Milos Dolnik  
Address:
Brandeis University
Chemistry Department
Waltham, MA 02254-9110
Telephone: (781) 736-2604
 Fax: (781) 736-2516
 E-mail: dolnik@brandeis.edu
Education: 
M.Sc Prague Institute of Chemical Technology
Ph.D. Prague Institute of Chemical Technology

List of publications
Research group

Research Interests:

Nonlinear Chemical Dynamics
My research interests include the study of dynamic behavior of nonlinear chemical systems. Experimental and numerical studies of chemical oscillating reactions are performed in well mixed flow-through, semibatch or batch reactors. Investigations of forced and coupled oscillating reactions are being conducted. The research concentrates also on the effects of delays in chemical kinetics.

Pattern Formation in Reaction-Diffusion Systems

When chemical reactions are combined with diffusion, spatial patterns and waves can be produced. Chemical reaction-diffusion patterns constitute the most convenient analog models for pattern formation in neurobiology, morphogenesis, chemotaxis, and ecology. My research program includes: a search for wave patterns that emerge in uniform stable systems via the wave bifurcation, spontaneous formation of target and spiral wave patterns in uniform and nonuniform reaction-diffusion systems and the study of chemical wave propagation and interaction resulting in new types of pattern formation in nonuniform media.

Modeling and Analysis of Gene Networks

We model the interactions in gene regulatory networks as biochemical reactions and analyse their dynamics. The existence of positive or negative feedbacks in gene networks lead to nonlinear rate equations and methods of analysis of nonlinear chemical systems are used to study dynamics of both synthetically engineered gene networks and naturally occurring gene networks.

Cell Cycle Dynamics

Using mathematical modeling of cell division cycle dynamics, we have studied a potential mechanism for controlling the frequency of cell division cycle. Control of the cell cycle is achieved by artificially expressing a protein that reversibly binds and inactivates one of the cell cycle proteins. In the simple case of the checkpoint-free situation, encountered in early amphibian embryos, the frequency of cell cycle oscillations can be increased or decreased by regulating the rate of synthesis, the binding rate, or the equilibrium constant of the binding protein.

Deterministic Chaos

Deterministic chaos is characterized by long-term unpredictability arising from an extreme sensitivity to initial conditions. Investigations are being conducted on emergence of chaos in chemical systems. Extended calculations are being performed on coupled chaotic systems to see under what conditions a pair or group of chaotic oscillators exhibits periodic or even steady state behavior. The study on coupled chaotic systems could be of considerable importance for the control and increased efficiency of industrial processes. It appears that a number of such processes behave chaotically and that this chaos can result in unpredictable and undesirable low yields.

Control of Chaos and Communication with Chaos

The sensitive dependence characteristic of chaos is advantageous for building a control system in which a small deliberate perturbation can have a large response. We demonstrate the possibility that information can be encoded into chaotic oscillations of the Belousov-Zhabotinsky reaction. The encoding technique is based on controlling the chaotic oscillations by applying small parameter perturbations and on learning the grammar of corresponding symbol dynamics produced by the free-running chaotic system. The encoding technique can be easily utilized for targeting and stabilization of any unstable periodic orbit.

Mathematical Modeling of Complex Chemical Reactions

Modeling of complex chemical reactions is performed for well mixed and distributed systems using solvers for integrating stiff ordinary and partial differential equations. Continuation of stationary and periodic solutions reveals bifurcation, multiplicity and stability of solutions. Statistical analysis (e.g. Fourier spectra) is used to characterize the numerical and experimental data.