CHIP:Data

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Supporting data and software

Fitness landscape data (rar archive): [1]

This is supporting information for the paper:
Nevozhay D, Adams RM, Van Itallie E, Bennett MR, Balázsi G,
Mapping the environmental fitness landscape of a synthetic gene circuit.
PLoS Comput. Biol. 8(4):e1002480 (2012).
PMID: 22511863, link to the PDF: [2]


Plasmids with sequences ready to be ordered from the public repository AddGene: [3]

These plasmids are supporting information for the following papers:
Nevozhay D, Zal T, Balázsi G,
Transferring a synthetic gene circuit from yeast to mammalian cells.
Nat Commun. 2013;4:1451.
PMID: 23385595, link to the PDF: coming soon

Nevozhay D, Adams RM, Van Itallie E, Bennett MR, Balázsi G,
Mapping the environmental fitness landscape of a synthetic gene circuit.
PLoS Comput. Biol. 8(4):e1002480 (2012).
PMID: 22511863, link to the PDF: [4]

Nevozhay D, Adams RM, Murphy KF, Josić K, Balázsi G,
Negative autoregulation linearizes the dose-response and suppresses the heterogeneity of gene expression.
Proc. Natl. Acad. Sci. U.S.A. 2009 Mar 31;106(13):5123-8.
PMID: 19279212, link to the PDF: [5]

Small RNA target prediction program (zip archive): [6]

This is research data by Diogo F. T. Veiga.
Additional Supplemental Materials are available from here: [7]

Data and R scripts for yeast multicellularity evolution project: [8]

This is supporting information for the Ecology & Evolution paper by Jennie J. Kuzdzal-Fick et al. (2019)

Temperature effects on gene expression and growth project: [9]

This is supporting information (Matlab scripts, experimental protocols, and flow cytometry and growth rate data) for the PNAS paper by Daniel A. Charlebois et al. (2018)

Role of network-mediated stochasticity in mammalian drug resistance: [10]

Data associated with experiments (DNA sequences and sequencing traces (ab1 files), growth curve data, and flow cytometry data) for the Nature Communications paper by Kevin S. Farquhar et al. (2019)

Noise-reducing optogenetic negative-feedback gene circuits in human cells: [11]

Data associated with experiments (Computational modeling, fluorescence microscopy, and flow cytometry data) for the Nucleic Acids Research paper by Michael Tyler Guinn & GB (2019).

Evolutionary regain of lost gene circuit function - Flow cytometry and cell count data: [12]; and raw Whole-Genome Sequencing (WGS) data: [13]

Data associated with experiments (Computational modeling, growth rate and flow cytometry data) for the PNAS paper by Mirna Kheir Gouda et al. (2019)

Drug-Dependent Growth Curve Reshaping Reveals Mechanisms of Antifungal Resistance: [14]

Data associated with the Communications Biology paper by Lesia Guinn et al. (2022) (gene knock-out sequencing, single cell/clump size calculations, growth curves, computational modeling, and data analysis)

Nonmonotone invasion landscape by noise-aware control of metastasis activator levels: [15]

Data associated with the Nature Chemical Biology paper Yiming Wan et al. (2023) (flow cytometry, immunofluorescence, qPCR, transwell assays, blots)

Adaptive DNA amplification of synthetic gene circuit opens new way to overcome cancer chemoresistance: [16]

Data and code associated with the provisionally accepted PNAS paper by Yiming Wan, Quanhua Mu, Rafał Krzysztoń et al. (2023) (flow cytometry, qPCR)

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