Singularly impulsive model of genetic regulatory networks

N. Kablar
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Abstract

In this paper we present singular and singularly impulsive model of genetic regulatory networks. Concrete example consists of two gene — two proteins simple synthetic network, and posses two fundamentally present parts in biochemical networks — positive and negative feedback loops. These are important structural parts of biochemical networks and are interesting for control theoreticians. By investigating purpose of positive and negative feedback presence, we see for example that they lead either to bistability (or multistability, in general) in case of positive feedback or generate oscillatory behaviour in case of negative feedback what we show. Mathematical model is derived in form of nonlinear two dimensional dynamical system which is further approximated to singular and singularly impulsive dynamical system. Importance of this example is singular systems approximation which in this particular example leads to interesting and well known phenomena — of relaxation oscillation. This phenomena is consequence of multiple-scale network, i.e. fast-slow decomposition. Jump phenomena that appears as a consequence of time scale differences is very different from jumps (impulsive behaviour) that we have in impulsive systems approximation of, for example, sigmoidal function response by piece-wise linear function in order to reduce complexity.
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基因调控网络的奇异脉冲模型
本文提出了遗传调控网络的奇异和奇异脉冲模型。具体实例由两个基因-两个蛋白质的简单合成网络组成,并具有生化网络中基本存在的两个部分-正反馈回路和负反馈回路。这些是生物化学网络的重要结构部分,对控制理论家来说很有趣。通过调查正反馈和负反馈存在的目的,我们看到,例如,它们在正反馈的情况下导致双稳性(或多稳性),或者在我们所展示的负反馈的情况下产生振荡行为。推导了非线性二维动力系统的数学模型,并进一步逼近为奇异和奇异脉冲动力系统。这个例子的重要性在于奇异系统近似,在这个特殊的例子中,它导致了有趣和众所周知的松弛振荡现象。这种现象是多尺度网络分解的结果,即快慢分解。由于时间尺度差异而出现的跳跃现象与我们在脉冲系统中的跳跃(脉冲行为)非常不同,例如,通过分段线性函数近似s型函数响应以降低复杂性。
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