Biomedical subjects

Michal Jaros

Publications and source records attributed to Michal Jaros.

10 recordsLinked to original sources

PubMed2006

Oscillating electrolytes.

Chemical oscillations are driven by the gradient of the chemical potential so that they can appear in systems where the substances are not in chemical equilibrium. We show that under the influence of the electric field, concentrations of electrically charged substances in solutions can oscillate even if the system is in chemical equilibrium. The driving force here is not the gradient of the chemical potential but rather the gradient of the electric potential. Utilizing CE we found periodic structures invoked by the application of a constant driving voltage in BGEs possessing complex eigenmobilities. By analogy with the behavior of dynamic systems, complex eigenmobilities implicate that the system will be unstable. Instead of forming system zones (system peaks) in the separation channel (capillary) the originally uniform concentration of electrolyte constituents becomes periodically disturbed when the electric current passes through it.

Electrolytes↗

PubMed2006

Simul 5 - free dynamic simulator of electrophoresis.

We introduce the mathematical model of electromigration of electrolytes in free solution together with free software Simul, version 5, designed for simulation of electrophoresis. The mathematical model is based on principles of mass conservation, acid-base equilibria, and electroneutrality. It accounts for any number of multivalent electrolytes or ampholytes and yields a complete picture about dynamics of electromigration and diffusion in the separation channel. Additionally, the model accounts for the influence of ionic strength on ionic mobilities and electrolyte activities. The typical use of Simul is: inspection of system peaks (zones), stacking and preconcentrating analytes, resonance phenomena, and optimization of separation conditions, in either CZE, ITP, or IEF.

Computer Simulation↗

PubMed2006

Eigenmobilities in background electrolytes for CZE. V. Intensity (amplitudes) of system peaks.

We present a mathematical model of CZE based on the concept of eigenmobilities - the eigenvalues of matrix M tied to the linearized governing equations of electromigration, and the spectral decomposition of matrix M into matrices of amplitudes P(j). Any peak in an electropherogram, regardless of whether it is an analyte peak or a system peak (system zone), is matched with its matrix P(j). This enables calculation of the peak parameters, such as the transfer ratio and the molar conductivity detection response (which give the indirect detection signal and the conductivity detection signal, respectively), when the initial disturbance caused by the injection of the sample is known. We also introduce new quantities, such as the generalized transfer ratio and the conductivity response of system zones, and show how the amplitude (intensity, area) of the analyte peaks and the system peaks can be calculated. We offer a free software, PeakMaster (http://www.natur.cuni.cz/gas), which yields this information in a user-friendly way.

Acetic Acid↗

PubMed2005

Conductivity detection in capillary zone electrophoresis: inspection by PeakMaster.

A simple rule stating that the signal in conductivity detection in capillary zone electrophoresis is proportional to the difference between the analyte mobility and mobility of the background electrolyte (BGE) co-ion is valid only for systems with fully ionized electrolytes. In zone electrophoresis systems with weak electrolytes both conductivity signal and electromigration dispersion of analyte peaks depend on the conductivity and pH effects. This allows optimization of the composition of BGEs to give a good conductivity signal of analytes while still keeping electromigration dispersion near zero, regardless of the injected amount of sample. The demands to achieve minimum electromigration dispersion and high sensitivity in conductivity detection can be accomplished at the same time. PeakMaster software is used for inspection of BGEs commonly used for separation of sugars (carbohydrates, saccharides) at highly alkaline pH. It is shown that the terms direct and indirect conductivity detection are misleading and should not be used.

Electric Conductivity↗

PubMed2004

Detection of molecular interactions at membrane surfaces through colloid phase transitions.

The molecular architecture of-and biochemical processes within--cell membranes play important roles in all living organisms, with many drugs and infectious disease agents targeting membranes. Experimental studies of biochemical reactions on membrane surfaces are challenging, as they require a membrane environment that is fluid (like cell membranes) but nevertheless allows for the efficient detection and characterization of molecular interactions. One approach uses lipid membranes supported on solid substrates such as silica or polymers: although the membrane is trapped near the solid interface, it retains natural fluidity and biological functionality and can be implanted with membrane proteins for functional studies. But the detection of molecular interactions involving membrane-bound species generally requires elaborate techniques, such as surface plasmon resonance or total internal reflection fluorescence microscopy. Here we demonstrate that colloidal phase transitions of membrane-coated silica beads provide a simple and label-free method for monitoring molecular interactions on lipid membrane surfaces. By adjusting the lipid membrane composition and hence the pair interaction potential between the membrane-supporting silica beads, we poise our system near a phase transition so that small perturbations on the membrane surface induce dramatic changes in the macroscopic organization of the colloid. We expect that this approach, used here to probe with high sensitivity protein binding events at membrane surfaces, can be applied to study a broad range of cell membrane processes.

Antibodies↗

PubMed2004

Eigenmobilities in background electrolytes for capillary zone electrophoresis: III. Linear theory of electromigration.

A mathematical model of capillary zone electrophoresis (CZE) based on the conception of eigenmobilities, which are the eigenvalues of a matrix M tied to the linearized governing equations is presented. The model considers CZE systems, where constituents, either analytes or components of the background electrolyte (BGE), are weak electrolytes--acids, bases, or ampholytes. There is no restriction on the number of components nor on the valence of the constituents nor on pH of the BGE. An electrophoretic system with N constituents has N eigenmobilities. In most BGEs one or two eigenmobilities are very close to zero so their corresponding eigenzones move very slowly. However, there are BGEs where no eigenmobility is close to zero. The mathematical model further provides: the transfer ratio, the molar conductivity detection response, and the relative velocity slope. This allows the assessment of the indirect detection, conductivity detection and peak broadening (distortion) due to electromigration dispersion. Also, we present a spectral decomposition of the matrix M to matrices allowing the assessment of the amplitudes of system eigenpeaks (system peaks). Our model predicted the existence of BGEs having no stationary injection zone (or water zone, gap, peak, dip). A common practice of using the injection zone as a marker of the electroosmotic flow must fail in such electrolytes.

Buffers↗

PubMed2004

Eigenmobilities in background electrolytes for capillary zone electrophoresis: IV. Computer program PeakMaster.

We are introducing a computer implementation of the mathematical model of zone electrophoresis (CZE) described in Stedry, M., Jaros, M., Hruska, V., Gas, B., Electrophoresis 2004, 25, 3071-3079 program PeakMaster. The computer model calculates eigenmobilities, which are the eigenvalues of the matrix tied to the linearized continuity equations, and which are responsible for the presence of system eigenzones (system zones, system peaks). The model also calculates other parameters of the background electrolyte (BGE)-pH, conductivity, buffer capacity, ionic strength, etc., and parameters of the separated analytes--effective mobility, transfer ratio, molar conductivity detection response, and relative velocity slope. This allows the assessment of the indirect detection, conductivity detection and peak broadening (peak distortion) due to electromigration dispersion. The computer model requires the input of the BGE composition, the list of analytes to be separated, and the system instrumental configuration. The output parameters of the model are directly comparable with experiments; the model also simulates electropherograms in a user-friendly way. We demonstrate a successful application of PeakMaster for inspection of BGEs having no stationary injection zone.

Computer Simulation↗

PubMed2003

Eigenmobilities in background electrolytes for capillary zone electrophoresis: II. Eigenpeaks in univalent weak electrolytes.

We analyze in detail a mathematical model of capillary zone electrophoresis (CZE) based on the conception of eigenmobilities, which are eigenvalues of the matrix tied to the linearized continuity equations. Our model considers CZE systems, where constituents are weak electrolytes and where pH of the background electrolyte may reach the full range from 0 to 14. Both hydrogen and hydroxide ions are taken into account in relations for conductivity and electroneutrality. An electrophoretic system with N constituents has N eigenmobilities. We reveal that two of the eigenmobilities have a special meaning as they exist due to the presence of hydrogen ions and hydroxide ions (in water solutions). These two eigenmobilities are responsible for the existence of two corresponding system zones (system peaks). We show that the stationary zone (injection zone, water zone, gap, peak, dip) is in many common background electrolytes composed of these two eigenzones which overlap, due to their very low electrophoretic mobility, into one zone. Other eigenmobilities give rise to system zones originating due to a possible existence of double (or multiple) coconstituents in the background electrolyte. The last group of eigenmobilities is connected with the movement of eigenzones accompanying analytes and enabling their indirect UV or conductivity detection. The model allows assessing experimentally available quantities such as effective mobility of the analyte, molar conductivity detection response, transfer ratio, and relative velocity slope and gives a picture about migration of analytes, their electromigration dispersion and signals obtained in detectors. It allows computer simulation of electropherograms and enables optimization of background electrolytes.

Electric Conductivity↗

PubMed2002

Eigenmobilities in background electrolytes for capillary zone electrophoresis. I. System eigenpeaks and resonance in systems with strong electrolytes.

A background electrolyte system for capillary zone electrophoresis which is composed of three strong univalent ionic constituents is investigated. The ion 1 is considered as a counter-ion and two ions, 2 and 3, are considered as co-ions in relation to the analyte ion 4. We investigate the linearized model of electromigration in such a system and calculate the eigenvalues of a corresponding matrix. The model is formulated in such a way that the eigenvalues of the system are certain mobilities, which we call eigenmobilites, which characterize specific features of the electtrophoretic migration. One of the eigenmobilities is the system eigenmobility u(s) causing the rise of the system peak, called here the system eigenpeak. A situation when the analyte has the same mobility as the system eigenmobility, u(4) = u(s), is analyzed in detail. We show that it leads to the resonance-the mutual jump in the concentration profile of both co-ions, 2 and 3, has a shape of the spatial derivation of the originally sampled analyte profile and, moreover, it grows linearly with time. After a sufficiently long time it can be "amplified" to any value. The resonance has then a great impact on signals of indirect detection methods, like indirect UV detection or conductivity detection. In the framework of the linearized model the relative velocity slope S(x), a measure of electromigration dispersion, is expressed as S(x) = F(u(1) + u(4))(u(2) - u(4))(u(3) - u(4))/[u(4)(u(s) - u(4))], where u(i) is the mobility of the ith ion and F is the Faraday constant. As in practice the concentration of the analyte is not infinitely small and has a certain finite value, the analyte will be at the resonance severely dispersed to a much broader spatial interval. When a specific detector is used, the signal of such an analyte can apparently be missed without any notice.

Electrolytes↗

PubMed2002

Optimization of background electrolytes for capillary electrophoresis: II. Computer simulation and comparison with experiments.

A mathematical and computational model described in the previous paper (Gas, B., Coufal, P., Jaros, M., Muzikár, J., Jelínek, L., J. Chromatogr. A 2001, 905, 269-279) is adapted, algorithmized, and a computer program PeakMaster having a status of freeware (http://natur.cuni.cz/ approximately gas) is introduced. The model enables optimization of background electrolyte (BGE) systems for capillary zone electrophoresis. The model allows putting to use uni- or di- or trivalent electrolytes and allows also for modeling highly acidic or alkaline BGEs. It takes into account the dependence of ionic mobilities and dissociation of weak electrolytes on the ionic strength. The model calculates the effective mobility of analytes and predicts parameters of the system that are experimentally available, such as the transfer ratio, which is a measure of the sensitivity in the indirect UV detection or the molar conductivity detection response, which expresses the sensitivity of the conductivity detection. Further, the model enables evaluation of a tendency of the analyte to undergo electromigration dispersion or peak broadening. The suitability of the model is verified by comparison of the predicted results with experiments, even under conditions that are far from ideal (under extreme pH and a high ionic strength).

Algorithms↗