Cell Modeling

TR&D2: Cell modeling

The goal of TR&D2 is to provide an extensible, state-of-the-art model building and simulation platform for spatially realistic simulation and analysis of cellular and subcellular biochemistry, meeting the diverse needs of biologists, and which interfaces synergistically with the technologies developed in TR&Ds 1, 3, and 4.


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

Scientific discovery is driven by testable hypotheses which derive from our intuition and questions surrounding our current understanding of reality. But when daunting complexity confounds our intuition we struggle to conceive new hypotheses and the cycle of discovery grinds to a halt. Computational models allow investigators to probe the complex relationships between biological components, obtain new insights and intuition -- the genesis of new hypotheses. Models are often developed in an iterative fashion where results from an initial computational experiment is compared against results of bench experiments. The model is then refined appropriately, simulated again and the cycle continues until satisfactory agreement is reached. This iterative process can lead to new insights, and these in turn can be tested by further experimentation.

cellmodimgMonte Carlo Simulation of Cellular Microphysiology. MCell is a highly successful modeling tool for realistic simulation of cellular signaling in the complex 3D subcellular microenvironment in and around living cells – what we call cellular microphysiology. At such small subcellular scales the familiar macroscopic concept of concentration is not useful and stochastic behavior dominates. MCell uses highly optimized Monte Carlo algorithms to track discrete molecules in space and time as they diffuse and interact with other effector molecules such as membrane channels, receptors, transporters or enzymes. CellBlender is the 3D CAD system we have developed over the previous funding period. CellBlender is an extension for the popular 3D content creation software Blender (blender.org) which transforms Blender into a sophisticated platform allowing researchers to build complex 3D cellular models and explore, visualize, and analyze their dynamics as computed by MCell.

Our modeling approach employs a technique we call “computational reconstitution”– attempting to recapitulate the structure and function of a cellular system from its component parts, including molecules, reaction networks, subcellular organelles, and cellular membrane architecture. The MCell/CellBlender platform for cell modeling and simulation was designed expressly for this purpose. With MCell/CellBlender, cellular systems of extraordinary scope and complexity can be reconstituted and new insights obtained by observing how microscopic interactions and organization give rise to macroscopic behaviors. Most importantly, because models constructed this way include rich mechanistic detail, predictions from simulations constitute testable hypotheses at the biochemical and molecular biological level.

Specific Aims

  1. Expand Simulation Capabilities for MCell


    1.1. Spatially structured, multi-state multi-component molecules. We will work with TR&D3 to add spatial structure to an expanded BioNetGen language, and create new algorithms for network-free, particle-based spatial simulations into MCell.

    1.2. State-dependent dynamic geometries. We will improve MCell’s dynamic geometry algorithms by making the geometry update rules be a function of the simulation state.

    1.3. Parallel MCell - We will parallelize the MCell code to run both in a multithreaded and/or multiprocess fashion to obtain good speedups when simulating models large enough to be efficiently decomposed into many subvolumes. This will dramatically shorten the cycle of model modification and evaluation, and allow researchers to explore model space much more rapidly.

  2. Create a Geometry Preparation Pipeline for MCell/CellBlender  We will streamline the process of preparing simulation-quality 3D meshes obtained from stacks of images, especially electron-microscopic images, and obtained from CellOrganizer (TR&D4). We will develop an intuitive and flexible GUI in CellBlender that harnesses the power of advanced algorithms and expert knowledge.


    2.1. Improved workflow for high quality alignment of 3DEM datasets. Accurate image registration is a critical first step in transforming raw serial-section EM image sets into geometric forms needed for accurate geometric analysis and MCell simulations.

    2.2. Improved workflow for transforming segmented structures into computational quality meshes. We will integrate several disjoint tools and process steps into CellBlender to create a coherent and streamlined process. This workflow will also benefit TR&D3 Subaim 1.3.

  3. Expand interfaces for MCell and CellBlender


    3.1. libMCell API for advanced simulation event-scheduler/event-handler. This will enable advanced capabilities such as simulation-state-dependent dynamic geometry and multiscale/hybrid simulations via coupling with external physics engines (e.g TR&D3 Subaims 1.2 and 1.3).

    3.2. libMCell API for model building from C and Python. Expose a set of high-level routines, which will allow users to create and simulate MCell models via API calls alone, i.e., without invoking any parsed MDL description of the model.

    3.3. Interface to Web/Cloud computing resources for MCell/CellBlender. We will create an interface for advanced simulation control of Web/Cloud computing resources to enable parameter sweep/estimation applications, closely coordinated with TR&D3.

Cell Modeling Research Highlights

BioNetGen Gives Insight into Immune System

A mix of modeling with BioNetGen and laboratory experiments has painted a sharper picture of how T cells decide when to protect the body from immune attack —and when to lead the attack.  Read more


Memory Capacity of Brain 10 Times More than Previously Thought

MMBioS members Terry Sejnowski and Tom Bartol, along with other Salk Institute collaborators, have achieved critical insight into the size of neural connections, putting the memory capacity of the brain far higher than common estimates.  Read more


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. Read more


Development and improvements to MCell

MCell is a modeling and simulation platform which provides the core capabilities for spatial simulation reaction-diffusion dynamics at complex biological interfaces. Recent improvements include libMCell, an MCell testing framework and new simulation capabilities that extend the range of simulations that can be performed. Read more


Development of CellBlender

CellBlender is a graphical interface for model construction, simulation, and analysis of complex spatial models of reaction-diffusion systems. The interface has been redesigned and functionality expanded. Read more



figure good 170Synaptic Facilitation Revealed

An investigation of several mechanisms of short-term facilitation at the frog neuromuscular junction concludes that the presence of a second class of calcium sensor proteins distinct from synaptotagmin can explain known properties of facilitation.  Read more



Junction Crossing Ahead

Synapses are the connections between neurons in the brain or between neurons and muscle fibers...read more.



View all Research Highlights



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