microarrays
•
Demo
Knockout mice
Oscillations in NF

.B Signaling
Control the Dynamics of Gene
Expression
Background
•
NF

kB Signaling has been observed by
electromobility shift assay (EMSA) in
studies of knockout mice
•
Florescence can be observed in a cell

by

cell basis
Proposed model
Culture Oscillations
Oscillations
Different Frequencies
Published by AAAS
A. Hoffmann et al., Science 298, 1241

1245 (2002)
Computational Model
Fig. 2. A computational model based on genetically reduced systems. (A) Analysis of
NF

Bn by EMSAs of nuclear extracts prepared at indicated times after stimulation with
TNF

(10 ng/ml) of fibroblasts of the indicated genotype. Arrows indicate specific nuclear
NF

B binding activity; asterisks indicate nonspecific DNA binding complexes. (B) The
NF

B

specific mobility shift in cells of the indicated genotype was quantitated by
phosphoimager and normalized and graphed against a linear time scale. (C)
Computational modeling of each genetically simplified signaling module results in
characteristic kinetics of the NF

Bn response. Model

fitting allows previously
undetermined biochemical parameters to be estimated. (D) Models of the simplified
signaling modules are combined, with increasing IB and

transcription rates, while
keeping the IB transcription rate constant. Model behaviors are shown that result as the
constitutive mRNA synthesis parameters for IB and IB are increased fivefold (top to
middle) and then sevenfold (middle to bottom). The bottom panel represents the NF

Bn
output predicted by a model with mRNA synthesis parameters identical to those
employed in the single IB isoform models shown in Fig. 2C. (E) Biochemical analysis of
NF

B and IB isoforms in wild

type fibroblasts. NF

Bn (top) assayed by EMSA at the
indicated times after persistent stimulation with TNF

. The specific NF

B

specific mobility
shift was quantitated by phosphoimager and normalized and graphed at the indicated
nonlinear time scale. Western blots of corresponding cytoplasmic fractions are probed
with anti

bodies specific to IB and

(bottom) and IB (above). (F) Verifica

tion of the
computational model for wild

type cells. IB and

mRNA synthesis parameters were
determined by qualitative model fitting to yield the graphed outputs in response to
persistent stimulation of NF

Bn (top) and total cellular concentrations of IB,

, and

Chapter 4
•
Bayesian Inference
•
Bayesian Networks
•
What about cycles
Basic Bayes
In Biology
•
Given
–
A is a hypothesis (pathway diagram)
–
X is prior data
–
D is new data
Example
•
Thermometer accurate to 2.5K
•
Determine the prorbability that the liquid is
water, given the temperature reading T on the
thermometer
•
X is the true temperature of the liquid
•
Priors p(water)=p(ethanol)=.5
•
P(Xwater)=1/100 for 273<X<373
•
P(Xethanol)=1/160 for 193<X<353
•
Given quantized values in each range
Likelihood
•
p(TX),water or ethanol)=0.2
–
For X

2.5<T<X+2.5
•
p(water,X)=p(Xwater)p(water)=p(Xwat
er)*0.5
•
P(waterT)=0.14, P(ethanolT)=6.14
•
Larger range increases the probability
Bayesian Approach
MCMC Approach
•
Small amount of data
–
Microarray data
–
Expert information
•
Large number of variables
•
Start with a model Y0
•
Compute p(Y0D)
•
Change rate constants or network
•
Optimize model based on the probability
generated by the Data
•
Genetic Algorithms can be used to search the
space
Evaluating Bayesian
Approaches
•
Reasoning in the presence of
uncertainty
•
Don’t work well with cycles
•
Example, take KEGG,
–
Compute ratio of the frequency with which
the two genes operated in the same
pathway vs the frequency with which the
two genes operated in different pathways
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