Computational Methods in Synthetic Biology

A special issue of Biology (ISSN 2079-7737).

Deadline for manuscript submissions: closed (15 April 2021) | Viewed by 9032

Special Issue Editors

Department of Cellular, Computational and Integrative Biology (CIBIO) & Department of Mathematics, University of Trento, Via Sommarive, 14, 38123 Povo, Italy
Interests: algebraic logic; artificial intelligence; bioinformatics; biostatistics; concurrency; formal languages; formal methods; mathematical biology; mathematics and computer science; modeling and simulation; programming languages; proof theory; stochastic simulation; synthetic biology; systems biology
Department of Computer Science, Clarkson University, 8 Clarkson Avenue Potsdam, NY 13699-5815, USA
Interests: combinatorics; logic; finite model theory; systems biology

Special Issue Information

The field of synthetic biology has witnessed a rapid transformation from being the academic stage of a sci-fi story to industrial reality. In a melting pot of biology and quantitative sciences, the joint effort in this field has been giving rise to methodologies for designing living technologies. The resulting modified organisms yield products that ordinarily depend on petrochemicals, e.g., fuels, plastic, and cosmetics. Others can be used for purifying greywater or generating electricity. Within the last decade, many companies have been emerging to harness the developments in organism engineering, backed up by numerous research groups around the world.

Very much like in the early days of other engineering fields, the early progress in synthetic biology was shaped by collections of test-cases. Yet, in the way that computer-aided design became an essential element of mature engineering disciplines, the prospects of synthetic technologies call for computational methods for containing and accelerating the progress. Due to the close proximity to systems biology, computational methods are now common in this field as well. Consequently, synthetic biology is starting to benefit from advances in computational methods. In particular, computational methods can be used to assist with and guide a variety of tasks, including modelling and simulation, circuit design, lab automation, and data analysis, to name a few.

This Special Issue welcomes the submission of original research and review manuscripts focusing on computational aspects of synthetic biology as well as specific quantitative technologies that address various aspects of organism engineering. The resulting Special Issue will provide an overview of the role of computational methods in this exciting and interdisciplinary field.

Dr. Ozan Kahramanoğulları
Prof. Dr. James Lynch
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 submissions that pass pre-check are 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. Biology 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 2700 CHF (Swiss Francs). 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 
  • Systems biology 
  • Bioinformatics 
  • Modelling and simulation 
  • Computer-aided design 
  • Machine learning 
  • Synthetic circuit design 
  • Nucleic acid computation 
  • Stochasticity and noise

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 1288 KiB  
Article
Stochastic Simulations as a Tool for Assessing Signal Fidelity in Gene Expression in Synthetic Promoter Design
Biology 2021, 10(8), 724; https://doi.org/10.3390/biology10080724 - 29 Jul 2021
Cited by 3 | Viewed by 3020
Abstract
The design and development of synthetic biology applications in a workflow often involve connecting modular components. Whereas computer-aided design tools are picking up in synthetic biology as in other areas of engineering, the methods for verifying the correct functioning of living technologies are [...] Read more.
The design and development of synthetic biology applications in a workflow often involve connecting modular components. Whereas computer-aided design tools are picking up in synthetic biology as in other areas of engineering, the methods for verifying the correct functioning of living technologies are still in their infancy. Especially, fine-tuning for the right promoter strength to match the design specifications is often a lengthy and expensive experimental process. In particular, the relationship between signal fidelity and noise in synthetic promoter design can be a key parameter that can affect the healthy functioning of the engineered organism. To this end, based on our previous work on synthetic promoters for the E. coli PhoBR two-component system, we make a case for using chemical reaction network models for computational verification of various promoter designs before a lab implementation. We provide an analysis of this system with extensive stochastic simulations at a single-cell level to assess the signal fidelity and noise relationship. We then show how quasi-steady-state analysis via ordinary differential equations can be used to navigate between models with different levels of detail. We compare stochastic simulations with our full and reduced models by using various metrics for assessing noise. Our analysis suggests that strong promoters with low unbinding rates can act as control tools for filtering out intrinsic noise in the PhoBR context. Our results confirm that even simpler models can be used to determine promoters with specific signal to noise characteristics. Full article
(This article belongs to the Special Issue Computational Methods in Synthetic Biology)
Show Figures

Figure 1

17 pages, 725 KiB  
Article
A Modelling Framework Linking Resource-Based Stochastic Translation to the Optimal Design of Synthetic Constructs
Biology 2021, 10(1), 37; https://doi.org/10.3390/biology10010037 - 07 Jan 2021
Cited by 4 | Viewed by 4900
Abstract
The effect of gene expression burden on engineered cells has motivated the use of “whole-cell models” (WCMs) that use shared cellular resources to predict how unnatural gene expression affects cell growth. A common problem with many WCMs is their inability to capture translation [...] Read more.
The effect of gene expression burden on engineered cells has motivated the use of “whole-cell models” (WCMs) that use shared cellular resources to predict how unnatural gene expression affects cell growth. A common problem with many WCMs is their inability to capture translation in sufficient detail to consider the impact of ribosomal queue formation on mRNA transcripts. To address this, we have built a “stochastic cell calculator” (StoCellAtor) that combines a modified TASEP with a stochastic implementation of an existing WCM. We show how our framework can be used to link a synthetic construct’s modular design (promoter, ribosome binding site (RBS) and codon composition) to protein yield during continuous culture, with a particular focus on the effects of low-efficiency codons and their impact on ribosomal queues. Through our analysis, we recover design principles previously established in our work on burden-sensing strategies, namely that changing promoter strength is often a more efficient way to increase protein yield than RBS strength. Importantly, however, we show how these design implications can change depending on both the duration of protein expression, and on the presence of ribosomal queues. Full article
(This article belongs to the Special Issue Computational Methods in Synthetic Biology)
Show Figures

Graphical abstract

Back to TopTop