New Mexicostmcallcenters Presentations 2

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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

http://stmc.health.unm.edu/index.html



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


http://images.google.com/imgres?imgurl=http://math.lanl.gov/~yi/images/yi1.jpg&imgrefurl=https://www.cvit.org/freelinking/Yi%20Jiang,%20Ph.D.&h=259&w=194&sz=7&hl=en&start=4&um=1&tbnid=I2u0REuRThaTqM:&tbnh=112&tbnw=84&prev=/images?q=Yi+Jiang+LANL&um=1&hl=en

http://images.google.com/imgres?imgurl=http://www.nature.com/nature/journal/v447/n7145/images/447743a-i1.0.jpg&imgrefurl=http://www.nature.com/nature/journal/v447/n7145/full/447743a.html&h=194&w=180&sz=36&hl=en&start=3&um=1&tbnid=NDFUvw5QN3BYrM:&tbnh=103&tbnw=96&prev=/images?q=Andrew+Bradbury+National+Laboratory&um=1&hl=en&sa=G

http://images.google.com/imgres?imgurl=http://roswell.ca.sandia.gov/img/aksingh.jpg&imgrefurl=http://roswell.ca.sandia.gov/anup/&h=150&w=138&sz=8&hl=en&start=7&um=1&tbnid=5B2dH_1EGF6dGM:&tbnh=96&tbnw=88&prev=/images?q=Anup+Singh+National+Laboratory&um=1&hl=en&sa=N

http://cellpath.health.unm.edu/faculty/Diane/index.htm



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




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.




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




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)





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

http://www.flickr.com/photos/gutenkunst/4516722674/





STMC Community Impact: OUTREACH to ALL AGES

“ART & SCIENCE of SYSTEMS BIOLOGY” (Santa Fe, NM, March 26-27, 2010)





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





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)





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




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



EM grid

coverslipcell

plasmamembrane sheetson EM gridready for labeling

Nanoscale Spatial Resolution of Membranes by Electron Microscopy



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” ?



Resting FcεRI is distributed in singlets, small clusters



After crosslinking, FcεRI is found in signaling patches

FcεRI

Coated vesicle

J. Cell Biology, 2000



You might ask, how relevant is this to OTHER receptors?

Such as the EGFR family…

ErbB3

PI3K



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



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



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



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



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)



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



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



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



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



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



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



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



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



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



New Technology: 3D Tracking of Endocytosis

Werner

Mathematics Algorithm Development Key



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



Slide 34

A rule-based model of early events inimmunoreceptor signaling

William S. Hlavacek

Center for the Spatiotemporal Modeling of Cell Signaling (STMC)



Outline

Motivation for modeling

Our approach

Model for T cell receptor (TCR) signaling (a work in

progress)



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



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)



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)



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



T cell activation is stimulated by co-crosslinking of TCRand the co-receptor CD28



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



A model was formulated on the basis of a data-guidedliterature search

~20 signaling proteins

>100 rules for protein-protein interactions~300 literaturecitations



Antibody-mediated crosslinking of TCR and CD28



Recruitment of Lck to CD28 via SH3-PRS interaction



Lck mediates phosphorylation of ITAM Ys in TCR



Recruitment of ZAP-70 to TCR via SH2-pY interactions



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/

http://tgen.org/public/RuleMonkey/

http://tgen.org/public/RuleMonkey/



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



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



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



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…..



3

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



4

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



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

New Mexicostmcallcenters Presentations 2

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