SYMPOSIUM
ON COMPUTATIONAL CELL BIOLOGY
AGENDA
Saturday, March 22, 2003
3:00PM
- 7:00PM Registration Olmsted Lobby
7:00PM
- 9:00PM Opening Reception (Sponsored by Proctor
and Gamble) BallRoom
Sunday, March 23, 2003
7:00AM
- 8:30AM Continental Breakfast Music Room
8:45AM: Opening Remarks Bret Peterson, NCRR Ballroom
9:00AM - 10:00AM Plenary
Lecture Ballroom
Alfred Gilman, University of Texas Southwestern Medical
Center
“Dissecting cellular signaling systems”
10:00AM - 10:30AM Break (Sponsored
by Hoffman-LaRoche, Inc.)
10:30AM - 12:00PM Session
I Signaling Networks I Ballroom
10:30 Adam Arkin, University
of California, Berkeley
"Playing
practical games with bacteria and viruses. Exploring the molecular mechanisms
behind clever cellular stratagems."
11:00 Douglas Lauffenberger, Massachusetts Institute of Technology
“Cue / Signal / Response Analysis of Cell Functional
Behavior”
11:30 Steven Wiley, Pacific
Northwest National Laboratories
“A quantitative
approach for understanding the role of receptor and ligand dynamics in cell
signaling”
12:00PM - 2:00PM Lunch Music Room
2:00PM - 5:00PM
Overview of
Software for Computational Cell Biology: Ballroom
Short presentations on software that will be demonstrated
in workshops on Monday and Tuesday.
5:00PM
- 7:00PM Dinner Break
7:00PM - 9:30PM Session II
Calcium Signaling Ballroom
7:00 Erwin Neher, Max Planck Institute, Gottingen, Germany
“Modeling neurotransmitter release and short-term
plasticity”
7:30 Michael Sanderson, University of Massachusetts Medical School
“Calcium
waves and oscillations: models and functions”
8:00
Break (Sponsored by Hoffman-LaRoche,
Inc.)
8:30 Raimond Winslow, Johns Hopkins University
"Relating
“Microscopic” Properties of Calcium-Induced Calcium-Release to “Macroscopic”
Function of the Ventricular Myocyte"
9:00 Peter Hunter, University
of Aukland
“The IUPS Physiome Project”
9:30PM
- 10:30PM Social Hour (Sponsored by Carl Zeiss, Inc.) Music Room
Monday, March 24
7:00AM
- 8:30AM Continental Breakfast Music Room
9:00PM – 12:00PM Session
III: Cell Motility and Trafficking Ballroom
9:00 Marie France-Carlier, CNRS, Gif-sur Yvette, France
“A biomimetic
motility assay provides insight into the mechanism of actin-based motility”
9:30 Alan Rick Horwitz, University
of Virginia
“Quantifying adhesive dynamics”
10:00 Francois Nedelec, EMBL Heidelberg, Germany
“Studying
the mitotic spindle bit by bit”
10:30
Break (Sponsored by Hoffman-LaRoche,
Inc.)
11:00
Robert Singer, Albert Einstein College of Medicine
“Computing single mRNA movements in single cells”
11:30 Raymond Goldstein, University
of Arizona
TBA
12:00PM
- 1:30PM Lunch Music Room
1:30PM - 3:00 PM
Software
Workshops Olmstead:
Tanglewood Room, Greylock Room and Foyer
Copasi (Stefan
Hoops, Virginia Polytechnical Institute)
MCell and DReAMM
(Joel Stiles, Pittsburgh
Supercomputing Center)
SigPath (Fabien
Campagne, Ravi Iyengar and Harel Weinstein, Mount Sinai Sch. of Med.)
Virtual Cell (Jim Schaff and Ion Moraru, University of Connecticut Health Center)
3:00PM - 5:00 PM
Poster Session
Berkshire
Room
5:00PM
- 7:00PM Dinner Break
7:00PM - 9:30PM Session
IV Signalling Networks II Ballroom
7:00 Tamas Balla, National Institute of Child Health & Human Development, NIH
“Live cell imaging of inositol lipids with GFP-fused
protein domains”
7:30 Susana Neves, Mount Sinai School of Medicine
“Regulation
of the Dynamics of Intracellular Microdomains of Signaling Molecules in
Neurons”
8:00 Break (Sponsored
by Hoffman-LaRoche, Inc.)
8:30 Upinder
Singh Bhalla, National Centre for
Biological Sciences, India
“Honey I shrunk the cell: signaling networks at ever-decreasing volumes”
9:00 Robert Sinkovits, University
of California, San Diego
“Reconstruction
of cellular networks from experimental data and legacy knowledge”
9:30PM
- 10: 30PM Social Hour (Sponsored by Physiome Sciences) Music
Room
Tuesday, March
25
7:30AM
- 8:30AM Continental Breakfast Music Room
9:00AM-12:00PM Session V.
Cellular Regulation Ballroom
9:00 Ian Macara, University of Virginia
Health Sciences
"Modeling
the transport of proteins in and out of the cell nucleus"
9:30 Mark Terasaki,
University of Connecticut Health Center
“Cyclin aggregation and robustness of bio-switching –
Part I"
10:00 Boris Slepchenko, University
of Connecticut Health Center
“Cyclin aggregation and robustness of bio-switching –
Part II"
10:30
Break (Sponsored by Hoffman-LaRoche,
Inc.)
11:00 Thomas Misteli, National
Cancer Institute, NIH
“Computational cell biology of an RNA polymerase
complex in living cells - Part I"
11:30 Robert Phair, BioInformatics
Services
“Computational
cell biology of an RNA polymerase complex in living cells - Part II”
12:00PM
– 1:30AM Lunch Music Room
1:30AM - 3:00PM
Software
Workshops: Olmstead:
Tanglewood Room, Greylock Room and Foyer
JDesigner and Jarnac (Herbert Sauro, Keck Graduate Institute)
Pathway
Prism (Peter Brooks and Andrew LeBeau, Physiome
Sciences)
Process DB (Robert Phair and Ann Chasson, Integrative Bioinformatics, Inc.)
3:00AM – 5:00PM
Poster Session
Berkshire
Room
6:00PM
- 7:00PM Cocktail Hour (Sponsored by IBM) Music
Room
7:00PM Ending Banquet (Sponsored by
IBM and NRCAM) Ballroom
Keynote
Presentation
Garrett Odell, University
of Washington
Wednesday, March
26
7:00AM
- 9:00AM Continental Breakfast Music Room
Poster Presentations
Monday
M1.
In silico biology
and integrative computational methods drive cardiovascular drug discovery in
the “omics” era. Reza Mazhari, Ph.D. and Craig M. Liddell, Ph.D. Artesian Therapeutics, Inc., Gaithersburg,
MD
M2.
A Reaction-Diffusion
Model for Gradient Sensing in Chemotaxis. K.K.Subramanian1 Atul Narang1 D.A.Lauffenburger2, 1Dept of Chemical Engineering,
University of Florida, Gainesville, FL, 2Environmental Health &
Dept. of Chemical Engineering, MIT, Cambridge, MA
M3.
A Dynamical Model of
the NF-κB Activation Module.
Myong-Hee Sung and Richard Simon, Biometric Research Branch, National
Cancer Institute, National Institutes of Health
M4.
Preliminary
Quantitative Analysis of Renal Cell Mechanosignal transduction in Response to a Physiologically Relevant
Pressure Stimulus. Alissa L.
Russ, Julie S. Martin, Karen M. Haberstroh, and Ann E. Rundell Department of Biomedical Engineering, Purdue
University, West Lafayette, IN
M5.
Quantitative Analysis
of Chromatin Protein Dynamics In Vivo. Stan Gorski, Thierry Cheutin and Tom
Misteli, National Cancer Institute,
National Institutes of Health, Bethesda, MD
M6.
Modeling of Ca2+
Flux in Pancreatic B-Cells: Role of NA+, the Plasma Membrane and
Intracellular Stores. Leonide E.
Fridlyand, Natalia Tamarina, and Louis H. Philipson, Department of Medicine, University of Chicago, Chicago, IL
M7.
Computational Analysis
of the Modulation of Calcium Oscillations and Waves by Calreticulin.. K. Yano, O.H. Petersen and A.V.
Tepikin. The Physiological laboratory, University of Liverpool, Liverpool, UK
M8.
Delineating
T-Cell Antigen Activated MAPK Signaling Pathway: A System Engineering Approach. Zheng, A. Rundell, V. Balakrishnan, R.
Geahlen, M. Harrison
M9.
Cell Organization in Soft Media.
Ilka B. Bischofs and Ulrich S. Schwarz,Max-Planck-Institute of Colloids and Interfaces, Theory
Division, Potsdam, Germany
M10.
Topographical
Analysis of the IgE Receptor Signaling Pathway of Mast Cells. #Stanly
L. Steinberg, Bridget S. Wilson*, Jun Zhang+, Karin Leiderman# , Janet R. Pfeiffer &
Janet M. Oliver*,*Departments of Pathology; +Computer
Sciences and #Mathematics and Statistics and University of New
Mexico, Albuquerque, New Mexico.
M11. Miniature
Ca2+ Release Events in Nerve Terminals (Ca2+ Syntillas)
Are Increased in Frequency by Depolarisation in the Absence of Extracellular Ca2+. Valérie DeCrescenzo1, Ronghua ZhuGe1,2, Cristina
Velázquez-Marrero1, Lawrence M. Lifshitz1,2, Edward
Custer1, Jeffrey Carmichael2, Anthony Lai3,
Richard A. Tuft1,2, Kevin E. Fogarty1,2, José R. Lemos1
and John V. Walsh, Jr.1. 1Department of Physiology,
University of Massachusetts Medical School, Worcester, MA. 2Biomedical
Imaging Group, University of Massachusetts Medical School, Worcester, MA. 3University
of Wales, Division of Medicine-Cardiology, WHRI Building, Heath Park, Cardiff,
UK
M12. Reaction Diffusion Modeling of ER Calcium
Levels with Realistic Geometry: Effects of IP3 Receptor Clustering
. Shawn A. Means#,
Alexander Smith*, Jason Shepard#, John Shadid#, Gregory D. Smith^
and Bridget S. Wilson*. *Department of Pathology, University of New
Mexico, Albuquerque, N.M.; #Sandia National Laboratory, Albuquerque,
N.M.; ^College of William and Mary, Williamsburg, VA.
M13. The Virtual Chromaffin Cell: Computational
Modeling Of CA2+ Signaling In a Classic Neurosecretory Cell. A.S. Schneider*, T.E. Davis* and I.
Moraru#†. *Center for Neuropharmacology &
Neuroscience, Albany Medical College, Albany, NY & †Univ
Connecticut Health Center, Farmington CT.
M14. Cell Dynamics in the Premorphogenetic Phase of
the Nematode C. Elegans . Alex Kraemer.
Department of Biochemistry, C.A.
University Kiel, Kiel, Germany
M15. Untangling the wires: a novel strategy to
infer the architecture of signaling and gene networks. Kholodenko BN, Kiyatkin A, Bruggeman FJ,
Sontag E, Westerhoff HV and Hoek JB. Department of Pathology, Anatomy and
Cell Biology, Thomas Jefferson University,
Philadelphia, PA, USA. Boris.Kholodenko@mail.tju.edu
M16. The Secret of the ErbB-Family: With the Help
of a Mathematical Model to New Insights.
B. Schoeberl1, U. B. Nielsen2, J. Beusmanns3,
P. K. Sorger4 and D. A. Lauffenburger1,4. 1
Division of Biological Engineering, MIT, 2 Merrimack
Pharmaceuticals, Cambridge MA, 3 AstraZeneca, Waltham, 4 Department
of Biology, MIT
Tuesday
T1.
Effect of Complex
Synaptic Topology on mEPC Variability: Insights from Spatially Realistic Monte
Carlo Simulations. William C.
Ford,1,2 Philip Davidson,3 Thomas E. Deerinck,4
Mark H. Ellisman,4 Thomas M. Bartol,5 Terrence J.
Sejnowski,5 and Joel R. Stiles1,2. 1Biomedical
Supercomputing Initiative, Pittsburgh Supercomputing Center; 2Dept.
of Neuroscience, University of Pittsburgh; 3Cornell University; 4Dept.
of Neuroscience, UCSD; 5Computational Neurobiology Laboratory, The
Salk Institute
T2.
Subcellular
Architecture, Ca2+ Dynamics, and Neurotransmitter Release: Insights from Spatially Realistic Monte
Carlo Simulations. John M.
Pattillo,1 Jason B. Castro,1 Stephen D. Meriney,1
and Joel R. Stiles1,2 . 1Department of Neuroscience,
University of Pittsburgh, 2Biomedical Supercomputing Initiative,
Pittsburgh Supercomputing Center
T3.
CellML 1.1: A Standard
for Specifying and Annotating Biological Models. Poul F. Nielsen, Autumn A. Cuellar, David P.
Bullivant, Peter J. Hunter. Bioengineering Institute, the University of
Auckland
T4.
GEM Project: An Effective
Development Strategy for Cell Models Based on Genomic Sequences. Nobuyoshi Ishii, Kazuharu Arakawa,
Katsuyuki Yugi, Yoichi Nakayama and Masaru Tomita. Institute for Advanced
Biosciences, Tsuruoka, Japan
T5.
GEM
System: Automatic Conversion of Genome Sequences into Cell Simulation
Models. Yohei Yamada, Kazuharu Arakawa, Kosaku Shinoda, Hiromi Komai, Kenji
Higashi, Yoichi Nakayama and Masaru Tomita,.
Institute for Advanced Biosciences, Tsuruoka, Japan
T6.
Linear Dynamic Model of Gene Regulation Network for the Yeast Cell Cycle. Seung Kee Han and Chang No Yoon. Department of Physics, Chungbuk
National University, Korea
T7.
Kinetic Analysis of
Receptor Activated Phosphoinositide Turnover. Chang Xu, James Watras and Leslie M. Loew. Department
of Physiology and National Resource for Cell Analysis and Modeling, University
of Connecticut Health Center, Farmington, CT
T8.
Integration of
Functional and Structural Genomic Data to Derive Models of Transcriptional
Regulation During Neuromodulation.
Gregory Gonye, Rajanikanth Vadigepalli, Hui Liu, Daniel Zak, Paul
Labhart*, Mary Harper*, and James Schwaber.
Daniel Baugh Institute for
Functional Genomics/Computational Biology, Department of Pathology, Anatomy,
and Cell Biology, Jefferson Medical College, Thomas Jefferson University,
Philadelphia, PA and *Genpathway Inc., San Diego, CA
T9.
Integrin Dimerization
and Ligand Arrangement: Key Components in Integrin Clustering for Cell Adhesion. Christopher J. Brinkerhoff & Jennifer J.
Linderman. Department of Chemical Engineering, The University of Michigan, Ann
Arbor, MI
T10. Understanding Ca2+-Dependent
Regulation of Exocytosis in Adrenal Chromaffin Cells: A Strategy using High Resolution Electrical and Optical
Measurements and Computer Modeling.
Jonathan R. Monck1 and Fernando D Marengo 2. 1
UCLA School of Medicine, Los Angeles,2 Universidad de Buenos
Aires, Facultad de Cs. Exactas y Naturales, Buenos Aires
T11. G-Protein Threshold Behavior in Human
Neutrophil Responses: Measurement,
Analysis, and the Role of Statistical Variation in Signaling Parameters. Peter S. Chang*, Geneva M. Omann†,
and Jennifer J. Linderman*. Univ. of Michigan, *Dept. of
Chemical Engineering and †Depts. of Biological Chemistry and General
Surgery and VA Med. Ctr., Ann Arbor, MI
T12. High Resolution RNA Dynamics in Living Yeast. A. M. Femino1, K. Fogarty2,
L. M. Lifshitz2, E. Powrie1, R. A. Tuft2, R.
H. Singer1. 1Department of Anatomy and
Structural Biology and Cell Biology, Albert Einstein College of Medicine,
Bronx, NY and 2Biomedical Imaging Facility and Department of
Physiology, University of Massachusetts Medical School, Worcester, MA.
T13. Modeling Currents and Fluxes Across the Outer
Mitochondiral Membrane. I. I.
Moraru, C. A. Mannella#†., L. M. Loew. University of Connecticut
Health Center, Farmington CT and †Wadsworth Center, Albany, NY.
T14. Study of Cell Fate Decision by Activated FAS
Signaling Network Using Integrated Experimental and Computational Approach. Fei Hua and Luk Van Parijs. Center
for Cancer Research, Massachusetts Institute of Technology, Cambridge, MA
T15. Modeling of Serotonin 5-HT2A Receptor
Signaling Pathway. Chiung-wen Chang1,
Ravi Iyengar 2, and Harel Weinstein 1, 3. Departments
of 1Physiology and Biophysics and 2Pharmacology and
Biological Chemistry, Mount Sinai School of Medicine, New York, NY and 3Department
of Physiology and Biophysics, Weill Cornell Medical College New York, NY.
T16. Modeling Electrical
and Biochemical Processes with the Virtual Cell. James C Schaff, Boris
M Slepchenko, Anuradha Lakshminarayana, Ion I Moraru, Leslie M LoewUniversity
of Connecticut Health Center, Farmington, CT
T17. The Systems Biology Workbench (SBW) Version
1.0: Framework and Modules.
Michael Hucka, Andrew Finney, Herbert Sauro, Hamid Bolouri, John Doyle,
Hiroaki Kitano, California Institute of
Technology
PLENARY LECTURE
Dissecting Cellular Signaling Systems.
Alfred Gilman
University of Southwest Texas
PLATFORM SESSSIONS
SESSION I SIGNALING NETWORKS I
Playing Practical
Games With Bacteria and Viruses. Exploring the Molecular Mechanisms Behind
Clever Cellular Stratagems.
Adam Arkin
University of
California, Berkeley
How do pathogenic bacteria
sense their environment to deploy different survival strategies? Why do some
viruses, like HIV, allow their host to live for long periods whereas others
like Ebola do not? How precisely are these strategies encoded in the organism's
biochemistry and genetics and how closely do they need to be followed to
guarantee its survival? What are the optimal strategies for defeating these
organisms or forcing them to do our bidding for industrial or medical benefit?
Here I will demonstrate, using examples from our research on Bacillus subtilis
stress response and the design of HIV gene therapeutic strategies, how
molecular biology combined with methods from statistical physics, nonlinear
dynamics, and game theory can be used to pose and partially answer these
questions as well as illustrate some of the profound challenges in doing so.
Cue / Signal / Response Analysis of Cell Functional
Behavior
Douglas
Lauffenberger
Environmental
Health & Dept. of Chemical Engineering, MIT, Cambridge, MA
A Quantitative Approach for Understanding the Role of
Receptor and Ligand Dynamics in Cell Signaling
Steven
Wiley
Biological
Sciences Division, Pacific Northwest National Laboratory, Richland, WA
Cell signaling operates
through an intricate network of biochemical pathways whose complex functioning
is only partly understood. Receptors are central players in signaling networks
and their trafficking through the endocytic machinery is highly regulated. Understanding cell signaling is dependent on
knowledge of spatial and temporal dynamics for receptors and signaling
pathways, but this information is usually qualitative in nature and poorly
integrated. To create a more integrated, “systems-level” approach to
understanding cell signaling, we have combined quantitative cell imaging,
antibody-based probes and mathematical modeling to understand the spatial
regulation of signaling. Kinetic modeling of receptor dynamics provides a
quantitative hypothesis by which to evaluate experimental studies.
Significantly, the computer models are designed so that they yield
experimentally testable predictions. We have designed experimental assays to
provide quantitative results, thus facilitating their use in testing model
predictions. Using the EGF receptor system as our test case, we have modeled
ligand production, ligand transport, binding, receptor trafficking and
degradation. In addition, we have included aspects of receptor homo and
heterodimerization as well as the influence of receptor overexpression on these
processes. We have used a stochastic, Monte Carlo-based simulation framework to
combine cell-signaling networks with our models of receptor and ligand dynamics
and have demonstrated their ability to predict experimental results. The
predictive capability of our models demonstrates that a combined
modeling-experimental approach is useful for understanding complex cellular
systems.
Session
II Calcium Signaling
Modeling Neurotransmitter Release and Short-Term
Synaptic Plasticity
Erwin Neher
Max Planck
Institute for Biophysical Chemistry, Goettingen, Germany
Neurotransmitter release
undergoes use-dependent short-term changes on the second to sub-second scale
known as short-term depression and short term (or paired-pulse) facilitation.
These changes in synaptic strength are very important for understanding dynamic
network behavior. We studied various presynaptic aspects of short-term
plasticity at the Calyx of Held, a presynaptic glutamatergic terminal, which
allows voltage clamp recordings (Schneggenburger et al., TINS, 25, 206-212; Felmy et al., Neuron, in
press; Sakaba and Neher, Neuron 32, 1119-1131) and developed models which include the dynamics of
Ca++-signals in the presynaptic terminal, vesicle depletion and
recruitment and heterogeneity within the vesicle population. All these aspects
were found to be necessary for a faithful description of facilitation and
depression (Trommershaeuser et al., Biophys. J., March 2003).
Calcium Waves and Oscillations: Models and Functions
Michael
Sanderson
University of
Massachusetts Medical School, Worcester, MA.
A wide variety of cells
display Ca2+ waves and oscillations. However, the relationship between these
two processes and their function in multicellular tissues is not well
understood. We examined intercellular waves in airway epithelial cells and
found that they were mediated by the local diffusion of IP3 through gap
junctions. Because it is difficult to visualize the diffusion of IP3, this
hypothesis was explored with models to verify that IP3 moving through gap
junctions had the ability to generate Ca2+ waves. This model led to certain
predictions, namely that the Ca2+ behavior of a cell would be determined by its
distance from the source of the wave. Experiments with glia cells confirmed
this prediction and illustrated the relationship between asynchronous
oscillations and propagating waves. In addition, the model analysis predicts
the entrainment of asynchronous Ca2+ oscillations and may also be used to
address the affects of stimulation by diffusing extracellular agonists. Because
the significance of Ca2+ oscillations is unclear, we correlated Ca2+ responses
with physiological processes in lung tissue and found that Ca2+ oscillations
contribute to both the maintenance of airway contraction and ciliary activity.
These two events are regulated by phosphorylation suggesting that Ca2+
oscillations regulate cell function by maintaining protein phosphorylation.
Relating “Microscopic” Properties of Calcium-Induced
Calcium-Release to “Macroscopic” Function of the Ventricular Myocyte
Raimond L. Winslow and Antti Tanskanen
Whitaker
Biomedical Engineering Institute, Department of Biomedical Engineering, The
Johns Hopkins University School of Medicine & Whiting School of
Engineering, Baltimore, MD
Cardiac
electrophysiology is a field with a rich history of integrative modeling. A
particularly important milestone was the development of the first
biophysically-based cell model describing interactions between voltage-gated
membrane currents, pumps and exchangers, and intracellular calcium (Ca2+)
cycling processes (DiFrancesco & Noble, Phil.
Trans. Roy. Soc. Lond. B 307: 353), and the subsequent elaboration of this
model to describe the cardiac ventricular myocyte action potential (Noble et
al. Ann. N. Y. Acad. Sci. 639: 334;
Luo, C-H and Rudy, Y. Circ. Res. 74:
1071). These, and all other integrative models of the myocyte developed to date
are of a type known as “common pool” models (Stern, Biophys. J. 63: 497). In such models, Ca2+ flux through
L-type Ca2+ channels (LCCs) and ryanodine sensitive Ca2+
release channels (RyRs) in the junctional sarcoplasmic reticulum (JSR) membrane
is directed into a common Ca2+ compartment. Ca2+ within
this common pool also serves as activator Ca2+ triggering JSR Ca2+
release. In a modeling tour de force,
Stern demonstrated that common pool models are structurally unstable,
exhibiting all-or-none Ca2+ release except (possibly) over some
narrow range of model parameters. Despite this inability to reproduce
experimentally measured properties of graded JSR Ca2+ release,
common pool models have been very successful in reproducing and predicting a
range of myocyte behaviors. This includes properties of interval-force
relationships that depend heavily on intracellular Ca2+ uptake and
release mechanisms (Rice et al. Am. J.
Physiol. 278: H913). Given these findings, one may wonder whether or not it
is important to incorporate an accurate biophysical description of graded JSR
Ca2+ release in computational models of the cardiac myocyte.
Stern went on to propose
the “local-control” theory of Ca2+ release. In this theory,
individual LCCs, the set of RyR with which they communicate, and the subspace
within which they communicate, defines a functional release unit (FRU). Local
control theory holds that while Ca2+ release within each FRU may be
all or none, the averaged behavior of many independent FRUs reflects the
probability of opening of LCCs. We have previously developed a model of the
functional release unit (FRU) consisting of one LCC, eight RyR, and the volume
in which they are enclosed (Biophys J 77:1871-84). To study the impact of local Ca2+ control in the
context of the whole cell AP, we have developed a ventricular cell model which
combines the stochastic simulation of a large number of independent FRUs with
the solution of a system of coupled ordinary differential equations describing
the full complement of cardiac membrane currents and intracellular fluxes. We
will describe development of this local-control myocyte model, and numerical
methods used for efficient simulation of model properties. We will demonstrate
that this model exhibits the graded release property, as well as a
voltage-dependent EC coupling gain function which agrees well with experimental
data. We will use this local-control myocyte model to suggest reasons why
graded release of JSR Ca2+ is of critical importance to myocyte
function.
(Supported by NIH HL60133,
the NIH Specialized Center of Research on Sudden Cardiac Death P50 HL52307, the
Whitaker Foundation, the Falk Medical Trust, and IBM Corporation)
The IUPS Physiome Project
Peter Hunter
Bioengineering
Institute, the University of Auckland
The Physiome Committee of
the International Union of Physiological Sciences (IUPS) is helping to lead the
world-wide Physiome Project effort to develop computational modeling of the
human body for improving our understanding of human physiology and for the
diagnosis and treatment of human diseases. The Physiome Project aims to develop
databases, markup languages and tools for modeling biological structure and
function at the cell, tissue and organ levels. It links down to biochemical
pathways and the existing genomic and proteomic databases. The talk will
describe some of these models which use an anatomically and biophysically based
approach that incorporates detailed anatomical and microstructural measurements
and material properties into continuum models. The interrelated electrical,
mechanical and biochemical functions of the heart, for example, have been
modelled in the first ‘physiome’ model of an organ.
SESSION
iii cELL mOTILITY AND TRAFFICKING
A Biomimetic Motility Assay Provides In sight Into
the Mechanism of Actin-Based Motility.
Marie-France Carlier, Sebastian Wiesner,
Emmanuèle Helfer, and Dominique Pantaloni
Dynamics of
Cytoskeleton and Motility, LEBS, CNRS, Gif-sur Yvette, France
Abstract. Site-directed
polymerization of actin generates cell protrusions that are at the origin of
cell motility and of the propulsive movement of intracellular pathogens like
Listeria or Shigella. We have developed a biomimetic motility assay to analyze the mechanism of force production
by site-directed polymerization of actin. Polystyrene microspheres,
functionalized in a controlled fashion by the N-WASP protein, the ubiquitous
activator of Arp2/3 complex, undergo actin-based propulsion in a medium that
consists of five pure proteins including actin, Arp2/3 complex, profilin, Actin
Depolymerizing Factor and a capping protein. We
have analyzed the dependence of velocity on N-WASP surface density, on the
concentration of capping protein and on external force. Movement was not slowed
down by increasing the diameter of the beads (0.2 to 3 µm) nor by increasing
the viscosity of the medium by 105-fold. This result shows that
forces due to actin polymerization are balanced by internal forces due to
transient attachment of filament ends at the surface. These forces are greater
than the viscous drag. Using Alexa488-labeled Arp2/3, we show that Arp2/3 is
incorporated in the actin tail like G-actin by barbed end branching of
filaments at the bead surface, not by side branching, and that filaments are
more densely branched upon increasing gelsolin concentration. The data are in
agreement with biochemical studies of the mechanism of Arp2/3 complex and
support models in which the rates of filament branching and capping control
velocity, and autocatalytic branching of filament ends rather than filament
nucleation occurs at the particle surface.
Quantifying Adhesive Dynamics
Rick Horwitz
