Modelling of methanol synthesis in a network of forced unsteady-state ring reactors by artificial neural networks for control purposes

A numerical model based on artificial neural networks (ANN) was developed to simulate the dynamic behaviour of a three reactors network (or ring reactor), with periodic change of the feed position, when low-pressure methanol synthesis is carried out. A multilayer, feedforward, fully connected ANN was designed and the history stack adaptation algorithm was implemented and tested with quite good results both in terms of model identification and learning rates. The influence of the ANN parameters was addressed, leading to simple guidelines for the selection of their values. A detailed model was used to generate the patterns adopted for the learning and testing phases. The simplified model was finalised to develop a model predictive control scheme in order to maximise methanol yield and to fulfil process constraints

Kinetic modelling of in vitro data of PI3K, mTOR1, PTEN enzymes and on-target inhibitors Rapamycin, BEZ235, and LY294002

The phosphatidylinositide 3-kinases (PI3K) and mammalian target of rapamycin-1 (mTOR1) are two key targets for anti-cancer therapy. Predicting the response of the PI3K/AKT/mTOR1 signalling pathway to targeted therapy is made difficult because of network complexities. Systems biology models can help explore those complexities but the value of such models is dependent on accurate parameterisation. Motivated by a need to increase accuracy in kinetic parameter estimation, and therefore the predictive power of the model, we present a framework to integrate kinetic data from enzyme assays into a unified enzyme kinetic model. We present exemplar kinetic models of PI3K and mTOR1, calibrated on in vitro enzyme data and founded on Michaelis-Menten (MM) approximation. We describe the effects of an allosteric mTOR1 inhibitor (Rapamycin) and ATP-competitive inhibitors (BEZ2235 and LY294002) that show dual inhibition of mTOR1 and PI3K. We also model the kinetics of phosphatase and tensin homolog (PTEN), which modulates sensitivity of the PI3K/AKT/mTOR1 pathway to these drugs. Model validation with independent data sets allows investigation of enzyme function and drug dose dependencies in a wide range of experimental conditions. Modelling of the mTOR1 kinetics showed that Rapamycin has an IC50 independent of ATP concentration and that it is a selective inhibitor of mTOR1 substrates S6K1 and 4EBP1: it retains 40% of mTOR1 activity relative to 4EBP1 phosphorylation and inhibits completely S6K1 activity. For the dual ATP-competitive inhibitors of mTOR1 and PI3K, LY294002 and BEZ235, we derived the dependence of the IC50 on ATP concentration that allows prediction of the IC50 at different ATP concentrations in enzyme and cellular assays. Comparison of the drug effectiveness in enzyme and cellular assays showed that some features of these drugs arise from signalling modulation beyond the on-target action and MM approximation and require a systems-level consideration of the whole PI3K/PTEN/AKT/mTOR1 network in order to understand mechanisms of drug sensitivity and resistance in different cancer cell lines. We suggest that using these models in systems biology investigation of the PI3K/AKT/mTOR1 signalling in cancer cells can bridge the gap between direct drug target action and the therapeutic response to these drugs and their combinations

UHMWPE/SBA-15 nanocomposites synthesized by in situ polymerization

Different nanocomposites have been attained by in situ polymerization based on ultra-high molecular weight polyethylene (UHMWPE) and mesoporous SBA-15, this silica being used for immobilization of the FI catalyst bis [N-(3-tert-butylsalicylidene)-2,3,4,5,6-pentafluoroanilinato] titanium (IV) dichloride and as filler as well. Two distinct approaches have been selected for supporting the FI catalyst on the SBA-15 prior polymerization. A study on polymerization activity of this catalyst has been performed under homogenous conditions and upon heterogenization. A study of the effect of presence of mesoporous particles and of the immobilization method is also carried out. Moreover, the thermal characterization, phase transitions and mechanical response of some pristine UHMWPEs and UHMWPE/SBA-15 materials have been carried out. Relationships with variations on molar mass, impregnation method of catalyst and final SBA-15 content have been established

Likelihood based observability analysis and confidence intervals for predictions of dynamic models

Mechanistic dynamic models of biochemical networks such as Ordinary Differential Equations (ODEs) contain unknown parameters like the reaction rate constants and the initial concentrations of the compounds. The large number of parameters as well as their nonlinear impact on the model responses hamper the determination of confidence regions for parameter estimates. At the same time, classical approaches translating the uncertainty of the parameters into confidence intervals for model predictions are hardly feasible. In this article it is shown that a so-called prediction profile likelihood yields reliable confidence intervals for model predictions, despite arbitrarily complex and high-dimensional shapes of the confidence regions for the estimated parameters. Prediction confidence intervals of the dynamic states allow a data-based observability analysis. The approach renders the issue of sampling a high-dimensional parameter space into evaluating one-dimensional prediction spaces. The method is also applicable if there are non-identifiable parameters yielding to some insufficiently specified model predictions that can be interpreted as non-observability. Moreover, a validation profile likelihood is introduced that should be applied when noisy validation experiments are to be interpreted. The properties and applicability of the prediction and validation profile likelihood approaches are demonstrated by two examples, a small and instructive ODE model describing two consecutive reactions, and a realistic ODE model for the MAP kinase signal transduction pathway. The presented general approach constitutes a concept for observability analysis and for generating reliable confidence intervals of model predictions, not only, but especially suitable for mathematical models of biological systems

Kinetic and thermodynamic analysis of leech-derived tryptase inhibitor interaction with bovine tryptase and bovine trypsin

The interaction of leech-derived tryptase inhibitor (LDTI) with bovine liver capsule tryptase (BLCT) and bovine trypsin has been studied using both thermodynamic and kinetic approaches. Several differences were detected: (i) the equilibrium affinity of LDTI for BLCT (K-a = 8.9 x 10(5) M-1) is about 600-fold lower than that for bovine trypsin (K-a = 5.1 x 10(8) M-1); (ii) LDTI behaves as a purely non-competitive inhibitor of BLCT, while it is a purely competitive inhibitor of bovine trypsin. These functional data are compared with those previously reported for the LDTI binding to human tryptase, where tight inhibition occurs at two of the four active sites of the tetramer (K-a = 7.1 x 10(8) M-1). Amino acid sequence alignment of BLCT, human beta II-tryptase and bovine trypsin allows us to infer some possible structural basis for the observed functional differences

Closed loop identification based on quantization

This paper proposes a new closed-loop identification scheme for a single-input-single-output control loop. It is based upon a quantizer inserted into the feedback path. The quantizer can be used to generate an equivalent persistently exciting signal with which the well known two-stage and/or two-step method can be used directly. Simulation examples and an experimental demonstration are used to illustrate the proposed scheme

Singular Langmuir-Hinshelwood reaction-diffusion problems. strong absorption under quasi-isothermal conditions

The steady state reaction-diffusion problem is considered for a permeable catalytic particle with Langmuir-Hinshelwood kinetics under isothermal and quasi-isothermal conditions. It is known that there may be multiple solutions due to either strong adsorption or external thermal effects; in the first case, an arbitrarily large number of solutions may appear for symmetric pellets in two and three dimensions. An asymptotic analysis provides analytical expressions for the response curve of the particle and for the multiplicity bounds. The approximate results compare quite well with those computed numerically, even in cases in which the gauge functions of the approximation scheme are of the logarithmic type

Hybrid materials based on polyethylene and MCM-41 microparticles functionalized with silanes: catalytic aspects of in situ polymerization, crystalline features and mechanical properties

New nanocomposites based on polyethylene have been prepared by in situ polymerization of ethylene in presence of mesoporous MCM-41. The polymerization reactions were performed using a zirconocene catalyst either under homogenous conditions or supported onto mesoporous MCM-41 particles, which are synthesized and decorated post-synthesis with two silanes before polymerization in order to promote an enhanced interfacial adhesion. The existence of polyethylene chains able to crystallize within the mesoporous channels in the resulting nanocomposites is figured out from the small endothermic process, located at around 80 C, on heating calorimetric experiments, in addition to the main melting endotherm. These results indicate that polyethylene macrochains can grow up during polymerization either outside or inside the MCM-41 channels, these keeping their regular hexagonal arrangements. Mechanical response is observed to be dependent on the content in mesoporous MCM-41 and on the crystalline features of polyethylene. Accordingly, stiffness increases and deformability decreases in the nanocomposites as much as MCM-41 content is enlarged and polyethylene amount within channels is raised. Ultimate mechanical performance improves with MCM-41 incorporation without varying the final processing temperature

The Galactic IMF: origin in the combined mass distribution functions of dust grains and gas clouds

We present here a theoretical model to account for the stellar IMF as a result of the composite behaviour of the gas and dust distribution functions. Each of these has previously been modelled and the models tested against observations. The model presented here implies a relation between the characteristic size of the dust grains and the characteristic final mass of the stars formed within the clouds containing the grains, folded with the relation between the mass of a gas cloud and the characteristic mass of the stars formed within it. The physical effects of dust grain size are due to equilibrium relations between the efficiency of grains in cooling the clouds, which is a falling function of grain size, and the efficiency of grains in catalyzing the production of molecular hydrogen, which is a rising function of grain size. We show that folding in the effects of grain distribution can yield a reasonable quantitative account of the IMF, while gas cloud mass function alone cannot do so.Comment: 8 pages, 6 figures, MNRAS accepted for publicatio