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"Methods Mol Biol" Category Publications:

Title Authors PubMed ID
1 Using Models to (Re-)Design Synthetic Circuits. McCallum G, Potvin-Trottier L 33405217
BIOLOGY
2 Computer-Aided Design of Active Pseudoknotted Hammerhead Ribozymes. Najeh S, Zandi K, Djerroud S, Kharma N, Perreault J 32712917
ENCS
3 Metabolomic and lipidomic analyses of chronologically aging yeast. Richard VR, Bourque SD, Titorenko VI 25213255
BIOLOGY
4 A Cell-Free Content Mixing Assay for SNARE-Mediated Multivesicular Body-Vacuole Membrane Fusion. Karim MA, Samyn DR, Brett CL 30317513
BIOLOGY
5 Visualization of SNARE-Mediated Organelle Membrane Hemifusion by Electron Microscopy. Mattie S, Kazmirchuk T, Mui J, Vali H, Brett CL 30317518
BIOLOGY
6 Identification of Genes Involved in the Degradation of Lignocellulose Using Comparative Transcriptomics. Gruninger RJ, Reid I, Forster RJ, Tsang A, McAllister TA 28417376
CSFG
7 Isolation and Preparation of Extracellular Proteins from Lignocellulose Degrading Fungi for Comparative Proteomic Studies Using Mass Spectrometry Robert J Gruninger 28417377
CSFG
8 Introduction: Overview of Fungal Genomics. de Vries RP, Grigoriev IV, Tsang A 29876804
CSFG
9 Fungal Genomic DNA Extraction Methods for Rapid Genotyping and Genome Sequencing. Bellemare A, John T, Marqueteau S 29876805
CSFG
10 Mass Spectrometry-Based Proteomics Marcos Rafael Di Falco 29876812
CSFG
11 Evolutionary Adaptation to Generate Mutants. de Vries RP, Lubbers R, Patyshakuliyeva A, Wiebenga A, Benoit-Gelber I 29876815
BIOLOGY
12 Manual Gene Curation and Functional Annotation. McDonnell E, Strasser K, Tsang A 29876819
CSFG
13 Evaluating Programs for Predicting Genes and Transcripts with RNA-Seq Support in Fungal Genomes. Reid I 29876820
CSFG
14 Phylogenetic Analysis of Protein Family. Song L, Wu S, Tsang A 29876824
CSFG

 

Title:Using Models to (Re-)Design Synthetic Circuits.
Authors:McCallum GPotvin-Trottier L
Link:https://www.ncbi.nlm.nih.gov/pubmed/33405217
DOI:10.1007/978-1-0716-1032-9_3
Publication:Methods in molecular biology (Clifton, N.J.)
Keywords:Biological oscillationsDynamical gene networkGillespie algorithmMathematical modelingStochastic simulationsSynthetic biologySynthetic gene circuitsSynthetic oscillator
PMID:33405217 Category:Methods Mol Biol Date Added:2021-01-07
Dept Affiliation: BIOLOGY
1 Department of Biology, Concordia University, Montreal, QC, Canada.
2 Department of Biology, Concordia University, Montreal, QC, Canada. laurent.potvin@concordia.ca.
3 Center for Applied Synthetic Biology, Concordia University, Montreal, QC, Canada. laurent.potvin@concordia.ca.
4 Department of Physics, Concordia University, Montreal, QC, Canada. laurent.potvin@concordia.ca.

Description:

Using Models to (Re-)Design Synthetic Circuits.

Methods Mol Biol. 2021; 2229:91-118

Authors: McCallum G, Potvin-Trottier L

Abstract

Mathematical models play an important role in the design of synthetic gene circuits, by guiding the choice of biological components and their assembly into novel gene networks. Here, we present a guide for biologists to build and utilize models of gene networks (synthetic or natural) to analyze dynamical properties of these networks while considering the low numbers of molecules inside cells that results in stochastic gene expression. We start by describing how to write down a model and discussing the level of details to include. We then briefly demonstrate how to simulate a network's dynamics using deterministic differential equations that assume high numbers of molecules. To consider the role of stochastic gene expression in single cells, we provide a detailed tutorial on running stochastic Gillespie simulations of a network, including instructions on coding the Gillespie algorithm with example code. Finally, we illustrate how using a combination of quantitative experimental characterization of a synthetic circuit and mathematical modeling can guide the iterative redesign of a synthetic circuit to achieve the desired properties. This is shown using a classic synthetic oscillator, the repressilator, which we recently redesigned into the most precise and robust synthetic oscillator to date. We thus provide a toolkit for synthetic biologists to build more precise and robust synthetic circuits, which should lead to a deeper understanding of the dynamics of gene regulatory networks.

PMID: 33405217 [PubMed - in process]





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