J
James J. Collins
Researcher at Massachusetts Institute of Technology
Publications - 700
Citations - 105255
James J. Collins is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Synthetic biology & Population. The author has an hindex of 151, co-authored 669 publications receiving 89476 citations. Previous affiliations of James J. Collins include Baylor College of Medicine & University at Albany, SUNY.
Papers
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Journal ArticleDOI
Introduction to focus issue: quantitative approaches to genetic networks.
TL;DR: This focus issue provides a selection of papers reflecting current research directions in the quantitative analysis of genetic networks, from molecular models for the binding of proteins, to realistic detailed models of cellular metabolism.
Journal ArticleDOI
ZSCAN10 expression corrects the genomic instability of iPSCs from aged donors
Maria Skamagki,Cristina Correia,Percy Yeung,Timour Baslan,Samuel Beck,Cheng Zhang,Christian A. Ross,Lam Dang,Zhong Liu,Simona Giunta,Tzu-Pei Chang,Joye Wang,Aparna Ananthanarayanan,Martina Bohndorf,Benedikt Bosbach,James Adjaye,Hironori Funabiki,Jonghwan Kim,Scott W. Lowe,James J. Collins,Chi Wei Lu,Hu Li,Rui Zhao,Kitai Kim +23 more
TL;DR: It is demonstrated that A-iPSCs exhibit excessive glutathione-mediated reactive oxygen species (ROS) scavenging activity, which blocks the DNA damage response and apoptosis and permits survival of cells with genomic instability.
Journal ArticleDOI
Using Engineered Bacteria to Characterize Infection Dynamics and Antibiotic Effects In Vivo
TL;DR: A host-derived Escherichia coli strain engineered with a genetic toggle switch is utilized as a research tool to examine in vivo replicative states in a mouse model of chronic infection, and to compare in-vivo and in- vitro bacterial behavior.
Journal ArticleDOI
Plug-and-play with RNA.
TL;DR: Ligand-responsive riboregulators enable extracellular control of post-transcriptional gene expression in response to Tournaisian reprograming.
Patent
Real-time and adaptive method and system for suppressing a pathological non-chaotic rhythm
TL;DR: In this paper, a model-independent control technique that does not require knowledge of the system's governing equations or a pre-control learning stage is proposed for low-dimensional chaotic or non-chaotic dynamical systems.