Special Issue "Computational Synthetic Biology"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Systems".

Deadline for manuscript submissions: closed (14 December 2018)

Special Issue Editors

Guest Editor
Prof. Dr. Julio R. Banga

(Bio)Process Engineering Group, IIM-CSIC (Spanish National Research Council), 36208 Vigo, Spain
Website | E-Mail
Interests: systems identification of biological networks; computer-aided design in synthetic biology; optimality principles in biological systems; dynamics and optimal control of biosystems
Guest Editor
Dr. Filippo Menolascina

School of Engineering, Institute for BioEngineering and SynthSys—Centre for Synthetic and Systems Biology, The University of Edinburgh, Scotland, UK.
Website | E-Mail
Interests: in vivo identification and control of biological systems; microfluidics; cyberphysical systems

Special Issue Information

Dear Colleagues,

Synthetic biology has witnessed phenomenal progress: In a little over a decade it has evolved from demonstrating proof-of-concept gene circuits in bacteria, to developing a whole new class of cell factories able to produce compounds that would otherwise been prohibitively costly and/or unsustainable to obtain, e.g., squalene.

However, despite a thriving community and some notable successes, the basic task of assembling a predictable gene network from biomolecular parts is still a key challenge; it often takes many months to produce a gene circuit with the desired behavior. Mathematical models, and Model-Based Systems Engineering methods, offer a unique opportunity to address this issue and change fundamental procedures in Synthetic Biology.

The central hypothesis is that we should be able to apply model-based methods and tools to enable the rational development of novel biosystems. However, despite its potentially disruptive impact on many sectors (including industrial biotechnology, medicine, drug development, bioremediation and energy) and several significant advances,  many challenges still remain.

This Special Issue on “Computational Synthetic Biology” aims to (i) review recent progress and/or (ii) report novel advances in the development and application of computational modeling to synthetic biology.

Contributions touching, but not limited to, the following topics are welcome:

  • Abstraction and multi-scale modelling (e.g., microbial consortia) in synthetic biology;
  • System identification and control methods in synthetic biology;
  • Biophysical and soft computing approaches to modelling biological dynamics;
  • Bio-design Automation and, in general, computer aided design of synthetic parts;
  • Standards in modelling of biological parts;
  • Software tools and workflows in synthetic biology.

Prof. Dr. Julio R. Banga
Dr. Filippo Menolascina
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1100 CHF (Swiss Francs). Please note that for papers submitted after 30 June 2019 an APC of 1200 CHF applies. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • synthetic biology
  • control theory
  • system identification
  • computer-aided design
  • standards
  • biological parts
  • software tools

Published Papers (7 papers)

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Editorial

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Open AccessEditorial Computational Methods Enabling Next-Generation Bioprocesses
Processes 2019, 7(4), 214; https://doi.org/10.3390/pr7040214
Received: 10 April 2019 / Accepted: 10 April 2019 / Published: 12 April 2019
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Abstract
Synthetic biology—the engineering of cells to rewire the biomolecular networks inside them—has witnessed phenomenal progress [...] Full article
(This article belongs to the Special Issue Computational Synthetic Biology)

Research

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Open AccessArticle Mechanistic Models of Inducible Synthetic Circuits for Joint Description of DNA Copy Number, Regulatory Protein Level, and Cell Load
Processes 2019, 7(3), 119; https://doi.org/10.3390/pr7030119
Received: 14 December 2018 / Revised: 5 February 2019 / Accepted: 19 February 2019 / Published: 26 February 2019
Cited by 1 | PDF Full-text (2939 KB) | HTML Full-text | XML Full-text
Abstract
Accurate predictive mathematical models are urgently needed in synthetic biology to support the bottom-up design of complex biological systems, minimizing trial-and-error approaches. The majority of models used so far adopt empirical Hill functions to describe activation and repression in exogenously-controlled inducible promoter systems. [...] Read more.
Accurate predictive mathematical models are urgently needed in synthetic biology to support the bottom-up design of complex biological systems, minimizing trial-and-error approaches. The majority of models used so far adopt empirical Hill functions to describe activation and repression in exogenously-controlled inducible promoter systems. However, such equations may be poorly predictive in practical situations that are typical in bottom-up design, including changes in promoter copy number, regulatory protein level, and cell load. In this work, we derived novel mechanistic steady-state models of the lux inducible system, used as case study, relying on different assumptions on regulatory protein (LuxR) and cognate promoter (Plux) concentrations, inducer-protein complex formation, and resource usage limitation. We demonstrated that a change in the considered model assumptions can significantly affect circuit output, and preliminary experimental data are in accordance with the simulated activation curves. We finally showed that the models are identifiable a priori (in the analytically tractable cases) and a posteriori, and we determined the specific experiments needed to parametrize them. Although a larger-scale experimental validation is required, in the future the reported models may support synthetic circuits output prediction in practical situations with unprecedented details. Full article
(This article belongs to the Special Issue Computational Synthetic Biology)
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Open AccessFeature PaperArticle Distilling Robust Design Principles of Biocircuits Using Mixed Integer Dynamic Optimization
Processes 2019, 7(2), 92; https://doi.org/10.3390/pr7020092
Received: 3 December 2018 / Revised: 24 January 2019 / Accepted: 31 January 2019 / Published: 13 February 2019
Cited by 1 | PDF Full-text (748 KB) | HTML Full-text | XML Full-text
Abstract
A major challenge in model-based design of synthetic biochemical circuits is how to address uncertainty in the parameters. A circuit whose behavior is robust to variations in the parameters will have more chances to behave as predicted when implemented in practice, and also [...] Read more.
A major challenge in model-based design of synthetic biochemical circuits is how to address uncertainty in the parameters. A circuit whose behavior is robust to variations in the parameters will have more chances to behave as predicted when implemented in practice, and also to function reliably in presence of fluctuations and noise. Here, we extend our recent work on automated-design based on mixed-integer multi-criteria dynamic optimization to take into account parametric uncertainty. We exploit the intensive sampling of the design space performed by a global optimization algorithm to obtain the robustness of the topologies without significant additional computational effort. Our procedure provides automatically topologies that best trade-off performance and robustness against parameter fluctuations. We illustrate our approach considering the automated design of gene circuits achieving adaptation. Full article
(This article belongs to the Special Issue Computational Synthetic Biology)
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Open AccessFeature PaperArticle Component Characterization in a Growth-Dependent Physiological Context: Optimal Experimental Design
Processes 2019, 7(1), 52; https://doi.org/10.3390/pr7010052
Received: 28 November 2018 / Revised: 10 January 2019 / Accepted: 15 January 2019 / Published: 21 January 2019
Cited by 1 | PDF Full-text (1059 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Computational Synthetic Biology)
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Open AccessArticle Towards an Aspect-Oriented Design and Modelling Framework for Synthetic Biology
Processes 2018, 6(9), 167; https://doi.org/10.3390/pr6090167
Received: 29 June 2018 / Revised: 3 September 2018 / Accepted: 12 September 2018 / Published: 15 September 2018
Cited by 2 | PDF Full-text (1995 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Work on synthetic biology has largely used a component-based metaphor for system construction. While this paradigm has been successful for the construction of numerous systems, the incorporation of contextual design issues—either compositional, host or environmental—will be key to realising more complex applications. Here, [...] Read more.
Work on synthetic biology has largely used a component-based metaphor for system construction. While this paradigm has been successful for the construction of numerous systems, the incorporation of contextual design issues—either compositional, host or environmental—will be key to realising more complex applications. Here, we present a design framework that radically steps away from a purely parts-based paradigm by using aspect-oriented software engineering concepts. We believe that the notion of concerns is a powerful and biologically credible way of thinking about system synthesis. By adopting this approach, we can separate core concerns, which represent modular aims of the design, from cross-cutting concerns, which represent system-wide attributes. The explicit handling of cross-cutting concerns allows for contextual information to enter the design process in a modular way. As a proof-of-principle, we implemented the aspect-oriented approach in the Python tool, SynBioWeaver, which enables the combination, or weaving, of core and cross-cutting concerns. The power and flexibility of this framework is demonstrated through a number of examples covering the inclusion of part context, combining circuit designs in a context dependent manner, and the generation of rule, logic and reaction models from synthetic circuit designs. Full article
(This article belongs to the Special Issue Computational Synthetic Biology)
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Open AccessFeature PaperArticle On-Line Optimal Input Design Increases the Efficiency and Accuracy of the Modelling of an Inducible Synthetic Promoter
Processes 2018, 6(9), 148; https://doi.org/10.3390/pr6090148
Received: 29 June 2018 / Revised: 24 August 2018 / Accepted: 27 August 2018 / Published: 1 September 2018
Cited by 2 | PDF Full-text (1974 KB) | HTML Full-text | XML Full-text
Abstract
Synthetic biology seeks to design biological parts and circuits that implement new functions in cells. Major accomplishments have been reported in this field, yet predicting a priori the in vivo behaviour of synthetic gene circuits is major a challenge. Mathematical models offer a [...] Read more.
Synthetic biology seeks to design biological parts and circuits that implement new functions in cells. Major accomplishments have been reported in this field, yet predicting a priori the in vivo behaviour of synthetic gene circuits is major a challenge. Mathematical models offer a means to address this bottleneck. However, in biology, modelling is perceived as an expensive, time-consuming task. Indeed, the quality of predictions depends on the accuracy of parameters, which are traditionally inferred from poorly informative data. How much can parameter accuracy be improved by using model-based optimal experimental design (MBOED)? To tackle this question, we considered an inducible promoter in the yeast S. cerevisiae. Using in vivo data, we re-fit a dynamic model for this component and then compared the performance of standard (e.g., step inputs) and optimally designed experiments for parameter inference. We found that MBOED improves the quality of model calibration by ∼60%. Results further improve up to 84 % when considering on-line optimal experimental design (OED). Our in silico results suggest that MBOED provides a significant advantage in the identification of models of biological parts and should thus be integrated into their characterisation. Full article
(This article belongs to the Special Issue Computational Synthetic Biology)
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Open AccessFeature PaperArticle Identifiability and Reconstruction of Biochemical Reaction Networks from Population Snapshot Data
Processes 2018, 6(9), 136; https://doi.org/10.3390/pr6090136
Received: 28 June 2018 / Revised: 31 July 2018 / Accepted: 15 August 2018 / Published: 22 August 2018
Cited by 1 | PDF Full-text (588 KB) | HTML Full-text | XML Full-text
Abstract
Inference of biochemical network models from experimental data is a crucial problem in systems and synthetic biology that includes parameter calibration but also identification of unknown interactions. Stochastic modelling from single-cell data is known to improve identifiability of reaction network parameters for specific [...] Read more.
Inference of biochemical network models from experimental data is a crucial problem in systems and synthetic biology that includes parameter calibration but also identification of unknown interactions. Stochastic modelling from single-cell data is known to improve identifiability of reaction network parameters for specific systems. However, general results are lacking, and the advantage over deterministic, population-average approaches has not been explored for network reconstruction. In this work, we study identifiability and propose new reconstruction methods for biochemical interaction networks. Focusing on population-snapshot data and networks with reaction rates affine in the state, for parameter estimation, we derive general methods to test structural identifiability and demonstrate them in connection with practical identifiability for a reporter gene in silico case study. In the same framework, we next develop a two-step approach to the reconstruction of unknown networks of interactions. We apply it to compare the achievable network reconstruction performance in a deterministic and a stochastic setting, showing the advantage of the latter, and demonstrate it on population-snapshot data from a simulated example. Full article
(This article belongs to the Special Issue Computational Synthetic Biology)
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