Determination of DNA cooperativity factor.

The paper presents measurements of the difference in the melting temperature of a colE1 DNA region when it is located inside the DNA helix and at its end. A direct comparison of calculations based on the rigorous theory of helix-coil transition with experimental data for .2 M Na+ (the conditions for fully reversible melting) yielded the value of 2.5-5x10(-5) for the cooperatively factor sigma. We discuss the reversibility of DNA melting and the possibility of applying the "all-or-nothing" concept to the melting of DNA regions.     

Fos-Jun heterodimers and Jun homodimers bend DNA in opposite orientations: implications for transcription factor cooperativity

Association of Fos and Jun with the AP-1 site results in a conformational change in the basic amino acid regions that constitute the DNA-binding domain. We show that Fos and Jun induce a corresponding alteration in the conformation of the DNA helix. Circular permutation analysis indicated that both Fos-Jun heterodimers and Jun homodimers induce flexure at the AP-1 site. Phasing analysis demonstrated that Fos-Jun heterodimers and Jun homodimers induce DNA bends that are directed in opposite orientations. Fos-Jun heterodimers bend DNA toward the major groove, whereas Jun homodimers bend DNA toward the minor groove. Fos and Jun peptides encompassing the dimerization and DNA-binding domains bend DNA in the same orientations as the full-length proteins. However, additional regions of both proteins influence the magnitude of the DNA bend angle. Thus, despite the amino acid sequence similarity in the basic region Fos-Jun heterodimers and Jun homodimers form topologically distinct DNA-protein complexes.     

Identification and estimation algorithm for stochastic neural system

An algorithm for the estimation of stochastic processes in a neural system is presented. This process is defined here as the continuous stochastic process reflecting the dynamics of the neural system which has some inputs and generates output spike trains. The algorithm proposed here is to identify the system parameters and then estimate the stochastic process called neural system process here. These procedures carried out on the basis of the output spike trains which are supposed to be the data observed in the randomly missing way by the threshold time function in the neural system. The algorithm is constructed with the well-known Kalman filters and realizes the estimation of the neural system process by cooperating with the algorithm for the parameter estimation of the threshold time function presented previously (Nakao et al., 1983). The performance of the algorithm is examined by applying it to the various spike trains simulated by some artificial models and also to the neural spike trains recorded in cat's optic tract fibers. The results in these applications are thought to prove the effectiveness of the algorithm proposed here to some extent. Such attempts, we think, will serve to improve the characterizing and modelling techniques of the stochastic neural systems.     

Analysing protein energy data by a stochastic model for cooperative interactions: comparison and characterization of cooperativity

 In the frame of a Markov chain model for cooperative interactions in proteins, previously introduced by us, we deal here with estimation of unknown parameters from protein energy data. One of these parameters characterizes the cooperativity of a protein; we propose to measure it also by the so-called approximate entropy. By our computations the approximate entropy turns out to be a decreasing function of the cooperativity. We analyse both simulated data of the Markov chain, and protein energy data obtained by molecular dynamics simulation. Moreover, we compare two rubredoxin proteins at different temperatures, according to their degrees of cooperativity. Received: 2 October 2000 / Revised version: 4 April 2001 / Published online: 14 March 2002     

A stochastic model for then-compartment irreversible system

This paper deals with a stochasticn-compartment irreversible system with a non-homogeneous Poisson input and arbitrary residence time for each of the compartments. Results relating to the number of particles present in each of the compartments as well as the total number of particles present in the system at any time are derived. Further, explicit expressions for the auto covariance function for each compartment and the cross-covariance function between any two compartments with a given time lag are obtained. As a particular case, then-compartment irreversible system is analyzed with homogeneous Poisson input and exponential residence time distribution for each of the compartments. The possible applications of the model are discussed.