(Semi)Formal Artificial Life Through P-systems & Learning Classifier Systems: An Investigation into InfoBiotics

通过 P 系统的（半）正式人工生命

• 批准号: EP/E017215/1
• 负责人: Natalio Krasnogor
• 金额: $ 65.69万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FE017215%2F1
• 关键词: Semi; Formal; Artificial; Life; Through

Artificial Life (ALife) has advanced enormously since A. Turing proposed in the early 50s models of pattern formation in living systems. It was Turing who first demonstrated how a simple system of coupled reaction-diffusion equations could give rise to spatial patterns in chemical concentrations through a process of chemical instability. J. von Newman, later, demonstrated that it was possible to build self-replicating abstract machines while A. Lindenmayer introduced L-systems for modelling artificial plants. The bulk of ALife research in the last 20 years has been done with a more ad-hoc bottom-up engineering approach by designing or evolving the rules that govern the local interactions of the entities in the system as to produce certain emergent behaviour. Emergence in this context is interpreted as a process within the system that could not have been predicted from merely inspecting the rules but that it is observed only by running the simulation. Some of the earliest landmarks in ALife were T. Rays' Tierra, J. Holland's Echo and L. Yaeger's Polyword. These early systems were all based on an individual based modelling framework, which were highly abstract and quite limited in the simulated details (i.e. physical and chemical laws) of the environment where the agents performed their interactions. K. Sims's virtual creatures and research like framsticks or swimmers incorporated a more accurate (albeit still arbitrary) physical reality into the ALife system. In turn, this increase in the detail of the environmental interactions allowed richer emergent processes to be observed. More recent work incorporated a more detailed biology through the addition of developmental processes, differential gene expression and genetic regulatory networks endowing ALife simulations with greater realism. Thus, as computing resources became more accessible and our biological knowledge deepened, more and more levels of biological, chemical and physical details were included in a bottom-up fashion into ALife simulations. Recent advances in analytical biotechnology, computational biology, bioinformatics and micro-biology are transforming our views of the complexity of biological systems, particularly the computations they perform (i.e. how information is processed, transmitted and stored) in order to survive, adapt and evolve in dynamic and sometimes hostile environments. We propose to capture some of these more recent biological insights, in particular those related to cell biology, as to develop sophisticated ALife simulations of cellular-like systems. Furthermore, while we propose to stick to the traditional engineering approach of building ALife systems from the bottom-up we would like to extend current research practice towards a more computationally formal and rigorous approach to the design and implementation of ALife research. In this proposal we seek a fundamental rethink on the way bottom-up Artificial Life research is conducted. Until now, much of this research has had a strong ad-hoc component with very little formalisations. We propose a new (semi) formal cellular Artificial Life methodology, which we call InfoBiotics. InfoBiotics proposes that a synergy between formal informatics methods, evolution and learning and biological and biochemical insights are a pre-requisite for a more principled practice of ALife research. The driving research issues behind this proposal are:i. What combinations of formal informatics, evolutionary and learning paradigms and biochemical insights are needed for a successful development of InfoBiotics as a principled approach to Artificial Cellular Life research? ii. What is the balance of each of the former that is needed in order to ask and, be able to, answer scientifically relevant and meaningful ALife questions from an InfoBiotics perspective?

自从图灵在 50 年代初提出生命系统模式形成模型以来，人工生命 (ALife) 取得了巨大进步。图灵首先证明了耦合反应扩散方程的简单系统如何通过化学不稳定性过程产生化学浓度的空间模式。 J. von Newman 后来证明了构建自我复制的抽象机器是可能的，而 A. Lindenmayer 则介绍了用于模拟人造植物的 L 系统。过去 20 年中的大部分 ALife 研究都是采用更加临时的自下而上的工程方法来完成的，通过设计或发展控制系统中实体的局部交互的规则来产生某些紧急行为。在这种情况下，出现被解释为系统内的一个过程，仅通过检查规则无法预测该过程，而只能通过运行模拟来观察它。 ALife 最早的里程碑包括 T. Rays 的 Tierra、J. Holland 的 Echo 和 L. Yaeger 的 Polyword。这些早期的系统都基于基于个体的建模框架，该框架高度抽象，并且在代理执行交互的环境的模拟细节（即物理和化学定律）方面非常有限。 K. Sims 的虚拟生物和研究（如 framsticks 或游泳者）将更准确（尽管仍然是任意的）物理现实融入到 ALife 系统中。反过来，环境相互作用细节的增加使得可以观察到更丰富的突发过程。最近的工作通过添加发育过程、差异基因表达和基因调控网络，结合了更详细的生物学，赋予 ALife 模拟更大的真实性。因此，随着计算资源变得更加容易获取以及我们的生物知识不断加深，越来越多级别的生物、化学和物理细节以自下而上的方式纳入 ALife 模拟中。分析生物技术、计算生物学、生物信息学和微生物学的最新进展正在改变我们对生物系统复杂性的看法，特别是它们执行的计算（即信息如何处理、传输和存储），以便在动态的、有时是敌对的环境中生存、适应和进化。我们建议捕捉一些最新的生物学见解，特别是与细胞生物学相关的见解，以开发复杂的类细胞系统 ALife 模拟。此外，虽然我们建议坚持自下而上构建 ALife 系统的传统工程方法，但我们希望将当前的研究实践扩展到计算上更加正式和严格的方法来设计和实施 ALife 研究。在本提案中，我们寻求对自下而上的人工生命研究的进行方式进行根本性的重新思考。到目前为止，大部分研究都有很强的临时性成分，很少有形式化。我们提出了一种新的（半）正式的细胞人工生命方法，我们称之为 InfoBiotics。 InfoBiotics 提出，正式信息学方法、进化和学习以及生物和生化见解之间的协同作用是 ALife 研究更具原则性实践的先决条件。该提案背后的驱动研究问题是：i．要成功开发 InfoBiotics 作为人工细胞生命研究的原则方法，需要将正式信息学、进化和学习范式以及生化见解进行哪些结合？二.为了从 InfoBiotics 的角度提出并能够回答科学相关且有意义的 ALife 问题，前者需要什么平衡？

项目成果

Deterministic and stochastic P systems for modelling cellular processes

• DOI: 10.1007/s11047-009-9158-4
• 发表时间: 2010-06
• 期刊: Natural Computing
• 影响因子: 2.1
• 作者: M. Gheorghe;V. Manca;F. Romero-Campero

P-systems and X-machines Papers dedicated to Mike Holcombe on the occasion of his 65th birthday

P 系统和 X 机器 在 Mike Holcombe 65 岁生日之际献给他的论文

• DOI: 10.1007/s11047-009-9110-7
• 发表时间: 2009
• 期刊: Natural Computing
• 影响因子: 2.1
• 作者: Gheorghe M

Performance and efficiency of memetic Pittsburgh learning classifier systems.

模因匹兹堡学习分类器系统的性能和效率。

• DOI: 10.1162/evco.2009.17.3.307
• 发表时间: 2009
• 期刊: Evolutionary computation
• 影响因子: 6.8
• 作者: Bacardit J

Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data.

• DOI: 10.1371/journal.pone.0039932
• 发表时间: 2012
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Glaab E;Bacardit J;Garibaldi JM;Krasnogor N
• 通讯作者: Krasnogor N

ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization.

• DOI: 10.1186/1471-2105-10-358
• 发表时间: 2009-10-28
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Glaab E;Garibaldi JM;Krasnogor N
• 通讯作者: Krasnogor N