Integrated Biosensor and Simulation System for Diagnosis and Therapy

• US 20100285082A1
• Filed: 10/24/2008
• Published: 11/11/2010
• Est. Priority Date: 08/22/2003
• Status: Active Grant

• Current Assignee: Dennis Fernandez
• Original Assignee: Dennis Fernandez
• Inventors: Fernandez, Dennis S.
• Granted Patent: US 8,370,070 B2
• Time in Patent Office: Days
• US Class Current: 424/422
• CPC Class Codes: A61B 2562/028 Microscale sensors, e.g. electromechanical sensors [MEMS]
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• CPC Class Codes: A61B 5/0215 by means inserted into the body catheters A61M25/00
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 5/076 Permanent implantations telemetry using implanted circuitry A61B5/0031; implanted stimulators for therapy A61N1/3605, A61N1/362, A61N1/372
• CPC Class Codes: A61B 5/145 Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissuemeasuring of blood pressure or blood flow A61B5/02; non-radiation detecting or locating of foreign bodies in blood A61B5/06
• CPC Class Codes: A61B 5/14539 for measuring pH
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/411 Detecting or monitoring allergy or intolerance reactions to an allergenic agent or substance
• CPC Class Codes: A61B 5/413 Monitoring transplanted tissue or organ, e.g. for possible rejection reactions after a transplant
• CPC Class Codes: A61B 5/415 the glands, e.g. tonsils, adenoids or thymus
• CPC Class Codes: A61B 5/418 lymph vessels, ducts or nodes
• CPC Class Codes: A61B 5/4839 combined with drug delivery
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/686 Permanently implanted devices, e.g. pacemakers, other stimulators, biochips A61B5/6861 takes precedence
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61P 9/00 Drugs for disorders of the cardiovascular system
• CPC Class Codes: C12Q 1/04 : Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor C12Q1/6897 takes precedence
• CPC Class Codes: C12Q 1/68 : involving nucleic acids
• CPC Class Codes: C12Q 1/6886 : for cancer immunoassay for cancer G01N33/574
• CPC Class Codes: C12Q 1/70 : involving virus or bacteriophage immunoassay for viruses G01N33/56983
• CPC Class Codes: C12Q 2600/156 : Polymorphic or mutational markers
• CPC Class Codes: G01N 33/53 : Immunoassay; Biospecific binding assay; Materials therefor
• CPC Class Codes: G06Q 40/08 : Insurance
• CPC Class Codes: G06Q 50/22 : Social work or social welfare, e.g. community support activities or counselling services
• CPC Class Codes: G16B 5/00 : ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: G16B 5/20 : Probabilistic models
• CPC Class Codes: G16B 5/30 : Dynamic-time models
• CPC Class Codes: G16H 10/60 : for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: G16H 70/60 : relating to pathologies
• CPC Class Codes: Y02A 90/10 : Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

BioMEMS/NEMS appliance biologically monitors an individual, using biosensors to detect cellular components. Data is simulated or analyzed using systems-biology software, which provides diagnostic or therapeutic guidance.

Claims

1. Integrated biosensor platform comprising:

a multifunctional array; and

a detection system comprising a plurality of biosensors coupled to the multifunctional array including a DNA sensor, an RNA sensor, a peptide or protein sensor, an antibody sensor, an antigen sensor, a tissue factor sensor, a vector or virus vector sensor, a lipid or fatty acid sensor, a steroid sensor, a neurotransmitter sensor, an inorganic ion or electrochemical sensor, a pH sensor, a free radical sensor, a carbohydrate sensor, a neural sensor, a chemical sensor, a small molecule sensor, an exon sensor, a metabolites sensor, an intermediate sensor, a chromosome sensor, and a cell sensor.

2. Platform of claim 1 wherein one or more of the sensors sense food.

3. System biology platform comprising:

means for verifying, modeling and simulating genomics, proteomics, computational chemistry, pharmacogenomics, computational biology, computational biophysics, computational cell behavior, pharmacokinetics, metabolomics, and trnscriptomics; and

means for sensing coupled to the verifying, modeling and simulating means.

4. Platform of claim 3 wherein the verifying, modeling and simulating means diagnoses an infectious disease or infection, an immunity or auto-immunity disease, or neoplasia.

5. Platform of claim 3 further comprising a regulation overlay.

6. Therapeutic apparatus comprising:

a therapeutic manufacture;

a therapeutic reservoir; and

a sensor manufacture.

7. Apparatus of claim 6 wherein the therapeutic manufacture comprises pharmaceuticals, bio-pharmaceuticals, a reconfigurable biocatalytic chip and a tissue scaffold.

8. Apparatus of claim 6 wherein the therapeutic reservoir comprises a pre-filled reservoir, a pre-cursor and a therapeutic storage.

9. Apparatus of claim 6 wherein the sensor manufacture comprises a probe arranger and a biosensor chip.

10. DNA unit comprising:

a DNA sensor;

a DNA therapeutic manufacture; and

a sensor manufacture.

11. Computer-assisted method for diagnosis/therapy comprising steps:

pre-diagnosing;

preliminary modeling;

configuring a biosensor;

implanting the biosensor;

initializing a chip in vivo;

program executing;

initial in vivo sensing;

in vivo modeling; and

recommending diagnosis or therapy.

12. Method of claim 11 further comprising step:

commanding a therapy unit.

13. Method of claim 11 wherein at least one step comprises a neural network, artificial intelligence, fuzzy technique, knowledge-based, probabilistic or bayesian approach.

14. A systems biology platform for computational modeling and simulation of multi-cellular systems or unicellular systems comprising:

internal or external databases, wherein the databases comprise genomics database, protein database, gene expression database, transcriptomics database, proteomics database, metabolomics database, pharmacogenomics database, pharmacokinetics database, disease database or drug database;

bioinformatics analysis tools, wherein the bioinformatics analysis tools comprise network inference, sequence analysis or protein folding;

a set of mathematics equations, wherein the mathematics equations represent biological processes of multi-cellular or unicellular systems; and

computational software to support the said systems biology platform;

wherein such computational software accesses one or more databases, applies one or more mathematics equations and operates one or more bioinformatics analysis tools to integrate data from multi-cellular or unicellular systems at a system level, thereby aiding to understand the interrelationships and functional organization of cells and genomes, whereby monitoring or analyzing biological hosts and providing diagnostic or therapeutic guidance on cancer studies or diseases, organ generation, and drug development.

15. The platform of claim 14 further comprising modeling and simulation of tissue engineering, genomic engineering, mutation studies, animal testing, chemical gradients, tissue, plants, bacteria, virus or single cell living things.

16. The platform of claim 14, wherein the modeling and simulation of biological processes of the multi-cellular systems or unicellular systems further comprises gene transcription, RNA translation, signal transduction pathways and networks, genomic networks, protein networks, whole cell simulation, or cell growth.

17. The platform of claim 14, wherein the bioinformatics analysis tools further comprise neural networks, artificial intelligence, fuzzy systems, probabilistic systems, inference engines, or Bayesian analysis.

18. The platform of claim 14 further comprising verification of hypotheses on the regulation of developing organisms.

19. The platform of claim 14 further comprising monitoring of effects of changing genome on the dynamics of developing organisms.

20. The platform of claim 14 further comprising determination of appropriate therapies or prevention of adverse therapy reactions.

21. The platform of claim 14 further comprising analysis of drug toxicity or drug level in a biological system.

22. The platform of claim 14 further comprising analysis of genetic composition of a biological host, or prediction of drug and food interactions in the host.

23. The platform of claim 14 further comprising generation of automated diagnostic or therapeutic recommendation or report on optimum level of nutritional substance or identification of alternative substance for consumption.

24. The platform of claim 14 further comprising one or more hardware, firmware, source or object code software, configurable logic chip or device, digital signal processor, systolic processing array, finite state machine, secured distributed network, or functionally equivalent components, thereby such system biology platform may be integrated within one or more integrated circuit, module or processor, wherein the systems biology platform may be a standalone system or integrated as a subsystem of a multi-component system.

25. A computational software-based method for modeling and simulating multi-cellular or unicellular systems comprising the steps of:

collecting biological data from multi-cellular or unicellular systems via one or more biosensors;

storing and retrieving the biological data in corresponding databases, wherein the databases may be internal or external, and may be accessed locally or remotely via wired or wireless network communications, whereby the databases comprise genomics database, protein database, gene expression database, transcriptomics database, proteomics database, metabolomics database, pharmacogenomics database, pharmacokinetics database, disease database or drug database;

representing one or more biological processes in mathematical formats by using one or more mathematics equations;

running one or more bioinformatics analysis tools to analyze and simulate the biological data from multi-cellular or unicellular systems; and

generating diagnostic or therapeutic guidance on cancer studies or diseases, organ generation, and drug development.

26. The method of claim 25 further comprising verifying hypotheses on the regulation of developing organisms.

27. The method of claim 25 further comprising monitoring effects of changing genome on the dynamics of developing organisms.

28. The method of claim 25 further comprising determining appropriate therapies or preventing of adverse therapy reactions.

29. The method of claim 25 further comprising analyzing drug toxicity or drug level in a biological system.

30. The method of claim 25 further comprising analyzing genetic composition of a biological host, or predicting drug and food interactions in the host.

31. The method of claim 25 further comprising generating automated diagnostic or therapeutic recommendation or report on optimum level of nutritional substance, or identifying of alternative substance for consumption.

32. A method for predicting the complete genome of a host comprising:

inputting biological material in the form of a single cell to a multifunctional biosensor wherein the multifunctional biosensor senses biological materials from the single cell wherein the biological materials are selected from a group comprising concentrations, inorganic or organic materials, cellular material, genetic material, nucleic acids, proteins, amino acids, peptides, antibodies, antigens, fatty acids, lipids, steroids, neurotransmitters, inorganic ions, pH levels, free radicals, carbohydrates, chemicals, small molecules, cells, tissue, pharmaceuticals, toxins, metabolites, and physiological levels;

receiving data from a multifunctional biosensor;

accessing a systems biology platform via a network to model biological processes based on one or more genomic sequences of the host, wherein the systems biology platform comprises of one or more of genomics database, transcriptomics database, proteomics database, metabolomics database, or disease database;

running a set of simulations based on data in the models;

comparing data obtained from said multifunctional biosensor platform with the results of said simulations; and

generating a report predicting the genomic sequence or structure of the host.

33. The method of claim 32, wherein the predicted genomic sequence or structure is used to diagnose genetic disorders.

34. The method of claim 32 further comprising predicting the diagnosis of genetic disorders in a host.

35. The method of any of preceding claims 32-34, wherein the generated predictive or diagnostic outcome is visually displayed.

36. A method for predicting the complete genome of a host comprising:

inputting biological material in the form of a single cell to a multifunctional biosensor platform wherein the multifunctional biosensor senses data from one or more of deoxyribonucleic acid sensor, exon sensor, or chromosome sensor;

receiving genomic or genetic sequence data of a single cell of the host from the multifunctional biosensor;

accessing a systems biology platform via a network to model the genome of the host based on a genomics database in the systems biology platform and data received from said multifunctional biosensor platform; and

using one or more of pattern recognition, feature extraction, supervised learning, unsupervised learning, learning algorithms, neural networks, artificial intelligence or fuzzy systems to reconstruct the genomic sequence of the host.

37. The method of claim 36, wherein the predicted genomic sequence or structure is used to diagnose genetic disorders.

38. The method of claim 36 further comprising predicting the diagnosis of genetic disorders in a host.

39. The method of any of preceding claims 36-38, wherein the generated predictive or diagnostic outcome is visually displayed.

40. A network system for predicting the genomic sequence of a subject comprising:

a multifunctional biosensor comprising one or more of deoxyribonucleic acid sensor, exon sensor, or chromosome sensor;

a processor;

a memory comprising genomics database; and

means for transmitting information obtained by said biosensor to the processor;

wherein the processor compares genomic data of the subject received from inputting biological material to the biosensor, to genomic data from genomics database and utilizes predictive or comparative algorithms to predict the genomic sequence of the subject.

41. A method for predicting the metabolic output of an organism comprising:

inputting biological material to a multifunctional biosensor platform wherein the multifunctional biosensor senses and provides data on biological materials selected from, concentrations, inorganic or organic materials, cellular material, genetic material, nucleic acids, proteins, amino acids, peptides, antibodies, antigens, fatty acids, lipids, steroids, neurotransmitters, inorganic ions, pH levels, free radicals, carbohydrates, chemicals, small molecules, cells, tissue, pharmaceuticals, toxins, metabolites, or physiological levels;

receiving data from a multifunctional biosensor;

representing one or more biological processes or molecular interactions as mathematical models;

accessing a systems biology platform via a network to model biological processes based on genomic sequences, wherein the systems biology platform comprises of one or more of genomics database, transcriptomics database, proteomics database, or metabolomics database;

running a set of simulations based on data and the administration of a substance or manipulation of the organism;

comparing data obtained from said multifunctional biosensor platform with the results of said simulations; and

generating a simulation report for predicting the metabolic out come of the administration of a substance or manipulation of the organism to aid in the bio-manufacturing or biopharming of the substance.

42. A method for evaluating drug toxicity in a biological system comprising the steps of:

administering a drug to the biological system;

monitoring one or more responses of the biological system to said drug using one or more sensors;

accessing a systems biology platform via a network to said sensor(s), wherein the systems biology platform comprises a genomics database, a transcriptomics database, a metabolomics database, a pharmacogenomics database, pharmacokinetics database or a drug database;

implementing knowledge-based techniques from the systems biology platform to analyze data from said one or more sensors; and

generating a report evaluating toxicity of the drug administered to the biological system.

43. The method of claim 42, wherein the generated simulation outcome is visually displayed.

44. The method of claim 42, wherein the generated simulation outcome is stored in a data storage retrievable through the network.

45. A network system for evaluating drug toxicity in a biological host comprising:

a processor and

a memory wherein the memory represents mathematically, via the network, genomic, transcriptomic, metabolomic, or pharmacogenomic data; and

the processor runs one or more simulations to evaluate toxicity of one or more drugs in the biological host.

46. The system of claim 45 further comprising:

an electronic data storage to store and access simulation data.

47. An operator-independent network system for diagnosing or making therapy recommendation comprising:

a non-implantable or implantable networked biosensor for monitoring cardiac performance, blood flow, blood metabolites, antigen, antibody, viruses, hormones, neurotransmitter, pH, free radical, carbohydrate, proteins, or small molecules such that physiological conditions of the individual are determined;

a processor using mathematical methods comprising neural networks, artificial intelligence, fuzzy systems, or probabilities on the patient records to simulate one or more conditions of a patient;

a network database comprising systems biology platform wherein the systems biology platform comprises genomics database, proteomics database, pharmaco-genomic database, chemical compound database etc.; and

a data analysis module or a set of computer readable instructions for providing immediate actionable physiologic feedback and reporting one or more diagnoses or therapy recommendation based on the physiological condition of the patient to a medical professional.

48. A method for diagnosing or making therapy recommendation comprising the steps of:

using a non-implantable or implantable sensor for monitoring cardiac performance, blood flow, blood metabolites, antigen, antibody, viruses, hormones, neurotransmitter, pH, free radical, carbohydrate, proteins, or small molecules such that physiological conditions of the individual are determined;

using mathematical methods comprising neural networks, artificial intelligence, fuzzy systems, or probabilities on the patient records to simulate one or more conditions of a patient; and

reporting one or more diagnoses or therapy recommendations based on the condition of the patient, to a medical professional on a user interface connected to a network.

49. An apparatus for diagnosing an individual or for making therapy recommendation comprising:

a non-implantable or implantable biosensor for monitoring cardiac performance, blood flow, blood metabolites, antigen, antibody, viruses, hormones, neurotransmitter, pH, free radical, carbohydrate, proteins, or small molecules such that physiological conditions of the individual are determined;

a data analyzer or a set of computer readable instructions for providing immediate actionable physiologic feedback; and

a user interface integrated to a network for accessing or for reporting one or more diagnoses or therapy recommendation based on the physiological condition of the patient to a medical professional.

50. An apparatus for monitoring a patient'"'"'s metabolic profile comprising of:

a non-implantable or implantable sensor comprising of biological microelectromechanical sensor (MEMS) chip or detection or transducer device that may be implemented or computer-modeled for operation in silicon, silica, glass, polymer or other substrate or instrumentation cavity, beam, surface, channel, encapsulated molecules, membrane, quantum dot or nanocrystal, matrix or array for single or multi-channel independent signal detection in two or three dimensions in vitro or in vivo for monitoring blood flow, blood metabolites, antigen, antibody, viruses, hormones, neurotransmitter, pH, free radical, carbohydrate, proteins, or small molecules;

a processor for providing immediate actionable physiologic feedback; and

a user interface integrated to a network for accessing or for generating report such that one or more diagnosis or therapy recommendations can be made based on the condition of the patient.

51. An apparatus of claim 50 wherein:

a non-implantable or implantable sensor comprises of a nanocrystal e.g., CdS, CdSe, CdTe, ZnSe, or other colloidal group II-VI semiconductor.

52. An apparatus for monitoring cardiac and hemodynamic effect on the patient, comprising of:

a non-implantable or implantable biosensor for monitoring cardiac performance, blood flow, pulse, myocardial wall contractility, arterial blood pressure, or oxygen content in right atrium of a patient to assess circulation to organ after transplant or reconstructive surgery;

a processor for analyzing and transmitting monitoring blood flow, pulse, myocardial wall contractility, arterial blood pressure, or oxygen content in right atrium data in a computer readable set of instructions; and

a user interface integrated to a network for accessing the cardiac and hemodynamic performance from a database coupled to the processor, and making therapy recommendations to a medical professional.

53. A drug discovery system comprising:

a high-throughput capability to generate crystals of a target protein;

a library of protein molecular fragments;

one or more computational design methods; and

one or more iterative synthetic chemistry;

wherein the computational design methods and the iterative synthetic chemistry optimize the protein molecular fragments into one or more drug-like lead compounds.

54. The system of claim 53, wherein the library of protein molecular fragments is further evaluated by utilizing crystallographic analysis.

55. The system of claim 53, wherein the computational design methods further comprises:

computational chemistry, wherein the computational chemistry uses algorithmic tools to facilitate chemical analyses; and

computational biology, wherein the computational biology uses algorithmic tools to facilitate biological analyses.

56. A method for drug discovery, the method comprising:

generating crystals of a target protein via a high-throughput capability;

maintaining a library of protein molecular fragments;

developing one or more computational design methods; and

developing one or more iterative synthetic chemistry;

wherein the computational design methods and the iterative synthetic chemistry optimize the protein molecular fragments into drug-like lead compounds.

57. The method of claim 56 further comprising:

utilizing crystallographic analysis to evaluate the protein molecular fragments in the library.

58. The method of claim 56 further comprising:

adopting computational chemistry, wherein the computational chemistry uses algorithmic tools to facilitate chemical analyses; and

adopting computational biology, wherein the computational biology uses algorithmic tools to facilitate biological analyses.

59. A method for making therapy recommendations comprising:

receiving information of prescription or dosage of a patient;

genotyping single nucleotide polymorphisms of the patient based on prescription or dosage;

storing information in a database such that patient records comprising reports indicating diet, present medications, family history of disease, or environmental or immunological factors is accessible using an on-screen search of a patient record; and

reporting therapy recommendations to a patient'"'"'s healthcare provider.

60. A method for analysis and interpretation of pharmacokinetic data comprising:

determining one or more interaction factors between a potential drug molecule and a biological molecule wherein the interaction factors comprise sterics, charge, or dipole forces;

predicting pharmacokinetic interactions between the potential drug molecule and the biological molecule;

statistically assessing the pharmacokinetic data; and

pharmacokinetic modeling for proving the therapeutic value of the drug molecule as a new pharmaceutical or generic drug.

61. A method of centralized comprehensive data-management comprising:

electronic remote data capture and scanning and indexing through;

data validation and review to promote data integrity through coordination with data managers, clinical personnel, and programmers; and

use of data to provide therapy, diagnosis, or other automated feedback.

62. The method of claim 61 further comprising reconfiguring a sensor.

63. A method comprising:

sensing one or more biological molecules wherein the biological molecules comprise organic or inorganic substances, cells, tissue, nutrients, chemicals, intracellular materials, extra-cellular materials, charged ions, pharmaceuticals, or molecular materials affecting host physiology and wherein the biological molecules may be accessed from a group comprising of plasma, serum, urine, cerebrospinal fluid, tissue, xenograft and cell lines;

screening one or more biomarkers by mass spectrometry-based proteomics and metabolomics; and

identifying useful biological markers to create a better understanding of disease and therapeutic response.

64. A method for identification of relevant biomolecules comprising:

tandem mass spectrometry to establish one or more structural characteristics of a peptide or protein biomolecule; and

identification of one or more post-translational modifications including unusual proteolytic processing and mutations in proteins with a label-free method that provides differential quantification and molecular identification.

65. A computerized method comprising:

profiling one or more chemical compounds;

identifying the said one or more compounds as therapeutic targets;

prioritizing the said therapeutic targets;

implementing data-based techniques wherein the data-based techniques comprise decision theory, pattern recognition, or Bayesian analysis or knowledge-based techniques wherein the knowledge-based techniques comprise mathematical modeling, simulation or symbolic reasoning;

analyzing one or more mechanisms of toxicity of the said one or more compounds; and

generating a report about toxicity and safety assessment of the one or more compounds.

66. A method for integrated biosensor and simulation system comprising:

obtaining biological data from one or more biosensors;

identifying and prioritizing one or more biomarkers via analysis of gene expression arrays, proteomics, or genotyping data;

correlating one or more molecular events to higher-order cellular and disease processes; and

generating a report showing relevance to organismal physiology and pathophysiology.

67. The method of claim 66 further comprising analyzing one or more differences in one or more mechanisms of disease between one or more biological hosts or patients.

68. The method of claim 66 further comprising analyzing the effect of time and dose on a biological host.

69. A computerized method for evaluating toxicity and safety comprising:

correlating one or more gene expression data to one or more clinical pathologies;

identifying one or more mechanisms of toxicity of one or more chemical compounds;

analyzing pharmacological response, mechanism of action, or mechanism of toxicity; and

generating a report about biological responses in the liver, kidney, or heart of a biological host to the one or more chemical compounds, thereby evaluating their toxicity or safety.

70. The method of claim 69 further comprising identifying one or more relevant biomarker candidates.

71. The method of claim 69 further comprising analyzing metabolomics data.

72. A network system based molecular phenotype diagnosis station comprising:

a multichannel assay block wherein the multichannel assay block generates a fingerprint of a molecular phenotype from a host for one or more biomarkers selected from a group of lipids, proteins, carbohydrates and nucleic acids;

an indicator; and

a processor wherein the processor compares the molecular phenotype of the host to a database and generates a sub-phenotype associated with a disease of a host and causes a display to indicate a molecular phenotype and a sub-phenotype associated with a disease of a host.

73. The station of claim 72 further comprising an interface for the processor to communicate with the multichannel assay block.

74. The station of claim 72 further comprising a memory for storing the molecular phenotype or sub-phenotype associated with the disease of the host.

75. In an network system comprising a processor, a multichannel assay block, and a indicator, a diagnosis method comprising:

submitting a biological sample from a host to the multichannel assay block wherein the biological sample is selected from a group of lipids, proteins, carbohydrates and nucleic acids;

analyzing the submitted sample in an assay to generate an assay result; and

converting the assay result into a molecular phenotype or sub-phenotype for a disease of the host.

76. The method of claim 75 further comprising:

saving one or more assay results or molecular phenotypes or sub-phenotypes in a memory.

77. The method of claim 75 further comprising:

displaying one or more assay results or molecular phenotypes or sub-phenotypes in an indicator.

78. The method of claim 75 further comprising:

comparing a molecular phenotype of a host to one or more molecular phenotypes of one or more healthy hosts to determine a disease sub-phenotype associated with a host.

79. The method of claim 75 further comprising:

comparing a molecular phenotype of a host before and after the host is associated with a disease to determine a disease sub-phenotype associated with a pathophysiology condition.

80. The method of claim 75 further comprising:

comparing a molecular phenotype of a host before and after medication to determine one or more target disease sub phenotypes of the medication associated with the host.

81. A network based molecular phenotype diagnosis system comprising:

a processor;

a multichannel assay block;

a memory; and

an indicator

wherein the processor accepts data from a multichannel assay block, converts the data from the multichannel assay block to one or more fingerprint molecular phenotypes, saves the fingerprint molecular phenotypes in memory, accesses data from memory and compares with each other and generates a molecular sub-phenotype as the diagnosis, saves the diagnosis in the memory and sends to display on the indicator as one or more molecular phenotype associated with the disease of the host.

82. The system of claim 81 wherein the processor compares a molecular phenotype of a host to one or more molecular phenotypes of one or more healthy hosts to determine a disease sub-phenotype associated with the host.

83. The system of claim 81 wherein the processor compares a molecular phenotype of a host before and after the host is associated with a disease to determine a disease sub-phenotype associated with a pathophysiology condition.

84. The system of claim 81 wherein the processor compares a molecular phenotype of a host before and after medication to determine one or more target disease sub-phenotypes of the medication associated with the host.

85. A method for genotyping comprising:

applying PCR to amplify target DNA;

extending a primer used in PCR to detect sequence differences at the single nucleotide level; and

performing SELDI MS analysis of the primer extension products to detect one or more disease markers.

86. The method of claim 85 further comprising using SNP array hybridization or single-strand conformation polymorphism to analyze loss of heterozygosity.

87. The method of claim 85 further comprising measuring and mapping one or more DNA copy number aberrations.

88. A method for quantitative gene expression comprising:

reverse transcribing RNA to complementary DNA;

applying real-time PCR to amplify the complementary DNA;

performing single base primer extension; and

performing SELDI MS analysis of the primer extension products to identify one or more disease biomarkers.

89. The method of claim 88 further comprising utilizing a protein sensor to identify a biomarker.

90. The method of claim 88 further comprising using SNP array hybridization or single-strand conformation polymorphism to analyze loss of heterozygosity.

91. The method of claim 88 further comprising utilizing a DNA sensor to detect a disease marker.

92. The method of claim 88 further comprising measuring and mapping one or more DNA copy number aberrations.

93. A method for quantitative DNA methylation comprising:

applying bisulfite treatment to target DNA;

applying PCR to amplify treated target DNA;

performing RNA transcription and base specific RNA cleavage; and

performing SELDI MS analysis on cleavage products to identify one or more disease biomarkers.

94. A system for quantitative and qualitative applications for genetic research wherein:

SELDI ProteinChip interface and TOF tandem mass spectrometer provide automatic data acquisition; and

software modules provide automatic quantitative and qualitative analysis.

95. A sensor and an analysis method comprising:

implanting or attaching one or more biosensors to a biological host;

sensing native or foreign substances in the host wherein the substances comprise small molecules, metabolites, intermediates, toxins, pharmaceuticals, vitamins, minerals, or other organic or inorganic chemicals;

communicating electrical, electronic, or electromagnetic signals via a network server to a systems biology platform wherein the systems biology platform comprises computer equipment, software programs, or reconfigurable firmware or emulation logic devices to verify, model, simulate, or analyze stored or raw data using computational biology or other analysis software or hardware tools;

generating a report of the effectiveness or side effects of one or more therapies in the host; and

administering one or more therapies to the host.

96. The method of claim 95 further comprising detecting one or more of a group comprising oxygen concentration, blood pressure, pH, epigenetic changes in DNA, primary, secondary, tertiary, or quaternary structures of amino acid-based structures, and abnormalities in folding in chromosomes.

97. A sensor and analysis method comprising:

inserting a biosensor in a model system;

sensing native or foreign substances in the model system wherein the substances comprise small molecules, metabolites, intermediates, toxins, pharmaceuticals, vitamins, minerals, or other organic or inorganic chemicals;

communicating electrical, electronic, or electromagnetic signals via a network server to a systems biology platform wherein the systems biology platform comprises computer equipment, software programs, or reconfigurable firmware or emulation logic devices to verify, model, simulate, or analyze stored or raw data using computational biology or other analysis software or hardware tools; and

generating a report of the effectiveness or side effects of one or more therapies in the model system.

98. DNA unit for molecular diagnostics comprising:

a DNA sensor;

a DNA therapeutic manufacture; and

a sensor manufacture;

wherein a systems-biology platform causes the DNA sensor to sense an organism in-vivo in response to the DNA therapeutic or sensor manufacture dispensing therapy material in the organism, the systems-biology platform monitoring the DNA sensor according to one or more systems-biology models to diagnose automatically the organism and cause the DNA therapeutic or sensor manufacture to generate a therapy manufacture.

99. Unit of claim 98 wherein the DNA sensor detects an RNAi mechanism in the organism, such that the systems-biology platform causes generation of the therapy manufacture including small interfering RNA (siRNA).

100. Unit of claim 98 wherein after liver organ transplant in the organism, the DNA sensor senses such organ, such that the systems-biology modeling automates the systems-biology platform to diagnose hepato cellular carcinoma or chronic liver disease.

101. Unit of claim 98 wherein the DNA sensor comprises a bioMEMs sensor chip including one or more quantum dot or nanocrystal for detecting in the organism mutation of human immune deficiency virus.

102. Unit of claim 98 wherein the DNA sensor senses one or more genetic disorder of the organism comprising point mutations, mutation within non-coding sequences, deletions and insertions, trinucleotide repeat mutations, autosomal mutations, gain of function mutations, loss of function mutations, mutations in mitochondrial genes, enzyme defects, defects in receptors and transport systems, alterations in structure, function or quantity of non-enzyme proteins, defects in receptor proteins, defects in protooncogenes or tumor-suppressor genes, aneuploidy, unbalanced autosome, sex chromosome abnormality, fragile X syndrome, ring chromosome, chromosome inversion, isochromosome formation, translocation, or abnormal gene products.

103. In a DNA unit including a DNA sensor, a DNA therapeutic manufacture, and a sensor manufacture, a method for molecular diagnostics comprising steps:

sensing by the DNA sensor an organism in-vivo in response to the DNA therapeutic or sensor manufacture dispensing therapy material in the organism;

monitoring by a systems-biology platform the DNA sensor according to one or more systems-biology models to diagnose automatically the organism and thereby cause the DNA therapeutic or sensor manufacture to generate a therapy manufacture.

104. Method of claim 103 wherein the DNA sensor detects an RNAi mechanism in the organism, such that the systems-biology platform causes generation of the therapy manufacture including small interfering RNA (siRNA).

105. Method of claim 103 wherein after liver organ transplant in the organism, the DNA sensor senses such organ, such that the systems-biology modeling automates the systems-biology platform to diagnose hepato cellular carcinoma or chronic liver disease.

106. Method of claim 103 wherein the DNA sensor comprises a bioMEMs sensor chip including one or more quantum dot or nanocrystal for detecting in the organism mutation of human immune deficiency virus.

107. Method of claim 103 wherein the DNA sensor senses one or more genetic disorder of the organism comprising point mutations, mutation within non-coding sequences, deletions and insertions, trinucleotide repeat mutations, autosomal mutations, gain of function mutations, loss of function mutations, mutations in mitochondrial genes, enzyme defects, defects in receptors and transport systems, alterations in structure, function or quantity of non-enzyme proteins, defects in receptor proteins, defects in protooncogenes or tumor-suppressor genes, aneuploidy, unbalanced autosome, sex chromosome abnormality, fragile X syndrome, ring chromosome, chromosome inversion, isochromosome formation, translocation, or abnormal gene products.