Evolution of associative learning in chemical networks.
Evolution of associative learning in chemical networks.
复制标题
DOI：
10.1371/journal.pcbi.1002739
复制
发表时间：
2012
影响因子：
4.3
通讯作者：
Fernando C
中科院分区：
文献类型：
--
作者：
McGregor S;Vasas V;Husbands P;Fernando C
能够在其一生中了解其环境并适当地改变其行为的生物体比不了解的生物体更有可能生存和繁殖。虽然联想学习--检测环境相关特征的能力--在神经系统中得到了广泛的研究，其中的潜在机制已经得到了相当好的理解，但单细胞内可以实现联想学习的机制却很少受到关注。在这里，使用化学网络的计算机进化，我们表明，存在着非常简单和合理的化学解决方案的联想学习问题的多样性，其中最简单的只使用一个核心化学反应。然后，我们问，在给定刺激历史的情况下，网络中化学浓度的线性组合在多大程度上可以近似环境的理想贝叶斯后验？贝叶斯分析揭示了化学网络的“记忆痕迹”。这篇论文的含义是，没有理由相信缺乏合适的表型变异会阻止联想学习在细胞信号传导、代谢、基因调控或细胞中这些网络的混合中进化。虽然人们可能认为联想学习需要神经系统，但本文表明，化学网络可以在计算机中进化，只需少量反应即可承担一系列联想学习任务。机制出奇的简单。可以使用贝叶斯方法来分析网络，以识别负责学习的网络组件。进化出的网络在某些方面比手工设计的用于联想学习的合成生物学网络更简单。可以在生物化学网络中寻找这些基序，并且可以合理地接受它们进行联想学习的假设，例如在单细胞中或在发育期间。
Organisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning – the ability to detect correlated features of the environment – has been studied extensively in nervous systems, where the underlying mechanisms are reasonably well understood, mechanisms within single cells that could allow associative learning have received little attention. Here, using in silico evolution of chemical networks, we show that there exists a diversity of remarkably simple and plausible chemical solutions to the associative learning problem, the simplest of which uses only one core chemical reaction. We then asked to what extent a linear combination of chemical concentrations in the network could approximate the ideal Bayesian posterior of an environment given the stimulus history so far? This Bayesian analysis revealed the ‘memory traces’ of the chemical network. The implication of this paper is that there is little reason to believe that a lack of suitable phenotypic variation would prevent associative learning from evolving in cell signalling, metabolic, gene regulatory, or a mixture of these networks in cells. Whilst one may have believed that associative learning requires a nervous system, this paper shows that chemical networks can be evolved in silico to undertake a range of associative learning tasks with only a small number of reactions. The mechanisms are surprisingly simple. The networks can be analysed using Bayesian methods to identify the components of the network responsible for learning. The networks evolved were simpler in some ways to hand-designed synthetic biology networks for associative learning. The motifs may be looked for in biochemical networks and the hypothesis that they undertake associative learning, e.g. in single cells or during development may be legitimately entertained.
登录
查看更多内容
影响因子：
5.5
作者：
DUNLAP, K
通讯作者：
DUNLAP, K
影响因子：
1.6
作者：
Femando, Chrisantha;Rowe, Jonathan
通讯作者：
Rowe, Jonathan
影响因子：
2
作者：
Gandhi, Nikhil;Ashkenasy, Gonen;Tannenbaum, Emmanuel
通讯作者：
Tannenbaum, Emmanuel
DOI：
10.3758/bf03209695
发表时间：
1979-01-01
期刊：
ANIMAL LEARNING & BEHAVIOR
影响因子：
--
作者：
HENNESSEY, TM;RUCKER, WB;MCDIARMID, CG
通讯作者：
MCDIARMID, CG
影响因子：
2.9
作者：
Maass, W;Natschläger, T;Markram, H
通讯作者：
Markram, H