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TinkerCell: Modular CAD Tool for Synthetic Biology

Deepak Chandran, Frank T. Bergmann, Herbert M. Sauro

arXiv:0907.3976v1q-bio.MNq-bio.QM

TL;DR

Synthetic biology needs CAD tools that connect biological data with computational modeling while accommodating emerging standards and diverse analyses. TinkerCell provides a visual, modular modeling environment with part-specific information and an extensible C and Python interface. Its architecture supports automatic propagation of part-associated parameters and equations, multiple modeling methods, and community-contributed functionality.

  • Problem

    Synthetic biology needs computational design tools that bridge biological data and modeling while addressing emerging standards and diverse possible analyses.

  • Method

    TinkerCell combines visual construction of modular biological networks with part- and connection-associated information and an open C and Python API for external analyses.

  • Results

    TinkerCell supports automatic updates to equations and parameters when parts are replaced, diverse analysis functions, and openness to different modeling methods.

  • Takeaways & Limitations

    TinkerCell provides a flexible host for biological algorithms, data, ontologies, and community-contributed functionality.

  • Takeaways & Limitations

    Some modeling plug-ins may not support every modeling approach, although TinkerCell’s underlying structure is not limited to any particular approach.

Abstract

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Synthetic biology brings together concepts and techniques from engineering and biology. In this field, computer-aided design (CAD) is necessary in order to bridge the gap between computational modeling and biological data. An application named TinkerCell has been created in order to serve as a CAD tool for synthetic biology. TinkerCell is a visual modeling tool that supports a hierarchy of biological parts. Each part in this hierarchy consists of a set of attributes that define the part, such as sequence or rate constants. Models that are constructed using these parts can be analyzed using various C and Python programs that are hosted by TinkerCell via an extensive C and Python API. TinkerCell supports the notion of a module, which are networks with interfaces. Such modules can be connected to each other, forming larger modular networks. Because TinkerCell associates parameters and equations in a model with their respective part, parts can be loaded from databases along with their parameters and rate equations. The modular network design can be used to exchange modules as well as test the concept of modularity in biological systems. The flexible modeling framework along with the C and Python API allows TinkerCell to serve as a host to numerous third-party algorithms. TinkerCell is a free and open-source project under the Berkeley Software Distribution license. Downloads, documentation, and tutorials are available at www.tinkercell.com.

Background

Synthetic biology is developing standards and modeling practices for interchangeable, programmable biological parts and networks. TinkerCell addresses these needs with modular, extensible CAD and modeling support.

  • Synthetic biology lacks established best practices for making molecular biology interchangeable and programmable.
  • The field is developing terminology, software, and laboratory procedures, while standardization and modularity are expected to become commonplace.
  • Synthetic-biology models must support both kinetic information and the information needed to construct biological networks.
  • Because the field merges multiple disciplines, analyses can span network dynamics, DNA sequences, and statistics of part usage.
  • TinkerCell provides third-party library support, modular network design, a flexible modeling framework, and flexible visual formats.
  • Unlike many modeling applications, TinkerCell does not impose a particular modeling method, visual representation, or strict model definition.

Results and Discussion

TinkerCell is a visual CAD tool for constructing modular biological networks while storing part-specific model information. Its open C and Python interfaces connect visual models to diverse analysis programs and future standards.

  • TinkerCell visually constructs and analyzes synthetic-biology networks using nodes, connections, compartments, and interoperable modules.
  • The framework remains open to different modeling methods because C and Python programs assign meaning to stored model information.
  • TinkerCell stores parameters and functions with the parts and connections they describe, so replacing a part automatically updates associated equations or parameters.
  • C and Python libraries and modules perform analyses through TinkerCell’s API, which also integrates their outputs with the visual interface.
  • The separation of visual interface and functionality lets users add and share new functions, while TinkerCell hosts existing libraries and modules.
  • Parts and reactions are loaded from XML files, allowing the application to accommodate future standards for biological-part and connection ontologies.
  • TinkerCell’s three distinguishing features are catalog-based construction, modular networks, and broad functionality enabled by an open architecture and API.

Methods

TinkerCell uses a layered, extensible architecture built around a C++ Core library and plug-ins. Plug-ins store model information, provide visual and modeling features, and can support multiple modeling approaches.

  • Architecture: TinkerCell’s layered architecture places a generic API at the bottom and a scripting interface at the top, with extensibility at each layer.XML files add flexibility by defining the ontology of parts and connections.
  • Core library: The C++ Core library, built with Qt Toolkit 4.5.0, provides data structures and functions for drawing parts and connections and storing their information.It also supports C functions and a console window currently used as a Python console.
  • Plug-ins: The complete application is a collection of plug-ins built on the Core library, while “TinkerCell” refers to both components because the Core alone lacks modeling capability.Plug-ins provide visual features, catalog loading, part insertion, and model-editing interfaces.
  • Model representation: Plug-ins determine which information models store, including parameters, rate equations, sequences, and construction details.Numerical Attributes and Reaction Rates plug-ins provide information needed to generate stoichiometry matrices and rate equations for differential-equation or stochastic models.
  • Extensibility: Although current plug-ins focus on stoichiometry and rates, TinkerCell’s underlying structure is not limited to these modeling approaches.The text identifies rule-based and Boolean modeling as possible extensions, although they are not included in the current plug-in set.

C and Python interface

TinkerCell exposes a broad C and Python interface through plug-ins, enabling external analysis, simulation, model generation, and modular network construction. Its attribute-based and interface-based design supports reusable biological parts and composable networks.

  • API capabilities: Plug-ins expose C and Python APIs that let third-party programs retrieve or modify kinetic information and visual properties.The API includes functions such as “getParameter”, “getRate”, and “getStoichiometry”, with over a hundred functions available.
  • Simulation: TinkerCell’s default simulators compile models as separate C programs, providing compiled-C execution speed while keeping long-running C and Python tasks on separate threads.The speed gain is more visible for the stochastic simulator.
  • Integrated tools: The interface hosts deterministic and stochastic simulation, steady-state and flux-balance analysis, SciPy and PySCeS modules, NetworkX, and custom sequence or rate-generation programs.The listed integrations use both the C and Python APIs.
  • Extending functionality: Users can add C dynamic libraries or Python scripts, which appear as buttons or menu items in TinkerCell.This makes integrating new functions simple.
  • Modular design of networks: Modules are networks with interfaces that can be connected by merging corresponding components or by introducing new reactions between interfaces.Connecting a kinase to a phosphorylated protein merges the represented molecular species without altering the original modules.
  • Composite parts: TinkerCell represents composite biological parts by preserving sub-part attributes while allowing the composite part to have its own family and attributes.The API can access parameters, annotations, authorship, and identification numbers for both sub-parts and composites.
  • Parts and attributes: Every model item belongs to a family in an XML-defined hierarchy with attributes, while connection families likewise define parameters such as Hill coefficients and dissociation constants.This structure allows scripts to use known attributes to generate rate equations or populate part attributes from RegulonDB.
  • Standards and automation: The family-based framework is intended to accommodate future visual standards and ontologies, and the included algorithms are general for future ontologies adopted by the community.A Hill-equations script uses transcription-regulation parameters to generate rate equations, while another script retrieves E. coli parts from RegulonDB.

Conclusion

TinkerCell combines a flexible visual interface with programming interfaces, modular modeling, and support for exchanging models, information, and algorithms. Its framework is intended both for engineering-style network construction and for investigating whether biological modules retain functionality across contexts.

  • Platform and integration: TinkerCell combines visual modeling with C and Python interfaces, allowing simulation, linear programming, and Python libraries to be integrated.The interfaces make TinkerCell a front-end and host for external programs rather than an analysis engine itself.
  • Platform and integration: The platform is designed as a hub where programmers, experimentalists, modellers, and theoreticians can exchange models and information.Its architecture and visual framework are kept open to different modeling methods and programs.
  • Modular modeling: The modular modeling framework supports constructing new networks from existing modules and exploring the implications of functional modularity in biology.Modules can be studied at different levels of detail, including fractional saturation models and explicitly modeled reactions.
  • Modular modeling: Whether a module retains its functionality when placed in different situations remains an open question relevant to synthetic biology and engineering.TinkerCell provides a platform for testing module behavior and identifying modules able to retain their functional identity.
  • Future development: TinkerCell supports multiple modeling approaches, while visual construction remains more feature-complete than the current text-based Antimony workflow.A text-based framework intended to contain all visual-construction features was being added.
  • Future development: Planned work includes improved direct SBML conversion, layout exchange, expanded program interfacing, and further experiments connecting simple modules.The paper also describes continuing standardization and interface efforts shaped by user needs.

Appendix Division of labor between the Core library and the plug-ins

The Core library supplies general infrastructure for visual model construction and plug-in interaction, while plug-ins add domain-specific editing, visualization, module, and genetic-network features.

  • Core library: The Core library provides command-window interaction, XML-based part and connection drawing, item manipulation, undo and redo, and memory management.It also supports loading C libraries as separate threads, variable indexing, and mathematical formula parsing.
  • Plug-ins: Plug-ins provide parts and connections trees, insertion tools, selection, copying, aliasing, replacement, family editing, and collision detection.Connection insertion is restricted to permitted part types, such as transcription-factor and regulator relationships.
  • Plug-ins: The Container, Plot, and Module tools support organizing parts into modules, displaying parameters, graphing multiple outputs, and connecting module interfaces.C and Python programs can output directly to the graph window.
  • Plug-ins: The Gene Regulatory Network Tool automates network construction by updating transcription rates and generating kinetics when compatible parts are connected.These updates occur when a promoter is placed upstream of a gene or a transcription factor is connected to a regulator.
  • Plug-ins: The Antimony Tool uses libAntimony to support text-based interaction with TinkerCell models.The paper separately notes that current text-based construction does not yet expose every feature available through visual construction.

Linear algebra operations on the Stoichiometry matrix

A TinkerCell script selects reactions, constructs their stoichiometry matrix, and applies SciPy linear-algebra routines to that matrix.

  • Workflow: The script retrieves reactions selected by the user and generates a stoichiometry matrix containing only those reactions.The selected reactions are represented in the script output by reaction labels such as J0 through J5.
  • Workflow: SciPy performs QR and LU factorization on the generated stoichiometry matrix.Most command output is omitted for reading clarity.

Get all the part sequences in the model as a multi-FASTA file

A TinkerCell script finds DNA-family components, retrieves each part’s sequence, and prints the sequences in FASTA format.

  • Sequence extraction: The script selects all model components belonging to the DNA family, including promoters, RBS, genes, and other DNA parts.DNA is described as the root family for these components.
  • Sequence extraction: The script obtains each selected part’s sequence and prints the results to the screen in FASTA format.The loop processes the collected components when the list is nonempty.

Getting annotation and sequence information about a module and its sub-components

TinkerCell scripts can locate a named module, traverse its sub-parts, and retrieve annotations, sequences, parameters, formats, types, and family classifications.

  • A script finds the module BBa_I0462 and obtains all of its sub-parts.The module is accessed through the paper’s composite-part example.
  • The script loops through sub-parts to print annotation information and sequence data for each component.Displayed outputs include annotations and nucleotide sequences, with some longer sequences truncated.
  • Names in the example output are replaced with “no name,” and some longer sequences are truncated.
  • The retrieved annotations identify sub-parts including Terminator, Coding, luxR repressor/activator, and RBS based on the Elowitz repressilator.The outputs also include descriptions such as a 64 bp stem-loop transcriptional terminator and a luxR Protein Generator.
  • The script can print a sub-part’s strength parameter and retrieve the composite part’s format, type, and family classifications.Family outputs include BBa_I0462_B0012 as a Terminator.

Figures

The figures present TinkerCell as a visual environment for nested biological systems, modular networks, layered software, plug-ins, and third-party analysis functions.

  • Figure 1 depicts a module inside a compartment, with a regulated gene producing an output protein that regulates receptor production on the compartment membrane.
  • Figure 2 shows a network involving three cells and illustrates TinkerCell’s use for both modeling and pathway-style visual representation.
  • Figure 3 presents a main window with a network canvas, parts and reactions lists, available C or Python functions, parameter editing, history, and a Python command line.Parts and reactions are loaded from XML files, while available functions are loaded from a text file.
  • Figure 4 describes a layered structure containing a generic Core library, C++ plug-ins, and C functions extendable to Python or other scripting languages.
  • Figures 5–7 illustrate the Core API, plug-ins for numerical attributes and rates, and connections among duplicated modules forming a larger network.The module example is a phosphorylation/dephosphorylation cycle whose interfaces connect duplicated modules.

Figure 9 - BioBrick composite part as a module

The figures describe TinkerCell’s composite parts, biological-part ontology, sequence visualization, and multiple ways to model gene regulatory networks.

  • Figure 9 - BioBrick composite part as a module: A composite part is composed of other parts, while retaining its own parameters, annotation, authorship, and information.Sub-parts retain individual attributes such as sequence and parameter values.
  • Ontology of parts and connections: TinkerCell assigns items to family types, supporting part and connection ontologies and interaction with databases.Connection families can be distinguished by arrowheads that are replaceable through the interface or scripts.
  • Viewing DNA sequence: The DNA-sequence view represents shared regulation abstractly when one promoter regulates multiple gene segments.The example implies two promoter copies, one upstream of each regulated gene.
  • Visual representations: TinkerCell supplies replaceable visual representations for genes, promoters, proteins, transcription factors, receptors, reporters, and cells.Visual objects and decorators are created by a polygon drawing program and can be replaced through a dialog.
  • Gene regulatory network modeling: Fractional-saturation modeling assumes transcription-factor association and dissociation at operator sites are in equilibrium.Under this assumption, protein production is proportional to active promoter states divided by all possible promoter states.
  • Gene regulatory network modeling: Part-based regulatory modeling provides more flexible rate expressions and automatically adjusts transcription rates when upstream promoters change.The adjustment accounts for the new promoter’s strength parameter.
  • Gene regulatory network modeling: Reaction-based modeling represents transcription and translation as multiple reactions, allowing delays and explicit transcription-factor binding and unbinding.
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