Ch 4 - Computational Science Laboratory

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7 Νοε 2013 (πριν από 3 χρόνια και 1 μήνα)

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


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


Given quantized values in each range

Likelihood


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


For X
-
2.5<T<X+2.5


p(water,X)=p(X|water)p(water)=p(X|wat
er)*0.5


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
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