Rule-based Modeling Language Research Articles

Long lived transients in gene regulation

Gene expression is regulated by the set of transcription factors (TFs) that bind to the promoter. The ensuing regulating function is often represented as a combinational logic circuit, where output (gene expression) is determined by current input values (promoter bound TFs) only. However, the simultaneous arrival of TFs is a strong assumption, since transcription and translation of genes introduce intrinsic time delays and there is no global synchronisation among the arrival times of different molecular species at their targets. We present an experimentally implementable genetic circuit with two inputs and one output, which in the presence of small delays in input arrival, exhibits qualitatively distinct population-level phenotypes, over timescales that are longer than typical cell doubling times. From a dynamical systems point of view, these phenotypes represent long-lived transients: although they converge to the same value eventually, they do so after a very long time span. The key feature of this toy model genetic circuit is that, despite having only two inputs and one output, it is regulated by twenty-three distinct DNA-TF configurations, two of which are more stable than others (DNA looped states), one promoting and another blocking the expression of the output gene. Small delays in input arrival time result in a majority of cells in the population quickly reaching the stable state associated with the first input, while exiting of this stable state occurs at a slow timescale. In order to mechanistically model the behaviour of this genetic circuit, we used a rule-based modelling language, and implemented a grid-search to find parameter combinations giving rise to long-lived transients. Our analysis shows that in the absence of feedback, there exist path-dependent gene regulatory mechanisms based on the long timescale of transients. The behaviour of this toy model circuit suggests that gene regulatory networks can exploit event timing to create phenotypes, and it opens the possibility that they could use event timing to memorise events, without regulatory feedback. The model reveals the importance of (i) mechanistically modelling the transitions between the different DNA-TF states, and (ii) employing transient analysis thereof.

Multi-Level Modeling and Simulation of Cellular Systems: An Introduction to ML-Rules.

ML-Rules is a rule-based language for multi-level modeling and simulation. ML-Rules supports dynamic nesting of entities and applying arbitrary functions on entity attributes and content, as well as for defining kinetics of reactions. This allows describing and simulating complex cellular dynamics operating at different organizational levels, e.g., to combine intra-, inter-, and cellular dynamics, like the proliferation of cells, or to include compartmental dynamics like merging and splitting of mitochondria or endocytosis. The expressiveness of the language is bought with additional efforts in executing ML-Rules models. Therefore, various simulators have been developed from which the user and automatic procedures can select. The experiment specification language SESSL facilitates design, execution, and reuse of simulation experiments. The chapter illuminates the specific features of ML-Rules as a rule-based modeling language, the implications for an efficient execution, and shows ML-Rules at work.

Generalizing Gillespie's Direct Method to Enable Network-Free Simulations.

Gillespie's direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie's direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termed network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie's direct method for network-free simulation. Finally, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology.

Developing and validating a multi-level ecological model of eastern Baltic cod (Gadus morhua) in the Bornholm Basin – A case for domain-specific languages

Recent changes in environmental and ecological conditions are a major reason that established stock assessment macro-models no longer provide reliable estimates on the stock status of eastern Baltic cod (Gadus morhua). To account for a complex ecological system, an individual-based modelling approach is needed that turns the attention from the overall population to the individual fish, its metabolic processes, behaviour, and interaction with environmental conditions.The multi-level rule-based modelling language ML-Rules was used to develop the first iteration of an individual-based model. The different stratification layers of the estuarine environment are modelled as explicit entities with varying properties and are populated by individual cod and its prey. The cod model focuses on growth and mortality dependent on abiotic factors and behaviour within the stratified environment of the Bornholm Basin.Despite the complexity of the formalised interactions, the resulting model specification is succinct and compact. Its structure and declarative nature facilitates reuse and subsequent extension. To allow easy replication of simulation experiments, we used the simulation experiment specification language SESSL for calibration and validation. The extendibility of the model and replicability of simulation experiments, as provided by the domain specific languages ML-Rules and SESSL respectively, will be key assets in future research, as the model provides a first, but essential, step toward studying the impact of specific environmental changes and certain behaviour pattern on the cod population in the Bornholm Basin.

Semantics and Efficient Simulation Algorithms of an Expressive Multilevel Modeling Language

The domain-specific modeling and simulation language ML-Rules is aimed at facilitating the description of cell biological systems at different levels of organization. Model states are chemical solutions that consist of dynamically nested, attributed entities. The model dynamics are described by rules that are constrained by arbitrary functions, which can operate on the entities’ attributes, (nested) solutions, and the reaction kinetics. Thus, ML-Rules supports an expressive hierarchical, variable structure modeling of cell biological systems. The formal syntax and semantics of ML-Rules show that it is firmly rooted in continuous-time Markov chains. In addition to a generic stochastic simulation algorithm for ML-Rules, we introduce several specialized algorithms that are able to handle subclasses of ML-Rules more efficiently. The algorithms are compared in a performance study, leading to conclusions on the relation between expressive power and computational complexity of rule-based modeling languages.

Rigid Geometric Constraints for Kappa Models

Rule-based modeling languages such as Kappa [Danos, V. and C. Laneve, Formal molecular biology, Theor. Comput. Sci. 325 (2004), pp. 69–110, Danos, V., J. Feret, W. Fontana, R. Harmer and J. Krivine, Rule-based modelling of cellular signalling, in: L. Caires and V. Vasconcelos, editors, Proceedings of the Eighteenth International Conference on Concurrency Theory (CONCUR07), Lecture Notes in Computer Science 4703 (2007), pp. 17–41, available at http://www.pps.univ-paris-diderot.fr/~danos/pdf/ka-fix.pdf] and BNGL [Blinov, M. L., J. R. Faeder, B. Goldstein and W. S. Hlavacek, Bionetgen: software for rule-based modeling of signal transduction based on the interactions of molecular domains, Bioinformatics 20 (2004), pp. 3289–3291. URL http://bioinformatics.oxfordjournals.org/content/20/17/3289.abstract, Blinov, M., J. Yang, J. Faeder and W. Hlavacek, Graph theory for rule-based modeling of biochemical networks, in: C. Priami, A. Ingólfsdóttir, B. Mishra and H. Riis Nielson, editors, Transactions on Computational Systems Biology VII, Lecture Notes in Computer Science 4230, Springer Berlin/Heidelberg, 2006 pp. 89–106. URL http://dx.doi.org/10.1007/11905455_5] allow for a concise description of combinatorially complex biochemical processes as well as efficient simulations of the resulting models [Danos, V., J. Feret, W. Fontana and J. Krivine, Scalable simulation of cellular signaling networks, invited paper, in: Z. Shao, editor, Proceedings of the Fifth Asian Symposium on Programming Systems, APLAS2007, Singapore, Lecture Notes in Computer Science 4807 (2007), pp. 139–157, available at http://www.pps.univ-paris-diderot.fr/~danos/pdf/scalability.pdf, Danos, V., J. Feret, W. Fontana, R. Harmer and J. Krivine, Rule-based modelling of cellular signalling, in: L. Caires and V. Vasconcelos, editors, Proceedings of the Eighteenth International Conference on Concurrency Theory (CONCUR07), Lecture Notes in Computer Science 4703 (2007), pp. 17–41, available at http://www.pps.univ-paris-diderot.fr/~danos/pdf/ka-fix.pdf]. A key aspect of the rule-based modeling approach is to partially expose the structure of the chemical species involved. However, the above-mentioned languages do not provide means to directly express the three-dimensional geometry of chemical species. As a consequence models typically capture only the network-topological structure of the species involved. For certain biochemical processes, such as the assembly of molecular complexes, in which steric constraints play a key role, it would seem natural to also model the geometric structure of species. We propose an extension to the Kappa modeling language allowing the annotation of the structure of chemical species with three-dimensional geometric information. This naturally introduces rigidity constraints on the species and reduces the state space of the resulting model by excluding species that are not geometrically sound. We show that models extended in this way can still be simulated efficiently, albeit at the cost of a greater number of null-events occurring during the simulation. The geometric constraints introduced by the extension are inherently non-local in that they may entangle the position and orientation of sub-structures at arbitrary distances in large species such as polymers. We give a formal definition of the notion of locality based on the intuition that local modifications should only affect sub-structures within a finite radius around the point where the modification occurred. We show that there are indeed geometrically enhanced Kappa models that are non-local, and conversely, that every local model can be simulated accurately using a finite classical Kappa model at the expense of a possible combinatorial explosion of its rule set. We also give some sufficient conditions for the locality of a model but show that locality is undecidable in general.

Phenotypic Properties of Scaffold-Based Signaling Paradigms

Scaffold proteins are key components of signaling networks, recruiting and organizing other signaling proteins. Certain scaffolds have been extensively characterized and a number of hypotheses exist regarding general scaffold functions, including the linearization of dose-response behavior and the mediation of feedback. Despite intense investigation, however, we lack a complete understanding of how scaffolds might influence signal transduction. The goal of this work is to systematically determine how various properties of signaling cascades are impacted in the presence or absence of scaffold proteins. Using rule-based modeling languages, we constructed three basic models of signaling based loosely on the network structure of the yeast pheromone signaling cascade. The first model lacks a scaffold; this solution cascade involves a series of Goldbeter-Koshland covalent modification cycles where each activated substrate is the kinase for a subsequent substrate. The second, machine, model contains a scaffold that acts as a nucleation point for the hierarchical assembly of kinases into a discrete multi-subunit signaling complex. Finally, the ensemble model contains a scaffold to which kinases bind independently. This ensemble network therefore exhibits considerably more combinatorial complexity in terms of the numbers of possible signaling species compared to the others. We performed a comparative analysis of these three paradigms, and found significant phenotypic differences. Notably, scaffold-based models exhibited far less noise as cascade depth increases. Furthermore, only machine-like models are capable of preventing crosstalk in instances where two pathways share components. More generally, we observed that a machine-like cascade generally offers greater functional robustness, though at the cost of evolutionary plasticity and time to signal response. The evolution of scaffold proteins, and the rules that govern the assembly of signaling complexes, has likely been driven by the distinct functional roles that scaffold-based signaling complexes can play.

A simulator for spatially extended kappa models

Summary: Spatial Kappa is a simulator of models written in a variant of the rule-based stochastic modelling language Kappa, with spatial extensions.Availability: The spatial kappa simulator is an open-source project licensed under the LGPLv3, with Java source, binaries and manual available at http://github.com/lptolik/SpatialKappa.Contact: oksana.sorokina@ed.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Spatial Rule-Based Modeling: A Method and Its Application to the Human Mitotic Kinetochore

A common problem in the analysis of biological systems is the combinatorial explosion that emerges from the complexity of multi-protein assemblies. Conventional formalisms, like differential equations, Boolean networks and Bayesian networks, are unsuitable for dealing with the combinatorial explosion, because they are designed for a restricted state space with fixed dimensionality. To overcome this problem, the rule-based modeling language, BioNetGen, and the spatial extension, SRSim, have been developed. Here, we describe how to apply rule-based modeling to integrate experimental data from different sources into a single spatial simulation model and how to analyze the output of that model. The starting point for this approach can be a combination of molecular interaction data, reaction network data, proximities, binding and diffusion kinetics and molecular geometries at different levels of detail. We describe the technique and then use it to construct a model of the human mitotic inner and outer kinetochore, including the spindle assembly checkpoint signaling pathway. This allows us to demonstrate the utility of the procedure, show how a novel perspective for understanding such complex systems becomes accessible and elaborate on challenges that arise in the formulation, simulation and analysis of spatial rule-based models.

The design of FFML: A rule-based policy modelling language for proactive fraud management in financial data streams

Developing fraud management policies and fraud detection systems is a vital capability for financial institutions towards minimising the effect of fraud upon customer service delivery, bottom line financial losses and the adverse impact on the organisation’s brand image reputation. Rapidly changing attacks in real-time financial service platforms continue to demonstrate fraudster’s ability to actively re-engineer their methods in response to ad hoc security protocol deployments, and highlights the distinct gap between the speed of transaction execution within streaming financial data and corresponding fraud technology frameworks that safeguard the platform. This paper presents the design of FFML, a rule-based policy modelling language and encompassing architecture for facilitating the conceptual level expression and implementation of proactive fraud controls within multi-channel financial service platforms. It is demonstrated how a domain specific language can be used to abstract the financial platform into a data stream based information model to reduce policy modelling complexity and deployment latencies through an innovative policy mapping language usable by both expert and non-expert users. FFML is part of a comprehensive suite of assistive tools and knowledge-based systems developed to support fraud analysts’ daily work of designing new high level fraud management policies, mapping into executable code of the underpinning application programming interface and deployment of active monitoring and compliance functionality within the financial platform.

Modular Modelling in Synthetic Biology: Light-Based Communication in E. coli

Modularity – the notion that individual components can be described as self-contained units and composed in different combinations – is a concept integral to synthetic biology. In vitro, it is embodied by the notion of BioBrickTMparts: standardised DNA sequences of defined structure, function, and common interfaces that can be composed and integrated into living cells. A similar modularity in the modelling of such systems, however, has not been explored until much more recently. In the course of the 2010 International Genetically Engineered Machine (iGEM) competition, the University of Edinburgh team adopted an iterative rule-based approach to developing a BioBrick model of light-based communication in Escherichia coli. The system was modelled in the Kappa stochastic rule-based biological modelling language, allowing for the modular development and analysis of a complicated biological system. It also utilised newly developed spatial extensions to aid in the depiction of intercellular communication via light emitting and light sensing pathways, demonstrating the extensibility of the language as a whole. This paper provides a case study of modelling synthetic biology projects using the rule-based techniques outlined above, documenting the benefits of this modular iterative approach.