Titlebook: Synthetic Gene Circuits; Methods and Protocol Filippo Menolascina Book 2021 Springer Science+Business Media, LLC, part of Springer Nature 2

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,Prediction of Cellular Burden with Host–Circuit Models,sign parameters on both burden and circuit functionality. Our approach relies on a new generation of computational models for microbial growth that can flexibly accommodate resource bottlenecks encountered in gene circuit design. Adoption of this modeling paradigm can facilitate fast and robust design cycles in synthetic biology.

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Book 2021l techniques to model and simulate biological systems, redesign biological systems, setting up of an automated biolaboratory, step-by-step guide on how to perform computer aided design, RNA sequencing, microfluidics -using bacterial cell free extracts, live mammalian cells, computational and experim

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Qualitative Modeling, Analysis and Control of Synthetic Regulatory Circuits,s. We also briefly present the problem of controlling synthetic circuits, an emerging topic that could profit from the capacity of qualitative modeling approaches to rapidly scan a space of design alternatives.

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Optimal Experimental Design for Systems and Synthetic Biology Using AMIGO2,OED can be used to define the set of experiments that would (a) identify the best model or (b) improve the identifiability of unknown parameters. In this chapter, we present a detailed practical procedure to compute optimal experiments using the AMIGO2 toolbox.

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Stochastic Differential Equations for Practical Simulation of Gene Circuits,ules of the species involved is not extremely low. Here, we describe methods and a computational framework to simulate a population of cells containing gene circuits of interest. These methods account for both intrinsic and extrinsic noise sources, and allow us to have both individual cell-related s

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Using Models to (Re-)Design Synthetic Circuits, 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