Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Biological or biochemical
Reexamination Certificate
2000-05-24
2002-12-31
Marschel, Ardin H. (Department: 1631)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Biological or biochemical
C435S006120
Reexamination Certificate
active
06502039
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to a mathematical analysis for the quantitative estimation of the level of differential gene expression. More specifically, the present invention relates to the mathematical derivation of an a posteriori distribution of all the fold-changes of the level of gene expression which may be inferred from the given experimental measurements.
BACKGROUND OF THE INVENTION
Cells rely upon their numerous protein components for a wide variety of functions. These functions include, e.g., the production of energy, the biosynthesis of all component macromolecules, the maintenance of cellular architecture, the ability to act upon intra- and extracellular stimuli, and the like. Each cell within an organism in contains the information necessary to produce the repertoire of proteins that organism can expressed. This information is stored as genes within the organism's genome. The number of unique human genes is estimated to be 30,000 to 100,000.
For any given cell, only a portion of the gene set is expressed in the form of protein. Some of the proteins are likely to be present in all cells (i.e., are ubiquitously expressed) because they serve biological function(s) which are required in every type of cell, and can be thought of as “housekeeping” proteins. In contrast, other proteins serve specialized functions that are only required in particular cell types. For example, muscle cells contain specialized proteins that form the dense contractile fibers of a muscle. Given that a large part of a cell's specific functionality is determined by the genes it is expressing, it is logical that transcription, the first step in the process of converting the genetic information stored in an organism's genome into protein, would be highly regulated by the control network that coordinates and directs cellular activity.
Regulation of gene expression is readily observed in studies that examine activities evident in cells configuring themselves for a particular function (e.g., specialization into a muscle cell) or state (e.g., active multiplication or quiescence). Hence, as cells alter their status, the coordinated transcription of the protein(s) which are requited for this molecular biological/physiological “state” can be observed. This highly detailed, global knowledge of the cell's transcriptional state provides information on the cell's status, as well as on the biological system(s) controlling this status. For example, knowledge of when and in what types of cell the protein product of a gene of unknown function is expressed would provide useful clues as to the likely function of that gene. Determination of gene expression patterns in normal cells could provide detailed knowledge of the way in which the control system achieves the highly coordinated activation and deactivation required for development and differentiation of a mature organism from a single fertilized egg. Comparison of gene expression patterns in normal and pathological cells could provide useful diagnostic “fingerprints” and help identify aberrant functions that would be reasonable targets for therapeutic intervention.
Unfortunately, the ability to carry out studies in which the transcriptional state of a large number of genes is determined has, until recently, been inhibited by limitations on the ability to survey cells for the presence and abundance of a large number of gene transcripts in a single experiment. One limitation can be in the small number of identified genes. In the case of humans, only a few thousand proteins encoded within the human genome have been physically purified and quantitatively characterized to any extent. Another limitation can be in the manner of transcription analysis.
Two recent technological advances address have aided analyses of gene transcription. The cloning of molecules derived from mRNA transcripts in particular tissues, followed by the application of high-throughput sequencing to the DNA ends of the members of these libraries has yielded a catalog of expressed sequence tags (ESTs). See e.g., Boguski and Schuler,
Nat. Genetics
10: 369-370 (1995). These “signature sequences” can provide unambiguous identifiers for a large cohort of genes.
In addition, the clones from which these sequences were derived provide analytical reagents that can be used in the quantitation of transcripts front biological samples. The nucleic acid polymers, DNA and RNA, are biologically synthesized in a copying reaction in which one polymer serves as a template for the synthesis of an opposing strand, which is termed its complement. Following the separation of the strands from one another (i.e., denaturation), these strands can be induced to pair, quite specifically, with other nucleic acid strands possessing a complementary sequence in a process called hybridization. This specific binding can be the basis of analytical procedures for measuring the amount of a particular species of nucleic acid, such as the mRNA specifying a particular protein gene product.
A second advance involves microarray/microassay technology. This is a hybridization-based process which allows simultaneous quantitation of many nucleic acid species. See e.g., DeRisi et al.,
Nat. Genetics
14: 457-460 (1996); Schena et al.,
Proc. Natl. Acad. Sci. USA
93: 10614-10619 (1996). This technique combines robotic placement (i.e., spotting) of small amounts of individual, pure nucleic acid species on a glass surface, hybridization to this array with multiple fluorescently-labeled nucleic acids, and detection and quantitation of the resulting fluorescent-labeled hybrids with, for example, a scanning confocal microscope. When used to detect transcripts, a particular RNA transcript (i.e., an mRNA) can be copied into DNA (i e., a cDNA) and this copied form of the transcript is subsequently immobilized onto, for example, a glass surface.
A problem in the analysis of gene expression data is the estimation of the overall fold-change in the expression level of a gene in one experiment relative to its expression in another experiment. Given these two raw measurements of the fold-change in gene expression level, the simplest approach, as utilized by previous methodologies, has been to take the arithmetic ratio of the values as an estimate of the overall fold-change. While for very strong signals this leads to a meaningful estimate of the fold-change in the underlying mRNA concentrations, for weaker signals the results are much more ambiguous because of contamination by the “noise” which is indigenous to the particular experimental system utilized. Another previously utilized technology for the estimation of the fold-change in gene expression level is based upon differential-signal intensities (e.g., the Affymetrix® chip). However, the values assigned to expression levels by use of the aforementioned methodology can be negative, thus leading to the awkward situation of negative or undefined gene expression ratios.
SUMMARY OF THE INVENTION
The present invention provides a highly accurate and reproducible mathematically-based methodology for quantifying the levels of differential gene expression from microassay protocols.
The methods of the present invention can be used to calculate differences in the level of gene expression in one or more arrays of genes. The methods involve defining the experimental noise associated with intensity of hybridization signal for each gene in the array(s). The experimental noise is variations in observed levels on chips or other microarrays rather than biological noise, which is the variation of expression level seen in biological systems. Detection of genes is often, but not always, based on fluorescence. Other detection systems have been used which may be adapted here. Such systems include luminescent or radioactive labels, biotinylated, haptenated, or other chemical tags that allow for easy detection of labeled probes.
For a mathematical description, see Section I—Formulation of the Noise Model below. The noise is assumed to be Gaussian and Bayes Theorem is applied.
Bushnell Steven
Fuchs Rainer
Theilhaber Joachim
Aventis Pharmaceuticals
Marschel Ardin H.
Mintz Levin Cohn Ferris Glovsky and Popeo P.C.
Sheinberg Monika
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