COMPUTATIONAL MODELING AND SIMULATION AS ENABLERS FOR BIOLOGICAL DISCOVERY 183
puter-aided methods for extracting accurate surfaces and defining in-silico representations of their
molecular properties; and physiological underpinnings from a variety of studies conducted by the
involved laboratories and from the literature.
These data are then used as the framework for advanced simulations using MCell running on high-
performance supercomputers as well as distributed or grid-based computational resources. This project
pushes development of tools for acquisition of improved large-scale tomographic reconstructions of
cellular interfaces down to supramolecular scales. It also drives improvements in the software tools
both for the distribution of molecular components within the surface models extracted from the tomo-
graphic reconstructions and for the deposition and retrieval of relevant information for the MCell
simulator (Box 5.18) in the tomography and Cell-Centered Database (CCDB) environment.
Realistic modeling of synaptic microphysiology (as illustrated in Figure 5.17) requires the following:
- Acquisition of high-resolution, three-dimensional synaptic ultrastructure—this is accomplished
with serial EM tomography. - Segmentation of pre- and postsynaptic membrane from the tomographic volume—this is accom-
plished using the tracing tool in Xvoxtrace. - Three-dimensional reconstruction of the membrane surface topology to form a triangle mesh—
this is accomplished using the marching cubes isosurface extraction tool in Xvoxtrace. - Subdivision of the membrane surface meshes into physiologically relevant regions (e.g., spine
versus nonspine membrane and PSD [phosphorylation site domain] versus non-PSD regions)—this is
accomplished using the mesh tagging tool in DReAMM. - Placement of effector molecules (e.g., receptors, enzymes, reuptake transporters) onto membrane
surfaces with the desired distribution and density—this is accomplished using the MCell model de-
scription language (MDL). Effector distribution and density may be determined by labeling and imag-
ing studies. - Specification of the diffusion constant, quantity, and location of neurotransmitter release—this is
accomplished using MCell MDL. - Specification of the reaction mechanisms and kinetic rate constants governing the mass action
kinetics interaction of neurotransmitter and effector molecules—this is accomplished using MCell MDL. - Specification of what quantitative measures should be made during the simulation—this is ac-
complished using MCell MDL. - Simulation of the defined system—this is accomplished using the MCell compute kernel.
- Analysis of the results at various points in the parameter space defined by the system—this is
accomplished using analysis tools of the investigator’s discretion.
Analysis of miniature excitatory postsynaptic currents (mEPSCs) recorded in electrophysiological
experiments shows that mEPSCs in the CG somatic spine mat occur in a broad spectrum of amplitudes,
rise times, and fall times. The differential kinetics and complementary distributions of α3 and α 7
nAChRs are expected to lead to mEPSCs whose characteristics are highly dependent on the location of
neurotransmitter release within the spine mat. Realistic simulation makes it possible to explore and
quantify the degree to which this hypothesis is true and to make quantitative comparisons of the
simulation and electrophysiological results. Figure 5.18 summarizes the results of simulations designed
to explore the limits of mEPSC behavior by virtue of the choice of neurotransmitter release locations.
The results not only confirm the qualitative expectations at each site but also predict their quantitative
behavior, allowing fine discriminations to be made.
The process briefly outlined above represents a significant advance in the ability to create realistic
computational models of subcellular microdomains from actual cellular ultrastructure. The preliminary
results presented are just the beginning of exciting computational experiments that can now be per-
formed on the CG model in an effort to illuminate and inform further bench experiments. Among all of
the things learned, perhaps the most important is which of the physical characteristics of the CG are the