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Thomas S. Shimizu

Researcher at University of Cambridge

Publications -  32
Citations -  6350

Thomas S. Shimizu is an academic researcher from University of Cambridge. The author has contributed to research in topics: Chemotaxis & Kinase activity. The author has an hindex of 18, co-authored 27 publications receiving 5992 citations. Previous affiliations of Thomas S. Shimizu include Harvard University & Keio University.

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The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.

TL;DR: This work summarizes the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks, a software-independent language for describing models common to research in many areas of computational biology.
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E-CELL: software environment for whole-cell simulation.

TL;DR: E-CELL, a modeling and simulation environment for biochemical and genetic processes, has been developed and a model of a hypothetical cell with only 127 genes sufficient for transcription, translation, energy production and phospholipid synthesis is constructed.
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From molecular noise to behavioural variability in a single bacterium

TL;DR: It is shown that certain properties established by population measurements, such as adapted states, are not conserved at the single-cell level: for timescales ranging from seconds to several minutes, the behaviour of non-stimulated cells exhibit temporal variations much larger than the expected statistical fluctuations.
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Modeling the chemotactic response of Escherichia coli to time-varying stimuli

TL;DR: A general theoretical model based on receptor adaptation and receptor–receptor cooperativity is developed that provides a quantitative system-level description of the chemotaxis signaling pathway and can be used to predict E. coliChemotaxis responses to arbitrary temporal signals.
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A modular gradient-sensing network for chemotaxis in Escherichia coli revealed by responses to time-varying stimuli.

TL;DR: The results show how dynamic input–output measurements, time honored in physiology, can serve as powerful tools in deciphering cell‐signaling mechanisms.