TR&D3 Research Highlights
Modeling with BioNetGen Gives MMBioS Team Insight into How Immune System Decides to Attack — or Let Be
Friend or Foe?
A mix of computer modeling and laboratory experiments has helped reveal how the body differentiates “friend from foe.” Using their BioNetGen computer tool for simulating biochemistry, MMBioS members and colleagues have painted a sharper picture of how T cells, the advance scouts of the immune system, decide when to protect bodily tissues from immune attack—and when to lead the attack. The finding may guide future efforts to control human diseases like diabetes and cancer.
When T cells—a type of white blood cell—encounter other cells in the body, “they have to decide what kind of a response to make,” says James Faeder, project co-leader of MMBioS’s Technology Research and Development Project 2 Team and associate professor of computational & systems biology, University of Pittsburgh. “Is that a threat, or is it something benign? Depending on their assessment of a possible threat they can either become activated, immune-boosting cells that kill pathogens, or they can tamp down those responses.”
Improved Sampling of Cell-Scale Models using the Weighted Ensemble Strategy
The “weighted ensemble” (WE) strategy for orchestrating a large set of parallel simulations has been established as an effective tool for efficiently calculating kinetic and equilibrium observables in molecular systems – and now has been extended to spatially resolved cell-scale systems by MMBioS researchers. In a collaboration among several MMBioS groups, the WESTPA implementation of WE has been used to control MCell simulations, including models built using a BioNetGen-CellOrganizer pipeline for situating complex biochemistry within spatially realistic cell models. As sketched, the WE strategy enables computational effort to be focused on rare, difficult-to-sample events – essentially increasing the precision of measurements in the tails of distributions. In an application to an MCell model of the frog neuro-muscular junction (NMJ), WE simulation enabled calculation of vesicle release probabilities under particularly challenging low-calcium concentrations that could not be well sampled by conventional MCell simulation: see red-circled points in the figure. This in turn, enabled validation of the model by confirmation of an established NMJ empirical power-law relationship between calcium-concentration and release probability. WE yielded estimates of observables in less overall computing time than would be required in ordinary parallelization, thus exhibiting super-linear parallel performance.
Development and improvements to MCell
The MCell modeling and simulation platform provides the core capabilities for spatial simulation reaction-diffusion dynamics at complex biological interfaces. Highlights of progress include:
libMCell API We have developed a high-level set of routines, which enable the creation and simulation of MCell models via API calls alone, i.e., without invoking any parsed MDL description of the model. Current functionality includes creation of species, defining reactions, defining molecule releases, creating model geometry, and simulation and runtime control.
MCell testing framework This framework, called nutmeg (https://github.com/haskelladdict/nutmeg), has completely replaced our previous, python based unit test framework. During the year we made nutmeg much more robust and added many additional test cases.
Implementation of new simulation capabilities that extend the range of simulations that can be performed in order to address important biological questions, such as the how membrane dynamics and signal transduction interact or how multisite phosphorylation, binding, and cooperativity affect signaling. Highlights include:
- Dynamic geometries Users can now create dynamical meshes using Blender and use these to drive an MCell simulation of reaction and diffusion with moving boundaries.
- Modeling pipeline Users can build complex biochemical models using BioNetGen and sample complex cellular geometries from imaging data using CellOrganizer and combine those into a single model that can be simulated in CellBlender (in collaboration with TRD3).
- Recovery of protein-protein interactions and other aspect of biochemical mechanisms from reaction network models using Atomizer. The automated conversion of these models into a rule-based format allows for comparative analysis of models and reuse of existing model components.
- Visualization of regulatory structurein rule-based models New visualization methods have been developed that enable global visualization of models exhibiting combinatorial complexity.
Development of CellBlender
CellBlender is a graphical interface for model construction, simulation, and analysis of complex spatial models of reaction-diffusion systems Highlights of progress include
- A major redesign of the interface to consolidate functionality based on a single toolbar.
- Implementation of a robust parameter handling system
- Functionality for running simulations from within the interface taking advantage of parallel computing resources allowing users to manage on-going simulation streams.
- Direct access to run-time error logs within the Blender interface.
- Model and geometry importers that allow import of compartmental reaction network models in either SBML or compartmental BNGL format.
- Implementation of internal data model provides backward compatibility with future CellBlender versions.
- Release of CellBlender version 1.0 and publication of an article featuring MCell and CellBlender in the Encyclopedia of Computational Neuroscience
Stochastic Simulation
Systems biology, the quantitative study of complex interacting biological systems, is becoming increasingly demanding of computational resources. As models become able to capture truly physiological behavior, some of the most difficult computations may also be the most important, such as a rare transition from normal to pathological behavior.
A novel approach to meet growing computational demands is described in a paper recently accepted to the Journal of Chemical Physics ("Efficient Stochastic Simulation of Chemical Kinetics Networks Using A Weighted Ensemble Of Trajectories"). First-author Donovan, in collaboration with MMBioS investigators Faeder and Zuckerman, applied a sophisticated parallel simulation algorithm originally developed for small-scale molecular systems to systems biology models.
The results were eye-catching: the new approach was more computationally efficient at modeling rare events than standard methods by orders of magnitude, even for a very complex model with thousands of reacting species.