GeNeDA: An Open-Source Workflow for Design Automation of Gene Regulatory Networks Inspired from Microelectronics

Madec, Morgan; Pêcheux, François; Gendrault, Yves; Rosati, Elise; Lallement, Christophe; Haiech, Jacques

doi:10.1089/cmb.2015.0229

Cited by 5 publications

(3 citation statements)

References 48 publications

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“…The construction of GRNs from the Boolean equations, i.e. Eqs ( 1)-( 7), is performed with GeNeDA, a genetic design automation tool derived from the field of digital electronics [31].…”

Section: Grn Constructionmentioning

confidence: 99%

Feasibility and reliability of sequential logic with gene regulatory networks

2021

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Gene regulatory networks exhibiting Boolean behaviour, e.g. AND, OR or XOR, have been routinely designed for years. However, achieving more sophisticated functions, such as control or computation, usually requires sequential circuits or so-called state machines. For such a circuit, outputs depend both on inputs and the current state of the system. Although it is still possible to design such circuits by analogy with digital electronics, some particularities of biology make the task trickier. The impact of two of them, namely the stochasticity of biological processes and the inhomogeneity in the response of regulation mechanisms, are assessed in this paper. Numerical simulations performed in two use cases point out high risks of malfunctions even for designed GRNs functional from a theoretical point of view. Several solutions to improve reliability of such systems are also discussed.

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“…The construction of GRNs from the Boolean equations, i.e. Eqs ( 1)-( 7), is performed with GeNeDA, a genetic design automation tool derived from the field of digital electronics [31].…”

Section: Grn Constructionmentioning

confidence: 99%

Feasibility and reliability of sequential logic with gene regulatory networks

2021

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“…Model-based design approaches promise to (partially) replace costly experiments with computer simulations. A range of theoretical approaches to automated or computer-assisted design have been published [3][4][5][6][7][8][9][10]. When supported by reliable characterization of genetic regulatory components, automated design algorithms can greatly increase the efficiency of design.…”

Section: Introductionmentioning

confidence: 99%

Component Characterization in a Growth-Dependent Physiological Context: Optimal Experimental Design

Braniff¹,

Scott²,

Ingalls³

2019

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Synthetic biology design challenges have driven the use of mathematical models to characterize genetic components and to explore complex design spaces. Traditional approaches to characterization have largely ignored the effect of strain and growth conditions on the dynamics of synthetic genetic circuits, and have thus confounded intrinsic features of the circuit components with cell-level context effects. We present a model that distinguishes an activated gene’s intrinsic kinetics from its physiological context. We then demonstrate an optimal experimental design approach to identify dynamic induction experiments for efficient estimation of the component’s intrinsic parameters. Maximally informative experiments are chosen by formulating the design as an optimal control problem; direct multiple-shooting is used to identify the optimum. Our numerical results suggest that the intrinsic parameters of a genetic component can be more accurately estimated using optimal experimental designs, and that the choice of growth rates, sampling schedule, and input profile each play an important role. The proposed approach to coupled component–host modelling can support gene circuit design across a range of physiological conditions.

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“…Most of the time, they have been developed from scratch [ 23 – 27 ]. An alternative approach, which have been recently demonstrated, is to use and readapt existing tools from the domain of microelectronics (Electronic Design Automation or EDA) to synthetic biology [ 21 , 28 ]. As simulation is at the heart of EDA processesa prerequisite for the adaptation of EDA tools to biology is the possibility to simulate biological mechanisms with SPICE.…”

Section: Introductionmentioning

confidence: 99%

Modeling and simulation of biological systems using SPICE language

2017

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The article deals with BB-SPICE (SPICE for Biochemical and Biological Systems), an extension of the famous Simulation Program with Integrated Circuit Emphasis (SPICE). BB-SPICE environment is composed of three modules: a new textual and compact description formalism for biological systems, a converter that handles this description and generates the SPICE netlist of the equivalent electronic circuit and NGSPICE which is an open-source SPICE simulator. In addition, the environment provides back and forth interfaces with SBML (System Biology Markup Language), a very common description language used in systems biology. BB-SPICE has been developed in order to bridge the gap between the simulation of biological systems on the one hand and electronics circuits on the other hand. Thus, it is suitable for applications at the interface between both domains, such as development of design tools for synthetic biology and for the virtual prototyping of biosensors and lab-on-chip. Simulation results obtained with BB-SPICE and COPASI (an open-source software used for the simulation of biochemical systems) have been compared on a benchmark of models commonly used in systems biology. Results are in accordance from a quantitative viewpoint but BB-SPICE outclasses COPASI by 1 to 3 orders of magnitude regarding the computation time. Moreover, as our software is based on NGSPICE, it could take profit of incoming updates such as the GPU implementation, of the coupling with powerful analysis and verification tools or of the integration in design automation tools (synthetic biology).

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GeNeDA: An Open-Source Workflow for Design Automation of Gene Regulatory Networks Inspired from Microelectronics

Cited by 5 publications

References 48 publications

Feasibility and reliability of sequential logic with gene regulatory networks

Feasibility and reliability of sequential logic with gene regulatory networks

Component Characterization in a Growth-Dependent Physiological Context: Optimal Experimental Design

Modeling and simulation of biological systems using SPICE language

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