Combined Feed
https://feeds.library.caltech.edu/people/Singhal-Vipul/combined.rss
A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenSat, 13 Apr 2024 01:56:12 +0000Recursively constructing analytic expressions for equilibrium distributions of stochastic biochemical reaction networks
https://resolver.caltech.edu/CaltechAUTHORS:20170614-142315589
Authors: {'items': [{'id': 'Meng-X-Flora', 'name': {'family': 'Meng', 'given': 'X. Flora'}}, {'id': 'Baetica-Ania-Ariadna', 'name': {'family': 'Baetica', 'given': 'Ania-Ariadna'}, 'orcid': '0000-0003-0421-8181'}, {'id': 'Singhal-Vipul', 'name': {'family': 'Singhal', 'given': 'Vipul'}, 'orcid': '0000-0003-1670-1824'}, {'id': 'Murray-R-M', 'name': {'family': 'Murray', 'given': 'Richard M.'}, 'orcid': '0000-0002-5785-7481'}]}
Year: 2017
DOI: 10.1098/rsif.2017.0157
PMCID: PMC5454304
Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture its dynamics. The chemical master equation (CME) is a commonly used stochastic model of Kolmogorov forward equations that describe how the probability distribution of a chemically reacting system varies with time. Finding analytic solutions to the CME can have benefits, such as expediting simulations of multiscale biochemical reaction networks and aiding the design of distributional responses. However, analytic solutions are rarely known. A recent method of computing analytic stationary solutions relies on gluing simple state spaces together recursively at one or two states. We explore the capabilities of this method and introduce algorithms to derive analytic stationary solutions to the CME.
We first formally characterize state spaces that can be constructed by performing single-state gluing of paths, cycles or both sequentially. We then study stochastic biochemical reaction networks that consist of reversible, elementary reactions with two-dimensional state spaces. We also discuss extending the method to infinite state spaces and designing the stationary behaviour of stochastic biochemical reaction networks. Finally, we illustrate the aforementioned ideas using examples that include two interconnected transcriptional components and biochemical reactions with two-dimensional state spaces.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/zjdnn-bxw82A MATLAB toolbox for modeling genetic circuits in cell-free systems
https://resolver.caltech.edu/CaltechAUTHORS:20200806-153554109
Authors: {'items': [{'id': 'Singhal-Vipul', 'name': {'family': 'Singhal', 'given': 'Vipul'}, 'orcid': '0000-0003-1670-1824'}, {'id': 'Tuza-Zoltan-A', 'name': {'family': 'Tuza', 'given': 'Zoltan A.'}, 'orcid': '0000-0003-2896-1527'}, {'id': 'Sun-Zachary-Z', 'name': {'family': 'Sun', 'given': 'Zachary Z.'}, 'orcid': '0000-0002-9425-2924'}, {'id': 'Murray-R-M', 'name': {'family': 'Murray', 'given': 'Richard M.'}, 'orcid': '0000-0002-5785-7481'}]}
Year: 2021
DOI: 10.1093/synbio/ysab007
PMCID: PMC8102020
We introduce a MATLAB-based simulation toolbox, called txtlsim, for an Escherichia coli-based Transcription–Translation (TX–TL) system. This toolbox accounts for several cell-free-related phenomena, such as resource loading, consumption and degradation, and in doing so, models the dynamics of TX–TL reactions for the entire duration of solution phase batch-mode experiments. We use a Bayesian parameter inference approach to characterize the reaction rate parameters associated with the core transcription, translation and mRNA degradation mechanics of the toolbox, allowing it to reproduce constitutive mRNA and protein-expression trajectories. We demonstrate the use of this characterized toolbox in a circuit behavior prediction case study for an incoherent feed-forward loop.https://authors.library.caltech.eduhttps://authors.library.caltech.edu/records/bygwm-0wk74