Deepak Chandran

The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology

The re-use of previously validated designs is critical to the evolution of synthetic biology from a research discipline to an engineering practice. Here we describe the Synthetic Biology Open Language (SBOL), a proposed data standard for exchanging designs within the synthetic biology community. SBOL represents synthetic biology designs in a community-driven, formalized format for exchange between software tools, research groups and commercial service providers. The SBOL Developers Group has implemented SBOL as an XML/RDF serialization and provides software libraries and specification documentation to help developers implement SBOL in their own software. We describe early successes, including a demonstration of the utility of SBOL for information exchange between several different software tools and repositories from both academic and industrial partners. As a community-driven standard, SBOL will be updated as synthetic biology evolves to provide specific capabilities for different aspects of the synthetic biology workflow.

Standard Biological Parts Knowledgebase

We have created the Knowledgebase of Standard Biological Parts (SBPkb) as a publically accessible Semantic Web resource for synthetic biology (sbolstandard.org). The SBPkb allows researchers to query and retrieve standard biological parts for research and use in synthetic biology. Its initial version includes all of the information about parts stored in the Registry of Standard Biological Parts (partsregistry.org). SBPkb transforms this information so that it is computable, using our semantic framework for synthetic biology parts. This framework, known as SBOL-semantic, was built as part of the Synthetic Biology Open Language (SBOL), a project of the Synthetic Biology Data Exchange Group. SBOL-semantic represents commonly used synthetic biology entities, and its purpose is to improve the distribution and exchange of descriptions of biological parts. In this paper, we describe the data, our methods for transformation to SBPkb, and finally, we demonstrate the value of our knowledgebase with a set of sample queries. We use RDF technology and SPARQL queries to retrieve candidate “promoter” parts that are known to be both negatively and positively regulated. This method provides new web based data access to perform searches for parts that are not currently possible.

Antimony: a modular model definition language

MOTIVATION Model exchange in systems and synthetic biology has been standardized for computers with the Systems Biology Markup Language (SBML) and CellML, but specialized software is needed for the generation of models in these formats. Text-based model definition languages allow researchers to create models simply, and then export them to a common exchange format. Modular languages allow researchers to create and combine complex models more easily. We saw a use for a modular text-based language, together with a translation library to allow other programs to read the models as well. SUMMARY The Antimony language provides a way for a researcher to use simple text statements to create, import, and combine biological models, allowing complex models to be built from simpler models, and provides a special syntax for the creation of modular genetic networks. The libAntimony library allows other software packages to import these models and convert them either to SBML or their own internal format. AVAILABILITY The Antimony language specification and the libAntimony library are available under a BSD license from http://antimony.sourceforge.net/.

Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach

BackgroundAccurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants.ResultsWe proposed two challenges; in the first, participants were given the topology and underlying biochemical structure of a 9-gene regulatory network and were asked to determine its parameter values. In the second challenge, participants were given an incomplete topology with 11 genes and asked to find three missing links in the model. In both challenges, a budget was provided to buy experimental data generated in silico with the model and mimicking the features of different common experimental techniques, such as microarrays and fluorescence microscopy. Data could be bought at any stage, allowing participants to implement an iterative loop of experiments and computation.ConclusionsA total of 19 teams participated in this competition. The results suggest that the combination of state-of-the-art parameter estimation and a varied set of experimental methods using a few datasets, mostly fluorescence imaging data, can accurately determine parameters of biochemical models of gene regulation. However, the task is considerably more difficult if the gene network topology is not completely defined, as in challenge 2. Importantly, we found that aggregating independent parameter predictions and network topology across submissions creates a solution that can be better than the one from the best-performing submission.

Essential information for synthetic DNA sequences

Jean Peccoud1, J Christopher Anderson2, Deepak Chandran3, Douglas Densmore4, Michal Galdzicki5, Matthew W Lux1, Cesar A Rodriguez6, Guy-Bart Stan7 & Herbert M Sauro3 1Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA. 2Department of Bioengineering, QB3: California Institute for Quantitative Biological Research, University of California, Berkeley, California, USA. 3Department of Bioengineering, University of Washington, Seattle, Washington, USA. 4Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts, USA.5Biomedical and Health Informatics, University of Washington, Seattle, Washington, USA. 6BIOFAB, Emeryville, California, USA. 7Department of Bioengineering and Centre for Synthetic Biology and Innovation, Imperial College London, London, UK. e-mail: peccoud@vt.edu

Computer-aided design of biological circuits using TinkerCell.

Synthetic biology is an engineering discipline that builds on modeling practices from systems biology and wet-lab techniques from genetic engineering. As synthetic biology advances, efficient procedures will be developed that will allow a synthetic biologist to design, analyze, and build biological networks. In this idealized pipeline, computer-aided design (CAD) is a necessary component. The role of a CAD application would be to allow efficient transition from a general design to a final product. TinkerCell is a design tool for serving this purpose in synthetic biology. In TinkerCell, users build biological networks using biological parts and modules. The network can be analyzed using one of several functions provided by TinkerCell or custom programs from third-party sources. Since best practices for modeling and constructing synthetic biology networks have not yet been established, TinkerCell is designed as a flexible and extensible application that can adjust itself to changes in the field.

Computer-Aided Design for Synthetic Biology

Computer-aided design (CAD) for synthetic biology has been proposed to parallel similar efforts in other engineering disciplines, such as electrical engineering or mechanical engineering. However, there is an important distinction between the fields, which is that the mechanisms by which biological systems function are not currently fully understood in sufficient detail to make completely predictive tools. Computational models of biological systems provide, at best, a qualitative understanding of the system under investigation. Quantitative models are limited by the large number of unknown parameters in any given biological system as well the lack of understanding of the detailed mechanisms. It is difficult to determine how much detail is required for predictable design of biological systems. Even assembling individual DNA sequences has shown to be unpredictable due to secondary DNA structures. As a result, the phrase ‘computer-aided design’ takes a very different meaning in synthetic biology: designing biological systems is as much an exploratory process as it is a rational design process. Through design and experimentation, the science of engineering biology is furthered, and that knowledge must be explicitly fed back into the design process itself. Due to its complexity, the challenge of predictably designing biological systems has become a community effort rather than a competitive effort. Consequently, several software developers in synthetic biology have recognized that supporting a community is a necessary component in synthetic biology design applications. Existing software tools in synthetic biology can be categorized into a three broad categories. First, there are software tools for mathematical analysis of biological systems. This category also includes tools from the field of systems biology. Secondly, there are software tools for assembling DNA sequences and analyzing the structure of the resulting composition. This category builds on concepts from genetic engineering for manipulating DNA sequences. The third category of tools are for database access. Synthetic biologists need a catalog of biological components, or ‘parts’, from which systems can be built; therefore, databases, whether local or distributed, are integral for synthetic biology research. This chapter will cover these categories of tools and how they contribute to synthetic biology. We also consider design by combinatorial optimization, which may work well in biological engineering due to properties of DNA replication.

Toward Modularity in Synthetic Biology: Design Patterns and Fan-out

Modularity is a concept that is widely used in biological science with various interpretations. In this chapter we will first give a general overview of modularity in biology, and later focus on modularity in synthetic biology. In engineering, a module is a component whose intrinsic functionality is independent of its surrounding milieu. In biology, however, modularity is less clear-cut; for example, modules can be classified by network interactions or by functional distinctiveness such as the reuse of protein domains. In synthetic biology the question of modularity is more closely related to engineering where functional independence is important. One way of defining synthetic modules is by specifying a generic pattern of regulations that results in desired functionalities, which we term a design pattern. In this perspective, connections between modules are described by the regulatory links, which are represented by molecular reactions. Under these reactions, the output of an upstream module – the concentration of regulating molecules – is sequestered by the input of the downstream module. This sequestration can cause changes in the upstream module function. We quantify the maximally tolerable load from the downstream input, which we term gene circuit fan-out. We provide an efficient and practical way of estimating the fan-out by experiment.

Genetic Constructor: An Online DNA Design Platform

Genetic Constructor is a cloud Computer Aided Design (CAD) application developed to support synthetic biologists from design intent through DNA fabrication and experiment iteration. The platform allows users to design, manage, and navigate complex DNA constructs and libraries, using a new visual language that focuses on functional parts abstracted from sequence. Features like combinatorial libraries and automated primer design allow the user to separate design from construction by focusing on functional intent, and design constraints aid iterative refinement of designs. A plugin architecture enables contributions from scientists and coders to leverage existing powerful software and connect to DNA foundries. The software is easily accessible and platform agnostic, free for academics, and available in an open-source community edition. Genetic Constructor seeks to democratize DNA design, manufacture, and access to tools and services from the synthetic biology community.

Data Model Standardization for Synthetic Biomolecular Circuits and Systems

While biological engineers strive to capture the biophysical theory essential in predicting how a newly designed synthetic organism will behave, the current state of this knowledge is far from ideal. To facilitate the research towards this goal, specifically through the application of computational tools, the data required to engineer biological systems should be electronically accessible and interpretable. The challenge to represent such information computationally is complicated by the enormous diversity and size of biological data. There is a plethora of biological components, interacting physically and chemically, with implications for behavior at multiple time and spatial scales. The many scientists working to move the synthetic biology field forward have to communicate their research findings and should understand each other despite their diverse academic backgrounds. The challenge and demand for data standardization arises from the need to collaborate in order to engineer ever more complex biomolecular circuits and to understand and control biological systems. The bioinformatics field provides us with a history of experience in its attempts to facilitate collaboration in the biomedical research community. We draw on the lessons from the application of information technology solutions to inform and inspire the new efforts in synthetic biology. Furthermore, we acknowledge fundamental differences in the nature of the two fields and discuss the need to standardize data models for the purpose of engineering and design of novel biomolecular circuits and systems.