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caused by Sugarcane Mosaic Virus (SCMV) is one of the most important virus diseases of sugarcane. In the present
study, changes in the transcription profile obtained by cDNA-AFLP analysis were
investigated in two sugarcane varieties contrasting to SCMV resistance, when
challenged with a severe virus strain. Healthy plants derived from meristem tip
tissue culture were mechanically inoculated under greenhouse controlled
conditions and sampled at 24, 48 and 72 hours after inoculation. A total of 392
transcript-derived fragments (TDFs) were verified in the resistant variety
against 380 in the susceptible one. The two sugarcane genotypes showed differential
behavior in the number of induced and repressed TDFs along the time-course
samplings. Ten out of 23 sequenced TDFs (unique from the resistance variety),
showed identity with known plant sequences, mostly related to plant defense
mechanisms against pathogens. The cDNA-AFLP technique was effective in
revealing changes in the transcription profile within and between contrasting
varieties when challenged by SCMV.
Stochastic modeling of biochemical reactions taking place at the cellular level has become the subject of intense research in recent years. Molecular interactions in a single cell exhibit random fluctuations. These fluctuations may be significant when small populations of some reacting species are present and then a stochastic description of the cellular dynamics is required. Often, the biochemically reacting systems encountered in applications consist of many species interacting through many reaction channels. Also, the dynamics of such systems is typically non-linear and presents multiple time-scales. Consequently, the stochastic mathematical models of biochemical systems can be quite complex and their analysis challenging. In this paper, we present a method to reduce a stochastic continuous model of well-stirred biochemical systems, the Chemical Langevin Equation, while preserving the overall behavior of the system. Several tests of our method on models of practical interest gave excellent results.
Biochemical systems have important practical applications, in particular to understanding critical intra-cellular processes. Often biochemical kinetic models represent cellular processes as systems of chemical reactions, traditionally modeled by the deterministic reaction rate equations. In the cellular environment, many biological processes are inherently stochastic. The stochastic fluctuations due to the presence of some low molecular populations may have a great impact on the biochemical system behavior. Then, stochastic models are required for an accurate description of the system dynamics. An important stochastic model of biochemical kinetics is the Chemical Langevin Equation. In this work, we provide a numerical method for approximating the solution of the Chemical Langevin Equation, namely the derivative-free Milstein scheme. The method is compared with the widely used strategy for this class of problems, the Milstein method. As opposed to the Milstein scheme, the proposed strategy has the advantage that it does not require the calculation of exact derivatives, while having the same strong order of accuracy as the Milstein scheme. Therefore it may be used for an automatic simulation of the numerical solution of the Chemical Langevin Equation. The tests on several models of practical interest show that our method performs very well.