Constructing synthetic biology workflows in the cloud

The synthetic biology design process has traditionally been heavily dependent upon manual searching, acquisition and integration of existing biological data. A large amount of such data is already available from Internet-based resources, but data exchange between these resources is often undertaken manually. Automating the communication between different resources can be done by the generation of computational workflows to achieve complex tasks that cannot be carried out easily or efficiently by a single resource. Computational workflows involve the passage of data from one resource, or process, to another in a distributed computing environment. In a typical bioinformatics workflow, the predefined order in which processes are invoked in a synchronous fashion and are described in a workflow definition document. However, in synthetic biology the diversity of resources and manufacturing tasks required favour a more flexible model for process execution. Here, the authors present the Protocol for Linking External Nodes (POLEN), a Cloud-based system that facilitates synthetic biology design workflows that operate asynchronously. Messages are used to notify POLEN resources of events in real time, and to log historical events such as the availability of new data, enabling networks of cooperation. POLEN can be used to coordinate the integration of different synthetic biology resources, to ensure consistency of information across distributed repositories through added support for data standards, and ultimately to facilitate the synthetic biology life cycle for designing and implementing biological systems.

[1]  Cosimo Laneve,et al.  Formal molecular biology , 2004, Theor. Comput. Sci..

[2]  Matthew R. Pocock,et al.  Microbase2.0: a generic framework for computationally intensive bioinformatics workflows in the cloud. , 2012, Journal of integrative bioinformatics.

[3]  Paul Freemont,et al.  Synthetic biology – the state of play , 2012, FEBS letters.

[4]  J. Stelling,et al.  Computational design tools for synthetic biology. , 2009, Current opinion in biotechnology.

[5]  Melanie I. Stefan,et al.  BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models , 2010, BMC Systems Biology.

[6]  Zhen Zhang,et al.  Sharing Structure and Function in Biological Design with SBOL 2.0. , 2016, ACS synthetic biology.

[7]  H. Sauro,et al.  Standard Biological Parts Knowledgebase , 2011, PloS one.

[8]  H M Sauro,et al.  Mathematical modeling and synthetic biology. , 2008, Drug Discovery Today : Disease Models.

[9]  Carole A. Goble,et al.  myExperiment: a repository and social network for the sharing of bioinformatics workflows , 2010, Nucleic Acids Res..

[10]  Hiroaki Kitano,et al.  The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models , 2003, Bioinform..

[11]  Chris J. Myers,et al.  Platforms for Genetic Design Automation , 2013 .

[12]  Paul S. Freemont,et al.  Computational design approaches and tools for synthetic biology. , 2011, Integrative biology : quantitative biosciences from nano to macro.

[13]  Goksel Misirli,et al.  Composable Modular Models for Synthetic Biology , 2014, ACM J. Emerg. Technol. Comput. Syst..

[14]  Catherine M Lloyd,et al.  CellML: its future, present and past. , 2004, Progress in biophysics and molecular biology.

[15]  Jacob Beal,et al.  An end-to-end workflow for engineering of biological networks from high-level specifications. , 2012, ACS synthetic biology.

[16]  Matthew R. Pocock,et al.  The SBOL Stack: A Platform for Storing, Publishing, and Sharing Synthetic Biology Designs. , 2016, ACS synthetic biology.

[17]  Matthew R. Pocock,et al.  Data Integration and Mining for Synthetic Biology Design. , 2016, ACS synthetic biology.

[18]  Iñaki Sainz de Murieta,et al.  Toward the First Data Acquisition Standard in Synthetic Biology. , 2016, ACS synthetic biology.

[19]  Wolfgang Wiechert,et al.  A scientific workflow framework for (13)C metabolic flux analysis. , 2016, Journal of biotechnology.

[20]  Timothy S. Ham,et al.  Design, implementation and practice of JBEI-ICE: an open source biological part registry platform and tools , 2012, Nucleic acids research.

[21]  J. Peccoud,et al.  Targeted Development of Registries of Biological Parts , 2008, PloS one.