Biomedical subjects

Zoran Konkoli

Publications and source records attributed to Zoran Konkoli.

5 recordsLinked to original sources

PubMed2006

Controlling enzymatic reactions by geometry in a biomimetic nanoscale network.

We demonstrate that a transition from a compact geometry (sphere) to a structured geometry (several spheres connected by nanoconduits) in nanotube-vesicle networks (NVNs) induces an ordinary enzyme-catalyzed reaction to display wavelike properties. The reaction dynamics can be controlled directly by the geometry of the network, and such networks can be used to generate wavelike patterns in product formation. The results have bearing for understanding catalytic reactions in biological systems as well as for designing emerging wet chemical nanotechnological devices.

Biomimetic Materials↗

PubMed2005

Interplay between chemical reactions and transport in structured spaces.

The main motivation behind this study is to understand the interplay between the reactions and transport in a geometries that are not compact. Typical examples of compact geometries are a box or a sphere. A network made of containers C(1) , C(2),..., C(N) and tubes is an example of the space that is structured and noncompact. In containers, particles react with the rate lambda. Tubes connecting containers allow for the exchange of chemicals with the transport rate D. A situation is considered where a number of reactants is small and kinetics is noise dominated. A method is presented that can be used to calculate the average and higher moments of the reaction time. A number of different chemical reactions are studied and their performance compared in various ways. Two schemes are discussed in general, the reaction on a fixed geometry ensemble (ROGE) and the geometry on a fixed reaction ensemble, examples are given in the ROGE case. The most important findings are as follows. (i) There is a large number of reactions that run faster in a networklike geometry. Such reactions contain antagonistic catalytic influences in the intermediate stages of a reaction scheme that are best dealt with in a networklike structure. (ii) Antagonistic catalytic influences are hard to identify since they are strongly connected to the pattern of injected molecules (inject pattern) and depend on the choice of molecules that have to be synthesized at the end (task pattern). (iii) The reaction time depends strongly on the details of the inject and task patterns.

Algorithms↗

PubMed2005

Controlled initiation of enzymatic reactions in micrometer-sized biomimetic compartments.

We present a technique to initiate chemical reactions involving few reactants inside micrometer-scale biomimetic vesicles (10(-12) to 10(-15) L) integral to three-dimensional surfactant networks. The shape of these networks is under dynamic control, allowing for transfer and mixing of two or several reactants at will. Specifically, two nanotube-connected vesicles were filled with reactants (substrate and enzyme, respectively) by microinjection. Initially, the vesicles are far apart and any diffusive mixing (on relevant experimental time scales) between the contents of the separated vesicles is hindered because of the narrow diameter and long axial extension of the nanotube. To initiate a reaction, the vesicles were brought close together, the nanotube was consumed by the vesicles and at a critical distance, the nanotube-vesicle junctions were dilated leading to formation of one spherical reactor, and hence mixing of the contents. We demonstrate the concept using a model enzymatic reaction, which yields a fluorescent product (two-step hydrolysis of fluorescein diphosphate by alkaline phosphatase), where product formation was measured as a function of time using a FRAP fluorescence microscopy protocol. By comparing the enzymatic activity with bulk measurements, the enzyme concentration inside the vesicle could be determined. Reactions could be followed for systems having as few as approximately 15 enzyme molecules confined to a reactor vesicle. To describe the experiments we use a simple diffusion-controlled reaction model and solve it using a survival probability approach. The agreement with experiment is qualitative, but the model describes the trends well. It is shown that the model correctly predicts (i) single-exponential decay after a few seconds, and (ii) that the substrate decay constant depends on the number of enzymes and geometry of reaction container. The numerical correction factor Lambda is introduced in order to ensure semiquantitative agreement between experiment and theory. It was shown that this numerical factor depends weakly on vesicle radius and number of enzymes, thus it is sufficient to determine this factor only once in a single calibration measurement.

Alkaline Phosphatase↗

PubMed2004

Application of Bogolyubov's theory of weakly nonideal Bose gases to the A+A, A+B, B+B reaction-diffusion system.

Theoretical methods for dealing with diffusion-controlled reactions inevitably rely on some kind of approximation, and to find the one that works on a particular problem is not always easy. Here the approximation used by Bogolyubov to study a weakly nonideal Bose gas, referred to as the weakly nonideal Bose gas approximation (WBGA), is applied in the analysis of three reaction-diffusion models: (i) A+A-->Ø, (ii) A+B-->Ø, and (iii) A+A,B+B,A+B-->Ø (the ABBA model). Two types of WBGA are considered, the simpler WBGA-I and the more complicated WBGA-II. All models are defined on the lattice to facilitate comparison with computer experiment (simulation). It is found that the WBGA describes the A+B reaction well, it reproduces the correct d/4 density decay exponent. However, it fails in the case of the A+A reaction and the ABBA model. (To cure the deficiency of WBGA in dealing with the A+A model, a hybrid of the WBGA and Kirkwood superposition approximations is suggested.) It is shown that the WBGA-I is identical to the dressed-tree calculation suggested by Lee [J. Phys. A 27, 2633 (1994)], and that the dressed-tree calculation does not lead to the d/2 density decay exponent when applied to the A+A reaction, as normally believed, but it predicts the d/4 decay exponent. Last, the usage of the small n(0) approximation suggested by Mattis and Glasser [Rev. Mod. Phys. 70, 979 (1998)] is questioned if used beyond the A+B reaction-diffusion model.

Journal Article↗

PubMed2004

Biomimetic nanoscale reactors and networks.

Methods based on self-assembly, self-organization, and forced shape transformations to form synthetic or semisynthetic enclosed lipid bilayer structures with several properties similar to biological nanocompartments are reviewed. The procedures offer unconventional micro- and nanofabrication routes to yield complex soft-matter devices for a variety of applications for example, in physical chemistry and nanotechnology. In particular, we describe novel micromanipulation methods for producing fluid-state lipid bilayer networks of nanotubes and surface-immobilized vesicles with controlled geometry, topology, membrane composition, and interior contents. Mass transport in nanotubes and materials exchange, for example, between conjugated containers, can be controlled by creating a surface tension gradient that gives rise to a moving boundary or by induced shape transformations. The network devices can operate with extremely small volume elements and low mass, to the limit of single molecules and particles at a length scale where a continuum mechanics approximation may break down. Thus, we also describe some concepts of anomalous fluctuation-dominated kinetics and anomalous diffusive behaviours, including hindered transport, as they might become important in studying chemistry and transport phenomena in these confined systems. The networks are suitable for initiating and controlling chemical reactions in confined biomimetic compartments for rationalizing, for example, enzyme behaviors, as well as for applications in nanofluidics, bioanalytical devices, and to construct computational and complex sensor systems with operations building on chemical kinetics, coupled reactions and controlled mass transport.

Biological Transport↗