复杂性系统研究方法:从人工生命到人工社会

一、人工生命的两种途径 “人工生命”(Artificial　Life)简称为Alife,是指具有“自然生命”特性和功能的人造系统,人工生命中的“活体”是指有生命特征的个体或群体。人工生命研究是抽象地提取演化、生命规则等生物现象的基本原理来模拟生命系统的动态发展过程。人工生命有多种研究方法或技术,但基本上可以分为以下两种途径。 ?　 生命科学途径:通过生物实验方式,在试管中通过生物化学或遗传工程的方法合成或生成人工的生命。这种方法在目前并不能从无生命物质开始合成生命,而只能用克隆技术进行基于相应的自然生命的“母体”无性繁殖生…     

人工免疫系统的应用与发展

在现代信息科学和生命科学相互交叉渗透的研究领域,由生物免疫系统启发的人工免疫系统(AIS)是继脑神经系统(神经网络)和遗传系统(进化计算)之后的又一个研究热点。该文首先简要介绍了生物免疫系统的特点,然后系统综述了国内外对人工免疫系统的最新研究和应用成果,最后展望了人工免疫系统进一步的研究方向。     

设计视域下传统造物的小众化价值升华

文章通过对中国传统造物思想的重新审视与研究,探寻适应于现代需求的设计新范式。在经济全球化快速发展的今天,小众化设计是在市场发展及文化快消等多因素影响下产生的新的设计需求。文章通过浅析传统造物思想下的设计现状,总结出小众化设计的特性以及其与造物思想的关联,并围绕小众化设计的关键与层次,进行造物思想与设计方法的探寻和结合,以期通过现代设计创新,实现对传统造物价值的延续及最大化传播。     

基因调控网络的控制:机遇与挑战

众所周知,基因调控网络（Genetic regulatory networks,GRNs）是一类基本且重要的生物网络.基因调控网络可以通过输入、噪声、参数以及正负反馈等进行功能的鲁棒性调节与控制.本文首先简要回顾了基因调控网络控制方面的若干研究进展,然后提出了一些与控制相关的基因调控网络的基本科学问题.基因调控网络的控制以生命科学为背景,以控制理论为理论基础.过去几十年,控制论的基本思想与方法逐步渗透到基因调控网络的研究中.同时,来源于生命科学的控制问题也为我们提出了新的机遇与挑战.基因调控网络的控制对生命科学中困扰人类的基本问题,如延长寿命、治愈癌症、糖尿病等顽疾有着非常重要的现实意义.此外,基因调控网络控制研究对合成生物学、网络医学、个性化医学等相关学科的发展具有潜在的应用价值.     

中国传统造物中的符号学原理

中国的造物史有着上千年的历史,其中蕴涵着丰富的造物原理和造物哲学。西方的符号学是一门专门研究符号规律的人文学科,本文将符号学的某些原理与中国的传统造物哲学有机地结合起来,试述中国传统造物中潜藏的符号学原理,这对于当今所提倡的跨学科研究有着重要的意义。     

序言:合成生物学前沿研究及转化应用

合成生物学是近年来发展迅猛的新兴前沿交叉学科.作为自然科学与工程之间的桥梁,合成生物学一方面探索并提出新理论,增进人们对自然生命体与工程生命体的基础认知,即"造物致知";另一方面,它还解决工程中面临的方法、技术问题,设计改造乃至创建全新的生命体为人类社会所用,即"造物致用".作为一个兼顾前沿科学探索与国计民生需求的交叉...     

多重生物识别：“身体密码”如何改变既有识别模式？

多生物特征解决方案的潜在好处是能延伸到人体进入控制区域从而获取敏感数据。这样,就能使生物特征识别系统更安全,入侵者用人造物或模仿品来同时骗过多生物特征基本上是不可能的,而个人在某项特征不便时亦可灵活调换。     

基于纳米功能界面和新型传感机制的生物传感器件

纳米材料因其具有光、电、磁和高的比表面等特殊性质,在催化、电化学、生物电子器件等领域显示出重大的潜在应用前景.利用现有的和新开发的化学制备原理合成具有生物相容的纳米功能界面,探索其在生物分子组装、生物分子的直接电子转移、生物催化和传感等方面的应用,将为生命科学的发展开辟新的天地,同时也为重大疾病的早期诊断和预警提供有力的工具.     

生物网格的应用探索

网格技术快速发展对基础科学的研究提供了诸多帮助,尤其是对于那些对海量数据存储、传输和计算的领域,譬如生命科学的研究。生命科学的信息化和全球化已成为大势所趋,我国生命科学界对网格技术产生了强烈需求。面对海量的、呈指数快速增长的生物信息数据,业界必须抓好两个重点:首先是必须开发和应用全新的生物信息处理方法;另外,必须建立高效的超大规模数据信息处理系统。在生物科学领域,生物数据包含很多种类的数据且分属于不同的组织。组     

动力学与生命科学的交叉研究进展综述

随着各学科的不断交叉融合,动力学已广泛应用于生命科学.根据生命科学的研究对象进行分类,对动力学的理论方法在不同尺度生物系统上的应用做了回顾与总结,重点阐述了近年来的相关研究进展.未来的发展趋势在于动力学理论方法与生物学实验的有机结合,不仅可以利用动力学既有的方法研究和解决生命科学问题,也应当针对生命科学所提出的难题发展新的动力学方法,拓展与深化理论研究.     

Population-based microbial computing: a third wave of synthetic biology?

Synthetic biology is an emerging research field, in which engineering principles are applied to natural, living systems. A major goal of synthetic biology is to harness the inherent “biological nanotechnology” of living cells for the purposes of computation, production or diagnosis. As the field evolves, it is gradually developing from a single-cell approach (akin to using standalone computers) to a distributed, population-based approach (akin to using networks of connected machines). We anticipate this eventually representing the “third wave” of synthetic biology (the first two waves being the emergence of modules and systems, respectively, with the second wave still yet to peak). In this paper, we review the developments that are leading to this third wave, and describe some of the existing scientific and technological challenges.     

生物传感器的应用现状与发展趋势

生物传感器是一种以生物活性单元为敏感元件,结合化学、物理转换元件,对被分析物具有高度选择性的装置,它具有灵敏度高、检测速度快、操作简便、成本低、可进行连续动态监测等优点。在介绍生物传感器发展现状、组成及工作原理的基础上,对生物传感器在生命科学、医学、环境检测、食品工程及军事等领域中的应用研究进行了综述,并探讨了生物传感器的发展前景。     

Scaling up genetic circuit design for cellular computing: advances and prospects

Synthetic biology aims to engineer and redesign biological systems for useful real-world applications in biomanufacturing, biosensing and biotherapy following a typical design-build-test cycle. Inspired from computer science and electronics, synthetic gene circuits have been designed to exhibit control over the flow of information in biological systems. Two types are Boolean logic inspired TRUE or FALSE digital logic and graded analog computation. Key principles for gene circuit engineering include modularity, orthogonality, predictability and reliability. Initial circuits in the field were small and hampered by a lack of modular and orthogonal components, however in recent years the library of available parts has increased vastly. New tools for high throughput DNA assembly and characterization have been developed enabling rapid prototyping, systematic in situ characterization, as well as automated design and assembly of circuits. Recently implemented computing paradigms in circuit memory and distributed computing using cell consortia will also be discussed. Finally, we will examine existing challenges in building predictable large-scale circuits including modularity, context dependency and metabolic burden as well as tools and methods used to resolve them. These new trends and techniques have the potential to accelerate design of larger gene circuits and result in an increase in our basic understanding of circuit and host behaviour.     

全球合成生物行业发展前沿分析

合成生物学在经历早期的技术创新和初步商业化探索后,于21世纪的第二个十年迎来了高速发展和商业化落地.该文从市场规模、行业融资和行业发展3个方面对全球合成生物学行业现状进行了梳理和分析.分析显示,在市场规模上,合成生物学市场增长迅猛,但其规模在不同的地理区域和行业领域内均有明显差距;在行业融资上,合成生物学行业投融资趋势...     

The Challenges of Multidisciplinary Education in Computer Science

Some of the most important problems facing the United States and China, indeed facing our entire planet, require approaches that are fundamentally multidisciplinary in nature. Many of those require skills in computer science (CS), basic understanding of another discipline, and the ability to apply the skills in one discipline to the problems of another. Modern training in computer science needs to prepare students to work in other disciplines or to work on multidisciplinary problems. What do we do to prepare them for a multidisciplinary world when there are already too many things we want to teach them about computer science? This paper describes successful examples of multidisciplinary education at the interface between CS and the biological sciences, as well as other examples involving CS and security, CS and sustainability, and CS and the social and economic sciences. It then discusses general principles for multidisciplinary education of computer scientists.     

Biodiversity and ecosystem informatics

The field of Biodiversity and Ecosystem Informatics (BDEI) brings together computer scientists, biologists, natural resource managers, and others who wish to solve real-world challenges while advancing the underlying ecological, computer, and information sciences. The potential for synergies among these disciplines is high, because our need to understand complex, ecosystem-scale processes requires the solution to many groundbreaking technological problems. Fortunately, we are beginning to see increased support for applied computer science and information technology research in the context of environmental problem-solving. In July, 2001, the National Science Foundation (NSF), in collaboration with the United States Geological Survey (USGS), and the National Aeronautics and Space Administration (NASA), invited proposals for high-risk, small-scale planning and incubation activities to catalyze innovation and rapid advances in this new research community. The papers included in this special issue are selected, peer-reviewed summaries from principal investigators involved in this first NSF BDEI effort. These papers provide an overview of this emerging area and remind us that computer and information science and engineering play a crucial role in creating the technologies from which advances in the natural sciences evolve.     

Understanding mobile technology-fit behaviors outside the classroom

An increase in mobile device usage among college students has been documented in different countries. We provide a solid theoretical and empirical foundation for mobile learning in the context of distance education, and more guidance in terms of how to utilize emerging mobile technologies and to integrate them into their teaching more effectively. This research focuses on a deeper understanding of how learners use mobiles as learning tools outside the classroom. Our results are based on a specific population drawn from two different countries, in which the US (United States) population reflected students from the education field, while the students from Israel are drawn more from the engineering and science fields. The findings of this study contribute to the generalizations to the education field and information system designers who need to analyze and design mobile-learning (m-learning) applications to be used outside the classroom.     

深度学习生物医学实体关系抽取研究综述

随着生命科学技术的发展,生物医学领域文献呈指数级增长,如何从海量文献中挖掘、抽取有价值的信息成为生物医学领域新的研究契机。作为信息抽取的核心技术,命名实体识别和关系抽取成为生物医学文本挖掘的基础和关键,其主要工作为识别生物医学文本中的实体,并提取实体间存在的生物医学语义关系。当前深度学习技术在各领域自然语言处理任务中取得了长足的发展,旨在总结基于神经网络的生物医学实体识别和关系抽取的方法,从概念、进展、现状等多角度全面阐述各项技术在生物医学领域的发展历程,进一步明确生物医学文本信息抽取工作的探索方向。