Engineering Cell Fate with Adaptive Feedback Control.

IF 3.7 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Frank Britto Bisso, Giulia Giordano, Christian Cuba Samaniego
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引用次数: 0

Abstract

Engineering cell fate is fundamental to optimizing therapies based on stem cells, which are aimed at replacing cells in patients suffering from trauma or disease. By timely administering molecular regulators (e.g., transcription factors, RNAs, or small molecules) in a process that mimics in vivo embryonic development, stem cell differentiation can be guided toward a specific cell fate. However, scaling up these therapies is extremely challenging because such differentiation strategies often result in mixed cellular populations. While synthetic biology approaches have been proposed to increase the yield of desired cell types, designing gene circuits that effectively redirect cell fate decisions requires mechanistic insight into the dynamics of the endogenous regulatory networks that govern this type of decision-making. In this work, we present a biomolecular adaptive controller designed to favor a specific cell fate. The controller, whose topology is akin to that of an Incoherent Feedforward Loop (IFFL), requires minimal knowledge of the endogenous network as it exhibits adaptive, non-reference-based behavior. The synthetic circuit operates through a sequestration mechanism and a delay introduced by an intermediate species, producing an output that asymptotically approximates a discrete temporal derivative of its input if the sequestration rate is sufficiently fast. Allowing the controller to actuate over a target species involved in the decision-making process creates a tunable synthetic bias that favors the production of the desired species with minimal alteration to the overall equilibrium landscape of the endogenous network. Through theoretical and computational analysis, we provide design guidelines for the controller's optimal operation, evaluate its performance under parametric perturbations, and extend its applicability to various examples of common multistable systems in biology.

基于自适应反馈控制的工程细胞命运。
改造细胞命运是优化以干细胞为基础的疗法的基础,干细胞疗法旨在替代遭受创伤或疾病的患者的细胞。通过在模拟体内胚胎发育的过程中及时管理分子调节剂(如转录因子、rna或小分子),干细胞分化可以被引导到特定的细胞命运。然而,扩大这些治疗是极具挑战性的,因为这种分化策略往往导致混合细胞群。虽然已经提出了合成生物学方法来增加所需细胞类型的产量,但设计有效地重定向细胞命运决定的基因回路需要对控制这种类型决策的内源性调节网络的动力学机制有深入的了解。在这项工作中,我们提出了一种生物分子自适应控制器,旨在支持特定的细胞命运。该控制器的拓扑结构类似于非相干前馈环路(IFFL),由于其表现出自适应的、非参考的行为,因此对内源性网络的了解最少。合成电路通过一种隔离机制和由中间物种引入的延迟来工作,如果隔离率足够快,则产生一个渐近接近其输入的离散时间导数的输出。允许控制器对决策过程中涉及的目标物种进行驱动,可以创建一个可调的合成偏差,该偏差有利于在对内源性网络的整体平衡景观进行最小改变的情况下产生所需物种。通过理论和计算分析,我们为控制器的最佳运行提供了设计指南,评估了其在参数扰动下的性能,并将其应用于生物学中常见的多稳态系统的各种示例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.00
自引率
10.60%
发文量
380
审稿时长
6-12 weeks
期刊介绍: The journal is particularly interested in studies on the design and synthesis of new genetic circuits and gene products; computational methods in the design of systems; and integrative applied approaches to understanding disease and metabolism. Topics may include, but are not limited to: Design and optimization of genetic systems Genetic circuit design and their principles for their organization into programs Computational methods to aid the design of genetic systems Experimental methods to quantify genetic parts, circuits, and metabolic fluxes Genetic parts libraries: their creation, analysis, and ontological representation Protein engineering including computational design Metabolic engineering and cellular manufacturing, including biomass conversion Natural product access, engineering, and production Creative and innovative applications of cellular programming Medical applications, tissue engineering, and the programming of therapeutic cells Minimal cell design and construction Genomics and genome replacement strategies Viral engineering Automated and robotic assembly platforms for synthetic biology DNA synthesis methodologies Metagenomics and synthetic metagenomic analysis Bioinformatics applied to gene discovery, chemoinformatics, and pathway construction Gene optimization Methods for genome-scale measurements of transcription and metabolomics Systems biology and methods to integrate multiple data sources in vitro and cell-free synthetic biology and molecular programming Nucleic acid engineering.
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