RBM Agenda

Attendees and Abstracts

Thursday, June 14

8:30-8:40 Opening Remarks

8:40-9:10 William S. Hlavacek (LANL) An Overview of Rule-Based Modeling

9:10-9:40 Talk 1

9:40-10:10 Talk 2

10:10-10:40 Coffee Break Sponsored by SFI

10:40-11:10 Talk 3

11:10-11:40 Talk 4

11:40-12:10 Talk 5

12:10-1:30 Catered Lunch Sponsored by SFI

1:30-2:00 Michael Hucka (Caltech) An Overview of SBML

2:00-2:30 Talk 6

2:30-3:00 Talk 7

3:00-3:30 Coffee Break Sponsored by SFI

3:30-4:00 Talk 8

4:00-4:30 Talk 9

4:30-5:00 Talk 10

5:00-5:30 Talk 11

5:30-5:45 Day 1 Summary and Thoughts About Day 2 (Hucka)

6:30 Dinner at Ristra Sponsored by SFI

Friday, June 15

8:30-9:00 Talk 12

9:00-9:30 Talk 13

9:30-10:00 Talk 14

10:00-10:30 Coffee Break Sponsored by SFI

10:30-11:00 Talk 15

11:00-11:30 Talk 16

11:30-12:00 Talk 17

12:00-1:30 Lunch Sponsored by SFI

1:30-2:00 Talk 18

2:00-2:30 Talk 19

2:30-3:30 Presentation of SBML Level 3 Proposal (Blinov) Followed by Discussion

3:30-4:00 Coffee Break Sponsored by SFI

4:00-? Open Discussion about Level 3 and Other Topics in Rule-Based Modeling. Preparation of a conference report to be submitted to Molecular Systems Biology. Future meetings?

Talk 18

Talk 19

Saturday, June 16

Morning Departure

The following are the confirmed attendees and the current abstracts of their presentations.

• Nathan Addy (The Molecular Sciences Institute): Experiences Converting Between BNG and Moleculizer Input Formats

Abstract: This talk will detail my experiences writing a converter that converts between BNG models and Moleculizer models. Both programs are examples of chemical reaction network simulators that take input in the form of template based rules describing the conditions and rates under which new complexes form. In spite of their similarities, there are several important differences in their structure, which represent different schools of thought as to how rules should be specified. This talk will detail these differences on a practical level, and how one can be generated from the other, with the hope that this will be informative to discussions regarding different SBML Level 3 Rule based formats.

• Michael L. Blinov (U Connecticut Health Ctr):

• Will Chen (Harvard): Phospho-dynamic Experiments and Computational Modeling of the ErbB Pathways

Abstract: The ErbB receptors are a set of four plasma membrane-bound receptors that play a role in organismal development and disease, such as proliferation, invasion and metastasis of cancers. The four receptors are able to homodimerize and heterodimerize when bound to extracellular ligands, and couple to the MAP kinase and PI3K/AKT pathways to transduce extracellular signals to the nucleus. High precision, phospho-dynamic measurements are married to a biologically simple (but computationally complex) chemical reaction ODE model of early pathway events. The role of rule-based modeling for large chemical systems will be discussed in the context of studying the dynamics of the ErbB receptors.

• Joshua Colvin

• Holger Conzelmann (University of Stuttgart): Signal transduction and combinatorial complexity: Model reduction, reduced modeling and thermodynamic constraints

Abstract: Modeling signal transduction networks, which exhibit an enormous combinatorial complexity, is a challenging task. In the past years we have developed systematic methods to create strongly reduced models of these complex reaction networks. These methods mostly are independent of exact numerical values of the kinetic parameters. They mainly base on assumptions about domain interactions. However, the feasible interactions between domains of scaffold proteins and receptors are restricted by elementary thermodynamic constraints, which should be considered in modeling.

• Vincent Danos (CNRS): What is so special about biological signalling?

Abstract: Biological signalling networks can be described neatly in a language where agents bind and modify their internal states according to some specified rules. However, natural rule sets generate concurrent transition systems (or stochastic ones, given rates for rules), which are large (many agents are involved) and high dimensional (the number of non-isomorphic ways in which agents can connect is high or even infinite), so that even simple tasks such as simulation are costly. We develop here an abstract interpretation of those networks, based on the natural notion of an agent view, which leads, among other things, to an efficient computation of a superset of the system dimensions. We show this superset is the exact one, ie not just a superset, iff the latter is closed under swap (an operation whereby pairs of edges of the same type can permute their ends). That property can be taken as the definition of a local signalling network, a rather strong constraint which is implied by a simple syntactic restriction on rules. Suprisingly we can show that two substantial examples -- the EGF/FGF receptors early response pathways -- verify that syntactic condition (up to some transformations), and therefore despite their apparent complexity fall within the class of local networks.

• Emek Demir (Memorial Sloan-Kettering Cancer Center): Rule based aspects in BioPAX

Abstract: BioPAX is a collaborative effort to create a data exchange format for biological pathway data. Recently we have proposed rule based aspects for covering generic entities and unknown state variables for the next level of BioPAX. I will present the biological requirements driven from the data providers in the BioPAX community as well as our design rationale for the proposed features.

• James R. Faeder (Los Alamos): Rule-based Modeling of Biochemical Systems using BioNetGen

Abstract: Binding interactions among proteins and other biomolecules occur at the level of domains and motifs and often follow phosphorylation or other posttranslational modifications. The catalog of these functional elements and their interactions is continually growing and poses a major barrier to the development of predictive mathematical models because of combinatorial complexity, the explosion in the number of possible chemical species and reactions that can occur in such networks. The BioNetGen (BNG) language uses graphs to represent proteins and other biomolecules, with nodes representing functional subunits of these molecules and edges representing binding interactions. Graph rewriting rules describe biochemical transformations, such the formation or dissociation of bonds or state changes. This language enables the construction of precise and comprehensive models and greatly expands the scope of information that can be incorporated into models of cellular networks. BNG also incorporates a wide range of analytical and simulation tools, and networks generated by BNG can be exported in the Systems Biology Markup Language and other formats allowing interoperability with other modeling platforms. Standard methods for simulating reaction networks, such as ODEs and the Gillespie algorithm, are often not adequate to simulate networks arising from the rule-based description of realistic signaling cascades, requiring the development of simulation algorithms that avoid explicit generation of the reaction network. I will describe recent progress in the development of such algorithms and also present several applications to the modeling of signal transduction networks, including immune and growth factor receptors.

• Walter Fontana (Harvard):

• William S. Hlavacek (Los Alamos):

• Michael Hucka (Caltech):

• Krivine Jean

• Ozan Kahramanoğullari (Imperial College London):

• Sohyoung Kim (NIH/NCI): Depicting combinatorial complexity with the molecular interaction map notation

Abstract: To help us understand how bioregulatory networks operate, we need a standard notation for diagrams analogous to electronic circuit diagrams. Such diagrams must surmount the difficulties posed by complex patterns of protein modifications and multiprotein complexes. To meet that challenge, we have designed the molecular interaction map (MIM) notation. MIM notation is used to build three types of diagrams: (1) explicit diagrams that define specific pathway models for computer simulation; (2) heuristic maps that organize the available information about molecular interactions and encompass the possible processes or pathways; and (3) diagrams of combinatorially complex models. Our discussion will focus on combinatorially compelx models and its applications.

• Jean Krivine (École Polytechnique):

• Pat Lincoln (SRI):

• Larry Lok (MSI): Mechanism-based management of reaction network rules

Abstract: Some (better or worse) Moleculizer design decisions, including but not limited to rule-based reaction network generation, naturally impact the program's native input format. For Moleculizer or any modeling program to work effectively with SBML, an adapter of some sort seems necessary on the input side. In some cases, the adapter could be a simple XSLT transformation. My talk will consist of some half-baked ideas about balancing adapter functionality with program revision.

• Aneil Mallavarapu (Harvard):

• Anika Oellrich (EBI): Support for multistate complexes in SBML Level 3: StochSim SBML Support

Abstract: Biological entities may exist under different states, which are caused by covalent modifications, binding to modulators, alternate conformations or physical properties etc. Since most entities possess more than one state variable this results in a combinatorial explosion of possible states. StochSim is a mesoscopic stochastic biochemical simulator, which includes the possibility to simulate reactions between multistate complexes. The states are represented by vectors of state variables, and the number of reactions increases linearly with the number of state variables rather than exponentially. Complex configuration files are needed for setting-up a simulation. A comfortable and portable way would-be to use SBML natively. Because of the reason that SBML Level 2 does not provide structures to encode species state variables and the related reactions, this information has to be encoded in StochSim proprietary annotations. The state variables and their possible values are stored in SBML speciesTypes, the initial levels in SBML species and the description of multistate reactions in SBML reaction. We will also give an overview of how the simulator works currently and especially how the implementation of multistates is realised. Open questions remain on how best to encode instantaneous equilibriums between states and nearest-neighbour interactions in lattice. Although the SBML extension we developed is meant to be used in StochSim for now, we developed it with a much broader audience in mind, for instance not restricting us to binary state variables.

• Rick "Pax" Paxson (MathWorks): : Introduction to SimBiology and possible future enhancements for rule based modeling

Abstract: I will be presenting the current version of SimBiology, a software package intended for the modeling, simulation and analysis of biochemical pathways. It is a MATLAB based product that exploits the flexibility of having a programming interface to all of its functionality. Hinging on this flexibility we propose possible future enhancements to add rule based modeling capabilities. The focus of the talk is to get attendees acquainted with SimBiology for the purpose of founding conversations and/or collaborations that extend rule based modeling in this environment.

• Richard G. Posner (TGen): Mechanistic modeling of signaling pathways.

Abstract: Because cellular signaling is exceedingly complex, we believe a mathematical model predicting the dynamic behavior of a system of interest is essential for understanding the system and that such a model has the potential to guide rationale drug discovery, clinical practice and diagnosis. Mechanistic models of signal transduction and other cellular systems are difficult to formulate because of the problem of combinatorial complexity, which is present whenever a relatively small number of biomolecular interactions have the potential to generate a much larger number of chemical species and reactions. For example a complete model of the ErbB signaling pathway would have require keeping track of over 100 trillion potential molecular configurations. We have recently developed a C version of network modelng software that can read BioNetGen inpiut files and has improved capabilities and speed. The software has been specifically designed to allow one to make testable in silico predictions concerning the effects of drugs individually and as combinations.

• Stephen Racunas (Penn State): Representation and Evaluation of Biochemical Hypotheses

Abstract: In this talk, I will outline some recent work in both metabolic and regulatory systems that makes use of rule-based techniques to evaluate populations of competing hypotheses for the level of support or contradiction each hypothesis encounters with respect to multiple data sources. I will outline the features of some formal languages used to represent the rules and the interacting agents in several applications, and then present a possible unifying "base language" capable of expressing the salient features from all of the examples in a common framework. I will also show how these techniques can been used to proofread biomedical knowledge bases, to unify heterogenous data resources, and to evaluate hypotheses about regulation and metabolism.

• Martin Meier-Schellersheim (NIH/NIAID): A tool for multi-scale computational cell biology

Abstract: The modeling and simulation tool Simmune allows for the definition of detailed models of cell biological processes ranging from interactions between molecular binding sites to the behavior of populations of cells. Based on the inputs the user provides through a graphical interface, the software automatically constructs the resulting sets of partial differential equations describing intra- and extra-cellular reaction-diffusion and integrates them, providing numerous ways to display the behavior of the simulated systems and to interact in a way that closely resembles wet-lab manipulations with running simulations. In the talk, I will explain the technical foundations and typical use cases for simmune.

• Ty Thomson (MIT): A Collaborative Process for Building and Documenting Biological Models

Abstract: Evaluation, reuse, and extension of published computational models is made difficult by the fact that most model builders do not provide organized descriptions of the reasoning and decisions made during model construction (e.g., choice and parameterization of reactions, system boundaries, etc.). The lack of access to such information results in individual modelers repeatedly redeveloping models, potentially using different subsets of the literature or interpretations thereof. Here we develop a principled process for describing the construction of a model along with support for model promulgation, reuse, revision, and extension. We apply our methods to develop a model for the Saccharomyces cerevisiae pheromone signal transduction pathway. The website (www.YeastPheromoneModel.org) presents both a formal rule-based model as well as the natural language documentation drawn directly from the literature. Choices and assumptions are clearly stated to facilitate their evaluation by other researchers. Modeling statements can be automatically extracted and processed into a BioNetGen model file for study and analysis. We believe that the cooperative and open development of well-documented models, enabled by web-based platforms such as wikis, would greatly increase model quality, scale, and number.