Chemistry: molecular biology and microbiology – Measuring or testing process involving enzymes or... – Involving nucleic acid
Reexamination Certificate
1997-12-08
2001-12-04
Brusca, John S. (Department: 1631)
Chemistry: molecular biology and microbiology
Measuring or testing process involving enzymes or...
Involving nucleic acid
C536S023100, C536S024100, C702S019000
Reexamination Certificate
active
06326140
ABSTRACT:
FIELD OF THE INVENTION
The field of the invention is the generation and analysis of stimulus-response signal profiles adapted to computer-based artificial intelligence systems such as neural networks and expert systems and their use as models for systemic responses.
BACKGROUND
Artificial intelligence (AI) systems can integrate data accumulation, recognition and storage functions with higher order analysis and decision protocols. AI systems such as expert systems and neural networks find wide application in qualitative analysis. Expert systems typically generate an individual data structure which is analyzed according to a knowledge base working in conjunction with a resident database; see, e.g. Holloway et al. (1993) U.S. Pat. No. 5,253,164 which was subject to recent judicial review, GMIS Inc, 34 USPQ2d 1389 (1995). “MYCIN”, another example, is a computer protocol using individual clinical evaluations to generate a personal data structure which is analyzed according to a knowledge base to predict or diagnose myocardial infarction and to determine hospital admissibility (Goldman et al. (1988) New England Journal of Medicine 318,797-803).
Neural network systems are networks of interconnected processing elements, each of which can have multiple input signals, but generates only one output signal. A neural network is trained by inputting training set of signals and correlating responses. The trained network is then used to analyze novel signals. For example, neural networks have been used extensively in optical character and speech recognition applications (e.g. Colley et al. (1993) U.S. Pat. No. 5,251,268).
The analysis of complex systems such as biological organisms are particularly well-suited to AI systems. Otherwise intractable complex stimulus-response patterns can be effectively analyzed using deduction protocols applied through AI systems. Pharmaceutical development for example, requires large-scale studies of systemic responses to modifications of the structure, form or administration of a drug. Presently, such systemic information is usually provided by live animal models which are costly and provide limited, relatively uninformative output signals (death, weight loss, etc.) and often mask the myriad biochemical pathway repressions and activations which underlie the measured organismal response. A number of in vitro or cell culture-based methods have been described for identifying compounds with a particular biological effect through the activation of a linked reporter (e.g. Gadski et al. (1992) EP 92304902.7 describes methods for substances which regulate the synthesis of an apolipoprotein; Evans et al. (1991) U.S. Pat. No. 4,981,784 describes methods for identifying ligand for a receptor and Farr et al. (1994) WO 94/17208 describes methods and kits utilizing stress promoters to determine toxicity of a compound).
The present invention combines these approaches to provide an in vitro or cell culture-based analysis of systemic response patterns. In particular, the invention involves sophisticated methods for generating and analyzing highly informative stimulus—systemic repression and activation response patterns.
SUMMARY OF THE INVENTION
The invention provides systems and methods for generating an output signal matrix database and for analyzing an output signal matrix by comparison to an output signal matrix database for correlating candidate stimuli and responses.
Generating an output signal matrix database according to the invention involves: (I) constructing a stimulated physical matrix; (ii) detecting a physical signal at each unit of the physical matrix; (iii) transducing each physical signal to generate a corresponding electrical output signal; (iv) storing each output signal in an output signal matrix data structure associating each output signal with the X and Y coordinates of the corresponding physical matrix unit and the stimulus; and (v) repeating steps (I)-(iv) to iteratively store output signal matrix data structures for a plurality of stimuli to form an output signal matrix database indexing output signal matrix data structures by stimuli.
Analyzing an output signal matrix by comparison to an output signal matrix database according to the present invention involves: (a) constructing a stimulated physical matrix; (b) detecting a physical signal at each unit of the physical matrix; (c) transducing each physical signal to generate a corresponding electrical output signal; (d) storing each output signal in an output signal matrix data structure associating each output signal with the X and Y coordinates of the corresponding physical matrix unit and the stimulus; and (e) comparing the output signal matrix data structure of step (d) with an output signal matrix database produced by the foregoing method of generating an output signal matrix database.
The stimulated physical matrices comprise an ordered array of units having X and Y coordinates. Each unit conforms (1) either a different responder of a living thing or a probe corresponding to such a different responder and, (2) an identifier for the responder or probe. The living thing is provided a stimulus capable of repressing the responders of a plurality of the units and the identifier provides a physical signal corresponding to the repression of such different responder. The arrays may comprise the organism's entire repertoire of responders which may be genes, gene regulatory elements, gene transcripts or gene translates, or a predetermined functional class or subset of the organism's entire repertoire. In a preferred embodiment, the array comprises a sufficient ensemble of responders to deduce the action of a stimulus regardless of its mechanism of action.
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Ashby Mathew
Rine Jasper
Brusca John S.
Christensen O'Connor Johnson & Kindness PLLC
Regents of the University of California
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