基于自适应滤波的基因调控网络研究
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摘要
基因调控网络是生物网络中一类重要的复杂网络。基因表达之间的调控是相互联系,相互制约的,孤立地研究单个基因及其表达并不能确切地反映生命现象本身和内在规律,因此,必须从系统的观点研究基因之间的调控网络。众所周知,时滞现象普遍存在于自然系统中,比如生物系统、化学系统、电力系统以及物理系统等等。时滞的存在可以导致系统不稳定,从而产生持续震荡、分岔甚至是混沌等现象发生。因此,时滞是基因调控网络的研究中必须要考虑的一方面。同时,基因调控网络在实现其功能的过程中,不可避免地会有噪声因素的影响,因此,噪声也是基因调控网络的研究中必须要考虑的一方面。
人们还未对生物体的运行机制了解透彻,比如基因是如何在正确的时间、正确的位点、用合适的浓度来激活或抑制基因表达的。生命机理的研究离不开高通量的数据采集和数据分析。受技术条件所限,研究者们并不能获得所需的全部信息。因此,理论生物学家们致力于从获得的数据中探究生命活动中存在的未知规律。这样就产生了一个滤波问题：给定一个存在随机扰动和时变时滞的基因调控网络,如何根据网络的观测数据来对其它产物的浓度和参数进行估计。基于此,本文主要做了以下工作：
1、对具有时变时滞的基因调控网络,在存在外部噪声扰动的情况下,采用自适应滤波的方法设计自适应律,通过构造Lyapunov函数判断系统的随机稳定性。在网络结构和节点输出状态已知的情况下,实现了网络中未知参数和未知状态的估计。
2、基于自适应同步集,运用自适应滤波方法研究了一类信息不完全的不确定基因调控网络。提出了一类新的不确定基因调控网络模型,并考虑了网络中存在时间延迟和噪声的影响。在仅仅知道网络结构和部分节点的输出状态的情况下(假定只有部分mRNA和蛋白质浓度的值可以观测到),通过该模型对网络中的未知参数和未知状态进行了估计。
Genetic regulatory network is an important type of biological networks. The regulations among gene expressions are not independent; there are interactions and inter-constrains among them. Study on a single gene expression can not grasp the biology functions and patterns of the living system. Hence, it is necessary to study the regulatory network among gene expressions from the system point of view. It is well known that time delay is ubiquitous in biological, chemical, and electrical dynamical systems, etc. Time delays can derail the stability of the system thereby causing sustained oscillations, bifurcations, and even chaos. So, time delay is one of important aspects in the field of genetic regulatory networks. As well, noise is unavoidable in the process of genetic regulatory networks. So, noise is another important aspect that must be considered.
Moreover, in a real genetic regulatory network, it is still not completely understood today as how the genes are expressed in the right time and right place, with the right amount throughout the development of the organism. Studying living organisms requires significant work on observing, collecting, and analyzing data. Because of the limit of technologies, researchers can not obtain all information they need. Thus, theoretical biologists attempt to estimating the missing information from the available data, which gives rise to the following filtering problem:given a genetic regulatory network, how to estimate the unknown products and parameters by using the obtained data from the network. The main results of the thesis are as follows:
1. For genetic regulatory networks with time-varying delays and uncertain noise disturbances, some adaptive laws are derived by using adaptive filtering approach and to ensure the stochastic stability by constructing Lyapunov functional. The theoretical results estimate the unknown parameters and states requiring only the output and structure of the underlying network.
2. Based on adaptive synchronization setting, a class of uncertain genetic regulatory networks with partial information are investigated from an adaptive filtering approach. A new uncertain model of genetic regulatory network is proposed considering the time delay and noise disturbance. Using this model, uncertain parameters and stated of the network are estimated requiring only network structure and partial output of some nodes(assuming that only some of the concentrations of mRNA and protein could be observed).
人们还未对生物体的运行机制了解透彻,比如基因是如何在正确的时间、正确的位点、用合适的浓度来激活或抑制基因表达的。生命机理的研究离不开高通量的数据采集和数据分析。受技术条件所限,研究者们并不能获得所需的全部信息。因此,理论生物学家们致力于从获得的数据中探究生命活动中存在的未知规律。这样就产生了一个滤波问题：给定一个存在随机扰动和时变时滞的基因调控网络,如何根据网络的观测数据来对其它产物的浓度和参数进行估计。基于此,本文主要做了以下工作：
1、对具有时变时滞的基因调控网络,在存在外部噪声扰动的情况下,采用自适应滤波的方法设计自适应律,通过构造Lyapunov函数判断系统的随机稳定性。在网络结构和节点输出状态已知的情况下,实现了网络中未知参数和未知状态的估计。
2、基于自适应同步集,运用自适应滤波方法研究了一类信息不完全的不确定基因调控网络。提出了一类新的不确定基因调控网络模型,并考虑了网络中存在时间延迟和噪声的影响。在仅仅知道网络结构和部分节点的输出状态的情况下(假定只有部分mRNA和蛋白质浓度的值可以观测到),通过该模型对网络中的未知参数和未知状态进行了估计。
Genetic regulatory network is an important type of biological networks. The regulations among gene expressions are not independent; there are interactions and inter-constrains among them. Study on a single gene expression can not grasp the biology functions and patterns of the living system. Hence, it is necessary to study the regulatory network among gene expressions from the system point of view. It is well known that time delay is ubiquitous in biological, chemical, and electrical dynamical systems, etc. Time delays can derail the stability of the system thereby causing sustained oscillations, bifurcations, and even chaos. So, time delay is one of important aspects in the field of genetic regulatory networks. As well, noise is unavoidable in the process of genetic regulatory networks. So, noise is another important aspect that must be considered.
Moreover, in a real genetic regulatory network, it is still not completely understood today as how the genes are expressed in the right time and right place, with the right amount throughout the development of the organism. Studying living organisms requires significant work on observing, collecting, and analyzing data. Because of the limit of technologies, researchers can not obtain all information they need. Thus, theoretical biologists attempt to estimating the missing information from the available data, which gives rise to the following filtering problem:given a genetic regulatory network, how to estimate the unknown products and parameters by using the obtained data from the network. The main results of the thesis are as follows:
1. For genetic regulatory networks with time-varying delays and uncertain noise disturbances, some adaptive laws are derived by using adaptive filtering approach and to ensure the stochastic stability by constructing Lyapunov functional. The theoretical results estimate the unknown parameters and states requiring only the output and structure of the underlying network.
2. Based on adaptive synchronization setting, a class of uncertain genetic regulatory networks with partial information are investigated from an adaptive filtering approach. A new uncertain model of genetic regulatory network is proposed considering the time delay and noise disturbance. Using this model, uncertain parameters and stated of the network are estimated requiring only network structure and partial output of some nodes(assuming that only some of the concentrations of mRNA and protein could be observed).
引文
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