For a detailed description of the time-domain envelope modeling process, we refer to [37 39].
In order to enable the gene-protein network presented above to model processes at the tissue level, we added output nodes to the gene-protein network.
Therefore, for modeling upstream processes, we assumed that transgenic seed was available and that the resultant BuChE would have mammalian glycans and form tetrameric structures [ 29], and hence its biological activity and plasma half-life would be comparable to the native human enzyme [ 29, 36].
To balance the number of the true and false splice site-containing sequences and to avoid the overfitting problem in the model-training processes, we randomly selected out 2,800 false splice donor (acceptor) site-containing sequences from the 271,937 (329,374) false splice donor (acceptor) site-containing sequences.
To model this process, we consider a population in a one-dimensional space.
For modeling spontaneous dynamic rupture processes, we use a 3D boundary integral equation method (BIEM) (Fukuyama and Madariaga, 1995) assuming an infinite, homogeneous elastic medium.
To be able to develop, manipulate, and execute computer models of biological processes, we have developed PyBioS (pybios.molgen.mpg.de), an object oriented modeling platform [ 84, 85].
We chose a model process that we routinely use for senescence-related studies in which senescence in barley is induced by incubation in the dark (Jackowski 1996; Legocka and Szweykowska 1981; Żelisko and Jackowski 2004).
Although we cannot model all of the processes, we should examine the possible and more physically plausible scenarios predicted by various models.
Based on the extensive model calibration process, we believe that our model provides a realistic characterization of waning.
