Ch 4 - Computational Science Laboratory

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7 Νοε 2013 (πριν από 4 χρόνια και 5 μήνες)

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Knockout mice

Oscillations in NF
.B Signaling
Control the Dynamics of Gene


kB Signaling has been observed by
electromobility shift assay (EMSA) in
studies of knockout mice

Florescence can be observed in a cell
cell basis

Proposed model

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

(10 ng/ml) of fibroblasts of the indicated genotype. Arrows indicate specific nuclear
B binding activity; asterisks indicate nonspecific DNA binding complexes. (B) The
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
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
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
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


A is a hypothesis (pathway diagram)

X is prior data

D is new data


Thermometer accurate to 2.5K

Determine the prorbability that the liquid is
water, given the temperature reading T on the

X is the true temperature of the liquid

Priors p(water)=p(ethanol)=.5

P(X|water)=1/100 for 273<X<373

P(X|ethanol)=1/160 for 193<X<353

Given quantized values in each range


p(T|X),water or ethanol)=0.2

For X


P(water|T)=0.14, P(ethanol|T)=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(Y0|D)

Change rate constants or network

Optimize model based on the probability
generated by the Data

Genetic Algorithms can be used to search the

Evaluating Bayesian

Reasoning in the presence of

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