Article(id=1148993607555674663, tenantId=1146029695717560320, journalId=1146031712061968385, issueId=1148993605936669114, articleNumber=null, orderNo=null, doi=10.12211/2096-8280.2023-001, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1672416000000, receivedDateStr=2022-12-31, revisedDate=1688400000000, revisedDateStr=2023-07-04, acceptedDate=null, acceptedDateStr=null, onlineDate=1751871023310, onlineDateStr=2025-07-07, pubDate=1709136000000, pubDateStr=2024-02-29, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1751871023310, onlineIssueDateStr=2025-07-07, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1751871023310, creator=13701087609, updateTime=1751871023310, updator=13701087609, issue=Issue{id=1148993605936669114, tenantId=1146029695717560320, journalId=1146031712061968385, year='2024', volume='5', issue='1', pageStart='1', pageEnd='216', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1751871022923, creator=13701087609, updateTime=1752057333139, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1149775047658791591, tenantId=1146029695717560320, journalId=1146031712061968385, issueId=1148993605936669114, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1149775047658791592, tenantId=1146029695717560320, journalId=1146031712061968385, issueId=1148993605936669114, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=77, endPage=87, ext={EN=ArticleExt(id=1149999703677612212, articleId=1148993607555674663, tenantId=1146029695717560320, journalId=1146031712061968385, language=EN, title=Synthetic biology based on dynamical analysis, columnId=1149894683619635652, journalTitle=Synthetic Biology Journal, columnName=Invited Review, runingTitle=null, highlight=null, articleAbstract=

With the developments of biotechnology and other disciplines such as computational science, synthetic biology has made great progresses in theoretical analysis, functional design, and experimental implementation, which is attracted extensively in the interdisciplinary fields such as computational biology and artificial intelligence. From the perspective of mathematical science, theories of designing various synthetic biological elements with specific functions have been emerging, such as gene switches, gene oscillators, and biological logic gates. From the perspective of technological innovation, great progresses have been made in biosynthesis and functionalization strategies such as genetic engineering and chemical modifications on proteins (enzymes) for self-assembly. The rapid developments of these related aspects have also greatly promoted the development of synthetic biology. This review specifically focuses on theoretical basis and analysis methods behind various synthetic biological networks with specific functions from the perspective of biomolecular network dynamics, including functional biological devices such as switches and oscillators, as well as factors related to mathematics and network theory, including correlations between positive and negative feedback loops and nonlinear dynamics, nonlinear factors and the causes of time delays, stability and bifurcation-related theories, and theoretical basis and analysis methods related to dynamics, such as the robustness and period tunability of periodic oscillators are also addressed, which provides theoretical analysis methods that can be used as reference for further design of more complex or easily synthesized biological devices. Therefore, synthetic biology based on dynamics can start with mathematical modeling and dynamical system theory to construct synthetic gene regulatory networks with specific functions. By applying gene editing technology and adopting reasonable assembly strategies for experimental manipulations, we can verify the theoretical designs. By analyzing gene expression profiles, the feasibility and performance of the theoretical design can be explored. Further analysis of the topology and function of synthetic gene regulatory networks, as well as relationship between dynamics and parameters can help us better understand adjustable design strategies and key factors for redesign.

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随着生物技术与其他各学科如计算科学的发展,合成生物学在功能设计与实验实施方面都取得了长足的进展。合成生物学近年来在计算生物学与人工智能等交叉学科领域引起广泛兴趣。从数学科学的角度,设计各种具有特定功能的合成生物元件的理论不断涌现,如基因开关、基因振子、生物逻辑门等。从生物技术角度,基因工程、蛋白(酶)的化学修饰自组装等生物合成及功能化策略也取得了巨大进步。这些相关方面的长足发展,也大大促进了合成生物学的发展。本文重点从生物分子网络动力学的角度,深入阐述各种具有特定功能的合成生物网络背后的理论基础与分析方法,其中包括生物功能器件如开关与振子,以及数学与网络理论相关的因素,包括正负反馈回路与动力学的相关性、非线性因素与时间延迟产生的原因、稳定性与分支相关理论、周期振子的鲁棒性、周期可调性等动力学相关的理论基础与分析方法,为进一步设计更为复杂或者更易实验合成的生物器件提供可以借鉴的理论分析方法。

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陈洛南(1962—),男,研究员,博士生导师。研究方向为网络生物学、计算生物学、机器学习与人工智能等。 E-mail:
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王瑞琦(1974—),男,研究员,博士生导师。研究方向为计算生物学与非线性动力学中稳定性、摄动、与分支理论等。 E-mail:

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王瑞琦(1974—),男,研究员,博士生导师。研究方向为计算生物学与非线性动力学中稳定性、摄动、与分支理论等。 E-mail:

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language=CN, label=图1, caption=常见的开关动力学(a)和周期振荡(b)

(a)常见的开关动力学,其中在参数区间Ⅰ、Ⅲ时系统为单稳的,而在区间Ⅱ时系统为双稳的;(b)周期振荡

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基于动力学分析的合成生物学研究
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王瑞琦 1 , 陈洛南 2, 3
合成生物学 | 特约评述 2024,5(1): 77-87
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合成生物学 | 特约评述 2024, 5(1): 77-87
基于动力学分析的合成生物学研究
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王瑞琦1 , 陈洛南2, 3
作者信息
  • 1 上海大学数学系,上海 200444
  • 2 中国科学院分子细胞科学卓越创新中心,上海生物化学与细胞生物学研究所系统生物学重点实验室,上海 200031
  • 3 浙江省系统健康科学重点实验室,中国科学院中国科学院大学杭州高等研究院,浙江 杭州 310024
  • 王瑞琦(1974—),男,研究员,博士生导师。研究方向为计算生物学与非线性动力学中稳定性、摄动、与分支理论等。 E-mail:

通讯作者:

陈洛南(1962—),男,研究员,博士生导师。研究方向为网络生物学、计算生物学、机器学习与人工智能等。 E-mail:
Synthetic biology based on dynamical analysis
Ruiqi WANG1 , Luonan CHEN2, 3
Affiliations
  • 1 Department of Mathematics,Shanghai University,Shanghai 200444,China
  • 2 Key Laboratory of Systems Biology,Shanghai Institute of Biochemistry and Cell Biology,Center for Excellence in Molecular Cell Science,Chinese Academy of Sciences,Shanghai 200031,China
  • 3 Key Laboratory of Systems Health Science of Zhejiang Province,Hangzhou Institute for Advanced Study,University of Chinese Academy of Sciences,Chinese Academy of Sciences,Hangzhou 310024,Zhejiang,China
出版时间: 2024-02-29 doi: 10.12211/2096-8280.2023-001
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随着生物技术与其他各学科如计算科学的发展,合成生物学在功能设计与实验实施方面都取得了长足的进展。合成生物学近年来在计算生物学与人工智能等交叉学科领域引起广泛兴趣。从数学科学的角度,设计各种具有特定功能的合成生物元件的理论不断涌现,如基因开关、基因振子、生物逻辑门等。从生物技术角度,基因工程、蛋白(酶)的化学修饰自组装等生物合成及功能化策略也取得了巨大进步。这些相关方面的长足发展,也大大促进了合成生物学的发展。本文重点从生物分子网络动力学的角度,深入阐述各种具有特定功能的合成生物网络背后的理论基础与分析方法,其中包括生物功能器件如开关与振子,以及数学与网络理论相关的因素,包括正负反馈回路与动力学的相关性、非线性因素与时间延迟产生的原因、稳定性与分支相关理论、周期振子的鲁棒性、周期可调性等动力学相关的理论基础与分析方法,为进一步设计更为复杂或者更易实验合成的生物器件提供可以借鉴的理论分析方法。

动力学  /  网络  /  开关  /  振子  /  稳定性  /  分支

With the developments of biotechnology and other disciplines such as computational science, synthetic biology has made great progresses in theoretical analysis, functional design, and experimental implementation, which is attracted extensively in the interdisciplinary fields such as computational biology and artificial intelligence. From the perspective of mathematical science, theories of designing various synthetic biological elements with specific functions have been emerging, such as gene switches, gene oscillators, and biological logic gates. From the perspective of technological innovation, great progresses have been made in biosynthesis and functionalization strategies such as genetic engineering and chemical modifications on proteins (enzymes) for self-assembly. The rapid developments of these related aspects have also greatly promoted the development of synthetic biology. This review specifically focuses on theoretical basis and analysis methods behind various synthetic biological networks with specific functions from the perspective of biomolecular network dynamics, including functional biological devices such as switches and oscillators, as well as factors related to mathematics and network theory, including correlations between positive and negative feedback loops and nonlinear dynamics, nonlinear factors and the causes of time delays, stability and bifurcation-related theories, and theoretical basis and analysis methods related to dynamics, such as the robustness and period tunability of periodic oscillators are also addressed, which provides theoretical analysis methods that can be used as reference for further design of more complex or easily synthesized biological devices. Therefore, synthetic biology based on dynamics can start with mathematical modeling and dynamical system theory to construct synthetic gene regulatory networks with specific functions. By applying gene editing technology and adopting reasonable assembly strategies for experimental manipulations, we can verify the theoretical designs. By analyzing gene expression profiles, the feasibility and performance of the theoretical design can be explored. Further analysis of the topology and function of synthetic gene regulatory networks, as well as relationship between dynamics and parameters can help us better understand adjustable design strategies and key factors for redesign.

dynamics  /  networks  /  switches  /  oscillators  /  stability  /  bifurcation
王瑞琦, 陈洛南. 基于动力学分析的合成生物学研究. 合成生物学, 2024 , 5 (1) : 77 -87 . DOI: 10.12211/2096-8280.2023-001
Ruiqi WANG, Luonan CHEN. Synthetic biology based on dynamical analysis[J]. Synthetic Biology Journal, 2024 , 5 (1) : 77 -87 . DOI: 10.12211/2096-8280.2023-001
自从里程碑式的合成基因开关1与基因振子2出现,合成生物学已走过了二十多年,在此期间合成生物学在设计方法、建模与分析手段、网络的构建与实验实现等多方面取得了长足的进展3。这些进展为我们进一步设计具有理想特性的合成生物个体分子、模块乃至更大网络提供了保证。生物分子工程建立了一系列用于特定目的的蛋白质和核酸的产生方法。通过合理设计进行工程设计,预先改变生物分子的序列,以实现所需的功能。各种模块的动力学已经研究得较为充分,尤其是网络模块功能与网络拓扑结构的相关性已获得了广泛的研究,例如正反馈回路网络只会收敛到平衡点,而不会出现振荡等复杂的动力学行为4;而具有环状网络结构的系统也仅会出现周期振荡或平衡点,而不会出现更加复杂的动力学,如混沌等5。特别地,当负的反馈回路足够强时,系统只会出现周期振荡。一些基因振子具有这样的结构,例如 Repressilator2以及生物钟的单环模型6。一些其他振子在特定假设(如拟稳态近似)下,也可以转化为环状网络的结构。
合成生物学一般采用“自下而上”的思路,从合成具有调控功能的生物大分子,使其成为标准化“元件”开始,通过分子间的调控关系并创建调控通路或回路等全新生物部件与细胞“底盘”,从而构建具有各类特定用途的人造生命系统。合成生物学采用工程学“设计-合成-测试”的研究方法,或对自然生物系统“重编程”,或从头设计具有全新特征的人工生命体系。另外,大量新技术的发展如新的DNA组装、合成、编辑技术以及转录组上多种多样、普遍存在的化学修饰的准确鉴定技术,为设计和控制基因表达提供了新的技术支持。
本文重点从生物分子网络动力学的角度,深入阐述各种具有特定功能的合成生物网络背后的理论基础与分析方法,其中包括生物功能器件如开关与振子,以及从数学与网络理论相关的因素,包括正负反馈回路与动力学的相关性、非线性因素与时间延迟产生的原因、稳定性与分支相关理论、周期振子的鲁棒性、周期可调性等动力学相关的理论基础与分析方法,为进一步设计更为复杂或者更易实验合成的生物器件提供可以借鉴的理论分析方法。
为了研究某个或者某一些基因的作用,科学家需要在某一特定时间打开或关闭这些基因的表达。这就需要一个灵敏基因开关。最简单的基因开关模型包含一个转录因子TF-A7,该转录因子激活其自身的转录。TF-A形成同二聚体并通过与增强子(TF-RE)结合来实现激活转录。转录速率随TF-A二聚体浓度饱和至最大速率,其与TF-A磷酸化成比例。通过改变TF-A磷酸化程度可以模拟对刺激的反应,并进而实现TF-A在表达高低之间的切换,从而实现基因tf-a的开与关7。对于单个基因而言,产生基因开关一般有如下情形:单体转录因子且具有两个结合位点的单个基因或者具有二聚体转录因子的单个结合位点的基因8
更进一步,包含两个相互抑制的基因的合成基因开关也被设计出来,且在大肠杆菌中得以实施。该开关由排列在相互抑制网络中的任意两个可抑制启动子构成。在该开关中,一个启动子-抑制子对为Ptrc-2启动子与Lac抑制子(lacI),另外一个启动子-抑制子对为PLs1con启动子与温度敏感的 λ抑制子(cIts)或为PLtetO-1启动子与tetR抑制子1。两个稳定的状态为定义为“低”与“高”状态,分别对应第二个抑制子的表达与否。通过使用瞬态化学或热感应在稳定状态之间实现切换,从而实现开关的目的。
从开关实现的原理来讲,该开关类似于具有单个基因中二聚体转录因子的单个结合位点的情形,也就是需要协同绑定。事实上,没有协同也可以实现基因开关,例如对于一系列生物相关条件,网络结构和随机效应的适当组合,即使没有协同结合,也会产生双稳态9。从非线性的角度讲,产生多稳态这些不同的条件从本质上来讲是增加系统的非线性,并进而产生多个平衡态,因为较低的非线性难以产生多个平衡态。
从动力学分析的角度讲,以常微分方程为例,假设一个基因调控网络可以通过常微分方程[式(1)]来表示:
x ˙ = f ( x ; p )
式中, x为所有分子构成的向量,表示所有分子的浓度;而向量函数 f的每一个分量表示每一个分子的所有调控,包括该分子的生成、转化、退化等所有调控; p为系统的参数向量。对于基因开关的分析首先从系统的平衡态开始。如果 x ¯ ( p )满足式(2),则称 x ¯ ( p )为系统的平衡点。
f x ¯ ( p ) ; p = 0
事实上,当参数 p发生改变时,我们得到的 x ¯ ( p )为平衡点曲线。接下来,我们讨论平衡点的局部稳定性。系统在 x ¯ ( p )处的Jacobian矩阵为:
A = ( f i x j ) x = x ¯ ( p )
如果矩阵 A的所有特征值具有负实部,也就是如果 R e λ i < 0,则该平衡点为局部稳定的;相反,如果存在某个具有正实部的特征值,则该平衡点为不稳定的。从几何角度讲,所谓局部稳定,即从一个平衡点附近出发,系统最终仍然会收敛到该平衡点。如果平衡点为不稳定的,则当受到扰动时,系统会远离该平衡点。
基因开关的其中一个条件为多稳态,也就是对适当的参数 p,系统存在多个稳定平衡点。以当参数 p = p ¯时系统具有三个平衡点 x s , 1 , x u , x s , 2为例。一般情形下,三个平衡点中两个为稳定的,即 x s , 1 x s , 2,而一个为不稳定的,即 x u。通常情况下,动力系统的分支理论可以用来研究系统的动力学行为随参数的变化情况。如果在特定参数值下,其两侧的动力学行为发生了定性的改变,则称在该点发生了分支。常见的局部分支有鞍结点分支、岔分支、跨临界分支、Hopf分支等10-11。由于多个稳态的存在,稳态不会是全局性的。如果系统有唯一的稳定平衡点,一般可以采用构造Lyapunov函数的方法来研究其全局稳定性,尽管有时候构造该函数非常困难。在研究基因开关时,最常见的分支是鞍结点分支,此时Jacobian矩阵具有一个零实部特征值。在鞍结点分支点的一侧,系统具有多稳态,多稳态的产生与消失经常伴随着鞍结点分支的发生。另外一种分支为岔分支,其特征为在岔分支的一侧,具有一条稳定的平衡点曲线,岔分支发生后,原来稳定的平衡点曲线失去稳定性,同时伴随着出现两条稳定的平衡点曲线。由于这两种分支均在分支点的一侧具有多稳态,因而经常被用来研究基因开关的多稳态现象出现或者消亡。
从动力学的角度来讲,开关的实现需要两个条件:多稳态与扰动。从理论的角度来看,不管是在离散的布尔模型还是连续的微分方程模型中,正反馈回路对于多稳态都是必要的12-13。正反馈回路可以是直接的,就像单基因开关中转录因子激活自己基因的情形7;正反馈回路也可以是间接的,就像两个转录因子抑制彼此的转录一样1。在这种情况下,两个转录因子彼此抑制,第一个转录因子通过抑制第二个转录因子,间接有利于第一个转录因子进一步增加,类似的情形对第二个转录因子也成立,从而产生了双稳态。
从动力学方面来讲,每一个稳态,一般指的是平衡点,都具有一个吸引域,以该区域的点作为初始点,系统随着时间的演化,最终都会收敛到该稳态。因此,在没有外部干扰的情况下,系统一般不会跳出一个稳态的吸引域,进而进入另一个稳态的吸引域,并最终收敛到另一个稳态。要实现不同稳态间的切换,也就是实现开与关之间的切换,一般需要较大的外部干扰,能够使系统逐渐跳出原来的吸引域,并最终停留在另外的稳态,如图1(a)所示。在Toggle switch中,通过使用瞬态化学或热感应在稳定状态之间实现切换,就是这样的道理。
多稳态模型对扰动的响应将取决于扰动时网络的状态。区别于常微分方程模型,在时滞微分方程的模型中,对给定扰动的响应不仅取决于扰动时刻系统各分子的表达水平,而且取决于延迟所包含的先前时间范围内各分子表达水平的历史。因此,根据研究对象的不同,采用不同的动力学模型,其分析方法也有所不同。
如上所说,尽管不同的模型都提到正的反馈回路对于多稳态都是必要的14,但一直缺少严格的理论依据。2003年,东京大学合原教授以及现任职中国科学院分子细胞科学卓越创新中心的陈洛南研究员等4,基于单调动力系统理论,从理论上严格证明了只有正反馈环的网络没有稳定的振荡,而只有稳定的平衡点,其稳定性与时滞无关。基于此理论,他们进一步提出了一种利用单调动力学系统设计具有多基因和时滞的合成遗传开关网络的新方法,可以在不考虑时间延迟的情况下设计基因开关,因为系统可以从函数空间减少到欧几里得空间。因此,即使存在不确定的延迟,也可以确保设计的开关准确地工作。他们还进一步提出了一种设计开关的程序,这大大简化了开关的设计与分析难度,并使理论分析和设计(即使对于大规模系统)也变得容易起来。他们还设计了一个包含三个基因lacItetRcI的合成基因开关。
事实上,基因开关伴随着生命发育的几乎所有阶段,因为在整个发育过程中,始终伴随着不同基因的打开与关闭。例如Tet控制的可诱导基因表达系统允许动物(包括昆虫、苍蝇、小鼠和大鼠)个体基因活性的变化。在该开关中,当tTA与合成的tTA依赖性启动子Ptet结合时,tTA启动转录;而在存在Tet的情况下,tTA不能与启动子Ptet结合,Ptet控制的基因转录被关闭。在小鼠中,Tet控制的基因表达为我们了解各种生物学过程如发育、疾病和行为等提供帮助。在特定选择的时间点诱导控制基因表达将进一步促进相互关联分析,从而可靠地将基因活性的变化与细胞生理学和动物行为的表型变化联系起来15。该开关有诸多优点,例如原核来源的TetR的结合特异性高,哺乳动物细胞中没有类似的DNA靶向序列,所以Tet系统调控特异性高,并且宿主基因不受到影响,适合于体内外的各种基因表达的调控。另外,Tet系统的诱导药物为Tet或Dox,Tet作为一种抗生素已被人们应用了很长时间,是对人体较为安全的一种药物。除此之外,Tet系统具有可逆性,在去除诱导剂后可使系统关闭,也可反复加入诱导剂,多次启动诱导反应。
国内的研究者也在人工基因开关的研究方面取得了良好的进展,例如2010年,北京大学欧阳颀、娄春波等学者16设计出了一种push-on push-off开关,该基因开关通过两个模块的耦合来实现,两个模块分别为:①双稳态开关存储模块;②双抑制启动子NOR门模块。耦合后的调控网络可以被重复地或者由相同的输入信号触发,发挥作为推上推下开关的功能。更进一步地,该课题组提出了一种无需特别调整就能自动设计遗传设备的方法。通过将随机模型与核糖体结合位点(RBS)设计方法相结合,他们开发了一种将RBS DNA序列与基因开关双稳态联系起来的生物物理模型17
除了常见的双稳态之外,2021年,加州理工学院Elowitz课题组18提出了一种自然激发的合成网络的设计方法,该方法可以产生七种不同的稳定状态。他们通过锌指转录因子设计了一种基因电路,锌指转录因子通过同源二聚和异源二聚相互作用,并可由控制转录因子二聚和稳定性的小分子调控。在培养的哺乳动物细胞中引入三种设计好的转录因子可以使细胞进入七种不同的稳定状态。这种多稳定性在合成生物学中是有重要意义的,除了产生长期的多稳定性外,这些稳态以及它们之间的切换将为我们理解自然条件下细胞命运控制系统的关键因素与特性提供帮助。
基因开关在很多方面有着良好的应用,并且各种新的应用也在逐渐开发出来。例如,波士顿大学的Collins团队19设计的基于RNAi和阻遏蛋白的可调基因开关,可以有效地关闭任意基因。在关闭状态下,Lac抑制因子被表达并抑制Tet抑制因子(tetR)以及感兴趣基因(GOI)。一种短发夹RNA(shRNA)阻止任何残留的基因表达。在开启状态下,药物抑制lacI表达,导致GOI和Tet抑制因子的表达,从而抑制shRNA。事实上,一个具有良好特性的基因开关可以被结合到更复杂的调控网络设计中,从而实现一些特定的目标,例如打开或关闭一些特定的基因,或者借助特定的蛋白酶敏感基因开关来增加报告基因的表达并准确报告蛋白酶活性/抑制20
从单个基因到多个基因,在合适的调控条件下,均可以构成基因开关。通过不同的外源或内源刺激实现稳态间的切换并实现开关的功能。在生物分子调控的不同层次,大量地存在各种开关,例如上述提到开关主要是通过转录调控实现的。除此之外,调控也可能发生在转录后修饰、翻译以及蛋白调控等其他调节过程中。基因开关几乎应用于细胞生物学的所有领域,从基因治疗和组织工程到生物工程和药物发现,再到构建转基因小鼠,以及在基础和应用研究中建立基因功能的相关性等多方面。
与合成基因开关Toggle switch发生在同一年,也就是2000年,普林斯顿大学的学者实现了更为复杂的功能模块,即基因表达振荡器2。该器件利用三个基因模块彼此间的抑制作用实现了输出信号的规律振荡。这两项工作在实验层面证明了设计生物元器件的可能性,对合成生物学发展有重要指导意义,因此被称为“合成生物学的里程碑”21
从数学意义上来讲,基因振荡器的振荡类型为极限环,也就是孤立且稳定的周期振荡,如图1(b)所示。与基因开关主要由正反馈回路产生不同,基因振荡器的产生主要由负反馈回路产生。最简单的合成基因振子由一个带有时滞的负反馈回路产生22,在该振荡器中,其启动子为LlacO-1,单个基因为lacI。如果不考虑时滞,对于单个负反馈回路,只有当回路包含至少三个变量并且反馈项包含抑制物质浓度的高次项时,振荡才可能23。从这些研究成果来看,基因振子需要负反馈回路以及较强的非线性。
从动力学的角度,时滞会影响到平衡点的稳定性,也会影响到周期解的产生或消失,当然也会影响到周期解的周期和振幅。既然时滞对基因调控网络动力学的影响很大,那时滞在基因调控网络中是如何产生的,在数学建模的时候又如何引入时间延迟呢?数学模型中的时滞一般是对转录、翻译、转运、磷酸化等需要较长时间的调控过程所需的时间。时滞通常采用离散化的或者分布式的方式。更准确地说,假如每个分子从其合成位置转移到其发挥作用的位置需要相同的时间长度,则时间延迟可以设定为离散的。例如在单基因的具有时滞的反馈抑制模型中,时滞定义为mRNA开始形成的时间和功能性LacI四聚体产生的时间之间的延迟22。而如果每个分子从其合成位置转移到其发挥作用的位置需要时间长度变化的幅度较大,则分布式时滞将是合适的建模框架24。由于时滞对基因调控网络的动力学有着重要的影响,因此,时滞可以作为一种控制机制,例如控制周期或振幅等25
为了产生合成基因振子所需要的极限环振动,除了负的反馈回路、时间延迟外,非线性也是一个也无法回避的问题。在基因振子的建模过程,非线性通常体现在所采用的Hill函数上。一些简单的模块,即较少的分子个数,也不包含时滞,为了产生振荡行为,往往需要较大的Hill指数。事实上,Hill函数的产生可以通过生物化学反应,在一些拟稳态近似与保守条件下,即假设快反应处于平衡状态以及结合和未结合的蛋白总数守恒,可以推导出来。在转录水平上,Hill函数反映的是阻遏蛋白复合物的形成或阻遏物与基因启动子的协同结合。因此从Hill函数的描述对象看,Hill指数不应该大于4,因为从分子的角度讲,多于四倍体的聚合物比较少见。为了降低产生振荡所需要的Hill指数值,比如说对Goodwin振子模型,提出不同的策略,其中包括引入时间延迟、非线性退化(Michaelis-Menten)、多位点磷酸化等手段26。总体上来说,这些策略为引入时滞或者其他额外的非线性因素27
著名学者Tyson28总结了生物振子的要求。第一,负反馈是将反应网络带回其振荡的“起点”所必需的。第二,负反馈信号在时间上必须充分延迟,以使生物化学反应不会停留在稳定的平衡点。第三,反应机制的动力学速率定律必须足够“非线性”,从而使得稳定的平衡点失稳。第四,产生和消耗的生化反应必须在允许网络产生振荡的适当时间尺度上发生。文中对时滞的产生也做了总结,即分子的转录、翻译、转运等需要的时间,具有中间反应物的长反应链,或者动态滞后28。因而,在许多由常微分方程描述的动态模型中,如果没有时滞,则状态变量相对较多,其中的一些变量与它们之间的生化反应与时滞起到了类似的作用。
事实上,除了上述因素外,对于周期振荡而言,我们还需要研究其周期、振幅、可调性以及鲁棒性等问题。为了产生周期可调且鲁棒的基因振子29,斯坦福大学的学者们通过广泛的计算模拟发现,当负调控回路耦合到正调控回路时,可以容易地实现周期的可调性(同时保持振幅几乎恒定)。也正因为如此,我们发现大多数的基因振子都具有这种正负反馈回路的网络结构。例如,许多生物钟模型都具有这种结构,且表现出了良好的对抗环境波动的鲁棒性。还有研究表明增加负反馈回路的长度可以增加周期可调性,例如原来的Repressilator有三个负调控,进一步增加负调控的个数且保持整体上为负反馈回路,周期可调性会变得更好30
与增加负反馈回路长度的思想类似,我们在更早时候,也就是2006年与2007年,提出了一种网络分解的手段2531。把描述基因振子的网络分成几个子网络,也就是把原来的封闭系统变为几个开放系统,通过它们之间的相互输入输出进行耦合,并在不同的开放系统间引入时间延迟,我们同样发现原来的Repressilator周期可调性更好25。从数学角度讲,增加负反馈回路长度效果与引入时滞相似,这也在其他的振子网络中发现类似的结论,比如对Goodwin振子模型引入时间延迟或者引入其他调控过程如多位点磷酸化等手段26。网络分解的思想可以更好地帮我们从开放系统的平衡点来确定周期振动的振幅,而其周期可以通过时滞或者其他调控来进行调节。显然周期振动在波峰与波谷处对时间的导数为零,因此振动波峰与波谷的值为描述其动力学的微分方程系统中某些方程的平衡点,也可以说是拟稳态。如果系统的所有方程都为零,则该零点为整个系统的平衡点。因此,网络分解正好可以解决这些问题,也就是通过研究不同子模块或者子网络的平衡点来确定整个系统的振幅。对于更为复杂的通过互锁的正负反馈回路构建的生物钟网络,此网络分解的方法也非常有效2531
合成基因振子的类型以及其网络拓扑结构有很多。除了上述提到的一些因素外,还有一些其他因素,例如利用不同的时间尺度,并进而产生松弛振荡32。类似地,我们也在很久前构建了基因松弛振子33。通过一个正的自反馈的基因与两个基因间的负反馈回路的耦合,并利用两个蛋白的不同时间尺度,构建了基因松弛振子。此类松弛振子可以采用分析快慢流行的方法进行研究。除了松弛振子,不同的时间尺度在设计与构建基因振子时,起着重要的作用。众所周知,蛋白之间的结合或者蛋白与启动子的结合显然比转录与翻译等过程满得多。因此,在许多合成基因网络的设计与分析时,经常假设快反应处于拟平衡状态,从而可以大大简化分析34-36
鲁棒性也是基因振子所必须考虑的因素,生物鲁棒性是指在受到外部扰动或内部参数摄动等不确定因素干扰时,生物系统保持其结构和功能稳定的一种特性37。一般而言,正负耦合的反馈回路具有更好的鲁棒性。对比研究发现,调控网络的拓扑结构对动力学与鲁棒性有着重要的影响38。局部的调控结构也对鲁棒性有着重要的影响,例如系统的计算分析发现,相对于一致的输入,非一致的输入有助于增强振子的鲁棒性39。另外,在单扰动的情况下,相较于单反馈回路,双反馈回路的结构有助于生物钟的鲁棒微调40。对生物振荡鲁棒性的研究,通常采用参数敏感性分析,如周期敏感性、振幅敏感性、总体状态敏感性分析等40-41。除了振子的鲁棒性,其他网络的鲁棒性也同样重要。最新研究结果表明,分层反馈控制克服了合成生物分子网络中的鲁棒性与速度性能权衡,也就是分层反馈具有良好的鲁棒性与收敛速度42
从数学角度对基因振子进行建模与分析时,确定性的方法通常为时滞微分方程或者常微分方程。随机性方法包括化学主方程或朗之万方程等。对确定系统的分析,振动的产生通常通过Hopf分支产生,其为局部分支,往往伴随着某个平衡点的失稳。在Hopf分支点,Jacobian矩阵具有一对纯虚根,且随着参数的进一步变化,纯虚根变为一对具有正实部的复特征值,平衡点失去稳定性,同时产生周期振荡。全局分支也会产生周期振荡,如不变环上的鞍结点分支(saddle-node on invariant circle bifurcation,SNIC),有时也叫鞍结点同宿分支(saddle-node homoclinic bifurcation)43
总而言之,设计、分析以及实验实现理想的基因振子需要充分考虑到以上提到的诸多因素,包括鲁棒性、周期可调性、非线性、负反馈回路、时滞、时间尺度、网络的整体与局部拓扑结构、振荡产生与消失的机理等。我们希望本文能有助于大家更好地理解细胞振荡的机理,并认识到定量建模在研究这些振荡中的重要性。
合成基因振子的下一步是为其设计细胞信号机制,以创建多细胞尺度上的振荡动力学。在一个细胞群体中,细胞间耦合基因表达最常见方法是使用来自细菌的群体感应系统,它通常涉及细胞间通讯和细胞内信号处理44。这些系统使用一种酶将S-腺苷甲硫氨酸(SAM)代谢为N-酰基-丝氨酸内酯(AHL),该内酯可以扩散到群体内的细胞内外,并作为转录因子的配体。该群体感应模式分为三部分:信号分子的合成、扩散、接收45。群体感应系统已成功地应用于许多应用中,包括能够控制细胞种群个数的多细胞合成调控网络。通过细胞间通信将基因表达与细胞生存和死亡相结合,可以编程种群的动态。通过构建一个“种群控制”网络,便可以自主调节死亡率和大肠杆菌种群的密度。该合成多细胞网络中采用了蛋白LuxI与LuxR,它们由合成启动子plac/ara-132控制,用两种质粒pLuxRI2和pluxCcdB3进行实验实施,杀手基因(lacZa-ccdB)由pluxI15控制46
合成的多细胞振子间的协作行为也取得了长足进展47-48。例如通过构建合成的多细胞网络,合成生物学家可以研究单细胞中的振荡如何耦合以产生群体水平上的同步49-56。这些不同的合成多细胞网络均采用了细胞间通信的方式进行耦合,且从不同的方面对同步行为进行了研究,包括共同的外部波动(即噪声),外部输入控制等实现多细胞间的同步,但这些结论均需要一个共同的条件,即充分的细胞间通信。
模式形成也是单细胞和多细胞生物中协调细胞行为的标志,例如Basu等学者57通过构建合成的多细胞系统对空间模式形成的研究。在该系统中,“受体”细胞为基于由“发送”细胞合成的酰基高丝氨酸内酯(AHL)信号的化学梯度形成环状分化模式。在受体细胞中,“波段检测”基因网络对AHL浓度范围做出反应。通过融合不同荧光蛋白作为输出,他们得到了不同的空间模式,如椭圆和丁香等。这种合成的多细胞系统的构建和研究可以用于组织工程、生物材料制造和生物传感的应用等多方面。
本文回顾与展望了从生物分子网络动力学的角度,深入阐述各种具有特定功能的合成生物网络背后的理论基础与分析方法,其中包括:生物功能器件(如开关与振子);从数学与网络理论相关的因素(包括正负反馈回路与动力学的相关性、非线性因素与时间延迟产生的原因、稳定性与分支相关理论、周期振子的鲁棒性、周期可调性等动力学相关的理论基础与分析方法),为进一步设计更为复杂或者更易实验合成的生物器件提供可以借鉴的理论分析方法。
事实上,除了文中提到的开关与振子的动力学与网络结构有着密切的关系,还有一些其他简单的模块被广泛研究,即网络motif,它可以被认为是网络中频繁出现的子图模式,是复杂网络的“构建块”58。 这些频繁出现的motif很显然在信息处理过程中会发挥重要的功能,例如负的自调控可以加快反应并减少波动,而正的自调控可以减缓反应并增加细胞与细胞间的差异。当然,其他不同的motif展现了不同的动力学行为,详见文献[59]。另外,北京大学理论生物学中心汤超教授课题组和加州大学旧金山分校Lim教授课题组合作60,通过在理论上穷举所有可能的三个结点的调控网络,发现具有适应性的网络都可以被分为两大类:包含两条符号相反的从输入到输出的信号通路(不一致前馈回路);或者不包含前馈回路,却包含至少一个负反馈回路60
除了上述提到的关于简单模块分析的相关理论或计算研究,还有很多。这些简单网络的动力学性质为进一步研究更为复杂的合成生物学的设计与分析提供了良好的理论基础。无论在合成还是自然存在许多较为复杂的网络中,网络分解的思想经常被采用,通过分析各个子网络的性质,并最终让它们成功匹配,可以大大简化分析的难度。例如前文提到的push-on push-off 开关,就是通过两个模块的耦合来实现14。类似地,DNA在受到辐射损伤时,p53通过两个简单的开关模型与一个振荡模型实现其命运选择61
随着科技的进步,越来越多的新策略被提出。在合成生物学的第一波浪潮中,基因元件间的调控回路实现了简单的功能,并被用来控制单个细胞的功能。在第二波合成生物学研究浪潮中,简单的模块被整合成复杂的电路,形成系统级功能,从而使得理论分析的难度加大,因此,提出了一些策略如组合优化等62。其他方法如人工智能方法也逐渐被提出来63。和已有的方法一起,这些新的方法将大大推动合成生物学的发展,为进一步设计更复杂的合成基因网络以及实现不同的应用场景(例如在个体或者群体水平上设计或合成混沌振子)2864发挥重要作用。
  • 国家自然科学基金(12371497)
  • 国家自然科学基金(12131020)
  • 国家自然科学基金(31930022)
  • 国家自然科学基金(12026608)
  • 国家重点研发计划(2017YFA0505500)
  • 中国科学院战略优先研究计划(XDB38040400)
  • 华大基因深圳开放项目(BGIRSZ20210010)
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doi: 10.12211/2096-8280.2023-001
  • 接收时间:2022-12-31
  • 首发时间:2025-07-07
  • 出版时间:2024-02-29
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  • 收稿日期:2022-12-31
  • 修回日期:2023-07-04
基金
国家自然科学基金(12371497)
国家自然科学基金(12131020)
国家自然科学基金(31930022)
国家自然科学基金(12026608)
国家重点研发计划(2017YFA0505500)
中国科学院战略优先研究计划(XDB38040400)
华大基因深圳开放项目(BGIRSZ20210010)
作者信息
    1 上海大学数学系,上海 200444
    2 中国科学院分子细胞科学卓越创新中心,上海生物化学与细胞生物学研究所系统生物学重点实验室,上海 200031
    3 浙江省系统健康科学重点实验室,中国科学院中国科学院大学杭州高等研究院,浙江 杭州 310024

通讯作者:

陈洛南(1962—),男,研究员,博士生导师。研究方向为网络生物学、计算生物学、机器学习与人工智能等。 E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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