用蒙特卡罗方法计算光通过薄层随机分布粒子的散射

本文利用蒙特卡罗方法计算准直光束通过薄层随机分布粒子散射的透射和反射光强,并和输运理论的扩散近似结果做了比较。当散射接近各向同性时两者符合良好。当散射明显地成为各向异性时,蒙特卡罗方法的结果是合理的,而输运理论的扩散近似失效。     

Die identifizierung und untersuchung von isomeren O-acylribonucleosiden mit hilfe des circulardichroismus

The CD spectra of benzoyl, p-nitrobenzoyl and anisoyl derivatives of adenosine, uridine, 1-methylriboside and 1-D-ribofuranosylbenzimidazole are registrated. The possibility of identification of 2′-, 3′-O-acylribonucleosides and observation of acyl groups isomerisation kinetics is shown. The kinetics of 2′?3′ migration of a number of 2′(3′)-O-derivatives, the dependence of kinetic constants on the pH are studied. The features of acyl derivated ribonucleosides CD spectra are discussed.     

Integration of enzyme kinetic models and isotopomer distribution analysis for studies of <Emphasis Type="Italic">in situ</Emphasis>cell operation

A current trend in neuroscience research is the use of stable isotope tracers in order to address metabolic processes in vivo. The tracers produce a huge number of metabolite forms that differ according to the number and position of labeled isotopes in the carbon skeleton (isotopomers) and such a large variety makes the analysis of isotopomer data highly complex. On the other hand, this multiplicity of forms does provide sufficient information to address cell operation in vivo. By the end of last millennium, a number of tools have been developed for estimation of metabolic flux profile from any possible isotopomer distribution data. However, although well elaborated, these tools were limited to steady state analysis, and the obtained set of fluxes remained disconnected from their biochemical context. In this review we focus on a new numerical analytical approach that integrates kinetic and metabolic flux analysis. The related computational algorithm estimates the dynamic flux based on the time-dependent distribution of all possible isotopomers of metabolic pathway intermediates that are generated from a labeled substrate. The new algorithm connects specific tracer data with enzyme kinetic characteristics, thereby extending the amount of data available for analysis: it uses enzyme kinetic data to estimate the flux profile, and vice versa, for the kinetic analysis it uses in vivo tracer data to reveal the biochemical basis of the estimated metabolic fluxes.