Adsorption of mono- and multivalent cat- and anions on DNA molecules

Adsorption of monovalent and multivalent cat- and anions on a deoxyribose nucleic acid (DNA) molecule from a salt solution is investigated by computer simulation. The ions are modelled as charged hard spheres, the DNA molecule as a point charge pattern following the double-helical phosphate strands. The geometrical shape of the DNA molecules is modelled on different levels ranging from a simple cylindrical shape to structured models which include the major and minor grooves between the phosphate strands. The densities of the ions adsorbed on the phosphate strands, in the major and in the minor grooves are calculated. First, we find that the adsorption pattern on the DNA surface depends strongly on its geometrical shape: counterions adsorb preferentially along the phosphate strands for a cylindrical model shape, but in the minor groove for a geometrically structured model. Second, we find that an addition of monovalent salt ions results in an increase of the charge density in the minor groove while the total charge density of ions adsorbed in the major groove stays unchanged. The adsorbed ion densities are highly structured along the minor groove while they are almost smeared along the major groove. Furthermore, for a fixed amount of added salt, the major groove cationic charge is independent on the counterion valency. For increasing salt concentration the major groove is neutralized while the total charge adsorbed in the minor groove is constant. DNA overcharging is detected for multivalent salt. Simulations for a larger ion radii, which mimic the effect of the ion hydration, indicate an increased adsorbtion of cations in the major groove.Comment: 34 pages with 14 figure

On compartmental modelling of mixing phenomena

In this paper the problem of modelling partial mixing phenomena, mostly relevant in environmental and reactors modelling practice, is considered. The ultimate modelling goal is to find identifiable, finite-dimensional state-space models, which are physically interpretable, realisable and which describe partial mixing. Hence, realization theory will be linked to prior physical systems knowledge to answer the question which mixing models are good candidates in environmental/reactor systems modelling. The starting point is compartmental systems modelling with backflows. It appears however that only a limited set of low-dimensional structures is identifiable. From the real world example given in this paper it appears that an appropriate, physically interpretable and realisable model within this class of models cannot be easily foun

Optimal parametric sensitivity control of a fed-batch reactor

The paper presents an optimal parametric sensitivity controller for estimation of a set of parameters in an experiment. The method is demonstrated for a fed-batch bioreactor case study for optimal estimation of the half-saturation constant KS and the parameter combination µmaxX/Y in which µmax is the maximum specific growth rate, X is the biomass concentration, and Y the yield coefficient. The resulting parametric sensitivity controller for the parameter KS is utilized in two sequential experiments using a ‘bang–bang-singular’ control strategy. Comparison with an optimal solution for the weighted sum of squared sensitivities for both parameters are compared with the individual cases where only one specific parametric output sensitivity is controlled. The parametric uncertainty is handled in a completely deterministic way as to arrive at a control law that maximizes the parametric output sensitivity

Optimal parametric sensitivity control for a fed-batch reactor

The paper presents a method to derive an optimal parametric sensitivity controller for optimal estimation of a set of parameters in an experiment. The method is demonstrated for a fed batch bio-reactor case study for optimal estimation of the saturation constant Ks and, albeit intuitively, the parameter combination "mu-max X/Y" where mu-max is the maximum growth rate [g/min], Y is the yield coefficient [g/g], and X is the (constant) biomass [g]

On Adaptive Optimal Input Design

The problem of optimal input design (OID) for a fed-batch bioreactor case study is solved recursively. Here an adaptive receding horizon optimal control problem, involving the so-called E-criterion, is solved on-line, using the current estimate of the parameter vector at each sample instant {tk, k = 0, , N - h}, where N marks the end of the experiment and h is the control horizon for which the input design problem is solved. The optimal feed rate F(tk) thus obtained is applied and the observation y(tk+1) that becomes available is subsequently used in a recursive prediction error algorithm to find an improved estimate of the actual parameter estimate (tk). The case study involves an identification experiment with a Rapid Oxygen Demand TOXicity device (RODTOX) for estimation of the biokinetic parameters max and KS in a Monod type of growth model. It is assumed that the dissolved oxygen probe is the only instrument available, which is an important limitation. Satisfactory results are presented and compared to a naïve input design in which the system is driven by an independent binary random sequence. This comparison shows that the OID approach yields improved confidence intervals on the parameter estimates. © 2006 American Institute of Chemical Engineers AIChE J, 200

Optimal input design for parameter estimation in a single and double tank system through direct control of parametric output sensitivities

In this paper the traditional and well-known problem of optimal input design for parameter estimation is considered. In particular, the focus is on input design for the estimation of the flow exponent present in Bernoulli's law. The theory will be applied to a water tank system with a controlled inflow and free outflow. The problem is formulated as follows: Given the model structure (f, g), which is assumed to be affine in the input, and the specific parameter of interest (¿), find a feedback law that maximizes the sensitivity of the model output to the parameter under different flow conditions in the water tank. The input design problem is solved analytically. The solution to this problem is used to estimate the parameter of interest with a minimal variance. Real-world experimental results are presented and compared with theoretical solution

Sensor data fusion in electrochemical applications : An overview and its application to electrochlorination monitoring

Sensor Data Fusion (SDF) is a widely used means of monitoring electrochemical processes. The application of SDF contributes to solving challenges in process efficiency, control and reliability. Due to recent, stringent regulations, there is a need to monitor the formation of by-products in electrochlorination, such as chlorate. For this development, the knowledge of SDF produced in neighboring fields of research, such as on batteries or fuel cells, can be of great value. This paper presents an overview of the application of SDF algorithms to monitor electrochemical processes, and discusses how to best apply SDF to monitor by-product concentrations in the context of electrochlorination. Both first-principles and data-driven approaches are discussed. Successful application of SDF to electrochlorination monitoring will improve the safety of drinking water supply. In addition, this overview can inspire and improve the application of SDF in the monitoring of other electrochemical systems