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Editorial overview: ‘Mathematical modelling of high-throughput and high-content data’ 编辑概述:“高通量和高含量数据的数学建模”
IF 3.7 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2022-03-01 DOI: 10.1016/j.coisb.2021.100405
Jan Hasenauer, Julio R. Banga
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引用次数: 1
Using resource constraints derived from genomic and proteomic data in metabolic network models 利用代谢网络模型中基因组和蛋白质组学数据得出的资源约束
IF 3.7 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2022-03-01 DOI: 10.1016/j.coisb.2021.100400
Kobe De Becker , Niccolò Totis , Kristel Bernaerts , Steffen Waldherr

The increasing amount of available high-content data in genomics, proteomics, and metabolomics has significantly improved the predictive power and model accuracy of genome-scale metabolic network models in recent years. We review recent constraint-based modeling approaches that incorporate genomics and proteomics data to form resource allocation models. Different modeling approaches to build resource allocation models and the related enzyme-constrained genome-scale metabolic models are discussed and evaluated with respect to differences regarding model features. In addition, an overview of the data required to construct, simulate and validate models for the different approaches is given, together with a list of relevant databases.

近年来,基因组学、蛋白质组学和代谢组学中可用的高含量数据越来越多,显著提高了基因组尺度代谢网络模型的预测能力和模型准确性。我们回顾了最近基于约束的建模方法,这些方法结合了基因组学和蛋白质组学数据来形成资源分配模型。不同的建模方法来建立资源分配模型和相关的酶约束基因组尺度代谢模型进行了讨论和评估,相对于模型特征的差异。此外,还概述了为不同方法构建、模拟和验证模型所需的数据,并列出了相关数据库的清单。
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引用次数: 3
The evolution of the metabolic network over long timelines 长时间内代谢网络的进化
IF 3.7 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2021-12-01 DOI: 10.1016/j.coisb.2021.100402
Markus Ralser , Sreejith J. Varma , Richard A. Notebaart

Metabolism is executed by an efficient, interconnected and ancient biochemical system, the metabolic network. Its evolutionary origins are, however, barely understood. We here discuss that because of niche adaptation, the evolutionary selection acting on the metabolic network structure distinguishes modern species and early life forms. Yet, its basic structure remained conserved over more than three billion years of diverging evolution. We speculate that this situation attributes key roles in metabolic network evolution to (i) the reaction properties of central metabolites, (ii) simple catalysts (e.g. metal ions, amino acids) whose importance remained unchanged during evolution, and (iii) the interconnectivity of the network that limits its expansion. The conservation of network structure hence implies that early life forms already used similar metabolic reaction topologies as modern species.

新陈代谢是由一个有效的、相互联系的、古老的生化系统——代谢网络来完成的。然而,它的进化起源却鲜为人知。我们在这里讨论了由于生态位适应,作用于代谢网络结构的进化选择区分了现代物种和早期生命形式。然而,它的基本结构在30多亿年的分化进化中仍然保持不变。我们推测,这种情况将代谢网络进化的关键作用归因于(i)中心代谢物的反应性质,(ii)在进化过程中重要性保持不变的简单催化剂(如金属离子、氨基酸),以及(iii)限制其扩展的网络互连性。因此,网络结构的守恒意味着早期的生命形式已经使用了与现代物种相似的代谢反应拓扑结构。
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引用次数: 3
Experimental tools to reduce the burden of bacterial synthetic biology 实验工具减轻细菌合成生物学负担
IF 3.7 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2021-12-01 DOI: 10.1016/j.coisb.2021.100393
Alice Grob , Roberto Di Blasi , Francesca Ceroni

Cellular burden limits the applications of bacterial synthetic biology. Experimental approaches for burden minimisation have recently become available. Tools to identify construct design with low footprint on the host include capacity monitors that quantify cellular capacity, high-throughput approaches and cell-free systems for construct prototyping. Orthogonal ribosomes and feedback controllers are instead useful to seek control of resource allocation and lower burden. Other approaches include genome reduction to increase the available resource pool and synthetic addiction to couple cell fitness and product accumulation. However, controlling the cellular response to exogenous expression is still a challenge, and more tools are needed to widen the applications of synthetic biology. Further effort that combines novel evolutionary data with burden-aware tools can set the foundation to increase the stability and robustness of future genetic systems.

细胞负荷限制了细菌合成生物学的应用。减少负担的实验方法最近已经可用。在主机上识别低占用空间的结构设计的工具包括量化单元容量的容量监视器、高通量方法和用于结构原型的无单元系统。正交核糖体和反馈控制器可用于寻求资源分配控制和降低负担。其他方法包括基因组减少以增加可用资源库和合成成瘾以偶联细胞适应度和产物积累。然而,控制细胞对外源表达的反应仍然是一个挑战,需要更多的工具来扩大合成生物学的应用。将新的进化数据与负担感知工具相结合的进一步努力可以为增加未来遗传系统的稳定性和健壮性奠定基础。
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引用次数: 10
Convergence and divergence in the metabolic network of Mycobacterium tuberculosis 结核分枝杆菌代谢网络的趋同与分化
IF 3.7 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2021-12-01 DOI: 10.1016/j.coisb.2021.100384
Catherine B. Hubert, Luiz Pedro S. de Carvalho

Metabolism is still often regarded as a set of canonical reactions, identical in all organisms, yet that is far from correct. Metabolism and the metabolic networks required for cellular functions vary dramatically even within species. This diversity is also present in bacterial pathogens. This mini-review explores the role of metabolic convergence and divergence in shaping the metabolic network of Mycobacterium tuberculosis and its ability to survive in the host. With the help of a few selected examples, we aim to illustrate the magnitude of changes observed in M. tuberculosis metabolic network.

新陈代谢仍然经常被认为是一系列规范的反应,在所有生物体中都是一样的,但这远远不正确。新陈代谢和细胞功能所需的代谢网络即使在物种内也有很大差异。这种多样性也存在于细菌病原体中。这篇小型综述探讨了代谢趋同和分化在塑造结核分枝杆菌代谢网络及其在宿主中生存能力中的作用。在一些选定的例子的帮助下,我们的目的是说明结核分枝杆菌代谢网络中观察到的变化幅度。
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引用次数: 3
Adaptive circuits in synthetic biology 合成生物学中的自适应电路
IF 3.7 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2021-12-01 DOI: 10.1016/j.coisb.2021.100399
Timothy Frei, Mustafa Khammash

One of the most remarkable features of biological systems is their ability to adapt to the constantly changing environment. By harnessing principles of control theory, synthetic biologists are starting to mimic this adaptation in regulatory gene circuits. Doing so allows for the construction of systems that perform reliably under non-optimal conditions. Furthermore, making a system adaptive can make up for imperfect knowledge of the underlying biology and, hence, avoid unforeseen complications in the implementation. Here, we review recent developments in the analysis and implementation of adaptive regulatory networks in synthetic biology with a particular focus on genetic circuits that can realize perfect adaptation.

生物系统最显著的特征之一是它们适应不断变化的环境的能力。通过利用控制论的原理,合成生物学家开始在调控基因回路中模仿这种适应。这样做可以构建在非最佳条件下可靠运行的系统。此外,使系统具有适应性可以弥补对潜在生物学知识的不完善,从而避免在实现中出现不可预见的复杂情况。在这里,我们回顾了合成生物学中自适应调控网络的分析和实施的最新进展,特别关注可以实现完美适应的遗传电路。
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引用次数: 10
Antibiotic resistance: Insights from evolution experiments and mathematical modeling 抗生素耐药性:来自进化实验和数学模型的见解
IF 3.7 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2021-12-01 DOI: 10.1016/j.coisb.2021.100365
Gabriela Petrungaro , Yuval Mulla , Tobias Bollenbach

Antibiotic resistance is a growing public health problem. To gain a fundamental understanding of resistance evolution, a combination of systematic experimental and theoretical approaches is required. Evolution experiments combined with next-generation sequencing techniques, laboratory automation, and mathematical modeling are enabling the investigation of resistance development at an unprecedented level of detail. Recent work has directly tracked the intricate stochastic dynamics of bacterial populations in which resistant mutants emerge and compete. In addition, new approaches have enabled measuring how prone a large number of genetically perturbed strains are to evolve resistance. Based on advances in quantitative cell physiology, predictive theoretical models of resistance are increasingly being developed. Taken together, a new strategy for observing, predicting, and ultimately controlling resistance evolution is emerging.

抗生素耐药性是一个日益严重的公共卫生问题。为了获得对耐药性演变的基本理解,需要将系统的实验和理论方法结合起来。进化实验与新一代测序技术、实验室自动化和数学建模相结合,使抗药性发展的调查能够达到前所未有的详细水平。最近的研究直接追踪了细菌种群复杂的随机动力学,在这些随机动力学中,耐药突变体出现并竞争。此外,新的方法已经能够测量出大量基因受到干扰的菌株进化出耐药性的可能性。基于定量细胞生理学的进展,抗性的预测理论模型正在日益发展。总之,一种观察、预测并最终控制耐药性演变的新策略正在出现。
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引用次数: 1
Synthetic biology-based optogenetic approaches to control therapeutic designer cells 基于合成生物学的光遗传学方法控制治疗设计细胞
IF 3.7 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2021-12-01 DOI: 10.1016/j.coisb.2021.100396
Maysam Mansouri , Martin Fussenegger

Optogenetics uses light as a traceless inducer to remotely control cellular behavior with high safety and spatiotemporal precision, and its implementation for therapeutic synthetic biology enable customizable user-defined remedial outputs to be generated from suitably engineered cells. Here, we focus on non-neural optogenetics, describing the tools and strategies available to engineer light-responsive, therapeutic mammalian designer cells and highlighting recent advances in design and translational applications, including cell and gene therapies. We also discuss current limitations in engineering genetically encoded light-sensitive systems and suggest some possible solutions.

光遗传学利用光作为一种无痕诱导剂,以高安全性和时空精度远程控制细胞行为,其在治疗性合成生物学中的实施使定制的用户定义的补救输出能够从适当的工程细胞中产生。在这里,我们专注于非神经光遗传学,描述了可用于设计光响应的治疗性哺乳动物设计细胞的工具和策略,并强调了设计和转化应用的最新进展,包括细胞和基因治疗。我们还讨论了目前工程遗传编码光敏系统的局限性,并提出了一些可能的解决方案。
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引用次数: 5
Erratum to “Regarding missing Editorial Disclosure statements in previously published articles” – Part I “关于以前发表的文章中缺少编辑披露声明”的勘误-第一部分
IF 3.7 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2021-12-01 DOI: 10.1016/j.coisb.2021.100387
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引用次数: 0
Dynamic models for metabolomics data integration 代谢组学数据整合的动态模型
IF 3.7 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2021-12-01 DOI: 10.1016/j.coisb.2021.100358
Polina Lakrisenko , Daniel Weindl

As metabolomics datasets are becoming larger and more complex, there is an increasing need for model-based data integration and analysis to optimally leverage these data. Dynamic models of metabolism allow for the integration of heterogeneous data and the analysis of dynamic phenotypes. Here, we review recent efforts in using dynamic metabolic models for data integration, focusing on approaches based on ordinary differential equations that are applicable to both time-resolved and steady-state measurements and that do not require flux distributions as inputs. Furthermore, we discuss recent advances and current challenges. We conclude that much progress has been made in various areas, such as the development of scalable simulation tools, and although challenges remain, dynamic modeling is a powerful tool for metabolomics data analysis that is not yet living up to its full potential.

随着代谢组学数据集变得越来越大,越来越复杂,越来越需要基于模型的数据集成和分析,以最佳地利用这些数据。代谢的动态模型允许异质数据的整合和动态表型的分析。在这里,我们回顾了最近在使用动态代谢模型进行数据集成方面的努力,重点关注基于常微分方程的方法,这些方法既适用于时间分辨测量,也适用于稳态测量,而且不需要通量分布作为输入。此外,我们还讨论了最近的进展和当前的挑战。我们得出的结论是,在各个领域都取得了很大进展,例如可扩展模拟工具的开发,尽管仍然存在挑战,动态建模是代谢组学数据分析的强大工具,但尚未充分发挥其潜力。
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引用次数: 2
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Current Opinion in Systems Biology
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