Download Intelligent DNA Chips: Logical Operation of Gene Expression

Intelligent DNA Chips:
Logical Operation of Gene Expression
Profiles on DNA Computers
Yasubumi Sakakibara, Akira Suyama
Genome Informatics 11: 33-42(2000)
Ji Yoon Park
Dept. of Biochem
Hanyang University
Abstract
1. By combining with the DNA Coded Number(DCN)
method, we implement universal DNA chips which not
only detect gene expression but also find logical formula
of gene expressions.
2. Advantage:
- More quantitative analysis of gene expression profiles
and the logical operations.
Object
 Intelligent DNA chip and the DCN method
allow us more quantitative analyses of gene
expression profiles and the logical operations
1. DNA microarray
- The massive parallelism to enable to simultaneously
detect the expressions for a large number of genes
- Application: gene discovery/ disease diagnosis
2. DNA computing: Boolean functions on DNA strands
- Sakakibara:
- DNF formula to a DNA strand,
- evaluate the encoded DNF formula for a truth-value assignment
by using hybridization and primer extension with DNA pol
- Suyama: DNA Coded Number(DCN)
1) genome information is first converted into data expressions
in DCNs using a conversion table written with DNA
2) DCN-encoded genome information analyzed with a power of
the massive parallelism of DNA computing
3) The results of the analysis are finally obtained by reading out a
sequence of DCNs
DNA Coded Number(DCN)
- Molecular arithmetic numbers by DNA base sequences
chosen from a set of orthonormal DNA base seq
- Orthonormal DNA seq:
: Uniform melting temp. & no mishybridization or
folding
- Associated with expressed or unexpressed genes are
generated using DNA molecular reaction.
A DNA chip with logical operations executable
(A∧B) V
C in the figures means:
if the gene A is expressed and the gene B is expressed or
if the gene C is not expressed, the formula is satisfied.
* ∧: “ and ” operation
* V : “ or ” operation
cDNA Microarray scheme
Oncogene 18, 3666-3672(1999)
1. mRNA preparation and cDNA synthesis
2. Expressed genes:
- the truth-value of a Boolean variable for the gene becomes 1.
3. Unexpressed genes:
- the truth-value of a Boolean variable for the gene becomes 0.
4. DCN seqs are simultaneously applied to a DNA chip with DNA
strands encoding Boolean formulae
5. The complementary marker sequences fluorescently tagged are
applied to the DNA chip after the logical evaluation and annealed
to marker in the DNA chip which remain single-stranded.
6. * Color: truth-value of the Boolean formula at the element = 1
* No colar:
=0
Intelligent DNA chip
* The intensity of the fluorescence at each element
- proportional to the expression level of the genes in the sample
- satisfiability level of the Boolean formula at the element with the
gene expression pattern
(x1∧￢ x2)∨ (x3∧￢ x4) = 0 (: not satisfied); ds DNA No color
(x1∧￢ x2)∨ (x3∧￢ x4) = 1 (: satisfied); ss DNA
color
- More marker subseqs in the formula remain single stranded,
there exist more terms which satisfied with the expression pattern
- More complementary marker seqs fluorescently tagged are
annealed and the element shows the fluorescent color with greater
level
Applications
Intelligent DNA chip
- Logical inference for such diagnosis based on detected gene
expression patterns
- detect disease-related genes
- differential expression analysis
: more easy and precisely realized
To detect specific genes using two
different samples
1)
We prepare two DCNs which represent two Boolean
variables say “ A” and “ B”, for a gene expression in
the target sample and the same gene expression in the
other sample, and implement DNA chip with a probe
for (A ∧ B)
2)
By applying those samples, if the (A ∧ B) is found
satisfied, we conclude the gene is expressed in the
target sample and not expressed in the other sample,
and hence the gene is specific to the target sample
Conclusion
Intelligent DNA chip
1.By combining DNA chips with the DNA-computing
method for representing and evaluating Boolean
functions and the DCN method
2. Detect gene expression/ Find logical formula of gene
expression
3. Quantitative analysis of gene expression profiles using
the intensity of the fluorescence at each element
4. Model gene regulation networks