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5. Derivation of elephant induced pluripotent stem cells

Author

Appleton, Evan, primary, Hong, Kyunghee, additional, Rodríguez-Caycedo, Cristina, additional, Tanaka, Yoshiaki, additional, Ashkenazy-Titelman, Asaf, additional, Bhide, Ketaki, additional, Rasmussen-Ivey, Cody, additional, Ambriz-Peña, Xochitl, additional, Korover, Nataly, additional, Bai, Hao, additional, Quieroz, Ana, additional, Nelson, Jorgen, additional, Rathod, Grishma, additional, Knox, Gregory, additional, Morgan, Miles, additional, Malviya, Nandini, additional, Zhang, Kairui, additional, McNutt, Brody, additional, Kehler, James, additional, Kowalczyk, Amanda, additional, Bow, Austin, additional, McLendon, Bryan, additional, Cantarel, Brandi, additional, James, Matt, additional, Mason, Christopher E., additional, Gray, Charles, additional, Koehler, Karl R., additional, Pearson, Virginia, additional, Lamm, Ben, additional, Church, George, additional, and Hysolli, Eriona, additional

Published
2024
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6. A comprehensive library of human transcription factors for cell fate engineering

Author

Ng, Alex H. M., Khoshakhlagh, Parastoo, Rojo Arias, Jesus Eduardo, Pasquini, Giovanni, Wang, Kai, Swiersy, Anka, Shipman, Seth L., Appleton, Evan, Kiaee, Kiavash, Kohman, Richie E., Vernet, Andyna, Dysart, Matthew, Leeper, Kathleen, Saylor, Wren, Huang, Jeremy Y., Graveline, Amanda, Taipale, Jussi, Hill, David E., Vidal, Marc, Melero-Martin, Juan M., Busskamp, Volker, and Church, George M.

Published
2021
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10. Building biosecurity for synthetic biology

Author

Trump, Benjamin D, Galaitsi, SE, Appleton, Evan, Bleijs, Diederik A, Florin, Marie‐Valentine, Gollihar, Jimmy D, Hamilton, R Alexander, Kuiken, Todd, Lentzos, Filippa, Mampuys, Ruth, Merad, Myriam, Novossiolova, Tatyana, Oye, Kenneth, Perkins, Edward, Garcia‐Reyero, Natàlia, Rhodes, Catherine, and Linkov, Igor

Published
2020
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13. Iterative transcription factor screening enables rapid generation of microglia-like cells from human iPSC

Author

Liu, Songlei, primary, Li, Li, additional, Zhang, Fan, additional, van Sambeek, Björn, additional, Appleton, Evan, additional, Ng, Alex H. M., additional, Khoshakhlagh, Parastoo, additional, Chen, Yuting, additional, Garcia-Corral, Mariana, additional, Wu, Chun-Ting, additional, Huang, Jeremy Y., additional, Tan, Yuqi, additional, Chao, George, additional, Aach, John, additional, Tam, Jenny, additional, Lim, Elaine T., additional, Raychaudhuri, Soumya, additional, and Church, George M., additional

Published
2022
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15. Feasibility of On-Site Verification

Author

Millett, Piers, primary, Alexanian, Tessa, additional, Appleton, Evan, additional, Diggans, James, additional, Montague, Michael, additional, and Titus, Alexander, additional

Published
2022
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16. Regulating gene drives

Author

Oye, Kenneth A., Esvelt, Kevin, Appleton, Evan, Catteruccia, Flaminia, Church, George, Kuiken, Todd, Lightfoot, Shlomiya Bar-Yam, McNamara, Julie, Smidler, Andrea, and Collins, James P.

Published
2014
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19. Building biosecurity for synthetic biology

Author

Massachusetts Institute of Technology. Department of Political Science, Massachusetts Institute of Technology. Institute for Data, Systems, and Society, Trump, Benjamin D, Galaitsi, SE, Appleton, Evan, Bleijs, Diederik A, Florin, Marie-Valentine, Gollihar, Jimmy D, Hamilton, R Alexander, Kuiken, Todd, Lentzos, Filippa, Mampuys, Ruth, Merad, Myriam, Novossiolova, Tatyana, Oye, Kenneth, Perkins, Edward, Garcia-Reyero, Natàlia, Rhodes, Catherine, Linkov, Igor, Massachusetts Institute of Technology. Department of Political Science, Massachusetts Institute of Technology. Institute for Data, Systems, and Society, Trump, Benjamin D, Galaitsi, SE, Appleton, Evan, Bleijs, Diederik A, Florin, Marie-Valentine, Gollihar, Jimmy D, Hamilton, R Alexander, Kuiken, Todd, Lentzos, Filippa, Mampuys, Ruth, Merad, Myriam, Novossiolova, Tatyana, Oye, Kenneth, Perkins, Edward, Garcia-Reyero, Natàlia, Rhodes, Catherine, and Linkov, Igor

Abstract

© 2020 The Authors. Published under the terms of the CC BY 4.0 license The fast-paced field of synthetic biology is fundamentally changing the global biosecurity framework. Current biosecurity regulations and strategies are based on previous governance paradigms for pathogen-oriented security, recombinant DNA research, and broader concerns related to genetically modified organisms (GMOs). Many scholarly discussions and biosecurity practitioners are therefore concerned that synthetic biology outpaces established biosafety and biosecurity measures to prevent deliberate and malicious or inadvertent and accidental misuse of synthetic biology's processes or products. This commentary proposes three strategies to improve biosecurity: Security must be treated as an investment in the future applicability of the technology; social scientists and policy makers should be engaged early in technology development and forecasting; and coordination among global stakeholders is necessary to ensure acceptable levels of risk.

Published
2021

20. A comprehensive library of human transcription factors for cell fate engineering

Author

Ng, Alex H. M., primary, Khoshakhlagh, Parastoo, additional, Rojo Arias, Jesus Eduardo, additional, Pasquini, Giovanni, additional, Wang, Kai, additional, Swiersy, Anka, additional, Shipman, Seth L., additional, Appleton, Evan, additional, Kiaee, Kiavash, additional, Kohman, Richie E., additional, Vernet, Andyna, additional, Dysart, Matthew, additional, Leeper, Kathleen, additional, Saylor, Wren, additional, Huang, Jeremy Y., additional, Graveline, Amanda, additional, Taipale, Jussi, additional, Hill, David E., additional, Vidal, Marc, additional, Melero-Martin, Juan M., additional, Busskamp, Volker, additional, and Church, George M., additional

Published
2020
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23. Genetic design automation for autonomous formation of multicellular shapes from a single cell progenitor

Author

Appleton, Evan, primary, Mehdipour, Noushin, additional, Daifuku, Tristan, additional, Briers, Demarcus, additional, Haghighi, Iman, additional, Moret, Michael, additional, Chao, George, additional, Wannier, Timothy, additional, Chiappino-Pepe, Anush, additional, Huang, Jeremy, additional, Belta, Calin, additional, and Church, George, additional

Published
2019
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25. BBF RFC 94: Type IIS Assembly for Bacterial Transcriptional Units: A Standardized Assembly Method for Building Bacterial Transcriptional Units Using the Type IIS Restriction Enzymes BsaI and BbsI

Author

Haddock, Traci L., Densmore, Douglas M., Appleton, Evan, Carr, Swati, Iverson, Sonya, De Freitas, Monique, Jin, Shawn, Awtry, Jake, Desai, Devina, Lozanoski, Thomas, Shah, Pooja, Agarwal, Yash, Lewis, Kathleen, Pacheco, Alan, Haddock, Traci L., Densmore, Douglas M., Appleton, Evan, Carr, Swati, Iverson, Sonya, De Freitas, Monique, Jin, Shawn, Awtry, Jake, Desai, Devina, Lozanoski, Thomas, Shah, Pooja, Agarwal, Yash, Lewis, Kathleen, and Pacheco, Alan

Abstract

This RFC94 describes an assembly standard based on the Type IIS restriction enzymes BsaI and BbsI (also called BpiI). This assembly standard is based upon the Modular Cloning (MoClo) assembly strategy, which was introduced in 2011 by Weber et al. [1] and is based upon Golden Gate cloning [2]. In this RFC, we describe our proposed MoClo standard for generating a library of bacterial DNA parts for generating four-part transcriptional units (promoter : 5’UTR : CDS : 3’UTR). In this work, we define 5’UTRs as including ribosomal binding sites (RBS) and bi-cistronic design elements (BCDs) [3], and 3’UTRs as transcriptional terminators. The 2012-2014 BostonU iGEM teams completed this work and a more compact library has also been created based on this work [4].

Published
2015

27. Owl: Electronic Datasheet Generator

Author

Appleton, Evan, primary, Tao, Jenhan, additional, Wheatley, F. Carter, additional, Desai, Devina H., additional, Lozanoski, Thomas M., additional, Shah, Pooja D., additional, Awtry, Jake A., additional, Jin, Shawn S., additional, Haddock, Traci L., additional, and Densmore, Douglas M., additional

Published
2014
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29. An End-to-End Workflow for Engineering of Biological Networks from High-Level Specifications

Author

Beal, Jacob, primary, Weiss, Ron, additional, Densmore, Douglas, additional, Adler, Aaron, additional, Appleton, Evan, additional, Babb, Jonathan, additional, Bhatia, Swapnil, additional, Davidsohn, Noah, additional, Haddock, Traci, additional, Loyall, Joseph, additional, Schantz, Richard, additional, Vasilev, Viktor, additional, and Yaman, Fusun, additional

Published
2012
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31. A design-build-test-learn tool for synthetic biology

Author

Appleton, Evan M.

Subjects
Bioinformatics, Design automation, Predictive design, Synthetic biology
Abstract

Modern synthetic gene regulatory networks emerge from iterative design-build-test cycles that encompass the decisions and actions necessary to design, build, and test target genetic systems. Historically, such cycles have been performed manually, with limited formal problem-definition and progress-tracking. In recent years, researchers have devoted substantial effort to define and automate many sub-problems of these cycles and create systems for data management and documentation that result in useful tools for solving portions of certain workflows. However, biologists generally must still manually transfer information between tools, a process that frequently results in information loss. Furthermore, since each tool applies to a different workflow, tools often will not fit together in a closed-loop and, typically, additional outstanding sub-problems still require manual solutions. This thesis describes an attempt to create a tool that harnesses many smaller tools to automate a fully closed-loop decision-making process to design, build, and test synthetic biology networks and use the outcomes to inform redesigns. This tool, called Phoenix, inputs a performance-constrained signal-temporal-logic (STL) equation and an abstract genetic-element structural description to specify a design and then returns iterative sets of building and testing instructions. The user executes the instructions and returns the data to Phoenix, which then processes it and uses it to parameterize models for simulation of the behavior of compositional designs. A model-checking algorithm then evaluates these simulations, and returns to the user a new set of instructions for building and testing the next set of constructs. In cases where experimental results disagree with simulations, Phoenix uses grammars to determine where likely points of design failure might have occurred and instructs the building and testing of an intermediate composition to test where failures occurred. A design tree represents the design hierarchy displayed in the user interface where progress can be tracked and electronic datasheets generated to review results. Users can validate the computations performed by Phoenix by using them to create sets of classic and novel temporal synthetic genetic regulatory functions in E. coli.

Published
2016

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