Network Modeling

TR&D3: Network modeling

The goal of TR&D3 Network-based approaches have yielded many novel insights, in particular, the identification of common network motifs such as feedback and feedforward loops that give rise to specific classes of signal processing characteristics – like switching, oscillation, and adaptation.


Cellular systems are profoundly difficult to understand because of the interplay between spatial, biochemical and molecular complexity that occurs on multiple levels of organization, from macromolecular assemblies to synapse architecture to neural circuits. Current cell simulation tools are just beginning to address the complexity that spans these levels in an integrated fashion. Major advances in simulation tools are needed to enable the development of models that capture the required level of detail and at the same time remain computationally tractable. The MCell/CellBlender platform for cell modeling developed in TR&D2 is expressly designed to fulfill these needs, providing insight and understanding of complex cellular systems.

Background and Motivation

The integration of the network modeling component within MMBioS offers numerous opportunities for further developing RBM capabilities and to demonstrating their effectiveness for providing mechanistic insight into complex regulatory networks. A number of specific aims will be pursued to achieve these goals: Accelerated methods for stochastic simulation of rule-based models. Most of the parameters governing the behavior of network models cannot be measured independently but must instead be determined by solving the inverse problem to obtain the parameters from experimental data, which demands many orders of magnitude more computational effort that computing a single trajectory. The structure of rule-based models opens the door to development of more efficient simulation methods that have not been previously explored.

Novel coarse-grained spatial modeling capabilities MCell simulations are prohibitively expensive in terms of computing time and memory for many applications, particularly those that require simulation of protein copy numbers on the scale of a whole cell. Simulations at this scale require more coarse-grained spatial resolution for computational efficiency, which we will develop in Aims 1.2 and 1.3. These coarse-grained network-free simulators will enable parameter estimation and other intensive analysis tasks to be performed on spatially-resolved models, such as those developed in DBP 4 and 8 in collaboration with TR&D4 and TR&D1.

Novel interfaces for model construction, visualization, and comparative analysis Large-scale modeling efforts require scalable and zoomable approaches to visualization. It is currently difficult to compare models in the literature or even those downloaded from databases with each other. Rule-based encoding can facilitates management and comparative analysis of models using tools we developed and we will apply in Aim 2.

Increased accessibility of model analysis and parameter estimation tools for complex models The computational demands of many parameter estimation tasks require HPC resources that many end users lack expertise to utilize. We will develop novel parameter estimation interfaces in Aim 2 that wil provide users with transparent access to HPC resources available through the XSEDE platform in Aim 3.

Unique infrastructure for rule-based modeling capabilities In Aim 3 we will harden the software libraries and interfaces that are critical to the continued availability of RBM capabilities for developers and end users. 

Related work/software Other RBM software tools have been developed, but none appears to have the strengths of the BioNetGen platform. Indeed, some major packages use BioNetGen as an underlying RBM engine, including VCell and Smoldyn.

There is little overlap between current VCell capabilities and the aims proposed here, and in fact the proposed simulator development in Aim 1, the visualization capabilities in Aim 2, and the software infrastructure in Aim 3 may all be used to expand VCell functionality in the future (see Blinov letter of support). Besides BioNetGen, the other major RBM language based on citations, use by non-affiliated groups, and integration into other software frameworks is Kappa, which is open source and written in the OCaml language. Kappa is well-known in the computer science community, but has comparatively few biological applications. Another notable effort is Simmune,  which combines graphical, RBM specification and PDE and ODE simulations. Currently, Simmune does not support stochastic simulations and source code is not available, which limits its use for outside development. A number of other software tools have been developed for RBM, especially in recent years, but most of these were developed for relatively specialized applications and/or are not being actively developed.

Specific Aims

  1. Advance rule-based modeling technology for cell-scale simulations

    Network-free simulation is a key technology for the largest scale simulations because it avoids explicit generation of the full reaction network without sacrificing accuracy. The computational cost of these simulations remains a significant limitation to their application, particularly for computationally intensive tasks such as parameter estimation, which motivates Subaim 1.1: Adapt and further develop methods to accelerate stochastic simulations of rule-based models. The integration of rule-based modeling capabilities into MCell in TR&D2 enables spatially-resolved simulations of large networks exhibiting combinatorial complexity, but simulating cell-sized molecular populations remains prohibitive, which motivates Subaim 1.2: Develop a compartmental network-free simulator; and Subaim 1.3: Develop a network-free subvolume-based simulator to complement TR&D2 and enable simulation-intensive parameter estimation tasks required by multiple DBPs. Both coarse-grained simulators will initially be developed to handle static geometries and subsequently extended to handle dynamic geometries..

  2. Further develop advanced visual interfaces for building, managing and analyzing rule-based models

    Visual interfaces are essential to the modern collaborative modeling endeavor: they make modeling accessible to non-specialists and enable collaborative development and re-use of models developed by other groups, motivating Subaim 2.1: Extend RuleBender, the graphical front end to BioNetGen, to enable visual model building without detailed knowledge of the BioNetGen language and provide additional model visualization capabilities that we have developed as prototypes in the initial funding period. Facilitating model annotation, comparison, and re-use motivates Subaim 2.2: Develop a database of molecules, rules, and models that can be used for comparative analysis of existing models and development of new models. This aim will take advantage of the Atomizer tool we have developed that enables any reaction network model to be recast as a rule-based one. Finally, facilitating analysis of model dynamics to enable mechanistic discovery from data motivates Subaim 2.3: Develop RuleBender interfaces for parameter sweeps, sensitivity analysis, and parameter estimation.  

  3. Extend and refine software infrastructure for rule-based modeling

    Although the BioNetGen framework already provides rule-based modeling capabilities for a number of major software tools including the Virtual Cell, pySB, rxncon, BioUML, MCell and WESTPA, the core capabilities provided under the BioNetGen umbrella are currently distributed among multiple codebases in different languages, motivating Subaim 3.1: Develop a hardened C/C++ library and application programming interface (API) for BioNetGen that exposes both high- and low-level functionality. The computationally intensive nature of parameter estimation and other model analysis tasks motivates Subaim 3.2: Develop a web portal that will provide end users direct access to rule-based modeling capabilities and HPC resources.

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