Associative learning in biochemical networks
Associative learning in biochemical networks
复制标题
DOI：
10.1016/j.jtbi.2007.07.004
复制
发表时间：
2007-11-07
影响因子：
2
通讯作者：
Tannenbaum, Emmanuel
中科院分区：
文献类型：
--
作者：
Gandhi, Nikhil;Ashkenasy, Gonen;Tannenbaum, Emmanuel
最近有人提出，有可能是通用的特点，从一个或多个选择压力下的自我复制代理的自组织构建的系统的出现。因此，在一个长度尺度上的结构和行为可以用来推断在其他长度尺度上的类似结构和行为。受此启发，我们试图描述生物化学网络中的各种“有生命”行为，以及这些行为对基因组进化的影响。具体来说，在本文中，我们开发了一个简单的，基于恒化器的模型，说明了一个类似于联想学习的过程如何发生在生化网络中。联想学习是一种学习形式，系统通过它“学习”将两个刺激相互关联。联想学习，也被称为条件反射，被认为是大脑中一个强大的学习过程（联想学习本质上是“类比学习”）。在我们的模型中，两种类型的复制分子，表示为A和B，存在于恒化器中的一些初始浓度。分子A和B被一些生长因子刺激复制，分别表示为G（A）和G（B）。还假设A和B可以共价连接，并且缀合分子可以被G（A）或G（B）生长因子刺激（并且可以被降解）。我们表明，如果恒化器是由两种生长因子刺激一定的时间，然后由一个时间间隔期间，恒化器是不刺激的，在所有，如果恒化器，然后再次刺激只有一个生长因子，那么将有一个短暂的增加，由其他生长因子激活的分子的数量。因此，恒化器带有早期的印记，同时刺激两种生长因子，这是联想学习的指示。有趣的是，我们的模型的动力学与巴甫洛夫最初在狗身上进行的一系列条件反射实验的某些方面是一致的。我们讨论了如何在RNA，DNA或肽网络内进行联想学习。我们还描述了这样的机制如何参与基因组进化，并建议相关的生物信息学研究，可能会解决这些问题。(C)2007爱思唯尔有限公司保留所有权利。
It has been recently suggested that there are likely generic features characterizing the emergence of systems constructed from the self-organization of self-replicating agents acting under one or more selection pressures. Therefore, structures and behaviors at one length scale may be used to infer analogous structures and behaviors at other length scales. Motivated by this suggestion, we seek to characterize various "animate" behaviors in biochemical networks, and the influence that these behaviors have on genomic evolution. Specifically, in this paper, we develop a simple, chemostat-based model illustrating how a process analogous to associative learning can occur in a biochemical network. Associative learning is a form of learning whereby a system "learns" to associate two stimuli with one another. Associative learning, also known as conditioning, is believed to be a powerful learning process at work in the brain (associative learning is essentially "learning by analogy"). In our model, two types of replicating molecules, denoted as A and B, are present in some initial concentration in the chemostat. Molecules A and B are stimulated to replicate by some growth factors, denoted as G(A) and G(B), respectively. It is also assumed that A and B can covalently link, and that the conjugated molecule can be stimulated by either the G(A) or G(B) growth factors (and can be degraded). We show that, if the chemostat is stimulated by both growth factors for a certain time, followed by a time gap during which the chemostat is not stimulated at all, and if the chemostat is then stimulated again by only one of the growth factors, then there will be a transient increase in the number of molecules activated by the other growth factor. Therefore, the chemostat bears the imprint of earlier, simultaneous stimulation with both growth factors, which is indicative of associative learning. It is interesting to note that the dynamics of our model is consistent with certain aspects of Pavlov's original series of conditioning experiments in dogs. We discuss how associative learning can potentially be performed in vitro within RNA, DNA, or peptide networks. We also describe how such a mechanism could be involved in genomic evolution, and suggest relevant bioinformatics studies that could potentially resolve these issues. (C) 2007 Elsevier Ltd. All rights reserved.