jeffrey d. varner (speaker)
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Predictive Modeling and Diagnostics in Cornell Biomedical Engineering. Jeffrey D. Varner (speaker) Department of Chemical and Biomolecular Engineering, Cornell University, 244 Olin Hall, Ithaca NY 14853. Working hypothesis: - PowerPoint PPT PresentationTRANSCRIPT
Jeffrey D. Varner (speaker)Department of Chemical and Biomolecular Engineering,
Cornell University, 244 Olin Hall, Ithaca NY 14853
Predictive Modeling and Diagnostics in Cornell Biomedical Engineering
Working hypothesis:
Uncertain mathematical models of protein-protein and protein-DNA networks relevant to human health can be computationally screened for fragile mechanisms. Fragile
mechanisms represent potential therapeutic targets.
Man-made and evolved networks and systems maybe highly optimized or robust to certain perturbations and sensitive or fragile to others (Highly Optimized Tolerance)
Csete et.al., Trends in Biotechnology, 22:446 - 450, 2004
If we knew how and where cell logic could break, perhaps even on a patient specific basis, what could we do?
Novak, B. & Tyson, J. J. Theor. Biol., 230: 563–79, 2004.
Time [hr]
Time [hr]
Cel
lma
ss [
mas
s]
Cyc
lin
E a
nd
Cyc
lin
A [
con
cen
trat
ion
]
Cyclin E
Cyclin B
Mass single cell
Accumulation (m x 1)
Mass Balances and Algebraic constraints (m x 1) where p denotes the (p x 1) parameter vector
Which parameters are important is calculated by sensitivity analysis. The equation governing the first-order sensitivity coefficients can be obtained by differentiating the model equations with respect to p:
m x p matrix of first-order sensitivity coefficients
m x p matrix of first-order partials with respect to parameters
m x p matrix of first-order partials with respect to states
m x m state selection matrix (diagonal)
Qualitative protein-protein and protein-DNA network models can be quickly generated, approximately identified* and screened for fragile mechanisms
model equations (m = 213; p = 380)
Parameter Index (1 - 98)
Sca
led
Ove
rall
Sen
siti
vity
Co
effi
cien
t
0
1
Sorted Parameter Index (1 - 98)
Sca
led
Ove
rall
Sen
siti
vity
Co
effi
cien
t
0
1
Overall state sensitivity coefficient for parameter j
Scaled first-order statesensitivity coefficient
Sum over state and then time
Stelling et.al., Proc. Nat. Acad. Sci., 101:13210 - 13215, 2004
The Tyson model predicts: Fragility associated with the translational efficiency, the Ubiquitin Proteasome System (UPS) and E2F activated expression. Are these real?
There are a large number of reports linking deregulation of the Ubiquitin ligase family members with cancer development
Nakayama et.al., Nature Rev. Cancer., 6:369 - 381, 2006
(A)
(D)
(C)
(B)
Proof-of-concept G1-S checkpoint LNCaP model was developed using qualitative data (190 proteins or protein-complexes and 342 protein-protein and protein-DNA interactions) describing cyclin-D expression
following PaCP conformer interaction with HER-2
How can we build and manage large mechanistic network models?
How do we model the response of complex tissue? Our working hypothesis is that we can understand complex biology by understanding and assembling
simple logical pieces
Fragility Analysis - Which mechanisms are likely to break?
How can we run better experiments to test the models?
Can we simulate multicellular dynamics with ensembles of single cells?
Assemble network models
Assuming fast nutrient transients has allowed us to simulate much larger 3-D grids with the same number of CPUs.
Spatial-temporal distribution of breast cancer-cell density after 150K iterations for different parameter values (128 x 128 x 128). Simulations were conducted on NERSC (IBM p575 Power5 111-nodes,888 CPUs) at SLAC using LAM-MPI for communication and the PETSc library for the solution of the nutrient field balances (Galerkin, Krylov space with Jacobi preconditioning).
x-axisx-axis
y-axisy-axis
z-axisz-axis
HH17
HH25
A C
B D
Microscale Biomechanics of Tissue Elements Predictive
Modeling by Tissue Composition
Butcher et al, Circ Res 2007; Butcher et al, Phil Tras Royal Soc, 2007
RAV
LA L
A
RA
CCM
IVS
LV
RV
LVLV
3D Quantitative Modeling of Microscale Vascular
Geometries - Morphogenesis
Butcher et al, Dev Dyn, 2007
HH27 LAV
C
D
S
T
1
T
2 T
3
EL = ES +/- EF E
S
= S/S
0
E
F
= f(S,T,)
E
T
= T/T
0
In Vivo Strain Measurement A
B
Le
afle
t Str
ain
, EL
17
A
V HH17
AV
A
Non-Invasive Measurement and Predictive Modeling of
Small Scale 3D Cardiovascular Function
Butcher et al, Circ Res. 2007
Questions?