PROMOT: modular modeling for systems biology

Compositional modelling of immune response and virus transmission dynamics

Transmission models for infectious diseases are typically formulated in terms of dynamics between individuals or groups with processes such as disease progression or recovery for each individual captured phenomenologically, without reference to underlying biological processes. Furthermore, the construction of these models is often monolithic: they do not allow one to readily modify the processes involved or include the new ones, or to combine models at different scales. We show how to construct a simple model of immune response to a respiratory virus and a model of transmission using an easily modifiable set of rules allowing further refining and merging the two models together. The immune response model reproduces the expected response curve of PCR testing for COVID-19 and implies a long-tailed distribution of infectiousness reflective of individual heterogeneity. This immune response model, when combined with a transmission model, reproduces the previously reported shift in the population distribution of viral loads along an epidemic trajectory. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

NOT Gates Based on Protein Degradation as a Case Study for a New Modular Modeling via SBML Level 3—Comp Package

In 2008, we were among the first to propose a method for the visual design and modular modeling of synthetic gene circuits, mimicking the way electronic circuits are realized in silico. Basic components were DNA sequences that could be composed, first, into transcription units (TUs) and, then, circuits by exchanging fluxes of molecules, such as PoPS (polymerase per second) and RiPS (ribosomes per seconds) as suggested by Drew Endy. However, it became clear soon that such fluxes were not measurable, which highlighted the limit of using some concepts from electronics to represent biological systems. SBML Level 3 with the comp package permitted us to revise circuit modularity, especially for the modeling of eukaryotic networks. By using the libSBML Python API, TUs—rather than single parts—are encoded in SBML Level 3 files that contain species, reactions, and ports, i.e., the interfaces that permit to wire TUs into circuits. A circuit model consists of a collection of SBML Level 3 files associated with the different TUs plus a “main” file that delineates the circuit structure. Within this framework, there is no more need for any flux of molecules. Here, we present the SBML Level 3-based models and the wet-lab implementations of Boolean NOT gates that make use, in the yeast Saccharomyces cerevisiae, of the bacterial ClpX-ClpP system for protein degradation. This work is the starting point towards a new piece of software for the modular design of eukaryotic gene circuits and shows an alternative way to build genetic Boolean gates.

Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: a software engineering perspective

Reuse of mathematical models becomes increasingly important in systems biology as research moves toward large, multi-scale models composed of heterogeneous subcomponents. Currently, many models are not easily reusable due to inflexible or confusing code, inappropriate languages, or insufficient documentation. Best practice suggestions rarely cover such low-level design aspects. This gap could be filled by software engineering, which addresses those same issues for software reuse. We show that languages can facilitate reusability by being modular, human-readable, hybrid (i.e., supporting multiple formalisms), open, declarative, and by supporting the graphical representation of models. Modelers should not only use such a language, but be aware of the features that make it desirable and know how to apply them effectively. For this reason, we compare existing suitable languages in detail and demonstrate their benefits for a modular model of the human cardiac conduction system written in Modelica.

Modular Modeling of Genetic Circuits in SBML Level 3

The System Biology Markup Language (SBML) Level 2 has been used extensively to make models for biological systems of different complexity. However, the lack of modularity was a serious hurdle for its application to Synthetic Biology where genetic circuits are preferably modeled by putting together the models of their components. SBML Level 3 with the Hierarchical Composition Package overcame this limit. Here, we describe how to realize a modular model for a eukaryotic AND gate in SBML Level 3. Circuit modules, such as transcription units and pools of molecules, are modeled separately and connected, to close the circuit, via Python scripts that utilize the libSBML API. Circuit simulations with COPASI confirm the validity of this modeling approach.

Towards Design of Self-Organizing Biomimetic Systems

The ability of designing biosynthetic systems with well-defined functional biomodules from scratch is an ambitious and revolutionary goal to deliver innovative, engineered solutions to future challenges in biotechnology and process systems engineering. In this work, several key challenges including modularization, functional biomodule identification, and assembly are discussed. In addition, an in silico protocell modeling approach is presented as a foundation for a computational model-based toolkit for rational analysis and modular design of biomimetic systems.

Towards an Aspect-Oriented Design and Modelling Framework for Synthetic Biology

Work on synthetic biology has largely used a component-based metaphor for system construction. While this paradigm has been successful for the construction of numerous systems, the incorporation of contextual design issues-either compositional, host or environmental-will be key to realising more complex applications. Here, we present a design framework that radically steps away from a purely parts-based paradigm by using aspect-oriented software engineering concepts. We believe that the notion of concerns is a powerful and biologically credible way of thinking about system synthesis. By adopting this approach, we can separate core concerns, which represent modular aims of the design, from cross-cutting concerns, which represent system-wide attributes. The explicit handling of cross-cutting concerns allows for contextual information to enter the design process in a modular way. As a proof-of-principle, we implemented the aspect-oriented approach in the Python tool, SynBioWeaver, which enables the combination, or weaving, of core and cross-cutting concerns. The power and flexibility of this framework is demonstrated through a number of examples covering the inclusion of part context, combining circuit designs in a context dependent manner, and the generation of rule, logic and reaction models from synthetic circuit designs.

A Network-Based Integrative Workflow to Unravel Mechanisms Underlying Disease Progression

Unraveling mechanisms underlying diseases has motivated the development of systems biology approaches. The key challenges for the development of mathematical models and computational tool are (1) the size of molecular networks, (2) the nonlinear nature of spatio-temporal interactions, and (3) feedback loops in the structure of interaction networks. We here propose an integrative workflow that combines structural analyses of networks, high-throughput data, and mechanistic modeling. As an illustration of the workflow, we use prostate cancer as a case study with the aim of identifying key functional components associated with primary to metastasis transitions. Analysis carried out by the workflow revealed that HOXD10, BCL2, and PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state, STAT3, JUN, and JUNB are playing a central role. The identified key elements of each network are validated using patient survival analysis. The workflow presented here allows experimentalists to use heterogeneous data sources for the identification of diagnostic and prognostic signatures.

Integrative biological simulation praxis: Considerations from physics, philosophy, and data/model curation practices

Integrative biological simulations have a varied and controversial history in the biological sciences. From computational models of organelles, cells, and simple organisms, to physiological models of tissues, organ systems, and ecosystems, a diverse array of biological systems have been the target of large-scale computational modeling efforts. Nonetheless, these research agendas have yet to prove decisively their value among the broader community of theoretical and experimental biologists. In this commentary, we examine a range of philosophical and practical issues relevant to understanding the potential of integrative simulations. We discuss the role of theory and modeling in different areas of physics and suggest that certain sub-disciplines of physics provide useful cultural analogies for imagining the future role of simulations in biological research. We examine philosophical issues related to modeling which consistently arise in discussions about integrative simulations and suggest a pragmatic viewpoint that balances a belief in philosophy with the recognition of the relative infancy of our state of philosophical understanding. Finally, we discuss community workflow and publication practices to allow research to be readily discoverable and amenable to incorporation into simulations. We argue that there are aligned incentives in widespread adoption of practices which will both advance the needs of integrative simulation efforts as well as other contemporary trends in the biological sciences, ranging from open science and data sharing to improving reproducibility.

Genetic Design via Combinatorial Constraint Specification

We present a formal language for specifying via constraints a "design space" of DNA constructs composed of genetic parts, and an algorithm for automatically and correctly creating a novel representation of the space of satisfying designs. The language is simple, captures a large class of design spaces, and possesses algorithms for common operations on design spaces. The flexibility of this approach is demonstrated using a 16-gene nitrogen fixation pathway and genetic logic circuits.

Design Automation in Synthetic Biology

Design automation refers to a category of software tools for designing systems that work together in a workflow for designing, building, testing, and analyzing systems with a target behavior. In synthetic biology, these tools are called bio-design automation (BDA) tools. In this review, we discuss the BDA tools areas-specify, design, build, test, and learn-and introduce the existing software tools designed to solve problems in these areas. We then detail the functionality of some of these tools and show how they can be used together to create the desired behavior of two types of modern synthetic genetic regulatory networks.

Vesicles-on-a-chip: A universal microfluidic platform for the assembly of liposomes and polymersomes

In this study, we present a PDMS-based microfluidic platform for the fabrication of both liposomes and polymersomes. Based on a double-emulsion template formed in flow-focusing configuration, monodisperse liposomes and polymersomes are produced in a controlled manner after solvent extraction. Both types of vesicles can be formed from the exact same combination of fluids and are stable for at least three months under ambient storage conditions. By tuning the flow rates of the different fluid phases in the flow-focusing microfluidic design, the size of the liposomes and polymersomes can be varied over at least one order of magnitude. This method offers a versatile tool for future studies, e.g., involving the encapsulation of biological agents and the functionalization of artificial cell membranes, and might also be applicable for the controlled fabrication of hybrid vesicles.

Challenges in horizontal model integration

Background

Systems Biology has motivated dynamic models of important intracellular processes at the pathway level, for example, in signal transduction and cell cycle control. To answer important biomedical questions, however, one has to go beyond the study of isolated pathways towards the joint study of interacting signaling pathways or the joint study of signal transduction and cell cycle control. Thereby the reuse of established models is preferable, as it will generally reduce the modeling effort and increase the acceptance of the combined model in the field.

Results

Obtaining a combined model can be challenging, especially if the submodels are large and/or come from different working groups (as is generally the case, when models stored in established repositories are used). To support this task, we describe a semi-automatic workflow based on established software tools. In particular, two frequent challenges are described: identification of the overlap and subsequent (re)parameterization of the integrated model.

Conclusions

The reparameterization step is crucial, if the goal is to obtain a model that can reproduce the data explained by the individual models. For demonstration purposes we apply our workflow to integrate two signaling pathways (EGF and NGF) from the BioModels Database.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0266-3) contains supplementary material, which is available to authorized users.

Semi-automated Modular Program Constructor for physiological modeling: Building cell and organ models

The Modular Program Constructor (MPC) is an open-source Java based modeling utility, built upon JSim's Mathematical Modeling Language (MML) ( http://www.physiome.org/jsim/) that uses directives embedded in model code to construct larger, more complicated models quickly and with less error than manually combining models. A major obstacle in writing complex models for physiological processes is the large amount of time it takes to model the myriad processes taking place simultaneously in cells, tissues, and organs. MPC replaces this task with code-generating algorithms that take model code from several different existing models and produce model code for a new JSim model. This is particularly useful during multi-scale model development where many variants are to be configured and tested against data. MPC encodes and preserves information about how a model is built from its simpler model modules, allowing the researcher to quickly substitute or update modules for hypothesis testing. MPC is implemented in Java and requires JSim to use its output. MPC source code and documentation are available at http://www.physiome.org/software/MPC/.

Generating Systems Biology Markup Language Models from the Synthetic Biology Open Language

In the context of synthetic biology, model generation is the automated process of constructing biochemical models based on genetic designs. This paper discusses the use cases for model generation in genetic design automation (GDA) software tools and introduces the foundational concepts of standards and model annotation that make this process useful. Finally, this paper presents an implementation of model generation in the GDA software tool iBioSim and provides an example of generating a Systems Biology Markup Language (SBML) model from a design of a 4-input AND sensor written in the Synthetic Biology Open Language (SBOL).

Proposed data model for the next version of the synthetic biology open language

While the first version of the Synthetic Biology Open Language (SBOL) has been adopted by several academic and commercial genetic design automation (GDA) software tools, it only covers a limited number of the requirements for a standardized exchange format for synthetic biology. In particular, SBOL Version 1.1 is capable of representing DNA components and their hierarchical composition via sequence annotations. This proposal revises SBOL Version 1.1, enabling the representation of a wider range of components with and without sequences, including RNA components, protein components, small molecules, and molecular complexes. It also introduces modules to instantiate groups of components on the basis of their shared function and assert molecular interactions between components. By increasing the range of structural and functional descriptions in SBOL and allowing for their composition, the proposed improvements enable SBOL to represent and facilitate the exchange of a broader class of genetic designs.

Modular design of synthetic gene circuits with biological parts and pools

Synthetic gene circuits can be designed in an electronic fashion by displaying their basic components-Standard Biological Parts and Pools of molecules-on the computer screen and connecting them with hypothetical wires. This procedure, achieved by our add-on for the software ProMoT, was successfully applied to bacterial circuits. Recently, we have extended this design-methodology to eukaryotic cells. Here, highly complex components such as promoters and Pools of mRNA contain hundreds of species and reactions whose calculation demands a rule-based modeling approach. We showed how to build such complex modules via the joint employment of the software BioNetGen (rule-based modeling) and ProMoT (modularization). In this chapter, we illustrate how to utilize our computational tool for synthetic biology with the in silico implementation of a simple eukaryotic gene circuit that performs the logic AND operation.

Parts & pools: a framework for modular design of synthetic gene circuits

Published in 2008, Parts & Pools represents one of the first attempts to conceptualize the modular design of bacterial synthetic gene circuits with Standard Biological Parts (DNA segments) and Pools of molecules referred to as common signal carriers (e.g., RNA polymerases and ribosomes). The original framework for modeling bacterial components and designing prokaryotic circuits evolved over the last years and brought, first, to the development of an algorithm for the automatic design of Boolean gene circuits. This is a remarkable achievement since gene digital circuits have a broad range of applications that goes from biosensors for health and environment care to computational devices. More recently, Parts & Pools was enabled to give a proper formal description of eukaryotic biological circuit components. This was possible by employing a rule-based modeling approach, a technique that permits a faithful calculation of all the species and reactions involved in complex systems such as eukaryotic cells and compartments. In this way, Parts & Pools is currently suitable for the visual and modular design of synthetic gene circuits in yeast and mammalian cells too.

Multi-state modeling of biomolecules

Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behavior of biological molecules or complexes that can adopt a large number of possible functional states. Biological signaling systems often rely on complexes of biological macromolecules that can undergo several functionally significant modifications that are mutually compatible. Thus, they can exist in a very large number of functionally different states. Modeling such multi-state systems poses two problems: the problem of how to describe and specify a multi-state system (the “specification problem”) and the problem of how to use a computer to simulate the progress of the system over time (the “computation problem”). To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states and towards rule-based formalisms that allow for implicit model specification, including the κ-calculus [1], BioNetGen [2]–[5], the Allosteric Network Compiler [6], and others [7], [8]. To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on ordinary differential equations, partial differential equations, or the Gillespie stochastic simulation algorithm[9], [10]. Given current computing technology, particle-based methods are sometimes the only possible option. Particle-based simulators fall into two further categories: nonspatial simulators, such as StochSim [11], DYNSTOC [12], RuleMonkey [9], [13], and the Network-Free Stochastic Simulator (NFSim) [14], and spatial simulators, including Meredys [15], SRSim [16], [17], and MCell [18]–[20]. Modelers can thus choose from a variety of tools, the best choice depending on the particular problem. Development of faster and more powerful methods is ongoing, promising the ability to simulate ever more complex signaling processes in the future.

Toward modular biological models: defining analog modules based on referent physiological mechanisms

Background

Currently, most biomedical models exist in isolation. It is often difficult to reuse or integrate models or their components, in part because they are not modular. Modular components allow the modeler to think more deeply about the role of the model and to more completely address a modeling project’s requirements. In particular, modularity facilitates component reuse and model integration for models with different use cases, including the ability to exchange modules during or between simulations. The heterogeneous nature of biology and vast range of wet-lab experimental platforms call for modular models designed to satisfy a variety of use cases. We argue that software analogs of biological mechanisms are reasonable candidates for modularization. Biomimetic software mechanisms comprised of physiomimetic mechanism modules offer benefits that are unique or especially important to multi-scale, biomedical modeling and simulation.

Results

We present a general, scientific method of modularizing mechanisms into reusable software components that we call physiomimetic mechanism modules (PMMs). PMMs utilize parametric containers that partition and expose state information into physiologically meaningful groupings. To demonstrate, we modularize four pharmacodynamic response mechanisms adapted from an in silico liver (ISL). We verified the modularization process by showing that drug clearance results from in silico experiments are identical before and after modularization. The modularized ISL achieves validation targets drawn from propranolol outflow profile data. In addition, an in silico hepatocyte culture (ISHC) is created. The ISHC uses the same PMMs and required no refactoring. The ISHC achieves validation targets drawn from propranolol intrinsic clearance data exhibiting considerable between-lab variability. The data used as validation targets for PMMs originate from both in vitro to in vivo experiments exhibiting large fold differences in time scale.

Conclusions

This report demonstrates the feasibility of PMMs and their usefulness across multiple model use cases. The pharmacodynamic response module developed here is robust to changes in model context and flexible in its ability to achieve validation targets in the face of considerable experimental uncertainty. Adopting the modularization methods presented here is expected to facilitate model reuse and integration, thereby accelerating the pace of biomedical research.

A model integration approach linking signalling and gene-regulatory logic with kinetic metabolic models

Systems biology has to increasingly cope with large- and multi-scale biological systems. Many successful in silico representations and simulations of various cellular modules proved mathematical modelling to be an important tool in gaining a solid understanding of biological phenomena. However, models spanning different functional layers (e.g. metabolism, signalling and gene regulation) are still scarce. Consequently, model integration methods capable of fusing different types of biological networks and various model formalisms become a key methodology to increase the scope of cellular processes covered by mathematical models. Here we propose a new integration approach to couple logical models of signalling or/and gene-regulatory networks with kinetic models of metabolic processes. The procedure ends up with an integrated dynamic model of both layers relying on differential equations. The feasibility of the approach is shown in an illustrative case study integrating a kinetic model of central metabolic pathways in hepatocytes with a Boolean logical network depicting the hormonally induced signal transduction and gene regulation events involved. In silico simulations demonstrate the integrated model to qualitatively describe the physiological switch-like behaviour of hepatocytes in response to nutritionally regulated changes in extracellular glucagon and insulin levels. A simulated failure mode scenario addressing insulin resistance furthermore illustrates the pharmacological potential of a model covering interactions between signalling, gene regulation and metabolism.

Recent advances in modeling languages for pathway maps and computable biological networks

As our theories of systems biology grow more sophisticated, the models we use to represent them become larger and more complex. Languages necessarily have the expressivity and flexibility required to represent these models in ways that support high-resolution annotation, and provide for simulation and analysis that are sophisticated enough to allow researchers to master their data in the proper context. These languages also need to facilitate model sharing and collaboration, which is currently best done by using uniform data structures (such as graphs) and language standards. In this brief review, we discuss three of the most recent systems biology modeling languages to appear: BEL, PySB and BCML, and examine how they meet these needs.

Programming biological models in Python using PySB

PySB is a framework for creating biological models as Python programs using a high-level, action-oriented vocabulary that promotes transparency, extensibility and reusability. PySB interoperates with many existing modeling tools and supports distributed model development.

PySB models are programs and leverage existing programming tools for documentation, testing, and collaborative development.

Reusable functions can encode common low-level biochemical processes as well as high-level modules, making models transparent and concise.

Modeling workflow is accelerated through close integration with Python numerical tools and interoperability with existing modeling software.

We demonstrate the use of PySB to encode 15 alternative hypotheses for the mitochondrial regulation of apoptosis, including a new ‘Embedded Together' model based on recent biochemical findings.

Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule-based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule-based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high-level, action-oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open-source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis.

Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks

A central goal of systems biology is the construction of predictive models of bio-molecular networks. Cellular networks of moderate size have been modeled successfully in a quantitative way based on differential equations. However, in large-scale networks, knowledge of mechanistic details and kinetic parameters is often too limited to allow for the set-up of predictive quantitative models.Here, we review methodologies for qualitative and semi-quantitative modeling of cellular signal transduction networks. In particular, we focus on three different but related formalisms facilitating modeling of signaling processes with different levels of detail: interaction graphs, logical/Boolean networks, and logic-based ordinary differential equations (ODEs). Albeit the simplest models possible, interaction graphs allow the identification of important network properties such as signaling paths, feedback loops, or global interdependencies. Logical or Boolean models can be derived from interaction graphs by constraining the logical combination of edges. Logical models can be used to study the basic input-output behavior of the system under investigation and to analyze its qualitative dynamic properties by discrete simulations. They also provide a suitable framework to identify proper intervention strategies enforcing or repressing certain behaviors. Finally, as a third formalism, Boolean networks can be transformed into logic-based ODEs enabling studies on essential quantitative and dynamic features of a signaling network, where time and states are continuous.We describe and illustrate key methods and applications of the different modeling formalisms and discuss their relationships. In particular, as one important aspect for model reuse, we will show how these three modeling approaches can be combined to a modeling pipeline (or model hierarchy) allowing one to start with the simplest representation of a signaling network (interaction graph), which can later be refined to logical and eventually to logic-based ODE models. Importantly, systems and network properties determined in the rougher representation are conserved during these transformations.

Modular, rule-based modeling for the design of eukaryotic synthetic gene circuits

BACKGROUND

The modular design of synthetic gene circuits via composable parts (DNA segments) and pools of signal carriers (molecules such as RNA polymerases and ribosomes) has been successfully applied to bacterial systems. However, eukaryotic cells are becoming a preferential host for new synthetic biology applications. Therefore, an accurate description of the intricate network of reactions that take place inside eukaryotic parts and pools is necessary. Rule-based modeling approaches are increasingly used to obtain compact representations of reaction networks in biological systems. However, this approach is intrinsically non-modular and not suitable per se for the description of composable genetic modules. In contrast, the Model Description Language (MDL) adopted by the modeling tool ProMoT is highly modular and it enables a faithful representation of biological parts and pools.

RESULTS

We developed a computational framework for the design of complex (eukaryotic) gene circuits by generating dynamic models of parts and pools via the joint usage of the BioNetGen rule-based modeling approach and MDL. The framework converts the specification of a part (or pool) structure into rules that serve as inputs for BioNetGen to calculate the part's species and reactions. The BioNetGen output is translated into an MDL file that gives a complete description of all the reactions that take place inside the part (or pool) together with a proper interface to connect it to other modules in the circuit. In proof-of-principle applications to eukaryotic Boolean circuits with more than ten genes and more than one thousand reactions, our framework yielded proper representations of the circuits' truth tables.

CONCLUSIONS

For the model-based design of increasingly complex gene circuits, it is critical to achieve exact and systematic representations of the biological processes with minimal effort. Our computational framework provides such a detailed and intuitive way to design new and complex synthetic gene circuits.

Parallel in vivo DNA assembly by recombination: experimental demonstration and theoretical approaches

The development of synthetic biology requires rapid batch construction of large gene networks from combinations of smaller units. Despite the availability of computational predictions for well-characterized enzymes, the optimization of most synthetic biology projects requires combinational constructions and tests. A new building-brick-style parallel DNA assembly framework for simple and flexible batch construction is presented here. It is based on robust recombination steps and allows a variety of DNA assembly techniques to be organized for complex constructions (with or without scars). The assembly of five DNA fragments into a host genome was performed as an experimental demonstration.

The Process-Interaction-Model: a common representation of rule-based and logical models allows studying signal transduction on different levels of detail

Background

Signaling systems typically involve large, structured molecules each consisting of a large number of subunits called molecule domains. In modeling such systems these domains can be considered as the main players. In order to handle the resulting combinatorial complexity, rule-based modeling has been established as the tool of choice. In contrast to the detailed quantitative rule-based modeling, qualitative modeling approaches like logical modeling rely solely on the network structure and are particularly useful for analyzing structural and functional properties of signaling systems.

Results

We introduce the Process-Interaction-Model (PIM) concept. It defines a common representation (or basis) of rule-based models and site-specific logical models, and, furthermore, includes methods to derive models of both types from a given PIM. A PIM is based on directed graphs with nodes representing processes like post-translational modifications or binding processes and edges representing the interactions among processes. The applicability of the concept has been demonstrated by applying it to a model describing EGF insulin crosstalk. A prototypic implementation of the PIM concept has been integrated in the modeling software ProMoT.

Conclusions

The PIM concept provides a common basis for two modeling formalisms tailored to the study of signaling systems: a quantitative (rule-based) and a qualitative (logical) modeling formalism. Every PIM is a compact specification of a rule-based model and facilitates the systematic set-up of a rule-based model, while at the same time facilitating the automatic generation of a site-specific logical model. Consequently, modifications can be made on the underlying basis and then be propagated into the different model specifications – ensuring consistency of all models, regardless of the modeling formalism. This facilitates the analysis of a system on different levels of detail as it guarantees the application of established simulation and analysis methods to consistent descriptions (rule-based and logical) of a particular signaling system.

Hierarchical modeling for synthetic biology

One of the characteristics of synthetic biology is that it often combines mathematical modeling with experimental work. The link between modeling and experiments is carried out by human researchers who have a conceptual understanding of the underlying biological system. At present, there is no method for representing a conceptual description that can be used to connect mathematical models and experimental data, especially sequence annotations, pertaining to the same underlying biological system. One reason for this limitation is that there can exist different mathematical models of the same biological system. In such cases, the same annotation in a DNA sequence would map differently to different models of the same system. In order to enable software support for synthetic biology, a software framework is needed such that it is able to capture a conceptual description of a biological system, including quantitative values, without confining itself to one mathematical model. The novel use of hierarchical modeling inside TinkerCell (www.tinkercell.com) provides one potential software solution for representing a "conceptual diagram" of a biological system. The conceptual diagram does not assume any underlying model. Rather, the diagram is mapped automatically to one of several models. The diagram can then contain information relevant for both modeling and experimental work. Computer-aided design (CAD) can be very useful to synthetic biology. CAD allows engineers to spend more effort at the design stage and less at the construction stage by automatically performing many tasks that are currently performed by human researchers. The ability to automatically link models and experimental results will be one step in the development of practical CAD systems for synthetic biology.

Towards the rational design of synthetic cells with prescribed population dynamics

The rational design of synthetic cell populations with prescribed behaviours is a long-standing goal of synthetic biology, with the potential to greatly accelerate the development of biotechnological applications in areas ranging from medical research to energy production. Achieving this goal requires well-characterized components, modular implementation strategies, simulation across temporal and spatial scales and automatic compilation of high-level designs to low-level genetic parts that function reliably inside cells. Many of these steps are incomplete or only partially understood, and methods for integrating them within a common design framework have yet to be developed. Here, we address these challenges by developing a prototype framework for designing synthetic cells with prescribed population dynamics. We extend the genetic engineering of cells (GEC) language, originally developed for programming intracellular dynamics, with cell population factors such as cell growth, division and dormancy, together with spatio-temporal simulation methods. As a case study, we use our framework to design synthetic cells with predator–prey interactions that, when simulated, produce complex spatio-temporal behaviours such as travelling waves and spatio-temporal chaos. An analysis of our design reveals that environmental factors such as density-dependent dormancy and reduced extracellular space destabilize the population dynamics and increase the range of genetic variants for which complex spatio-temporal behaviours are possible. Our findings highlight the importance of considering such factors during the design process. We then use our analysis of population dynamics to inform the selection of genetic parts, which could be used to obtain the desired spatio-temporal behaviours. By identifying, integrating and automating key stages of the design process, we provide a computational framework for designing synthetic systems, which could be tested in future laboratory studies.

An integrative model links multiple inputs and signaling pathways to the onset of DNA synthesis in hepatocytes

During liver regeneration, quiescent hepatocytes re-enter the cell cycle to proliferate and compensate for lost tissue. Multiple signals including hepatocyte growth factor, epidermal growth factor, tumor necrosis factor α, interleukin-6, insulin and transforming growth factor β orchestrate these responses and are integrated during the G₁ phase of the cell cycle. To investigate how these inputs influence DNA synthesis as a measure for proliferation, we established a large-scale integrated logical model connecting multiple signaling pathways and the cell cycle. We constructed our model based upon established literature knowledge, and successively improved and validated its structure using hepatocyte-specific literature as well as experimental DNA synthesis data. Model analyses showed that activation of the mitogen-activated protein kinase and phosphatidylinositol 3-kinase pathways was sufficient and necessary for triggering DNA synthesis. In addition, we identified key species in these pathways that mediate DNA replication. Our model predicted oncogenic mutations that were compared with the COSMIC database, and proposed intervention targets to block hepatocyte growth factor-induced DNA synthesis, which we validated experimentally. Our integrative approach demonstrates that, despite the complexity and size of the underlying interlaced network, logical modeling enables an integrative understanding of signaling-controlled proliferation at the cellular level, and thus can provide intervention strategies for distinct perturbation scenarios at various regulatory levels.

Logical modelling of gene regulatory networks with GINsim

Discrete mathematical formalisms are well adapted to model large biological networks, for which detailed kinetic data are scarce. This chapter introduces the reader to a well-established qualitative (logical) framework for the modelling of regulatory networks. Relying on GINsim, a software implementing this logical formalism, we guide the reader step by step towards the definition and the analysis of a simple model of the lysis-lysogeny decision in the bacteriophage λ.

In silico implementation of synthetic gene networks

Computational synthetic biology has borrowed methods, concepts, and techniques from systems biology and electrical engineering. Features of tools for the analysis of biochemical networks and the design of electric circuits have been combined to develop new software, where Standard Biological Parts (physically stored at the MIT Registry) have a mathematical description, based on mass action or Hill kinetics, and can be assembled into genetic networks in a visual, "drag & drop" fashion. Recent tools provide the user with databases, simulation environments, formal languages, and even algorithms for circuit automatic design to refine and speed up gene network construction. Moreover, advances in automation of DNA assembly indicate that synthetic biology software soon will drive the wet-lab implementation of DNA sequences.

Automatic compilation from high-level biologically-oriented programming language to genetic regulatory networks

BACKGROUND

The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry.

METHODOLOGY/PRINCIPAL FINDINGS

To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes (~ 50%) and latency of the optimized engineered gene networks.

CONCLUSIONS/SIGNIFICANCE

Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems.

Automatic design of digital synthetic gene circuits

De novo computational design of synthetic gene circuits that achieve well-defined target functions is a hard task. Existing, brute-force approaches run optimization algorithms on the structure and on the kinetic parameter values of the network. However, more direct rational methods for automatic circuit design are lacking. Focusing on digital synthetic gene circuits, we developed a methodology and a corresponding tool for in silico automatic design. For a given truth table that specifies a circuit's input–output relations, our algorithm generates and ranks several possible circuit schemes without the need for any optimization. Logic behavior is reproduced by the action of regulatory factors and chemicals on the promoters and on the ribosome binding sites of biological Boolean gates. Simulations of circuits with up to four inputs show a faithful and unequivocal truth table representation, even under parametric perturbations and stochastic noise. A comparison with already implemented circuits, in addition, reveals the potential for simpler designs with the same function. Therefore, we expect the method to help both in devising new circuits and in simplifying existing solutions.

Synthetic Biology is a novel discipline that aims at the construction of new biological systems able to perform specific tasks. Following the example of electrical engineering, most of the synthetic systems so far realized look like circuits where smaller DNA-encoded components are interconnected by the exchange of different kinds of molecules. According to this modular approach, we developed, in a previous work, a tool for the visual design of new genetic circuits whose components are displayed on the computer screen and connected through hypothetical wires where molecules flow. Here, we present an extension of this tool that automatically computes the structure of a digital gene circuit–where the inputs and the output take only 0/1 values–by applying procedures commonly used in electrical engineering to biology. In this way, our method generalizes and simplifies the design of genetic circuits far more complex than the ones so far realized. Moreover, different from other currently used methods, our approach limits the use of optimization procedures and drastically reduces the computational time necessary to derive the circuit structure. Future improvements can be achieved by exploiting some more biological mechanisms able to mimic Boolean behavior, without a substantial growth of the algorithmic complexity.

Toward design-based engineering of industrial microbes

Engineering industrial microbes has been hampered by incomplete knowledge of cell biology. Thus an iterative engineering cycle of modeling, implementation, and analysis has been used to increase knowledge of the underlying biology while achieving engineering goals. Recent advances in Systems Biology technologies have drastically improved the amount of information that can be collected in each iteration. As well, Synthetic Biology tools are melding modeling and molecular implementation. These advances promise to move microbial engineering from the iterative approach to a design-oriented paradigm, similar to electrical circuits and architectural design. Genome-scale metabolic models, new tools for controlling expression, and integrated -omics analysis are described as key contributors in moving the field toward Design-based Engineering.