the discrete-state Markov chain model and also ensuring biologically realistic solutions. This framework could easily be extended to other biochemical reaction networks. © 2012 American Physical Society.

Development of a transformation system for sorghum (Sorghum bicolor L.)

Sorghum (Sorghum bicolor (L.) Moench) is the world’s fifth major cereal crop and holds importance as a construction material, food and fodder source. More recently, the potential of this plant as a biofuel source has been noted. Despite its agronomic importance, the use of sorghum production is being constrained by both biotic and abiotic factors. These challenges could be addressed by the use of genetic engineering strategies to complement conventional breeding techniques. However, sorghum is one of the most recalcitrant crops for genetic modification with the lack of an efficient tissue culture system being amongst the chief reasons. Therefore, the aim of this study was to develop an efficient tissue culture system for establishing regenerable embryogenic cell lines, micropropagation and acclimatisation for Sorghum bicolor and use this to optimise parameters for genetic transformation via Agrobacterium-mediated transformation and microprojectile bombardment. Using five different sorghum cultivars, SA281, 296B, SC49, Wray and Rio, numerous parameters were investigated in an attempt to establish an efficient and reproducible tissue culture and transformation system. Using immature embryos (IEs) as explants, regenerable embryogenic cell lines (ECLs) could only be established from cultivars SA281 and 296B. Large amounts of phenolics were produced from IEs of cultivars, SC49, Wary and Rio, and these compounds severely hindered callus formation and development. Cultivar SA281 also produced phenolics during regeneration. Attempts to suppress the production of these compounds in cultivars SA281 and SC49 using activated charcoal, PVP, ascorbic acid, citric acid and liquid filter paper bridge methods were either ineffective or had a detrimental effect on embryogenic callus formation, development and regeneration. Immature embryos sourced during summer were found to be far more responsive in vitro than those sourced during winter. In an attempt to overcome this problem, IEs were sourced from sorghum grown under summer conditions in either a temperature controlled glasshouse or a growth chamber. However, the performance of these explants was still inferior to that of natural summer-sourced explants. Leaf whorls, mature embryos, shoot tips and leaf primordia were found to be unsuitable as explants for establishing ECLs in sorghum cultivars SA281 and 296B. Using the florets of immature inflorescences (IFs) as explants, however, ECLs were established and regenerated for these cultivars, as well as for cultivar Tx430, using callus induction media, SCIM, and regeneration media, VWRM. The best in vitro responses, from the largest possible sized IFs, were obtained using plants at the FL-2 stage (where the last fully opened leaf was two leaves away from the flag leaf). Immature inflorescences could be stored at 25oC for up to three days without affecting their in vitro responses. Compared to IEs, the IFs were more robust in tissue culture and showed responses which were season and growth condition independent. A micropropagation protocol for sorghum was developed in this study. The optimum plant growth regulator (PGR) combination for the micropropagation of in vitro regenerated plantlets was found to be 1.0 mg/L BAP in combination with 0.5 mg/L NAA. With this protocol, cultivars 296B and SA281 produced an average of 57 and 13 off-shoots per plantlet, respectively. The plantlets were successfully acclimatised and developed into phenotypically normal plants that set seeds. A simplified acclimatisation protocol for in vitro regenerated plantlets was also developed. This protocol involved deflasking in vitro plantlets with at least 2 fully-opened healthy leaves and at least 3 roots longer than 1.5 cm, washing the media from the roots with running tap water, planting in 100 mm pots and placing in plastic trays covered with a clear plastic bag in a plant growth chamber. After seven days, the corners of the plastic cover were opened and the bags were completely removed after 10 days. All plantlets were successfully acclimatised regardless of whether 1:1 perlite:potting mix, potting mix, UC mix or vermiculite were used as potting substrates. Parameters were optimised for Agrobacterium-mediated transformation (AMT) of cultivars SA281, 296B and Tx430. The optimal conditions were the use of Agrobacterium strain LBA4404 at an inoculum density of 0.5 OD600nm, heat shock at 43oC for 3 min, use of the surfactant Pluronic F-68 (0.02% w/v) in the inoculation media with a pH of 5.2 and a 3 day co-cultivation period in dark at 22oC. Using these parameters, high frequencies of transient GFP expression was observed in IEs precultured on callus initiation media for 1-7 days as well as in four weeks old IE- and IF-derived callus. Cultivar SA281 appeared very sensitive to Agrobacterium since all tissue turned necrotic within two weeks post-exposure. For cultivar 296B, GFP expression was observed up to 20 days post co-cultivation but no stably transformed plants were regenerated. Using cultivar Tx430, GFP was expressed for up to 50 days post co-cultivation. Although no stably transformed plants of this cultivar were regenerated, this was most likely due to the use of unsuitable regeneration media. Parameters were optimised for transformation by particle bombardment (PB) of cultivars SA281, 296B and Tx430. The optimal conditions were use of 3-7 days old IEs and 4 weeks old IF callus, 4 hour pre- and post-bombardment osmoticum treatment, use of 0.6 µm gold microparticles, helium pressure of 1500 kPa and target distance of 15 cm. Using these parameters for PB, transient GFP expression was observed for up to 14, 30 and 50 days for cultivars SA281, 296B and Tx430, respectively. Further, the use of PB resulted in less tissue necrosis compared to AMT for the respective cultivars. Despite the presence of transient GFP expression, no stably transformed plants were regenerated. The establishment of regenerable ECLs and the optimization of AMT and PB parameters in this study provides a platform for future efforts to develop an efficient transformation protocol for sorghum. The development of GM sorghum will be an important step towards improving its agronomic properties as well as its exploitation for biofuel production.

Modelling passenger flow at airport terminals : individual agent decision model for stochastic passenger behaviour

Airport system is complex. Passenger dynamics within it appear to be complicate as well. Passenger behaviours outside standard processes are regarded more significant in terms of public hazard and service rate issues. In this paper, we devised an individual agent decision model to simulate stochastic passenger behaviour in airport departure terminal. Bayesian networks are implemented into the decision making model to infer the probabilities that passengers choose to use any in-airport facilities. We aim to understand dynamics of the discretionary activities of passengers.

Design and construction of fixed bed pyrolysis system and plum seed pyrolysis for bio-oil production

This work investigated the production of bio oil from plum seed (Zyziphus jujuba) by fixed bed pyrolysis technology. A fixed bed pyrolysis system has been designed and fabricated for production of bio oil. The major components of the system are: fixed bed reactor, liquid condenser and liquid collector. Nitrogen gas was used to maintain the inert atmosphere in the reactor where the pyrolysis reaction takes place. The feedstock considered in this study is plum seed as it is available waste material in Bangladesh. The reactor is heated by means of a cylindrical biomass external heater. Rice husk was used as the energy source. The products are oil, char and gas. The parameters varied are reactor bed temperature, running time and feed particle size. The parameters are found to influence the product yields significantly. The maximum liquid yield of 39 wt% at 5200C for a feed particle size of 2.36-4.75 mm and a gas flow rate of 8 liter/min with a running time of 120 minute. The pyrolysis oil obtained at these optimum process conditions are analyzed for some of their properties as an alternative fuel. The density of the liquid was closer with diesel. The viscosity of the plum seed liquid was lower than that of the conventional fuels. The calorific value of the pyrolysis oil is one half of the diesel fuel.

The effect of charge on ultrafine particle deposition : an experimental pilot study

Eepidemiological studies have linked exposure to ultrafine particles (UFPs, <100 nm) to a variety of adverse health effects. To understand the mechanisms behind these effects, it is essential to measure aerosol deposition in the human respiratory tract. Electrical charge is a very important property as it may increase the particle deposition in human respiratory tract (Melanderi et al., 1983). However, the effect of charge on UFP deposition has seldom been investigated. The aim of this study is to investigate the effect of charge on UFP deposition in human lung, by conducting a pilot study using a tube-based experimental system.

Experimental study of the effect of charge on ultrafine particle deposition

The health effects of ultrafine particles (UFPs, <100 nm) have received increasing attention in recent years and particles from a variety of indoor sources, such as combustion or printer emissions, fall within this size range. Since people spend most of their time indoors, knowledge on aerosol deposition in the human respiratory tract is essential to minimise the health risks associated with environmental or occupational exposure to aerosol particles. Among the factors that could alter particle deposition, electrical charge is important as it may increase particle deposition in human respiratory tract (Melanderi et al., 1983), even when particles carry only a few charges. However, evidence showing such an increase in particle deposition for UFPs is sparse. The aim of this study was to investigate the effect of charge on the deposition of UFPs in the human lung by studying the deposition of charged particles in the conductive tubing of an experimental laboratory system.

Maintenance optimisation of a multi-state series-parallel system considering economic dependence and state-dependent inspection intervals

This paper presents a maintenance optimisation method for a multi-state series-parallel system considering economic dependence and state-dependent inspection intervals. The objective function considered in the paper is the average revenue per unit time calculated based on the semi-regenerative theory and the universal generating function (UGF). A new algorithm using the stochastic ordering is also developed in this paper to reduce the search space of maintenance strategies and to enhance the efficiency of optimisation algorithms. A numerical simulation is presented in the study to evaluate the efficiency of the proposed maintenance strategy and optimisation algorithms. The simulation result reveals that maintenance strategies with opportunistic maintenance and state-dependent inspection intervals are more cost-effective when the influence of economic dependence and inspection cost is significant. The study further demonstrates that the optimisation algorithm proposed in this paper has higher computational efficiency than the commonly employed heuristic algorithms.

Optimal power system stabilizer tuning in multi-machine system via an improved differential evolution

Power system stabilizer (PSS) is one of the most important controllers in modern power systems for damping low frequency oscillations. Many efforts have been dedicated to design the tuning methodologies and allocation techniques to obtain optimal damping behaviors of the system. Traditionally, it is tuned mostly for local damping performance, however, in order to obtain a globally optimal performance, the tuning of PSS needs to be done considering more variables. Furthermore, with the enhancement of system interconnection and the increase of system complexity, new tools are required to achieve global tuning and coordination of PSS to achieve optimal solution in a global meaning. Differential evolution (DE) is a recognized as a simple and powerful global optimum technique, which can gain fast convergence speed as well as high computational efficiency. However, as many other evolutionary algorithms (EA), the premature of population restricts optimization capacity of DE. In this paper, a modified DE is proposed and applied for optimal PSS tuning of 39-Bus New-England system. New operators are introduced to reduce the probability of getting premature. To investigate the impact of system conditions on PSS tuning, multiple operating points will be studied. Simulation result is compared with standard DE and particle swarm optimization (PSO).