Automated Design of Synthetic Gene Circuits in the Presence of Molecular Noise

被引:5
作者
Sequeiros, Carlos [1 ]
Vazquez, Carlos [2 ,3 ]
Banga, Julio R. [1 ]
Otero-Muras, Irene [4 ]
机构
[1] Spanish Natl Res Council, Computat Biol Lab, MBG CSIC, Pontevedra 36143, Spain
[2] Univ A Coruna, Dept Math, La Coruna 15071, Spain
[3] Univ A Coruna, CITIC, La Coruna 15071, Spain
[4] Inst Integrat Syst Biol I2SysBio CSIC UV, Computat Synthet Biol Grp, Valencia 46980, Spain
来源
ACS SYNTHETIC BIOLOGY | 2023年 / 12卷 / 10期
关键词
genetic design automation; molecular noise; stochastic dynamics; robust oscillator; biochemicaladaptation; toggle switch; SIMULATION; BIOLOGY; NETWORK;
D O I
10.1021/acssynbio.3c00033
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Microorganisms (mainly bacteria and yeast) are frequently used as hosts for genetic constructs in synthetic biology applications. Molecular noise might have a significant effect on the dynamics of gene regulation in microbial cells, mainly attributed to the low copy numbers of mRNA species involved. However, the inclusion of molecular noise in the automated design of biocircuits is not a common practice due to the computational burden linked to the chemical master equation describing the dynamics of stochastic gene regulatory circuits. Here, we address the automated design of synthetic gene circuits under the effect of molecular noise combining a mixed integer nonlinear global optimization method with a partial integro-differential equation model describing the evolution of stochastic gene regulatory systems that approximates very efficiently the chemical master equation. We demonstrate the performance of the proposed methodology through a number of examples of relevance in synthetic biology, including different bimodal stochastic gene switches, robust stochastic oscillators, and circuits capable of achieving biochemical adaptation under noise.
引用
收藏
页码:2865 / 2876
页数:12
相关论文
共 33 条
  • [1] A new efficient approach to fit stochastic models on the basis of high-throughput experimental data using a model of IRF7 gene expression as case study
    Aguilera, Luis U.
    Zimmer, Christoph
    Kummer, Ursula
    [J]. BMC SYSTEMS BIOLOGY, 2017, 11
  • [2] α-Divergence Is Unique, Belonging to Both f-Divergence and Bregman Divergence Classes
    Amari, Shun-Ichi
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 2009, 55 (11) : 4925 - 4931
  • [3] Bayesian design of synthetic biological systems
    Barnes, Chris P.
    Silk, Daniel
    Sheng, Xia
    Stumpf, Michael P. H.
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2011, 108 (37) : 15190 - 15195
  • [4] Opportunities at the Intersection of Synthetic Biology, Machine Learning, and Automation
    Carbonell, Pablo
    Radivojevic, Tijana
    Garcia Martin, Hector
    [J]. ACS SYNTHETIC BIOLOGY, 2019, 8 (07): : 1474 - 1477
  • [5] A Network Approach to Genetic Circuit Designs
    Crowther, Matthew
    Wipat, Anil
    Goni-Moreno, Angel
    [J]. ACS SYNTHETIC BIOLOGY, 2022, 11 (09): : 3058 - 3066
  • [6] MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics
    Egea, Jose A.
    Henriques, David
    Cokelaer, Thomas
    Villaverde, Alejandro F.
    MacNamara, Aidan
    Danciu, Diana-Patricia
    Banga, Julio R.
    Saez-Rodriguez, Julio
    [J]. BMC BIOINFORMATICS, 2014, 15
  • [7] An evolutionary method for complex-process optimization
    Egea, Jose A.
    Marti, Rafael
    Banga, Julio R.
    [J]. COMPUTERS & OPERATIONS RESEARCH, 2010, 37 (02) : 315 - 324
  • [8] A synthetic oscillatory network of transcriptional regulators
    Elowitz, MB
    Leibler, S
    [J]. NATURE, 2000, 403 (6767) : 335 - 338
  • [9] A trust region SQP algorithm for mixed-integer nonlinear programming
    Exler, Oliver
    Schittkowski, Klaus
    [J]. OPTIMIZATION LETTERS, 2007, 1 (03) : 269 - 280
  • [10] Ge H., 2013, Encyclopedia of Systems Biology, P396