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NM Center for the SpatioTemporal Modeling of Cell Signaling
(Spatiotemporal Modeling Center; STMC)
PI/Director: Janet M. Oliver, PhD
stmc.health.unm.edu
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Bridget Wilson
Pathology
, cell biology
Janet Oliver,,
Pathology
cell biology
Diane Lidke
Pathology,
biophysics
Stanly Steinberg,
Math/Stat
Biomathematics,
spatial statistics
Jeremy Edwards,
MGM/ChemEng
spatial modeling,
stochastic methods
National Laboratory Modeling and Technology
UNM Biology and Technology
UNM Modeling
Elaine Bearer
Pathologyrainin
cell biology,
education
Anup Singh
SNL
Microfluidics
Andrew Bradbury
LANL Life Sci Div
affinity probes
Yi Jiang
LANL T Div
tumor
modeling
Bill Hlavacek
LANL T Div
rules-based
modeling
Byron Goldstein
LANL T Div
Immune system
modeling
Dion Vlachos
Chem Eng, UDel
multiscale modeling
Keith Lidke
Physics
biophysics,
instrumentation
Melanie Moses
Computer Sci
computer science,
education
STMC Leaders
U. Delaware
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Accomplishments of the P20 STMC (2002-2008)
-Faculty recruits, all now with independent funding (NIH, NSF, HFSP)
D. Lidke (Pathology)K. Lidke (Physics)
J. Edwards (Molecular Genetics /Chem Engineering)
- Interdisciplinary Training of Grad Students/Postdocs
>25 trainees from multiple UNM departments + LANL, Sandia
(leveraged through partnership with 2 NSF IGERT grants)
- Institutional Framework, Inter-Institutional Agreements
UNM Schools of Medicine and Engineering and College of Arts and Sciences,
Sandia & Los Alamos National Labs
- Launch of q-bio
-Track record
Multiple collaborative grants and publications
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Goals of the P50 STMC (2009-2014)
To develop predictive models of cell signalingthrough the effort of scientific teams with
expertise in measurement, mathematicalmodeling and the development of newtechnologies. In particular, to understand normal and abnormal cell behavior through the development and integration of spatial,temporal and biochemical measurements and
models of signaling pathways.
To prepare students and postdocs forsuccessful careers focused on quantitative,systems level analyses of complexbiomedical processes.
To establish systems biology research andtraining as a long-term area of scientificemphasis that integrates New Mexico’sflagship University with its two powerfulNational Laboratories.
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P50 STMC Structure
• 3 Multi-Scale Scientific Projects,
focused on measuring and
modeling signal transduction(Immune System, Cancer)
• 3 Next-Gen Technology Cores
(Affinity Reagents, Microfluidics,
Super Resolution Imaging)
• Pilot Projects (2 per year)
• Faculty Recruitment (1+ per year)
• Expanded Graduate Student,Postdoc Training
• Visitor Program, Seminar Series
• Scientific and Community Outreach
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Year 1 P50 Successes: Faculty Recruiting
Dr. Jennifer GilletteQuantitative Imaging of Hematopoietic Stem Cells, context of
Signal Transduction (to Dept. of Pathology, SOM, Oct 2010)
Dr. Lydia TapiaComputational Biology, especially Coarse Grain Approaches to
Protein Folding Motions (to Dept. of Computer Science, SOE, Jan 2011)
Letters of Offer: T Cell Signaling and Migration (to Pathology)
Cancer Modeling (to Pathology and Chemical Engineering)
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P50 Successes: STMC Students, Postdocs
Shal Low-Nam
Single particle
tracking
Samantha
Schwartz
Superresolution
Microscopy
Ksenia
Matlawska-
Wasowska
Cell signaling
Mario Paz
Microfluidics,
single particle
measurements
Patrick Cutler
Hyperspectral,
single particle
imaging
Michael
Malik
Hyperspectral
microscopy
development
Fang Huang
Computational
Analysis
Peiyou Song
Stochastic
Modeling
Kimberly Kanigal
Tumor Modeling
Stephanie
Jerman
Ciliary Signaling,
Receptor
Trafficking
Erin Zekas
Quantitative
imaging of
phosphoinositides
Not shown:
Duncan Wadsworth
Image Analysis
Bin Hu
Kinetic ModelingInformatics
Flor Espinoza
Spatial
Statistics
Ryan Gutenkunst
Mathematical
Modeling
Dipak Barua
Mathematical
Modeling
Megan McCabe
Hybrid
Modeling
Kathrin Spendier
Biophysics
Jerry Thomas
scFv probes
development
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STMC Community Impact: OUTREACH to ALL AGES
“ART & SCIENCE of SYSTEMS BIOLOGY” (Santa Fe, NM, March 26-27, 2010)
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STMC National/International Impact:
Annual q-bio Summer School and Conference
Conference
August 11-14, 2010
Santa Fe, NM
Keynote speakers:
Michael A. Savageau, UC DavisJames E Ferrell Jr, Stanford
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Leverage
Infrastructure, Training, Outreach:1S10RR025540-01 (Wandinger-Ness) 4/01/09-3/31/10
Confocal Stereology Microscope
NIH R13GM082162 (Hlavacek) 6/10/09-6/1/14
Information processing, cellular signaling and gene regulation (q-bio)
P30GM092317 (Williams, Oliver) 9/29/09-9/28/11
NIGMS Biomedical Research Core Center in Immunology and Imaging (ARRA)
R25CA153825 (Oliver, Datye) 8/01/10-7/31/15Integrative Cancer Nanoscience and Microsystems (IC-NSMS) Training Center
Next-Generation Imaging:1R01GM086237 (Bruchez, K. Lidke) 9/30/09-8/31/11
Bright blinking probes for fast multicolor superresolution imaging in live cells (ARRA)
NSF (K. Lidke) 7/01/10–06/30/15CAREER: A computational and analytical approach to single-molecule
fluorescence imaging for the quantitative analysis of protein interactions in living cells
NIH 1DP2OD006673 (Timlin) 30/09/09-31/8/14
Multiplexed Measurements of Protein Dynamics and Interactions at Extreme Resolutions
(New Innovator)
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STMC Presentations for the 2010 All-Centers Meeting
Bridget S. Wilson, PhD, Co-PI/ Measurement Core Director:
Spatial Measurements and Stochastic Models
William S. Hlavacek, PhD, Co-PI/LANL Modeling Core Director:
Phosphoproteomics Measurements and Rules-based Models
Elaine L. Bearer, MD/PhD, Co-I/Training and Outreach Core Director:
Community Outreach
Shalini Low-Nam, BS, Graduate student:
Lightning talk
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FcεRI
IgE
ImmunoReceptor
Signaling (FcR, TCR ...)
Tyrosine Kinase
Receptor Signaling
(EGFR, Erb2, Erb3…)
Activated by soluble crosslinking reagents
or cell-cell “synapses”Couple to cytosolic tyrosine kinases
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EM grid
coverslipcell
plasmamembrane sheetson EM gridready for labeling
Nanoscale Spatial Resolution of Membranes by Electron Microscopy
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Possible distributions of membrane constituents
typical results, Hopkins spatial statistics test
0 0.2 0.4 0.6 0.8 10
1
2
3
4
5
6
7
8
9
0 0.2 0.4 0.6 0.8 10
0.5
1
1.5
2
2.5
3
0
2
4
6
8
1 0
0 0.2 0.4 0.6 0.8 1
“Islands” or “Rafts” ?
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Resting FcεRI is distributed in singlets, small clusters
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After crosslinking, FcεRI is found in signaling patches
FcεRI
Coated vesicle
J. Cell Biology, 2000
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You might ask, how relevant is this to OTHER receptors?
Such as the EGFR family…
ErbB3
PI3K
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Cells Must Integrate Signals from Multiple Receptors
on Their Cell Surface
Growth Factor Receptors
GPCRs
IgE
FcγRIIb
FcεRI
IgG InhibitorySignals
hi lk l ll ? l i f d h
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Does this crosstalk occur locally? At least in some cases. We found that
FcεRI and a GPCR can co-localize while signaling.
1 min after addition of both ligands
Xue et al., Mol. Biol Cell 2007
100 200 300 400
- 4 0
0
2 0
4 0
distance(nm)
L ( t ) - t Ripley’s bivariant -
a spatial statistics testfor co-clustering
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Are these clusters & domains the
equivalent of "LIPID RAFTS"?
What about “Cytoskeletal Corrals”?
What about "PROTEIN ISLANDS"?
What is the significance of clustering to
signaling output?
WHAT'S IN A NAME?
rafts
corrals
islands
Drawings by Lillemeier
• Mathematical modeling
• Single Particle Tracking
• New quantitative live cell imaging
technologies
Thro gh sim lations e can e plore spatial aspects of
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Through simulations, we can explore spatial aspects ofsignaling from the membrane, propagation through the
cytosol and into the nucleus.
Costa et al, PLoS1, 2009
Edwards, Costa
C t i l ti t th t t i t t i
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x (nm)
y ( n m
)
0.5 10
5
8
P D F
random
data
3
1
B
x (nm)
y ( n m
)
0.5 10
5
8
P D F
random
data
3
1
0.5 10
5
8
P D F
random
data
3
1
B
Computer simulations suggest that transient trapping
in confinement zones is sufficient to cause the
clustering patterns we see for resting receptorsEdwards, Hsieh
Hsieh et al., IET Systems Biology,2008; BMC Systems Biol 2010
Si l i h h i h l (IP R)
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Simulations show that ion channel (IP3R)
clustering influences calcium flux dynamics
Means et al., Biophysical J . 2006Mazel et al., Biophysical J. 2009
P t Fitti f EM D t i S ti l St h ti Si
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Resting: 17 particles/µm2
EGF 2’: 71 particles/µm2
Parameter Fitting of EM Data in Spatial Stochastic Sims
Yang et al., J. Cell Science 2007Hsieh et al., BMC Systems Biol 2010
Rich New Data for Modeling Single Particle Tracking
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Rich New Data for Modeling: Single Particle Tracking
using Quantum Dot ProbesAndrews D Lidke K Lidke
Illustration by J. Werner
Quantum Dot labeled IgE
Capture diffusion ofResting IgE-FcεRI(~ 0.1 µm2/s)
Ag
Capture diffusion of
IgE-FcεRI complexesafter crosslinking& during signaling
Nature Cell Biology 2008; Immunity 2009
Use of Multi Color QD probes to follow mobile receptors often
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Use of Multi-Color QD probes to follow mobile receptors, often
occupying the same microdomain for short periods
Motion confined,not correlated
E l t j t i f 2 t th t t h
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Example trajectories of 2 receptors that encounter each
other repeatedly within the same microdomain
Li ll TIRF i i f QD I E l b l d F RI i ll
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Live cell TIRF imaging of QD-IgE labeled FcεRI in cells
expressing GFP-Actin: Proof for Cytoskeletal Corrals
Andrews et al.,Nature Cell Biology 2008
Scale bar =
5 micron
F t i bilit f F RI
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Fast immobility of FcεRI
can occur upon crosslinking
0 40 80 120 160 2000
8
16
24
D i s p l a c e m e
n t ( n m )
Time (sec)
add polyvalent antigen (DNP24-BSA)
I bili ti f F RI i D d t D
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Immobilization of FcεRI is Dependent on Dose
& Valency of Antigen
Manuscript in preparationAndrews et al, Immunity 2009
µg/mlDNP24-BSA
µg/ml DNP24-BSA
Hyperspectral Microscopy: new technology to track complexes with
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Hyperspectral Microscopy: new technology to track complexes with
multiple QD colors …. Proof that a receptor cluster of 3 can diffuse.
Andrews, Immunity 2009
Motion is correlated for thisTrio for >100 seconds……
New Technology: 3D Tracking of Endocytosis
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New Technology: 3D Tracking of Endocytosis
Werner
Mathematics Algorithm Development Key
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Mathematics, Algorithm Development Key
to Analysis with New Imaging Technologies
overlay
Polynomial
correctionLinear shift
• Single Particle Tracking
• Single Molecule Binding
Measurements
• Super Resolution Microscopy
• Fluorescence CorrelationSpectroscopy
• Hyperspectral Imaging
• Microfluidics
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Slide 34
A rule-based model of early events inimmunoreceptor signaling
William S. Hlavacek
Center for the Spatiotemporal Modeling of Cell Signaling (STMC)
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Outline
Motivation for modeling
Our approach
Model for T cell receptor (TCR) signaling (a work in
progress)
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The need for predictive models of signal-transductionsystems
These systems mediate cellular information processing andregulate cellular phenotypes
They are complex
Molecular changes that affect cell signaling cause/sustaindisease (e.g., cancer)
Numerous drugs that target signaling proteins are currently inclinical trials
• Spectacular successes (e.g., imatinib treatment of CML)
• But results are disappointing for many patients
Many clinical trials are underway to test combinations of drugs(clinicaltrials.gov)
• There are too many combinations to consider all possibilities in trials
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Value added by modeling
We can use models to organize information about a system withprecision
• Introduces greater rigor and discipline
We can determine the logical consequences of a model specification
• Design principles can be elucidated (key for synthetic biology)
•
Certification (essential for personalized medicine)
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Rule-based modeling: basic concepts
Graphs represent molecules, their component parts, and “internal states”Molecules, components, and states can be directly linked to annotation in
databases
Graph-rewriting rules represent molecular interactions
A rule specifies the addition or removal of an edge to represent binding or
unbinding, or the change of an internal state to represent, for example, post-translational modification of a protein at a particular site
TCR(Y111~p)+ZAP70(SH2)<->TCR(Y111~p!1).ZAP70(SH2!1)
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Rule-based modeling solves the problem ofcombinatorial complexity
Inside a Chemical Plant• Large numbers of molecules…
• …of a few types
• Conventional modeling works fine (a good idea since 1865)
Inside a Cell
•
Possibly small numbers of molecules…• …of many possible types
• Rule-based modeling is designed to deal with this situation (new)
ZAP-70
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T cell activation is stimulated by co-crosslinking of TCRand the co-receptor CD28
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Can we explain the temporal phosphoproteomics of thefirst minute of signaling?
A few of the time-courses measured during the first minutevia SILAC and quantitative mass spectrometry
J. Dengjel (Freiburg), B. Blagoev (Odense)
>280 phosphorylation sites>70 proteins
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A model was formulated on the basis of a data-guidedliterature search
~20 signaling proteins
>100 rules for protein-protein interactions~300 literaturecitations
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Antibody-mediated crosslinking of TCR and CD28
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Recruitment of Lck to CD28 via SH3-PRS interaction
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Lck mediates phosphorylation of ITAM Ys in TCR
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Recruitment of ZAP-70 to TCR via SH2-pY interactions
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Model parameters and model structure are beingadjusted to fit data: agreement for 12 of 26 time courses
Predicted time courses are noisy because our model can only be simulatedusing a “network-free” approach and such approaches are inherently stochastic
Simulation tool: RuleMonkey (Colvin et al., submitted),software is freely available at http://tgen.org/public/RuleMonkey/
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Conclusions
A detailed model of early events in TCR signaling can be formulated,simulated and used to provide a mechanistic interpretation of temporalphosphoproteomic data
Our initial model specification should serve as a launching pad forinvestigating a wide array of issues related to development of predictivemodels for signal-transduction systems
• What is required for model validation?
• What are the best strategies for certification (e.g., model-guided experimentaldesign)?
• Can we quantify and track how consistent this model is with available knowledge?
Possible collaboration with new hire in STMC who has expertise in TCR
signaling
Other work in progress is focused on IgE receptor signaling and will takeadvantage of STMC imaging capabilities
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Acknowledgments
Bin Hu (STMC-supported postdoc), Lily Chylek (UNM graduate student,became involved as a result of STMC-offered course)
Ryan Gutenkunst (CNLS postdoc, CNLS partners with STMC)
Joern Dengjel and Blagoy Blagoev (collaboration initiated as a result ofdiscussions at STMC-supported q-bio Conference)
The modeling work was supported by NIH grants P50 GM085273 and R01GM076570
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1
Outreach & Training ProgramOutreach & Training Program
E.L. Bearer and W.T.E.L. Bearer and W.T. HlavacekHlavacek
Spatiotemporal Modeling of Cell SignalingSpatiotemporal Modeling of Cell Signaling
Center (SMTC), University of New Mexico:Center (SMTC), University of New Mexico:
A National Center for Systems BiologyA National Center for Systems Biology
Spatiotemporal Modeling of Cell Signaling
Center (SMTC), University of New Mexico
OUTREACH AND TRAINING LEADERSHIP:
Elaine L. Bearer, MD-PhD, Professor of Pathology, UNM-HSC• Recent recruit to UNM from Brown University
• Multiple Dean’s Excellence in Teaching at Brown
• Experienced director of major medical and graduate school courses
• Currently on the Steering and Curriculum Committee for UNM programs
• NIH -funded investigator using imaging & computational modeling
• Serious and professional involvement in the creative arts
Bill Hlavacek, PhD
Melanie Moses, PhD Ryan Tanner,Center administrator
• NIH-funded investigator at Los Alamos National Labs In Theoretical andBiophysics Group
• Founder and Director of q-bio course and conference
• Mathematician and agent-based modeler
• Professor of computer sciences
• NIH-funded investigator in biological system
• Course leader in HHMI interdisciplinary course project at UNM
Media announcements:Media announcements:
Science magazine,Science magazine, Feb 19, 2010Feb 19, 2010
Santa FeSanta Fe Complex websiteComplex website
(NIGMS and UNM websites)(NIGMS and UNM websites)
Art and Science of Systems BiologyArt and Science of Systems Biology
First Event: March 26-27 2010First Event: March 26-27 2010
SantaSanta FeFe Complex, Santa Fe, NMComplex, Santa Fe, NM
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2
Friday, March 26, 2010
16:30-17:30 Private reception for registered participants
17:30-19:45 Public Lecture 1 & 2
Garrett M. Odell and Victoria E. Foe, University of Washington
19:45-21:00 Viewing Lumenscape/ARTS Lab dome projections
Winning pieces from the 2009 International Science & Engineering Visualization
Challenge (Branching Morphogenesis; Kuen's Surface: a Meditation on Euclid, Lobachevsky, and
Quantum Fields; Jellyfish Burger; Save our Earth, Let's Go Green; Flower Power)
Saturday, March 27, 2010
12:00-13:00 Kid-friendly opening reception and viewing
13:00-16:00 Workshop 1 - Nanoscience - Interactive experiments for kids of all ages - (Armstrong,
Program Coordinator, UNM Nanoscience and Microsystems Graduate Program)
14:00-15:00 Workshop 2 - Agent-based modeling
Introduction and software tutotial (Hlavacek, Hu and Gutenkunst)
14:30-15:00 Software tutorial - learn what executable biology is all about!
15:00-16:00 Workshop 3 - Imaging Take a peek at the molecular world
16:30-17:30 Private reception for registered participants
17:30-19:00 Public Lecture 2
Bridget S. Wilson, University of New Mexico Health Sciences Center
19:00-21:00 Viewing of Dome, Science winners, etc.
Schedule of events:Schedule of events: The event;
Attendance: 475 people!UNM Wilson and LANL Ecke
Steinberg and Art MatLab and STMC
Nanodays workshop
Tracking single particle(s)
Memory circuit mapped from 20 mouse brains
Our kids
Ideas for next year?Ideas for next year?
The SENSES
Wendell Lim modeling signaling
Conceptualizing art and The sciences on a Systems Biologyplatform…..
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Spatiotemporal Modeling of Cell SignalingSpatiotemporal Modeling of Cell Signaling
Center (SMTC), University of New MexicoCenter (SMTC), University of New Mexico
TRAINING PROGRAMS
Unique to our SysBio center:q-bio summer school
q-bio conference
Associated programs (leveraged): two funded IGERTS
Nanotechnology with Cell Biology and Neuroscience
Cancer Nanoscience and Microsystems
Unique at UNMHHMI interdisciplinary course offerings on main campusBioMedical Sciences Graduate Program in HSC
q-bio is a series of conferences and affiliated summer schools, which aim
at advancing predictive modeling of cellular regulation, decision making,formation of response, and other information processing phenomena. The
emphasis is on deep theoretical understanding, detailed modeling, and
quantitative experimentation directed at understanding the behavior of
particular regulatory systems and/or elucidating general principles of
cellular information processing. Unlike many biological conferences, which
focus on specific model systems, q-bio focuses on understanding of
phenomena, which manifest themselves in many biological systems.
UNM STMC>Training> q-bio
Three-week summer school, lecture-discussion format
4-day conference with invited talks on a specialized topic
2010: Cellular information processing, integrating modeling
and reality
http://cnls.lanl.gov/q-bio/wiki/index
The Fourth q-bio Summer School
“Cellular Information Processing”
• Spatiotemporal modeling of cellular regulatory systemsLectures: Hlavacek, Wilson, Bearer, Bauer, Faeder
• Stochastic biochemistryLectures: Marrone, Munsky, Nemenman, Raj, Voigt, Werner, Zilman
• Multiscale modeling of biomolecules
Lectures: Gnanakaran, Ramakrishnan, Sethi, Bellesia
School themes for 2010:
Los Alamos, July 26- Aug 10, 2010
Sponsored by: New Mexico Center for Systems Biology,
Center for Nonlinear Studies at Los Alamos National Laboratory, and
Los Alamos Institute for Advanced Studies
q-bioq-bio Summer School and ConferenceSummer School and Conference
28 junior researchers participated in the 2009 school - 36 in 2010
Trainees in STMC receive preferential admission and financial
support
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Spatiotemporal Modeling of Cell Signaling
Center (SMTC), University of New Mexico
Future directions for training programs of the STMC:
• develop a unified training philosophy for systemsbiology scientists in the unique environment of New
Mexico: Cultural Diversity, National Laboratories, local
industry (Intel), HSC, UNM campus, and NM StateUniversity systems.
• create links to existing programs at UNM and our partners
(leverage, cross-fertilization, and integration)
• design and implement outcomes analysis of training
programs and action plans for incorporation of new ideas
in education, based on performance reviews andfeedback from faculty and students.
Single particle tracking & Hidden Markov
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Single particle tracking & Hidden MarkovModeling of erbB1 homodimerization
1 – No interaction 2 - Dimer
Interaction Distance
LA1 LA2
t1
t1
t2
t2
τON
τOFF
Monomer (M)
Dimer (D)
0
2
4
6
8
10
D i m e
r L i f e t i m e ( s )
9.98 s 3.97 s 1.26 s
Activated RestingInhibited
Dimer
Monomers
2 μm