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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:

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slide1

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
slide2

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.

slide3

Man-made and evolved networks and systems maybe highly optimized or robust to certain perturbations and sensitive or fragile to others (Highly Optimized Tolerance)

If we knew how and where cell logic could break, perhaps even on a patient specific basis, what could we do?

Csete et.al., Trends in Biotechnology, 22:446 - 450, 2004

slide4

Cyclin E

Cyclin E and Cyclin A [concentration]

Cyclin B

Time [hr]

Mass single cell

Cellmass [mass]

Time [hr]

Novak, B. & Tyson, J. J. Theor. Biol., 230: 563–79, 2004.

slide5

m x p matrix of first-order partials with respect to states

m x m state selection matrix (diagonal)

m x p matrix of first-order partials with respect to parameters

m x p matrix of first-order sensitivity coefficients

Qualitative protein-protein and protein-DNA network models can be quickly generated, approximately identified* and screened for fragile mechanisms

Accumulation (m x 1)

model equations (m = 213; p = 380)

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:

slide6

1

The Tyson model predicts:

Fragility associated with the translational efficiency, the Ubiquitin Proteasome System (UPS) and E2F activated expression.

Are these real?

Scaled Overall Sensitivity Coefficient

Overall state sensitivity

coefficient for parameter j

Scaled first-order state

sensitivity coefficient

0

Parameter Index (1 - 98)

1

Sum over state and then time

Scaled Overall Sensitivity Coefficient

Stelling et.al., Proc. Nat. Acad. Sci., 101:13210 - 13215, 2004

0

Sorted Parameter Index (1 - 98)

slide7

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

slide9

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

(A)

(C)

(B)

(D)

slide10

How can we build and manage large

mechanistic network models?

Assemble network

models

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?

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

slide11

Assuming fast nutrient transients has allowed us to simulate much larger 3-D grids with the same number of CPUs.

z-axis

z-axis

y-axis

y-axis

x-axis

x-axis

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).

microscale biomechanics of tissue elements predictive modeling by tissue composition

C

A

HH17

HH25

D

B

Microscale Biomechanics of Tissue Elements Predictive Modeling by Tissue Composition

Butcher et al, Circ Res 2007; Butcher et al, Phil Tras Royal Soc, 2007

3d quantitative modeling of microscale vascular geometries morphogenesis

RAV

RA

CCM

LA

LA

RV

LV

LV

LV

IVS

3D Quantitative Modeling of Microscale Vascular Geometries - Morphogenesis

Butcher et al, Dev Dyn, 2007

non invasive measurement and predictive modeling of small scale 3d cardiovascular function

Leaflet Strain, EL

17

A

V

ET = T/T0

A

AV

HH17

A

In Vivo Strain Measurement

C

EL = ES +/- EF

ES = S/S0

S

EF = f(S,T,q)

q

T1

T2

T3

HH27 LAV

B

D

Non-Invasive Measurement and Predictive Modeling of Small Scale 3D Cardiovascular Function

Butcher et al, Circ Res. 2007