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Finding Steady State Solutions of Cybernetic Models of Biological Systems

机译:寻找生物系统的网络学模型稳态解决方案

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Cybernetic models are ODEs obtained by mass balances with the control/regulatory variables included. These regulatory variables named as cybernetic variables represent the regulatory actions taken by the cells. Cybernetic variables for induction/repression control the transcription of various enzymes which catalyze the metabolism of the uptake of a particular substrate. Cybernetic variables for inhibition/activation control the activity of the enzymes which metabolize a particular substrate. In this work, cybernetic model of a continuous culture of Klebsiella pneumoniae of Baloo and Ramkrishna (1991) which includes maintenance effects, is used. The inclusion of maintenance phenomena has made it more complicated. But this model with maintenance could very well describe the experimentally observed behavior of microorganisms at low dilution rates i.e. the biomass levels are very low and at zero dilution rates we have zero biomass values. This model is mathematically complicated as it contains non-differential max functions in the denominator of the cybernetic variables for inhibition/activation. A combinatorial approach proposed by Namjoshi and Ramkrishna (2001) is used to carry out the steady state analysis. The bifurcation analysis has been carried out for this model by Vasudeva Kumar et. al. (under review). We can detect washout/trivial and nontrivial steady state branches in bifurcation diagrams. Once, the trivial solutions are determined, it is possible to find out nontrivial solutions using branch point continuation from the transcritical bifurcation points where the trivial solutions intersect the non-trivial solutions. Finding the trivial steady states (washout solutions) is not possible analytically because of mathematical complexity of the model.
机译:网络学模型是通过包含控制/调节变量的质量平衡获得的杂物。这些称为Cyber​​etic变量的监管变量代表了细胞采取的监管行动。诱导/抑制的数字变量控制各种酶的转录,该酶催化特定基材摄取的代谢。用于抑制/活化的控制虫变量控制代谢特定基材的酶的活性。在这项工作中,使用包括维护效果的Baloo和Ramkrishna(1991)的连续培养的无线培养的网络学模型。纳入维护现象使它变得更加复杂。但是这种具有维护的模型可以很好地描述在低稀释率下微生物的实验观察到的微生物行为即,生物质水平非常低,并且在零稀释速率下我们具有零生物量值。该模型在数学上复杂,因为它包含用于抑制/激活的网络感染变量的分母中的非差分最大功能。 Namjoshi和Ramkrishna(2001)提出的组合方法用于进行稳态分析。 Vasudeva Kumar et的这种模型已经进行了分叉分析。 al。 (正在审查)。我们可以在分叉图中检测冲洗/琐碎和非稳态稳态分支。一旦确定了琐碎的解决方案,可以使用从跨临界分支点的分支点继续从跨临界分支点进行相交的非微不足道解决方案的非临界分支求解来找到非竞争解决方案。由于模型的数学复杂性,在分析上无法分析稳定状态(冲洗解决方案)是不可能的。

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