<scp>i</scp>B<scp>io</scp>S<scp>im</scp> 3: A Tool for Model-Based Genetic Circuit Design

Watanabe, Leandro; Nguyen, Tramy; Zhang, Michael; Zundel, Zach; Zhang, Zhen; Madsen, Curtis; Roehner, Nicholas; Myers, Chris J.

doi:10.1021/acssynbio.8b00078

Cited by 70 publications

(75 citation statements)

References 32 publications

(42 reference statements)

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“…The lack of robust predictive modelling-which accurately foresee the performance of a design before implementation-threatens to undermine the success of the field. Although model-based design [49] is a common practice, accurate predictions are difficult to obtain since the internal workings Figure 4: Impact of co-translational dimerisation and intergenic distance on a genetic toggle switch. A The two circuit modules are located in proximity (i.e., sharing the same chromosomal position).…”

Section: Resultsmentioning

confidence: 99%

Modelling co-translational dimerisation for programmable nonlinearity in synthetic biology

Stoof

Goñi-Moreno

2020

Preprint

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Nonlinearity plays a fundamental role in the performance of both natural and synthetic biological networks. Key functional motifs in living microbial systems, such as the emergence of bistability or oscillations, rely on nonlinear molecular dynamics. Despite its core importance, the rational design of nonlinearity remains an unmet challenge. This is largely due to a lack of mathematical modelling that accounts for the mechanistic basics of nonlinearity. We introduce a model for gene regulatory circuits that explicitly simulates protein dimerization—a well-known source of nonlinear dynamics. Specifically, our approach focusses on modelling co-translational dimerization: the formation of protein dimers during—and not after—translation. This is in contrast to the prevailing assumption that dimer generation is only viable between freely diffusing monomers (i.e., post-translational dimerization). We provide a method for fine-tuning nonlinearity on demand by balancing the impact of co- versus post-translational dimerization. Furthermore, we suggest design rules, such as protein length or physical separation between genes, that may be used to adjust dimerization dynamics in-vivo. The design, build and test of genetic circuits with on-demand nonlinear dynamics will greatly improve the programmability of synthetic biological systems.

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Section: Resultsmentioning

confidence: 99%

Modelling co-translational dimerisation for programmable nonlinearity in synthetic biology

Stoof

Goñi-Moreno

2020

Preprint

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“…Algorithms 1, 2 and 3 were implemented in Java as a prototype tool within the iBioSim genetic design automation (GDA) tool [36,30,39]. This tool constructs an approximate CTMC transition rate matrix, which is then analyzed with the help of the PRISM probabilistic model checking tool [27].…”

Section: Resultsmentioning

confidence: 99%

Approximation Techniques for Stochastic Analysis of Biological Systems

Neupane

Zhang

Madsen

et al. 2019

Computational Biology

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There has been an increasing demand for formal methods in the design process of safety-critical synthetic genetic circuits. Probabilistic model checking techniques have demonstrated significant potential in analyzing the intrinsic probabilistic behaviors of complex genetic circuit designs. However, its inability to scale limits its applicability in practice. This chapter addresses the scalability problem by presenting a state-space approximation method to remove unlikely states resulting in a reduced, finite state representation of the infinite-state continuous-time Markov chain that is amenable to probabilistic model checking. The proposed method is evaluated on a design of a genetic toggle switch. Comparisons with another state-of-art tool demonstrates both accuracy and efficiency of the presented method.

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“…Comprehensive comparisons of the BioUML platform with the above platforms, as well as comparisons with specialized tools for systems biology (e.g. CellDesigner (61), Tellurium (62), COPASI (63), iBioSim (64)), pathway visualization and analyses (e.g. Cytoscape (39)) and workflow platforms (Galaxy, Taverna (65)) are available at http://wiki.biouml.org/index.php/Tools_Comparison.…”

Section: Discussionmentioning

confidence: 99%

BioUML: an integrated environment for systems biology and collaborative analysis of biomedical data

Kolpakov

Akberdin

Kashapov³

et al. 2019

Nucleic Acids Research

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BioUML (homepage: http://www.biouml.org, main public server: https://ict.biouml.org) is a web-based integrated environment (platform) for systems biology and the analysis of biomedical data generated by omics technologies. The BioUML vision is to provide a computational platform to build virtual cell, virtual physiological human and virtual patient. BioUML spans a comprehensive range of capabilities, including access to biological databases, powerful tools for systems biology (visual modelling, simulation, parameters fitting and analyses), a genome browser, scripting (R, JavaScript) and a workflow engine. Due to integration with the Galaxy platform and R/Bioconductor, BioUML provides powerful possibilities for the analyses of omics data. The plug-in-based architecture allows the user to add new functionalities using plug-ins. To facilitate a user focus on a particular task or database, we have developed several predefined perspectives that display only those web interface elements that are needed for a specific task. To support collaborative work on scientific projects, there is a central authentication and authorization system (https://bio-store.org). The diagram editor enables several remote users to simultaneously edit diagrams.

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iBioSim 3: A Tool for Model-Based Genetic Circuit Design

Cited by 70 publications

References 32 publications

Modelling co-translational dimerisation for programmable nonlinearity in synthetic biology

Modelling co-translational dimerisation for programmable nonlinearity in synthetic biology

Approximation Techniques for Stochastic Analysis of Biological Systems

BioUML: an integrated environment for systems biology and collaborative analysis of biomedical data

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