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工业5.0——仿生学和合成生物学的关联及内涵 Artical
Peter Sachsenmeier
《工程（英文）》 2016年 第2卷 第2期 页码 225-229 doi: 10.1016/J.ENG.2016.02.015
仿生学(模仿生物特殊本领的学科) 以及合成生物学，将和过去50年的硅芯片一样与工程开发、工业发展产生紧密联系。化学工业已经将白色生物技术应用于新工艺、新材料和资源的可持续利用中。合成生物学也已经应用到第二代生物燃料的发展中，并利用特制的微生物或生物制催化剂获取太阳能。而仿生学在制药、处理工程以及DNA存储领域的市场潜力是巨大的。除此之外，还探讨了涉及人造食物链和食物的仿生学前沿——合成代谢，以及原材料生物工程。这些研究将给生物学带来新思考。生物工程将和今天的数字化技术一样驱动创新。大数据、分析学和海量存储将是未来的发展方向。虽然合成生物学在未来50年将和当今的数字化一样普遍且具有革新能力，但是目前它的应用和影响力还处在初级阶段。本文采用了将生物工程发展分为五个阶段(DNA分析、生物回路、最小基因组、原始细胞、异源生物学)的普遍分类方法，阐述了其对安全与保障、工业发展以及生物工程和生物技术作为跨学科领域发展的影响，同时讨论了伦理问题及公众对仿生学和合成生物学结果的公众讨论的重要性
关键词： 仿生学 合成生物学 生物工程 生物传感器 生物燃料 生物武器 虚拟进化 原始细胞 异种细胞 经济意义 工业5.0 德国 中国
高子航, 贾广禄, 谢宏钊, 黄强, 福田敏男, 石青
《工程（英文）》 2022年 第17卷 第10期 页码 232-243 doi: 10.1016/j.eng.2022.05.012
现有的仿生机器鼠仅可以执行一些基本的仿鼠运动基元（MP），并通过这些基元的刚性组合来形成简单的行为。
彭冬冬,贺明睿,杨 昊,张 蓓,高博鑫,范 琳,丁 鹤,南延虎,潘福生,苏延磊,吴 洪,姜忠义
《中国工程科学》 2014年 第16卷 第12期 页码 102-112
仿生与生物启发的思想和策略在众多基础与工程科学领域取得了重要进展。仿生和生物启发膜主要是以细胞膜、荷叶和贻贝等为仿生原型，以生物矿化、生物黏合和自组装等为工具，以绿色、高效、节能为目标，在资源、能源高效利用和可持续发展等方面会发挥越来越大的作用，并逐步发展成为膜和膜过程领域的重要分支本文将对仿生和生物启发膜的研究进展进行简要总结，重点介绍抗污染膜、杂化膜和复合膜的制备与应用。
仿生牙釉质材料的工程制造策略 Review
张凌云, 张云帆, 余婷婷, 彭丽颖, 孙倩男, 韩冰
《工程（英文）》 2022年 第14卷 第7期 页码 113-123 doi: 10.1016/j.eng.2021.02.027
基于蝶翅三级微纳米结构的定制光子工程辐射制冷纺织品 Article
郭竑宇, 牛田野, 俞建勇, 王学利, 斯阳
《工程（英文）》 2023年 第31卷 第12期 页码 120-126 doi: 10.1016/j.eng.2023.07.019
植物蛋白基仿生肉制品研究进展 Review
孙翠霞, 葛姣, 何君, 甘人友, 方亚鹏
《工程（英文）》 2021年 第7卷 第5期 页码 674-678 doi: 10.1016/j.eng.2020.10.011
代谢组扩展生物学的“旁中心法则”——对理解基因组学-糖组学-代谢组学-表观基因组学互作的意义
Albert Stuart Reece
《工程（英文）》 2023年 第26卷 第7期 页码 16-16 doi: 10.1016/j.eng.2022.07.011
The central dogma of biology holds that the transcription of DNA into RNA and the translation of RNA into proteins forms the primary axis of biological activity [1]. Following major advances in the description of the complex glycan and lipid chains that are added onto these basic building blocks, the glycome and lipidome have recently been added to this doctrine as an exciting new extension named the ‘‘paracentral dogma” [2]. However, it has been pointed out that biological systems can include many layers, which are described in modern omics technology platforms relating to both cell-intrinsic and cell-extrinsic layers of control, including metabolomic, microbiomic, immunological, epigenomic, epitranscriptomic, proteomic and phosphoproteomic layers [3].
It is well known that stem and progenitor cells have a metabolism that is based on glycolysis and glutaminolysis [4]. Although this provides less energy to the cell than oxidative phosphorylation, it suffices for these cells’ needs, since such cells are generally relatively quiescent and normally suppress energy-intensive processes such as genome duplication and transcription. Moreover, it has been shown that the high intracellular lactate levels involved in such states not only inhibits the key gatekeeper enzymes of oxidative phosphorylation (i.e., pyruvate dehydrogenase and carnitine palmitoyl acyltransferase) but also actually covalently modifies them by lactylation in order to maintain this inhibited metabolic–epigenomic state [5]. In addition, intermediate metabolism and nutrients are the source of the very extensive library of post-translational modifications to DNA, RNA, and proteins, as well as supplying cellular energy for many of the required reactions. Hence, the metabolic state locks in and reinforces the epigenomic state, and the metabolome and epigenome thereby play mutually reinforcing roles. This self-reinforcing coordination explains why it is so difficult to generate induced pluripotent cells and is a contributory explanation for why the described protocols typically have such low cellular yields.
These concepts become even more important when it is considered that cancer cells are de-differentiated, similarly rely on glycolysis and glutaminolysis, and are similarly metabolically–epigenomically–genomically synchronized. The disruption of this metabolic system is a key focus of mechanistic cancer research.
These important considerations imply that the descriptive and predictive power of the newly described ‘‘paracentral dogma” of biology may be usefully and meaningfully extended by including the metabolome, along with the genome, transcriptome, proteome, glycome, and lipidome, to describe cell-intrinsic regulation—not only in terms of another omics analytical layer but also as a fully predictive and interactive partner in the symphonic-like multilayer coordination that evidently comprises cellular regulatory layering.
海豚群算法 Article
Tian-qi WU,Min YAO,Jian-hua YANG
《信息与电子工程前沿（英文）》 2016年 第17卷 第8期 页码 717-729 doi: 10.1631/FITEE.1500287
关键词： 仿生算法；海豚；优化
新孢子虫病——分子流行病学及发病机制综述 Review
Asis Khan, Jahangheer S. Shaik, Patricia Sikorski, Jitender P. Dubey, Michael E. Grigg
《工程（英文）》 2020年 第6卷 第1期 页码 10-19 doi: 10.1016/j.eng.2019.02.010
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