Self-Organized Pluripotent Stem Cell Patterning by Automated Design

Briers, Demarcus; Libby, Ashley R.G.; Haghighi, Iman; Joy, David A; Conklin, Bruce R.; Belta, Călin; McDevitt, Todd C.

doi:10.2139/ssrn.3318933

Cited by 3 publications

(4 citation statements)

References 63 publications

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“…Future directions for these computational efforts could be a combination with artificial intelligence-based optimization through either evolutionary algorithms or machine learning. It has been recently shown that these could be used to generate morphologies that can then be recapitulated in vitro . , Algorithms could not only be trained to optimize parameters such as cell line, signaling network, and behavioral response but also incorporate subparameters such as motility, proliferation, differentiability, juxtacrine and soluble morphogen signaling, mechanotransduction, adhesion, chemotaxis, and differentiation, to list a few. Numerous other recent advances in synthetic biology ,− have made it possible to further control this process, facilitating the synthetic reconstruction of complex native morphogenic processes toward enabling control over custom tissue development.…”

Section: Discussionmentioning

confidence: 99%

Parameterized Computational Framework for the Description and Design of Genetic Circuits of Morphogenesis Based on Contact-Dependent Signaling and Changes in Cell–Cell Adhesion

et al. 2022

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Synthetic development is a nascent field of research that uses the tools of synthetic biology to design genetic programs directing cellular patterning and morphogenesis in higher eukaryotic cells, such as mammalian cells. One specific example of such synthetic genetic programs was based on cell–cell contact-dependent signaling using synthetic Notch pathways and was shown to drive the formation of multilayered spheroids by modulating cell–cell adhesion via differential expression of cadherin family proteins in a mouse fibroblast cell line (L929). The design method for these genetic programs relied on trial and error, which limited the number of possible circuits and parameter ranges that could be explored. Here, we build a parameterized computational framework that, given a cell–cell communication network driving changes in cell adhesion and initial conditions as inputs, predicts developmental trajectories. We first built a general computational framework where contact-dependent cell–cell signaling networks and changes in cell–cell adhesion could be designed in a modular fashion. We then used a set of available in vitro results (that we call the “training set” in analogy to similar pipelines in the machine learning field) to parameterize the computational model with values for adhesion and signaling. We then show that this parameterized model can qualitatively predict experimental results from a “testing set” of available in vitro data that varied the genetic network in terms of adhesion combinations, initial number of cells, and even changes to the network architecture. Finally, this parameterized model is used to recommend novel network implementation for the formation of a four-layered structure that has not been reported previously. The framework that we develop here could function as a testing ground to identify the reachable space of morphologies that can be obtained by controlling contact-dependent cell–cell communications and adhesion with these molecular tools and in this cellular system. Additionally, we discuss how the model could be expanded to include other forms of communication or effectors for the computational design of the next generation of synthetic developmental trajectories.

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Section: Discussionmentioning

confidence: 99%

Parameterized Computational Framework for the Description and Design of Genetic Circuits of Morphogenesis Based on Contact-Dependent Signaling and Changes in Cell–Cell Adhesion

et al. 2022

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“…Future directions for these computational efforts could be the combination with artificial intelligence-based optimization either through evolutionary algorithms or machine learning. It has been recently shown that these could be used to generate morphologies that can then be recapitulated in vitro (Briers et al, 2019;Kriegman et al, 2019). Algorithms could not only be trained to optimize parameters such as cell line, signaling network, and behavioral response, but could also incorporate subparameters such as: motility, proliferation, differentiability, juxtacrine and soluble morphogen signaling, mechanotransduction, adhesion, chemotaxis, and differentiation, to list a few.…”

Section: Discussionmentioning

confidence: 99%

“…In these situations where much is unknown, even semi-predictive computational models could be of significant value, allowing rapid implementation of many designs in silico. These various designs could be tested for viability and pre-optimized before or concurrently with, biological implementation, as was recently shown for simple genetic modifications and self-organization of stem cells (Briers et al, 2019). Here we develop a more general framework that can enable more complex genetic circuit engineering.…”

mentioning

confidence: 99%

A parametrized computational framework for description and design of genetic circuits of morphogenesis based on contact-dependent signaling and changes in cell-cell adhesion

Morsut¹,

Lam²

2019

Preprint

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Synthetic development is a nascent field of research that uses the tools of synthetic biology to design genetic programs directing cellular patterning and morphogenesis in higher eukaryotic cells, such as mammalian cells. Synthetic genetic networks comprising cell-cell communications and morphogenesis effectors (e.g. adhesion) are generated and integrated into a cellular genome. Current design methods for these genetic programs rely on trial and error, which limit the number of possible circuits and parameter ranges that can be explored. By contrast, computational models act as rapid testing platforms, revealing the networks, signals, and responses required for achieving robust target structures. Here we introduce a computational framework, based on cellular Potts, where contact dependent cell-cell signaling networks and cellular responses can be chosen in a modular fashion. We represent and tune a number of recently described synthetic morphogenic trajectories in silico, such as those resulting in multilayered synthetic spheroids. Our parameters were tuned using a comparison with published in vitro experimental results. Our tuned parameters were then used to design and explore novel developmental trajectories for the formation of elongated and oscillatory structures. Here, multiple rounds of optimization suggested critical parameters for the successful implementation of these trajectories. The framework that we develop here could function as a testing ground to explore how synthetic biology tools can be used to create particular multicellular trajectories, as well as for understanding both imagined and extant developmental trajectories.

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“…al. [39], demonstrated the ability to combine a 2D Cellular Potts model with Particle Swarm Optimization to automated the design and experimentally validate 2D symmetrical patterns in human induced pluripotent stem cells.…”

Section: Modeling Multicellular Self-assembly In 4d Spacementioning

confidence: 99%

Genetic design automation for autonomous formation of multicellular shapes from a single cell progenitor

Appleton¹,

Mehdipour

Daifuku

et al. 2019

Preprint

Self Cite

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Multi-cellular organisms originate from a single cell, ultimately giving rise to mature organisms of 1 heterogeneous cell type composition in complex structures. Recent work in the areas of stem cell 2 biology and tissue engineering have laid major groundwork in the ability to convert certain types of 3 cells into other types, but there has been limited progress in the ability to control the morphology of 4 cellular masses as they grow. Contemporary approaches to this problem have included the use of 5 artificial scaffolds, 3D bioprinting, and complex media formulations, however, there are no existing 6 approaches to controlling this process purely through genetics and from a single-cell starting point. 7Here we describe a computer-aided design approach for designing recombinase-based genetic circuits 8 for controlling the formation of multi-cellular masses into arbitrary shapes in human cells. 9

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Self-Organized Pluripotent Stem Cell Patterning by Automated Design

Cited by 3 publications

References 63 publications

Parameterized Computational Framework for the Description and Design of Genetic Circuits of Morphogenesis Based on Contact-Dependent Signaling and Changes in Cell–Cell Adhesion

Parameterized Computational Framework for the Description and Design of Genetic Circuits of Morphogenesis Based on Contact-Dependent Signaling and Changes in Cell–Cell Adhesion

A parametrized computational framework for description and design of genetic circuits of morphogenesis based on contact-dependent signaling and changes in cell-cell adhesion

Genetic design automation for autonomous formation of multicellular shapes from a single cell progenitor

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