Synthetic biology in mammalian cells: next generation research tools and therapeutics

Lienert, Florian; Lohmueller, Jason; Garg, Abhishek; Silver, Pamela A.

doi:10.1038/nrm3738

Cited by 254 publications

(221 citation statements)

References 146 publications

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“…In fact, attempts to fundamentally address the issue by recognizing and filtering out the effects of indirect interactions at a global scale have begun to surface (11). Meanwhile, parallel developments in synthetic biology (23) have endowed researchers with new tools that allow precise emulation of naturally occurring topologies (21,22). Networks orthogonal to the cellular milieu can serve as a biomolecular topological "ground truth" (20,24).…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, engineered synthetic gene circuits are orthogonal to the endogenous pathways yet operate within the natural cellular context using the available resources. Thus, synthetic networks are a versatile platform for investigating specific connectivities and topological properties and can ultimately guide us to deriving fundamental insights about biological systems and pathways (20)(21)(22)(23).…”

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confidence: 99%

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Discriminating direct and indirect connectivities in biological networks

Kang

Moore

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Reverse engineering of biological pathways involves an iterative process between experiments, data processing, and theoretical analysis. Despite concurrent advances in quality and quantity of data as well as computing resources and algorithms, difficulties in deciphering direct and indirect network connections are prevalent. Here, we adopt the notions of abstraction, emulation, benchmarking, and validation in the context of discovering features specific to this family of connectivities. After subjecting benchmark synthetic circuits to perturbations, we inferred the network connections using a combination of nonparametric single-cell data resampling and modular response analysis. Intriguingly, we discovered that recovered weights of specific network edges undergo divergent shifts under differential perturbations, and that the particular behavior is markedly different between topologies. Our results point to a conceptual advance for reverse engineering beyond weight inference. Investigating topological changes under differential perturbations may address the longstanding problem of discriminating direct and indirect connectivities in biological networks. A focal point of systems biology is the reverse engineering of gene regulatory networks (1-5). The methods have shifted from intuitive inference of local connectivities to comprehensive analysis of large networks, involving heterogeneous data sets from high-throughput experiments and complex theoretical tools (6-10). Despite significant advances, a fundamental reverse engineering bottleneck is the ability to discriminate between direct and indirect connections. In a simple case, assuming three nodes in a cascade formulation, where an input node is activating an intermediary node which in turn is activating an output node, a reverse engineering algorithm may infer an activating edge from the input node to the output, even though there is no direct biological interaction.Unfortunately, the limitation in correctly distinguishing the effects stemming from indirect connectivities is pervasive (11-13) and justifies the urgent need for new and reliable methods to eliminate spurious edges. Importantly, remedies to address this problem should not further muddle the interpretation by removing true network edges (14). A number of theoretical approaches have been proposed to overcome this hurdle (4, 15-18), but the ability to experimentally verify the conclusions drawn by reverse engineering tools remains paramount.The majority of efforts to address the verification issue adopt in silico benchmark suites that are based on biological pathway approximations (19). Although these models do include a number of commonly observed topologies and have provided significant insights, they do not fully capture the complexity of the biological realm and the associated heterogeneity and intrinsic variability. On the other hand, engineered synthetic gene circuits are orthogonal to the endogenous pathways yet operate within the natural cellular context using the available resources. Thus, sy...

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Discriminating direct and indirect connectivities in biological networks

Kang

Moore

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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“…The early, pioneering studies were predominantly focused on standardization of network components to increase transfection efficiency and applications in the prokaryotic cell [1,2], while more recent applications are emerging in yeast and mammalian cell lines [3][4][5]. Eukaryotic cells, in contrast to prokaryotic cells, have developed sophisticated mechanisms to withstand invasion of foreign RNA/DNA material, such as the major histocompatibility complex-dependent mechanism.…”

Section: Introductionmentioning

confidence: 99%

Mammalian synthetic biology: emerging medical applications

Kis

Pereira

Homma

et al. 2015

J. R. Soc. Interface.

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In this review, we discuss new emerging medical applications of the rapidly evolving field of mammalian synthetic biology. We start with simple mammalian synthetic biological components and move towards more complex and therapy-oriented gene circuits. A comprehensive list of ON-OFF switches, categorized into transcriptional, post-transcriptional, translational and posttranslational, is presented in the first sections. Subsequently, Boolean logic gates, synthetic mammalian oscillators and toggle switches will be described. Several synthetic gene networks are further reviewed in the medical applications section, including cancer therapy gene circuits, immuno-regulatory networks, among others. The final sections focus on the applicability of synthetic gene networks to drug discovery, drug delivery, receptor-activating gene circuits and mammalian biomanufacturing processes.

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“…synthetic circuits | optimal filtering | noise cancellation | adaptive design R ecent developments in synthetic biology have enabled a revolution of biomolecular engineering (1,2), prompting numerous applications in therapeutics (3)(4)(5), biocomputing (6)(7)(8), and plant engineering (9), for instance. However, a variety of practical limitations have to be addressed before the field can achieve its full promise.…”

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confidence: 99%

Molecular circuits for dynamic noise filtering

Zechner

Seelig

Rullan

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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The invention of the Kalman filter is a crowning achievement of filtering theory-one that has revolutionized technology in countless ways. By dealing effectively with noise, the Kalman filter has enabled various applications in positioning, navigation, control, and telecommunications. In the emerging field of synthetic biology, noise and context dependency are among the key challenges facing the successful implementation of reliable, complex, and scalable synthetic circuits. Although substantial further advancement in the field may very well rely on effectively addressing these issues, a principled protocol to deal with noise-as provided by the Kalman filter-remains completely missing. Here we develop an optimal filtering theory that is suitable for noisy biochemical networks. We show how the resulting filters can be implemented at the molecular level and provide various simulations related to estimation, system identification, and noise cancellation problems. We demonstrate our approach in vitro using DNA strand displacement cascades as well as in vivo using flow cytometry measurements of a light-inducible circuit in Escherichia coli.synthetic circuits | optimal filtering | noise cancellation | adaptive design

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Synthetic biology in mammalian cells: next generation research tools and therapeutics

Cited by 254 publications

References 146 publications

Discriminating direct and indirect connectivities in biological networks

Discriminating direct and indirect connectivities in biological networks

Mammalian synthetic biology: emerging medical applications

Molecular circuits for dynamic noise filtering

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