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DNA Microarray Analysis and Gene Expression Profiling - Biomatics.org

DNA Microarray Analysis and Gene Expression Profiling

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The study of Biomatics is central to the optimal utilization of DNA Microarray technology.  It is accepted that genes and proteins interract in complex networks.  Computer scientists speak the language of circuits and networks.  The finite state machine is a biological system just as are the endocrine, or circulatory sytems.  Biomatical models will help interpret and use DNA Microarray data.  This is a Biomatics Goal

If the hypercube model of genetic control is correct then DNA microarray experiments may reflect this model.  Three control atoms result in eight clusters of genes.  These eight clusters arranged as the corners of a cube have three sets of four orthogonal edges.  Thus these eight clusters could control twelve reversible processes, in effect acting as on/off switches. In addition the Cayley table shows sixty-four possible transitions which could control sixty-four  other processes. This architecture is in fact used today in hypercube networks. This archtecture has many computational advantages in certain classes of algorithms notably parallel processing

So, how would these processes reflect themselves on a DNA microchip?

DNA microarray experiments in cancer, asthma and other areas  have yielded eight clusters of genes. Other experiments have yielded 2 and 4 clusters which also fit the n to 2n  conversion model. Given a time series of experiments one could study the interplay of the eight clusters.  A change in the intensity  of a cluster is a transition along the cube. There are twelve such (reversible) edges. One heuristic then is to find three orthogonal sets of four.


Statistical analysis

The analysis of DNA microarrays poses a large number of statistical problems, including the normalization of the data. There are dozens of proposed normalization methods in the published literature; as in many other cases where authorities disagree, a sound conservative approach is to try a number of popular normalization methods and compare the conclusions reached: how sensitive are the main conclusions to the method chosen?

Also, experimenters must account for multiple comparisons: even if the statistical P-value assigned to a gene indicates that it is extremely unlikely that differential expression of this gene was due to random rather than treatment effects, the very high number of genes on an array makes it likely that differential expression of some genes represent false positives or false negatives. Statistical methods tailored to microarray analyses have recently become available that assess statistical power based on the variation present in the data and the number of experimental replicates, and can help minimize type I and type II errors in the analyses.[16]

A basic difference between microarray data analysis and much traditional biomedical research is the dimensionality of the data. A large clinical study might collect 100 data items per patient for thousands of patients. A medium-size microarray study will obtain many thousands of numbers per sample for perhaps a hundred samples. Many analysis techniques treat each sample as a single point in a space with thousands of dimensions, then attempt by various techniques to reduce the dimensionality of the data to something humans can visualize. [17]

DNA Microarray Experiments

DNA microarray experiments have found asthma to be related to eight clusters of genes. According to the biomatics model, there may be three binary dynamics, A B and C. responsible for asthma. Three proteins (Topoisomerases?), methylation, acetylation, or ubiquitination reactions manipulated in a specific combination can adjust this system through a set of state transitions. 

Consider the normal state of these eight clusters as a vector V1 as follows-

V1 =   |G0|
           |G1|
           |G2|
           |G3|
           |G4|
           |G5|
           |G7|

Similarly, define the cancerous state as V2-

V2 =   |G0|
           |G1|
           |G2|
           |G3|
           |G4|
           |G5|
           |G7|

The desired therapy result vector is then V3 = V2 - V1 to restore V2 to V1

 

*
0
A
B
C
AB
AC
BC
ABC
G0
G0
G1
G2
G3
G4
G5
G6
G7
G1
G1
G0
G4
G5
G2
G3
G7
G6
G2
G2
G4
G0
G6
G1
G7
G3
G5
G3
G3
G5
G6
G0
G7
G1
G2
G4
 G4
G4
G2
G1
G7
G0
G6
G5
G3
G5
G5
G3
G7
G1
G6
G0
G4
G2
G6
G6
G7
G3
G2
G5
G4
G0
G1
G7
G7
G6
G5
G4
G3
G2
G1
G0


So for example if GN is over expressed and GM is under expressed- locate GM on the left hand side of the table and apply the associated dynamic combination to transition to state GM. E.g. applying AB to G4 yields G0.


External Links:


 

We wished to quantify the state-of-the-art of our understanding of clusters in microarray data. To do this we systematically compared the clusters produced on sets of microarray data using a representative set of clustering algorithms (hierarchical, k-means, and a modified version of QT_CLUST) with the annotation schemes MIPS, GeneOntology and GenProtEC. We assumed that if a cluster reflected known biology its members would share related ontological annotations. This assumption is the basis of "guilt-by-association" and is commonly used to assign the putative function of proteins. To statistically measure the relationship between cluster and annotation we developed a new predictive discriminatory measure.

We found that the clusters found in microarray data do not in general agree with functional annotation classes. Although many statistically significant relationships can be found, the majority of clusters are not related to known biology (as described in annotation ontologies). This implies that use of guilt-by-association is not supported by annotation ontologies. Depending on the estimate of the amount of noise in the data, our results suggest that bioinformatics has only codified a small proportion of the biological knowledge required to understand microarray data. 

http://www.bioinfo.de/isb/2002/02/0046/

The GO project has developed three structured controlled vocabularies (ontologies) that describe gene products in terms of their associated biological processes, cellular components and molecular functions in a species-independent manner. There are three separate aspects to this effort: first, the development and maintenance of the ontologies themselves; second, the annotation of gene products, which entails making associations between the ontologies and the genes and gene products in the collaborating databases; and third, development of tools that facilitate the creation, maintenance and use of ontologies. 

http://www.geneontology.org/

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