4.6 Article

Towards the rational design of synthetic cells with prescribed population dynamics

Journal

JOURNAL OF THE ROYAL SOCIETY INTERFACE
Volume 9, Issue 76, Pages 2883-2898

Publisher

ROYAL SOC
DOI: 10.1098/rsif.2012.0280

Keywords

-

Ask authors/readers for more resources

The rational design of synthetic cell populations with prescribed behaviours is a long-standing goal of synthetic biology, with the potential to greatly accelerate the development of biotechnological applications in areas ranging from medical research to energy production. Achieving this goal requires well-characterized components, modular implementation strategies, simulation across temporal and spatial scales and automatic compilation of high-level designs to low-level genetic parts that function reliably inside cells. Many of these steps are incomplete or only partially understood, and methods for integrating them within a common design framework have yet to be developed. Here, we address these challenges by developing a prototype framework for designing synthetic cells with prescribed population dynamics. We extend the genetic engineering of cells (GEC) language, originally developed for programming intracellular dynamics, with cell population factors such as cell growth, division and dormancy, together with spatio-temporal simulation methods. As a case study, we use our framework to design synthetic cells with predator-prey interactions that, when simulated, produce complex spatio-temporal behaviours such as travelling waves and spatio-temporal chaos. An analysis of our design reveals that environmental factors such as density-dependent dormancy and reduced extracellular space destabilize the population dynamics and increase the range of genetic variants for which complex spatio-temporal behaviours are possible. Our findings highlight the importance of considering such factors during the design process. We then use our analysis of population dynamics to inform the selection of genetic parts, which could be used to obtain the desired spatio-temporal behaviours. By identifying, integrating and automating key stages of the design process, we provide acomputational framework for designing synthetic systems, which could be tested in future laboratory studies.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

Article Chemistry, Multidisciplinary

Predicting DNA hybridization kinetics from sequence

Jinny X. Zhang, John Z. Fang, Wei Duan, Lucia R. Wu, Angela W. Zhang, Neil Dalchau, Boyan Yordanov, Rasmus Petersen, Andrew Phillips, David Yu Zhang

NATURE CHEMISTRY (2018)

Article Nanoscience & Nanotechnology

A spatially localized architecture for fast and modular DNA computing

Gourab Chatterjee, Neil Dalchau, Richard A. Muscat, Andrew Phillips, Georg Seelig

NATURE NANOTECHNOLOGY (2017)

Article Biochemical Research Methods

Efficient Switches in Biology and Computer Science

Luca Cardelli, Rosa D. Hernansaiz-Ballesteros, Neil Dalchau, Attila Csikasz-Nagy

PLOS COMPUTATIONAL BIOLOGY (2017)

Article Multidisciplinary Sciences

Model reduction enables Turing instability analysis of large reaction - diffusion models

Stephen Smith, Neil Dalchau

JOURNAL OF THE ROYAL SOCIETY INTERFACE (2018)

Article Computer Science, Artificial Intelligence

Computing with biological switches and clocks

Neil Dalchau, Gregory Szep, Rosa Hernansaiz-Ballesteros, Chris P. Barnes, Luca Cardelli, Andrew Phillips, Attila Csikasz-Nagy

NATURAL COMPUTING (2018)

Article Computer Science, Artificial Intelligence

Computing with biological switches and clocks

Neil Dalchau, Gregory Szep, Rosa Hernansaiz-Ballesteros, Chris P. Barnes, Luca Cardelli, Andrew Phillips, Attila Csikasz-Nagy

NATURAL COMPUTING (2018)

Article Immunology

A Mechanistic Model for Predicting Cell Surface Presentation of Competing Peptides by MHC Class I Molecules

Denise S. M. Boulanger, Ruth C. Eccleston, Andrew Phillips, Peter Coveney, Tim Elliott, Neil Dalchau

FRONTIERS IN IMMUNOLOGY (2018)

Article Multidisciplinary Sciences

Synthesizing and tuning stochastic chemical reaction networks with specified behaviours

Niall Murphy, Rasmus Petersen, Andrew Phillips, Boyan Yordanov, Neil Dalchau

JOURNAL OF THE ROYAL SOCIETY INTERFACE (2018)

Article Computer Science, Artificial Intelligence

Scaling up genetic circuit design for cellular computing: advances and prospects

Yiyu Xiang, Neil Dalchau, Baojun Wang

NATURAL COMPUTING (2018)

Article Nanoscience & Nanotechnology

DNA-based communication in populations of synthetic protocells

Alex Joesaar, Shuo Yang, Bas Bogels, Ardjan van der Linden, Pascal Pieters, B. V. V. S. Pavan Kumar, Neil Dalchau, Andrew Phillips, Stephen Mann, Tom F. A. de Greef

NATURE NANOTECHNOLOGY (2019)

Article Multidisciplinary Sciences

Stochastic pulsing of gene expression enables the generation of spatial patterns in Bacillus subtilis biofilms

Eugene Nadezhdin, Niall Murphy, Neil Dalchau, Andrew Phillips, James C. W. Locke

NATURE COMMUNICATIONS (2020)

Article Biology

Turing Patterning in Stratified Domains

Andrew L. Krause, Vaclav Klika, Jacob Halatek, Paul K. Grant, Thomas E. Woolley, Neil Dalchau, Eamonn A. Gaffney

BULLETIN OF MATHEMATICAL BIOLOGY (2020)

Article Multidisciplinary Sciences

A systematic approach to inserting split inteins for Boolean logic gate engineering and basal activity reduction

Trevor Y. H. Ho, Alexander Shao, Zeyu Lu, Harri Savilahti, Filippo Menolascina, Lei Wang, Neil Dalchau, Baojun Wang

Summary: Split inteins are powerful tools for designing synthetic split proteins. Here the authors use a mini-Mu transposon screen to map split sites, enabling the development of protein-based logic gates and fine control of protein activity.

NATURE COMMUNICATIONS (2021)

Article Multidisciplinary Sciences

A deep learning model for predicting next-generation sequencing depth from DNA sequence

Jinny X. Zhang, Boyan Yordanov, Alexander Gaunt, Michael X. Wang, Peng Dai, Yuan-Jyue Chen, Kerou Zhang, John Z. Fang, Neil Dalchau, Jiaming Li, Andrew Phillips, David Yu Zhang

Summary: A deep learning model is developed to predict NGS depth using DNA probe sequences, applied to both human and non-human sequencing panels. The model shows high accuracy in cross-validation for different panels.

NATURE COMMUNICATIONS (2021)

Article Multidisciplinary Sciences

Interpretation of morphogen gradients by a synthetic bistable circuit

Paul K. Grant, Gregory Szep, Om Patange, Jacob Halatek, Valerie Coppard, Attila Csikasz-Nagy, Jim Haseloff, James C. W. Locke, Neil Dalchau, Andrew Phillips

NATURE COMMUNICATIONS (2020)

No Data Available