scholarly journals iGEM comes of age: trends in its research output

2021 ◽  
Author(s):  
Ashwin K. Jainarayanan ◽  
Anastasios Galanis ◽  
Amatullah Mustafa Nakara ◽  
Guilherme E. Kundlatsch ◽  
Roger Rubio-Sánchez
Keyword(s):  
Quantitative Analysis ◽  
Synthetic Biology ◽  
Translational Research ◽  
Research Output ◽  
Biological Systems ◽  
Genetically Engineered ◽  
Sustainable Future ◽  
Age Trends

AbstractThe international Genetically Engineered Machine (iGEM) is an educational benchmark in synthetic biology. Eighteen years after its inception, it has also catalysed the infusion of synthetic biology with interdisciplinary fundamental and translational research as well as with inspired young scientists. Here, we communicate a quantitative analysis of compiled published work associated to iGEM projects, highlighting trends in their dissemination and versatility. As iGEM comes of age, we anticipate it will continue to revolutionise, alongside SynBio, several disciplines of science and industries through the development of synthetic biological systems towards a sustainable future.

scholarly journals An amazing experience: “One that no one regrets and everyone will remember”

2015 ◽  
Vol 37 (1) ◽  
pp. 34-35
Author(s):  
Lewis Moffat
Keyword(s):  
Synthetic Biology ◽  
University Students ◽  
Biological Systems ◽  
Genetically Engineered ◽  
University College ◽  
University College London

In 2014, the Biochemical Society helped fund students taking part in the iGEM (the International Genetically Engineered Machine) competition. This synthetic biology competition allows university students to work in teams to solve real challenges by building genetically engineered biological systems using BioBricks, from the Registry of Standard Biological Parts. Each team has to manage their own project, secure funding and advocates their research. In 2014 iGEM celebrated its tenth anniversary, which meant that all teams had the opportunity to present their accomplishments at the Giant Jamboree in Boston, MA. Lewis Moffat (iGEM team, University College London, UK) and Jessica Martyn (iGEM team, Dundee, UK) took part in the competition. They have written accounts of their experiences throughout the project and what they gained from taking part.

scholarly journals BioMaster: An Integrated Database and Analytic Platform to Provide Comprehensive Information About BioBrick Parts

2021 ◽  
Vol 12 ◽  
Author(s):  
Beibei Wang ◽  
Huayi Yang ◽  
Jianan Sun ◽  
Chuhao Dou ◽  
Jian Huang ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Biological Systems ◽  
Relevant Information ◽  
Genetically Engineered ◽  
Major Obstacle ◽  
Genetic Circuit ◽  
Related Literature ◽  
Comprehensive Information ◽  
User Friendly ◽  
Biobrick Parts

Synthetic biology seeks to create new biological parts, devices, and systems, and to reconfigure existing natural biological systems for custom-designed purposes. The standardized BioBrick parts are the foundation of synthetic biology. The incomplete and flawed metadata of BioBrick parts, however, are a major obstacle for designing genetic circuit easily, quickly, and accurately. Here, a database termed BioMaster http://www.biomaster-uestc.cn was developed to extensively complement information about BioBrick parts, which includes 47,934 items of BioBrick parts from the international Genetically Engineered Machine (iGEM) Registry with more comprehensive information integrated from 10 databases, providing corresponding information about functions, activities, interactions, and related literature. Moreover, BioMaster is also a user-friendly platform for retrieval and analyses of relevant information on BioBrick parts.

scholarly journals On Robust State Estimation of Gene Networks

2010 ◽  
Vol 2 ◽  
pp. 117959721000200 ◽  
Author(s):  
Chia-Hua Chuang ◽  
Chun-Liang Lin
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Quantitative Analysis ◽  
State Estimation ◽  
Gene Networks ◽  
Gene Network ◽  
Biological Systems ◽  
Uncertain Process ◽  
Network Systems ◽  
Internal States

Gene networks in biological systems are not only nonlinear but also stochastic due to noise corruption. How to accurately estimate the internal states of the noisy gene networks is an attractive issue to researchers. However, the internal states of biological systems are mostly inaccessible by direct measurement. This paper intends to develop a robust extended Kalman filter for state and parameter estimation of a class of gene network systems with uncertain process noises. Quantitative analysis of the estimation performance is conducted and some representative examples are provided for demonstration.

scholarly journals Independent control of amplitude and period in a synthetic oscillator circuit with modified repressilator

2022 ◽  
Vol 5 (1) ◽  
Author(s):  
Fengyu Zhang ◽  
Yanhong Sun ◽  
Yihao Zhang ◽  
Wenting Shen ◽  
Shujing Wang ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Synthetic Biology ◽  
Biological Systems ◽  
Reporter Genes ◽  
Quantitative Model ◽  
Theoretical Level ◽  
Independent Control ◽  
Oscillator Circuit ◽  
Biological Circuits

AbstractSynthetic Biology aims to create predictable biological circuits and fully operational biological systems. Although there are methods to create more stable oscillators, such as repressilators, independently controlling the oscillation of reporter genes in terms of their amplitude and period is only on theoretical level. Here, we introduce a new oscillator circuit that can be independently controlled by two inducers in Escherichia coli. Some control components, including σECF11 and NahR, were added to the circuit. By systematically tuning the concentration of the inducers, salicylate and IPTG, the amplitude and period can be modulated independently. Furthermore, we constructed a quantitative model to forecast the regulation results. Under the guidance of the model, the expected oscillation can be regulated by choosing the proper concentration combinations of inducers. In summary, our work achieved independent control of the oscillator circuit, which allows the oscillator to be modularized and used in more complex circuit designs.

Integrating the whole from the sum of the parts: vignettes in computational biology

2017 ◽  
Vol 1 (3) ◽  
pp. 241-243
Author(s):  
Jeffrey Skolnick
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Synthetic Biology ◽  
Personalized Medicine ◽  
Computational Biology ◽  
Biological Systems ◽  
Deep Understanding ◽  
Biological Processes ◽  
Practical Applications ◽  
Biological Variables

As is typical of contemporary cutting-edge interdisciplinary fields, computational biology touches and impacts many disciplines ranging from fundamental studies in the areas of genomics, proteomics transcriptomics, lipidomics to practical applications such as personalized medicine, drug discovery, and synthetic biology. This editorial examines the multifaceted role computational biology plays. Using the tools of deep learning, it can make powerful predictions of many biological variables, which may not provide a deep understanding of what factors contribute to the phenomena. Alternatively, it can provide the how and the why of biological processes. Most importantly, it can help guide and interpret what experiments and biological systems to study.

Quantitative analysis of glycine related metabolic pathways for one-carbon synthetic biology

2020 ◽  
Vol 64 ◽  
pp. 70-78 ◽  
Author(s):  
Yaeseong Hong ◽  
Jie Ren ◽  
Xinyi Zhang ◽  
Wei Wang ◽  
An-Ping Zeng
Keyword(s):  
Quantitative Analysis ◽  
Synthetic Biology ◽  
Metabolic Pathways

scholarly journals Are the biomedical sciences ready for synthetic biology?

2020 ◽  
Vol 11 (1) ◽  
pp. 23-31
Author(s):  
Maxwell S. DeNies ◽  
Allen P. Liu ◽  
Santiago Schnell
Keyword(s):  
Synthetic Biology ◽  
Biomedical Research ◽  
Molecular Level ◽  
Biological Systems ◽  
Science Research ◽  
Functional System ◽  
Biomedical Sciences ◽  
Present Evidence ◽  
Experimental Control ◽  
Discovery Science

AbstractThe ability to construct a functional system from its individual components is foundational to understanding how it works. Synthetic biology is a broad field that draws from principles of engineering and computer science to create new biological systems or parts with novel function. While this has drawn well-deserved acclaim within the biotechnology community, application of synthetic biology methodologies to study biological systems has potential to fundamentally change how biomedical research is conducted by providing researchers with improved experimental control. While the concepts behind synthetic biology are not new, we present evidence supporting why the current research environment is conducive for integration of synthetic biology approaches within biomedical research. In this perspective we explore the idea of synthetic biology as a discovery science research tool and provide examples of both top-down and bottom-up approaches that have already been used to answer important physiology questions at both the organismal and molecular level.

Bottom-Up Synthetic Biology: Engineering in a Tinkerer’s World

Science ◽  
2011 ◽  
Vol 333 (6047) ◽  
pp. 1252-1254 ◽  
Author(s):  
Petra Schwille
Keyword(s):  
Synthetic Biology ◽  
Life Science ◽  
Biological Systems ◽  
Living Systems ◽  
Design Features ◽  
Literal Interpretation ◽  
Bottom Up ◽  
Systematic Construction ◽  
Science Discipline ◽  
Recent Developments

How synthetic can “synthetic biology” be? A literal interpretation of the name of this new life science discipline invokes expectations of the systematic construction of biological systems with cells being built module by module—from the bottom up. But can this possibly be achieved, taking into account the enormous complexity and redundancy of living systems, which distinguish them quite remarkably from design features that characterize human inventions? There are several recent developments in biology, in tight conjunction with quantitative disciplines, that may bring this literal perspective into the realm of the possible. However, such bottom-up engineering requires tools that were originally designed by nature’s greatest tinkerer: evolution.

Mammalian Synthetic Biology: Engineering Biological Systems

2017 ◽  
Vol 19 (1) ◽  
pp. 249-277 ◽  
Author(s):  
Joshua B. Black ◽  
Pablo Perez-Pinera ◽  
Charles A. Gersbach
Keyword(s):  
Synthetic Biology ◽  
Biological Systems
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