Standard Virtual Biological Parts: A Repository of Modular Modeling Components for Synthetic Biology

Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
Bioinformatics (Impact Factor: 4.98). 02/2010; 26(7):925-31. DOI: 10.1093/bioinformatics/btq063
Source: PubMed


Motivation: Fabrication of synthetic biological systems is greatly enhanced by incorporating engineering design principles and techniques such as computer-aided design. To this end, the ongoing standardization of biological parts presents an opportunity to develop libraries of standard virtual parts in the form of mathematical models that can be combined to inform system design. Results: We present an online Repository, populated with a collection of standardized models that can readily be recombined to model different biological systems using the inherent modularity support of the CellML 1.1 model exchange format. The applicability of this approach is demonstrated by modeling gold-medal winning iGEM machines. Availability and Implementation: The Repository is available online as part of We hope to stimulate the worldwide community to reuse and extend the models therein, and contribute to the Repository of Standard Virtual Parts thus founded. Systems Model architecture information for the Systems Model described here, along with an additional example and a tutorial, is also available as Supplementary information. The example Systems Model from this manuscript can be found at The Template models used in the example can be found at Contact: [email protected]
/* */ Supplementary information: Supplementary data are available at Bioinformatics online. © The Author 2010. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]
/* */

Full-text preview

Available from:
  • Source
    • "The modular nature of SVPs allows new templates to be defined at any desired level of abstraction. In this work, the set of SVP types was extended beyond those described previously [Cooling et al. 2010] to include operators and shims (spacer sequences). A set of promoters acting as two-input logic gates was also defined. "

    ACM Journal on Emerging Technologies in Computing Systems 12/2014; 11(3):1-19. DOI:10.1145/2631921 · 0.73 Impact Factor
    • "In particular, hybrid modular parts could be created that integrate standard mathematical formulations describing biological parts (e.g. Cooling et al., 2010; Rodrigo, Carrera, & Jaramillo, 2007) with nonparametric approaches that model the contained coefficients based on e.g. the DNA sequence. The combination of various hybrid modular parts can subsequently be used to either describe given systems or to design synthetic systems. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Hybrid semi-parametric models consist of model structures that combine parametric and nonparametric submodels based on different knowledge sources. The development of a hybrid semi-parametric model can offer several advantages over traditional mechanistic or data-driven modeling, as reviewed in this paper. These advantages, such as broader knowledge base, transparency of the modeling approach and cost-effective model development, have been widely recognized, not only in academia but also in the industry. In this paper, the most common hybrid semi-parametric modeling and parameter identification techniques are revisited. Applications in the areas of (bio)chemical engineering for process monitoring, control, optimization, scale-up and model-reduction are reviewed. It is outlined that the application of hybrid semi-parametric techniques does not automatically lead into better results but that rational knowledge integration has potential to significantly improve model-based process operation and design.
    Computers & Chemical Engineering 01/2014; 60:86-101. DOI:10.1016/j.compchemeng.2013.08.008 · 2.78 Impact Factor
  • Source
    • "Here, we focus on the use of CellML to provide general purpose " plug and play " of mathematical models and model configuration in OpenCMISS applications. CellML (Cuellar et al., 2003) 3 is an XML format for encoding mathematical models in a modular and reusable manner (Nickerson and Buist, 2008; Cooling et al., 2010). See Section 2 below for a general introduction to the mathematical framework provided by CellML. "
    [Show abstract] [Hide abstract]
    ABSTRACT: OpenCMISS is an open-source modeling environment aimed, in particular, at the solution of bioengineering problems. OpenCMISS consists of two main parts: a computational library (OpenCMISS-Iron) and a field manipulation and visualisation library (OpenCMISS-Zinc). OpenCMISS is designed for the solution of coupled multi-scale, multi-physics problems in a general-purpose parallel environment. CellML is an XML format designed to encode biophysically based systems of ordinary differential equations and both linear and non-linear algebraic equations. A primary design goal of CellML is to allow mathematical models to be encoded in a modular and reusable format to aide reproducibility and interoperability of modeling studies. In OpenCMISS we make use of CellML models to enable users to configure various aspects of their multi-scale physiological models. This avoids the need for users to be familiar with the OpenCMISS internal code in order to perform customised computational experiments. Examples of this are: cellular electrophysiology models embedded in tissue electrical propagation models; material constitutive relationships for mechanical growth and deformation simulations; time-varying boundary conditions for various problem domains; fluid constitutive relationships and lumped parameter models. In this paper we provide implementation details describing how CellML models are integrated into multi-scale physiological models in OpenCMISS. The external interface OpenCMISS presents to users will also be described, including specific examples exemplifying the extensibility and usability these tools provide the physiological modelling and simulation community. We conclude with some thoughts on future extension of OpenCMISS to make use other community developed information standards, such as FieldML, SED-ML, and BioSignalML. Plans for the integration of accelerator code (GPU and FPGA) generated from CellML models is also discussed.
    Frontiers in Bioengineering and Biotechnology 01/2014; 2:79. DOI:10.3389/fbioe.2014.00079
Show more