Binomial leap methods for simulating stochastic chemical kinetics

This paper discusses efficient simulation methods for stochastic chemical kinetics. Based on the tau-leap and midpoint tau-leap methods of Gillespie [D. T. Gillespie, J. Chem. Phys. 115, 1716 (2001)], binomial random variables are used in these leap methods rather than Poisson random variables. The motivation for this approach is to improve the efficiency of the Poisson leap methods by using larger stepsizes. Unlike Poisson random variables whose range of sample values is from zero to infinity, binomial random variables have a finite range of sample values. This probabilistic property has been used to restrict possible reaction numbers and to avoid negative molecular numbers in stochastic simulations when larger stepsize is used. In this approach a binomial random variable is defined for a single reaction channel in order to keep the reaction number of this channel below the numbers of molecules that undergo this reaction channel. A sampling technique is also designed for the total reaction number of a reactant species that undergoes two or more reaction channels. Samples for the total reaction number are not greater than the molecular number of this species. In addition, probability properties of the binomial random variables provide stepsize conditions for restricting reaction numbers in a chosen time interval. These stepsize conditions are important properties of robust leap control strategies. Numerical results indicate that the proposed binomial leap methods can be applied to a wide range of chemical reaction systems with very good accuracy and significant improvement on efficiency over existing approaches. (C) 2004 American Institute of Physics.

Accelerated leap methods for simulating discrete stochastic chemical kinetics

Biologists are increasingly conscious of the critical role that noise plays in cellular functions such as genetic regulation, often in connection with fluctuations in small numbers of key regulatory molecules. This has inspired the development of models that capture this fundamentally discrete and stochastic nature of cellular biology - most notably the Gillespie stochastic simulation algorithm (SSA). The SSA simulates a temporally homogeneous, discrete-state, continuous-time Markov process, and of course the corresponding probabilities and numbers of each molecular species must all remain positive. While accurately serving this purpose, the SSA can be computationally inefficient due to very small time stepping so faster approximations such as the Poisson and Binomial τ-leap methods have been suggested. This work places these leap methods in the context of numerical methods for the solution of stochastic differential equations (SDEs) driven by Poisson noise. This allows analogues of Euler-Maruyuma, Milstein and even higher order methods to be developed through the Itô-Taylor expansions as well as similar derivative-free Runge-Kutta approaches. Numerical results demonstrate that these novel methods compare favourably with existing techniques for simulating biochemical reactions by more accurately capturing crucial properties such as the mean and variance than existing methods.

A multi-scaled approach for simulating chemical reaction systems

In this paper we give an overview of some very recent work, as well as presenting a new approach, on the stochastic simulation of multi-scaled systems involving chemical reactions. In many biological systems (such as genetic regulation and cellular dynamics) there is a mix between small numbers of key regulatory proteins, and medium and large numbers of molecules. In addition, it is important to be able to follow the trajectories of individual molecules by taking proper account of the randomness inherent in such a system. We describe different types of simulation techniques (including the stochastic simulation algorithm, Poisson Runge–Kutta methods and the balanced Euler method) for treating simulations in the three different reaction regimes: slow, medium and fast. We then review some recent techniques on the treatment of coupled slow and fast reactions for stochastic chemical kinetics and present a new approach which couples the three regimes mentioned above. We then apply this approach to a biologically inspired problem involving the expression and activity of LacZ and LacY proteins in E. coli, and conclude with a discussion on the significance of this work.

On minimal models of process systems

Minimal representations are known to have no redundant elements, and are therefore of great importance. Based on the notions of performance and size indices and measures for process systems, the paper proposes conditions for a process model being minimal in a set of functionally equivalent models with respect to a size norm. Generalized versions of known procedures to obtain minimal process models for a given modelling goal, model reduction based on sensitivity analysis and incremental model building are proposed and discussed. The notions and procedures are illustrated and compared on a simple example, that of a simple nonlinear fermentation process with different modelling goals and on a case study of a heat exchanger modelling. (C) 2004 Elsevier Ltd. All rights reserved.

Oscillatory regulation of hes1: Discrete stochastic delay modelling and simulation

Discrete stochastic simulations are a powerful tool for understanding the dynamics of chemical kinetics when there are small-to-moderate numbers of certain molecular species. In this paper we introduce delays into the stochastic simulation algorithm, thus mimicking delays associated with transcription and translation. We then show that this process may well explain more faithfully than continuous deterministic models the observed sustained oscillations in expression levels of hes1 mRNA and Hes1 protein.

Stochastic models for regulatory networks of the genetic toggle switch

Bistability arises within a wide range of biological systems from the A phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. in this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.

Stochastic delay differential equations for genetic regulatory networks

Time delay is an important aspect in the modelling of genetic regulation due to slow biochemical reactions such as gene transcription and translation, and protein diffusion between the cytosol and nucleus. In this paper we introduce a general mathematical formalism via stochastic delay differential equations for describing time delays in genetic regulatory networks. Based on recent developments with the delay stochastic simulation algorithm, the delay chemical masterequation and the delay reaction rate equation are developed for describing biological reactions with time delay, which leads to stochastic delay differential equations derived from the Langevin approach. Two simple genetic regulatory networks are used to study the impact of' intrinsic noise on the system dynamics where there are delays. (c) 2006 Elsevier B.V. All rights reserved.

Advanced computing for systems biology

Systems biology is based on computational modelling and simulation of large networks of interacting components. Models may be intended to capture processes, mechanisms, components and interactions at different levels of fidelity. Input data are often large and geographically disperse, and may require the computation to be moved to the data, not vice versa. In addition, complex system-level problems require collaboration across institutions and disciplines. Grid computing can offer robust, scaleable solutions for distributed data, compute and expertise. We illustrate some of the range of computational and data requirements in systems biology with three case studies: one requiring large computation but small data (orthologue mapping in comparative genomics), a second involving complex terabyte data (the Visible Cell project) and a third that is both computationally and data-intensive (simulations at multiple temporal and spatial scales). Authentication, authorisation and audit systems are currently not well scalable and may present bottlenecks for distributed collaboration particularly where outcomes may be commercialised. Challenges remain in providing lightweight standards to facilitate the penetration of robust, scalable grid-type computing into diverse user communities to meet the evolving demands of systems biology.

Nanostructures, Semicrytalline Morphology, and Nanoscale Confinement Effect on the Crystallization Kinetics in Self-Organized Block Copolymer/Thermoset Blends

This work reports the first instance of self-organized thermoset blends containing diblock copolymers with a crystallizable thermoset-immiscible block. Nanostructured thermoset blends of bisphenol A-type epoxy resin (ER) and a low-molecular-weight (M-n = 1400) amphiphilic polyethylene-block-poly(ethylene oxide) (EEO) symmetric diblock copolymer were prepared using 4,4'-methylenedianiline (MDA) as curing agent and were characterized by transmission electron microscopy (TEM), atomic force microscopy (AFM), small-angle X-ray scattering (SAXS), and differential scanning calorimetry (DSC). All the MDA-cured ER/EEO blends do not show macroscopic phase separation but exhibit microstructures. The ER selectively mixes with the epoxy-miscible PEO block in the EEO diblock copolymer whereas the crystallizable PE blocks that are immiscible with ER form separate microdomains at nanoscales in the blends. The PE crystals with size on nanoscales are formed and restricted within the individual spherical micelles in the nanostructured ER/EEO blends with EEO content up to 30 wt %. The spherical micelles are highly aggregated in the blends containing 40 and 50 wt % EEO. The PE dentritic crystallites exist in the blend containing 50 wt % EEO whereas the blends with even higher EEO content are completely volume-filled with PE spherulites. The semicrystalline microphase-separated lamellae in the symmetric EEO diblock copolymer are swollen in the blend with decreasing EEO content, followed by a structural transition to aggregated spherical micellar phase morphology and, eventually, spherical micellar phase morphology at the lowest EEO contents. Three morphological regimes are identified, corresponding precisely to the three regimes of crystallization kinetics of the PE blocks. The nanoscale confinement effect on the crystallization kinetics in nanostructured thermoset blends is revealed for the first time. This new phenomenon is explained on the basis of homogeneous nucleation controlled crystallization within nanoscale confined environments in the block copolymer/thermoset blends.

Chemical and functional identification and characterization of novel sulfated alpha-conotoxins from the cone snail Conus anemone

An LC/MS analysis with diagnostic screening for the detection of peptides with posttranslational modifications revealed the presence of novel sulfated peptides within the -conotoxin molecular mass range in Conus anemone crude venom. A functional assay of the extract showed activity at several neuronal nicotinic acetylcholine receptors (nAChRs). Three sulfated alpha-conotoxins (AnIA, AnIB, and AnIC) were identified by LC/MS and assay-directed fractionation and sequenced after purification. The most active of these, alpha-AnIB, was further characterized and used to investigate the influence of posttranslational modifications on affinity. Synthetic AnIB exhibited subnanomolar potency at the rat alpha3/beta2 nAChR (IC50 0.3 nM) and was 200-fold less active on the rat alpha7 nAChR (IC50 76 nM). The unsulfated peptide [Tyr(16)]AnIB showed a 2-fold and 10-fold decrease in activities at alpha3beta2 (IC50 0.6 nM) and alpha7(IC50 836 nM) nAChR, respectively. Likewise, removal of the C-terminal amide had a greater influence on potency at the alpha7 (IC50 367 nM) than at the alpha3beta2 nAChR (IC50 0.5 nM). Stepwise removal of two N-terminal glycine residues revealed that these residues affect the binding kinetics of the peptide. Comparison with similar 4/7-alpha-conotoxin sequences suggests that residue 11 (alanine or glycine) and residue 14 (glutamine) constitute important determinants for alpha3beta2 selectivity, whereas the C-terminal amidation and sulfation at tyrosine-16 favor alpha7 affinity.

Kinetics of reduction of lead smelting slags with solid carbon

Research techniques and a methodology have been developed that enable the reduction kinetics of molten lead smelting slags with solid carbon to be studied. The rates of reduction of PbO-FeO-Fe2O3-CaO-SiO2 slags with carbon have been measured for a range of slag compositions for PbO concentrations between 3 and 100 weight percent, and temperatures between 1423 and 1573 K. The reduction rates were determined for both graphite and coke. Within the range of process conditions examined, it has been shown that the reaction rates are almost independent of carbon reactivity, SiO2/CaO and SiO2/Fe ratio in the range of compositions investigated and are not influenced by the presence of sulphur in the slag.The apparent first order rate constants for oxygen removal increase with increasing PbO concentration and oxygen activity in the slag. The data indicate that the rate limiting reaction step for the reduction of lead slags with solid carbon is the chemical reaction at the gas/slag interface.

Phosphate uptake and growth kinetics of Trichodesmium (Cyanobacteria) isolates from the North Atlantic Ocean and the Great Barrier Reef, Australia

We compared inorganic phosphate (P-i) uptake and growth kinetics of two cultures of the diazotrophic cyanobacterium Trichodesmium isolated from the North Atlantic Ocean (IMS101) and from the Great Barrier Reef, Australia (GBRTRLI101). Phosphate-limited cultures had up to six times higher maximum P-i uptake rates than P-replete cultures in both strains. For strain GBRTRLI101, cell-specific P-i uptake rates were nearly twice as high, due to larger cell size, but P-specific maximum uptake rates were similar for both isolates. Half saturation constants were 0.4 and 0.6 muM for P-i uptake and 0.1 and 0.2 muM for growth in IMS101 and GBRTRLI101, respectively. Phosphate uptake in both strains was correlated to growth rates rather than to light or temperature. The cellular phosphorus quota for both strains increased with increasing P-i up to 1.0 muM. The C:P ratios were 340-390 and N:P ratios were 40-45 for both strains under severely P-limited growth conditions, similar to reported values for natural populations from the tropical Atlantic and Pacific Oceans. The C:P and N:P ratios were near Redfield values in medium with >1.0 muM P-i. The North Atlantic strain IMS101 is better adapted to growing on P-i at low concentrations than is GBRTRLI101 from the more P-i-enriched Great Barrier Reef. However, neither strain can achieve appreciable growth at the very low (nanomolar) P-i concentrations found in most oligotrophic regimes. Phosphate could be an important source of phosphorus for Trichodesmium on the Great Barrier Reef, but populations growing in the oligotrophic open ocean must rely primarily on dissolved organic phosphorus sources.

Kinetics of reduction of FeO from slag by graphite and coal chars

The rates of reduction of FeO from iron-saturated FeO-CaO-Al2O3-SiO2 slags by graphite, coke, bituminous coal and anthracitic coal chars at temperatures in the range 1 673-1873 K have been measured using a sessile drop technique. The extents of reaction were determined using EPMA analysis of quenched samples, and on line gas analysis using a quadrupole mass spectrometer. The reaction rates have been shown to be dependent critically on carbon type. For the reaction geometry used in this investigation the reduction rates of graphite and coke are observed to be faster than with coal chars. This unexpected finding is shown to be associated with differences in the dominant chemical and mass transfer mechanisms occurring at the reaction interface. High reaction rates are observed to occur with the formation of liquid Fe-C alloy product and the associated gasification of carbon from the alloy. The rates of reduction by coal chars are determined principally by the chemical reaction at the carbon/gas interface and slag phase mass transfer.

Mg-based nanocomposites with high capacity and fast kinetics for hydrogen storage

Magnesium and its alloys have shown a great potential in effective hydrogen storage due to their advantages of high volumetric/ gravimetric hydrogen storage capacity and low cost. However, the use of these materials in fuel cells for automotive applications at the present time is limited by high hydrogenation temperature and sluggish sorption kinetics. This paper presents the recent results of design and development of magnesium-based nanocomposites demonstrating the catalytic effects of carbon nanotubes and transition metals on hydrogen adsorption in these materials. The results are promising for the application of magnesium materials for hydrogen storage, with significantly reduced absorption temperatures and enhanced ab/desorption kinetics. High level Density Functional Theory calculations support the analysis of the hydrogenation mechanisms by revealing the detailed atomic and molecular interactions that underpin the catalytic roles of incorporated carbon and titanium, providing clear guidance for further design and development of such materials with better hydrogen storage properties.