Methods and systems for analyzing complex biological systems

• US 20040002842A1
• Filed: 11/20/2002
• Published: 01/01/2004
• Est. Priority Date: 11/21/2001
• Status: Abandoned Application

• Current Assignee: Icoria, Inc. (Abbvie Incorporated)
• Original Assignee: Icoria, Inc. (Abbvie Incorporated)
• Inventors: Davis, Keith, Lawrence, Matthew, Coffin, Marie, Hamilton, Carol, Boyes, Douglas, Allen, Keith, Liddell, Craig M., Beecher, Christopher, Shuster, Jeffrey, Woessner, Jeffrey, Hoffman, Neil, Zhang, Weiwen, Hurban, Patrick
• Application Number:
  US10/300,204
• Publication Number:
  US 20040002842A1
• Time in Patent Office: Days
• US Class Current: 703/11
• CPC Class Codes: G01N 30/72 Mass spectrometers mass spectrometers per se H01J49/00
• CPC Class Codes: G01N 30/8655 Details of data formats
• CPC Class Codes: G01N 30/8658 Optimising operation parameters
• CPC Class Codes: G01N 30/8662 Expert systems; optimising a large number of parameters
• CPC Class Codes: G01N 30/8679 Target compound analysis, i.e. whereby a limited number of peaks is analysed
• CPC Class Codes: G01N 30/8693 Models, e.g. prediction of retention times, method development and validation
• 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 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 50/20 Heterogeneous data integration
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

The present invention provides methods and systems for organizing complex and disparate data. More specifically, the present invention provides methods and systems for organizing complex and disparate data into coherent data sets. Coherent data sets resulting from the methods and systems of the present invention serve as models for biological systems. Methods and systems for integrating data and creating coherent data sets are useful for numerous biological applications, such as, for example, determining gene function, identifying and validating drug and pesticide targets, identifying and validating drug and pesticide candidate compounds, profiling drug and pesticide compounds, producing a compilation of health or wellness profiles, determining compound site(s) of action, identifying unknown samples, and numerous other applications in the agricultural, pharmaceutical, forensic, and biotechnology industries.

Claims

1. A method for creating a coherent data set for modeling biological systems, comprising:

a) entering a unique identifier of at least one biological sample into a computer tracking system;

b) storing data comprising metabolite data in said computer tracking system, wherein said data comprising metabolite data are linked to said unique identifier;

c) converting said linked data to a numeric format; and

d) converting said numeric format data to a common unit system, wherein said common unit system data are a coherent data set which serves as a model for a biological system.

2. The method according to claim 1, wherein said metabolite data comprise data from at least three processes.

3. The method according to claim 2, wherein said three processes are selected from the group consisting of liquid chromatography (LC), high-pressure liquid chromatography (HPLC), mass spectroscopy (MS), hyphenated mass spectroscopy (MS-MS), gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), Fourier transform-ion cyclotron resonance-mass spectrometer (FT-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transform InfraRed (FT-IR), and inductively coupled plasma mass spectrometry (ICP-MS).

4. The method according to claim 2, wherein two of said three processes are LC-MS and GC-MS.

5. The method according to claim 2, wherein said three processes are LC-MS, GC-MS, and FT-MS.

6. The method according to claim 2, wherein said three processes are LC-MS, GC-MS, and NMR.

7. The method according to claim 2, wherein said three processes are LC-MS, GC-MS, and ICP-MS.

8. The method according to claim 1, wherein the computer tracking system is a Laboratory Information Management System (LIMS).

9. The method according to claim 1, wherein the biological sample is selected from the group consisting of animalia, plantae, protista, monera, and fungi.

10. The method according to claim 9, wherein the biological sample is selected from the group consisting of human primate, non-human primate, canine, feline, equine, bovine, porcine, rabbit, rodent, liver tissue, liver spheroids, primary hepatocytes, liver cell lines, and HepG2 cells.

11. The method according to claim 9, wherein the biological sample is selected from the group consisting of Arabidopsis, corn, wheat, barley, rye, legumes, mint, tobacco, tomatoes, rice, spinach, and peas.

12. The method according to claim 9, wherein the biological sample is selected from the group consisting of Magnaporthe, Candida, Mycosphaerella, Botrytis, Saccharomyces, Aspergillus, Puccinia, Erysiphe, Ustilago, Fursarium, Phytophthora and Penicillium.

13. The method according to claim 1, wherein said coherent data set is a model for a particular disease or disease stage.

14. The method according to claim 1, wherein said coherent data set is a model for the efficacy of a therapeutic program or exposure to a particular chemical.

15. The method according to claim 1, wherein the biological sample is selected from the group consisting of a healthy organism, a diseased organism, a drug-treated organism, and a genetically altered organism.

16. The method according to claim 1, wherein the biological sample is from an organism having received an environmental or chemical insult.

17. The method according to claim 1, wherein the common unit system is deviation from a standard.

18. A method for creating a coherent data set for modeling biological systems, comprising:

a) entering a unique identifier of at least one biological sample into a computer tracking system;

b) storing data comprising metabolite data in said computer tracking system, wherein said data comprising metabolite data are linked to said unique identifier;

c) converting said linked data to a numeric format;

d) transforming said numeric format data into a Gaussian distribution;

e) converting said Gaussian distribution data to a common unit system; and

f) reducing the dimensionality of said common unit system data, wherein said dimensionally reduced common unit system data are a coherent data set which serves as a model for a biological system.

19. The method according to claim 18, wherein said metabolite data comprise data from at least three processes.

20. The method according to claim 19, wherein said three processes are selected from the group consisting of liquid chromatography (LC), high-pressure liquid chromatography (HPLC), mass spectroscopy (MS), hyphenated mass spectroscopy (MS-MS), gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), Fourier transform-ion cyclotron resonance-mass spectrometer (FT-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transform InfraRed (FT-IR), and inductively coupled plasma mass spectrometry (ICP-MS).

21. The method according to claim 19, wherein two of said three processes are LC-MS and GC-MS.

22. The method according to claim 19, wherein said three processes are LC-MS, GC-MS, and FT-MS.

23. The method according to claim 19, wherein said three processes are LC-MS, GC-MS, and NMR.

24. The method according to claim 19, wherein said three processes are LC-MS, GC-MS, and ICP-MS.

25. The method according to claim 18, wherein the computer tracking system is a Laboratory Information Management System (LIMS).

26. The method according to claim 18, wherein the biological sample is selected from the group consisting of animalia, plantae, protista, monera, and fungi.

27. The method according to claim 26, wherein the biological sample is selected from the group consisting of human primate, non-human primate, canine, feline, equine, bovine, porcine, rabbit, rodent, liver tissue, liver spheroids, primary hepatocytes, liver cell lines, and HepG2 cells.

28. The method according to claim 26, wherein the biological sample is selected from the group consisting of Arabidopsis, corn, wheat, barley, rye, legumes, mint, tobacco, tomatoes, rice, spinach, and peas.

29. The method according to claim 26, wherein the biological sample is selected from the group consisting of Magnaporthe, Candida, Mycosphaerella, Botrytis, Saccharomyces, Aspergillus, Puccinia, Erysiphe, Ustilago, Fursarium, Phytophthora and Penicillium.

30. The method according to claim 18, wherein said coherent data set is a model for a particular disease or disease stage.

31. The method according to claim 18, wherein said coherent data set is a model for the efficacy of a therapeutic program or exposure to a particular chemical.

32. The method according to claim 18, wherein the biological sample is selected from the group consisting of a healthy organism, a diseased organism, a drug-treated organism, and a genetically altered organism.

33. The method according to claim 18, wherein the biological sample is from an organism having received an environmental or chemical insult.

34. The method according to claim 18, wherein the common unit system is deviation from a standard.

35. The method according to claim 18, wherein said reduction of dimensionality is achieved by applying one of the group consisting of principle components analysis, correlation analysis, regression analysis, and pre-clustering of said common unit system data.

36. The method according to claim 18, wherein said transformation into a Gaussian distribution occurs by conversion of said numeric format data to a logarithmic scale.

37. A method for creating a coherent data set for modeling biological systems, comprising:

a) entering a unique identifier of at least one biological sample into a computer tracking system;

b) storing data comprising metabolite data in said computer tracking system, wherein said data comprising metabolite data are linked to said unique identifier;

c) converting said linked data to a numeric format;

d) transforming said numeric format data into a Gaussian distribution; and

e) converting said Gaussian distribution data to a common unit system, wherein said common unit system data are a coherent data set which serves as a model for a biological system.

38. The method according to claim 37, wherein said metabolite data comprise data from at least three processes.

39. The method according to claim 38, wherein said three processes are selected from the group consisting of liquid chromatography (LC), high-pressure liquid chromatography (HPLC), mass spectroscopy (MS), hyphenated mass spectroscopy (MS-MS), gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), Fourier transform-ion cyclotron resonance-mass spectrometer (FT-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transform InfraRed (FT-IR), and inductively coupled plasma mass spectrometry (ICP-MS).

40. The method according to claim 38, wherein two of said three processes are LC-MS and GC-MS.

41. The method according to claim 38, wherein said three processes are LC-MS, GC-MS, and FT-MS.

42. The method according to claim 38, wherein said three processes are LC-MS, GC-MS, and NMR.

43. The method according to claim 38, wherein said three processes are LC-MS, GC-MS, and ICP-MS.

44. The method according to claim 37, wherein the computer tracking system is a Laboratory Information Management System (LIMS).

45. The method according to claim 37, wherein the biological sample is selected from the group consisting of animalia, plantae, protista, monera, and fungi.

46. The method according to claim 45, wherein the biological sample is selected from the group consisting of human primate, non-human primate, canine, feline, equine, bovine, porcine, rabbit, rodent, liver tissue, liver spheroids, primary hepatocytes, liver cell lines, and HepG2 cells.

47. The method according to claim 45, wherein the biological sample is selected from the group consisting of Arabidopsis, corn, wheat, barley, rye, legumes, mint, tobacco, tomatoes, rice, spinach, and peas.

48. The method according to claim 45, wherein the biological sample is selected from the group consisting of Magnaporthe, Candida, Mycosphaerella, Botrytis, Saccharomyces, Aspergillus, Puccinia, Erysiphe, Ustilago, Fursarium, Phytophthora and Penicillium.

49. The method according to claim 37, wherein said coherent data set is a model for a particular disease or disease stage.

50. The method according to claim 37, wherein said coherent data set is a model for the efficacy of a therapeutic program or exposure to a particular chemical.

51. The method according to claim 37, wherein the biological sample is selected from the group consisting of a healthy organism, a diseased organism, a drug-treated organism, and a genetically altered organism.

52. The method according to claim 37, wherein the biological sample is from an organism having received an environmental or chemical insult.

53. The method according to claim 37, wherein the common unit system is deviation from a standard.

54. The method according to claim 37, wherein said transformation into a Gaussian distribution occurs by conversion of said numeric format data to a logarithmic scale.

55. A method for creating a coherent data set for modeling biological systems, comprising:

a) entering a unique identifier of at least one biological sample into a computer tracking system;

b) storing data comprising metabolite data in said computer tracking system, wherein said data comprising metabolite data are linked to said unique identifier;

c) converting said linked data to a numeric format;

d) converting said numeric format data to a common unit system; and

e) reducing the dimensionality of said common unit system data, wherein said dimensionally reduced common unit system data are a coherent data set which serves as a model for a biological system.

56. The method according to claim 55, wherein said metabolite data comprise data from at least three processes.

57. The method according to claim 56, wherein said three processes are selected from the group consisting of liquid chromatography (LC), high-pressure liquid chromatography (HPLC), mass spectroscopy (MS), hyphenated mass spectroscopy (MS-MS), gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), Fourier transform-ion cyclotron resonance-mass spectrometer (FT-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transform InfraRed (FT-IR), and inductively coupled plasma mass spectrometry (ICP-MS).

58. The method according to claim 56, wherein two of said three processes are LC-MS and GC-MS.

59. The method according to claim 56, wherein said three processes are LC-MS, GC-MS, and FT-MS.

60. The method according to claim 56, wherein said three processes are LC-MS, GC-MS, and NMR.

61. The method according to claim 56, wherein said three processes are LC-MS, GC-MS, and ICP-MS.

62. The method according to claim 55, wherein the computer tracking system is a Laboratory Information Management System (LIMS).

63. The method according to claim 55, wherein the biological sample is selected from the group consisting of animalia, plantae, protista, monera, and fungi.

64. The method according to claim 63, wherein the biological sample is selected from the group consisting of human primate, non-human primate, canine, feline, equine, bovine, porcine, rabbit, rodent, liver tissue, liver spheroids, primary hepatocytes, liver cell lines, and HepG2 cells.

65. The method according to claim 63, wherein the biological sample is selected from the group consisting of Arabidopsis, corn, wheat, barley, rye, legumes, mint, tobacco, tomatoes, rice, spinach, and peas.

66. The method according to claim 63, wherein the biological sample is selected from the group consisting of Magnaporthe, Candida, Mycosphaerella, Botrytis, Saccharomyces, Aspergillus, Puccinia, Erysiphe, Ustilago, Fursarium, Phytophthora and Penicillium.

67. The method according to claim 55, wherein said coherent data set is a model for a particular disease or disease stage.

68. The method according to claim 55, wherein said coherent data set is a model for the efficacy of a therapeutic program or exposure to a particular chemical.

69. The method according to claim 55, wherein the biological sample is selected from the group consisting of a healthy organism, a diseased organism, a drug-treated organism, and a genetically altered organism.

70. The method according to claim 55, wherein the biological sample is from an organism having received an environmental or chemical insult.

71. The method according to claim 55, wherein the common unit system is deviation from a standard.

72. The method according to claim 55, wherein said reduction of dimensionality is achieved by applying one of the group consisting of principle components analysis, correlation analysis, regression analysis, and pre-clustering of said common unit system data.

73. A method for creating a coherent data set for modeling biological systems, comprising:

a) entering a unique identifier of at least one biological sample into a computer tracking system;

b) storing in said computer tracking system disparate data, wherein said disparate data comprise metabolite data and other data, and said disparate data are linked to said unique identifier;

c) converting said linked disparate data to a numeric format; and

d) converting said numeric format data to a common unit system, wherein said common unit system data are a coherent data set which serves as a model for a biological system.

74. The method according to claim 73, wherein said metabolite data comprise data from at least three processes.

75. The method according to claim 74, wherein said three processes are selected from the group consisting of liquid chromatography (LC), high-pressure liquid chromatography (HPLC), mass spectroscopy (MS), hyphenated mass spectroscopy (MS-MS), gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), Fourier transform-ion cyclotron resonance-mass spectrometer (FT-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transform InfraRed (FT-IR), and inductively coupled plasma mass spectrometry (ICP-MS).

76. The method according to claim 74, wherein two of said three processes are LC-MS and GC-MS.

77. The method according to claim 74, wherein said three processes are LC-MS, GC-MS, and FT-MS.

78. The method according to claim 74, wherein said three processes are LC-MS, GC-MS, and NMR.

79. The method according to claim 74, wherein said three processes are LC-MS, GC-MS, and ICP-MS.

80. The method according to claim 73, wherein said other data are selected from the group consisting of RNA data, DNA data, protein data, and phenotypic data.

81. The method according to claim 73, wherein the computer tracking system is a Laboratory Information Management System (LIMS).

82. The method according to claim 73, wherein the biological sample is selected from the group consisting of animalia, plantae, protista, monera, and fungi.

83. The method according to claim 82, wherein the biological sample is selected from the group consisting of human primate, non-human primate, canine, feline, equine, bovine, porcine, rabbit, rodent, liver tissue, liver spheroids, primary hepatocytes, liver cell lines, and HepG2 cells.

84. The method according to claim 82, wherein the biological sample is selected from the group consisting of Arabidopsis, corn, wheat, barley, rye, legumes, mint, tobacco, tomatoes, rice, spinach, and peas.

85. The method according to claim 82, wherein the biological sample is selected from the group consisting of Magnaporthe, Candida, Mycosphaerella, Botrytis, Saccharomyces, Aspergillus, Puccinia, Erysiphe, Ustilago, Fursarium, Phytophthora and Penicillium.

86. The method according to claim 73, wherein said coherent data set is a model for a particular disease or disease stage.

87. The method according to claim 73, wherein said coherent data set is a model for the efficacy of a therapeutic program or exposure to a particular chemical.

88. The method according to claim 73, wherein the biological sample is selected from the group consisting of a healthy organism, a diseased organism, a drug-treated organism, and a genetically altered organism.

89. The method according to claim 73, wherein the biological sample is from an organism having received an environmental or chemical insult.

90. The method according to claim 73, wherein the common unit system is deviation from a standard.

91. A method for creating a coherent data set for modeling biological systems, comprising:

a) entering a unique identifier of at least one biological sample into a computer tracking system;

b) storing in said computer tracking system disparate data, wherein said disparate data comprise metabolite data and other data, and said disparate data are linked to said unique identifier;

c) converting said linked disparate data to a numeric format;

d) transforming said numeric format data into a Gaussian distribution;

e) converting said Gaussian distribution data to a common unit system; and

f) reducing the dimensionality of said common unit system data, wherein said dimensionally reduced common unit system data are a coherent data set which serves as a model for a biological system.

92. The method according to claim 91, wherein said metabolite data comprise data from at least three processes.

93. The method according to claim 92, wherein said three processes are selected from the group consisting of liquid chromatography (LC), high-pressure liquid chromatography (HPLC), mass spectroscopy (MS), hyphenated mass spectroscopy (MS-MS), gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), Fourier transform-ion cyclotron resonance-mass spectrometer (FT-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transform InfraRed (FT-IR), and inductively coupled plasma mass spectrometry (ICP-MS).

94. The method according to claim 92, wherein two of said three processes are LC-MS and GC-MS.

95. The method according to claim 92, wherein said three processes are LC-MS, GC-MS, and FT-MS.

96. The method according to claim 92, wherein said three processes are LC-MS, GC-MS, and NMR.

97. The method according to claim 92, wherein said three processes are LC-MS, GC-MS, and ICP-MS.

98. The method according to claim 91, wherein said other data are selected from the group consisting of RNA data, DNA data, protein data, and phenotypic data.

99. The method according to claim 91, wherein the computer tracking system is a Laboratory Information Management System (LIMS).

100. The method according to claim 91, wherein the biological sample is selected from the group consisting of animalia, plantae, protista, monera, and fungi.

101. The method according to claim 100, wherein the biological sample is selected from the group consisting of human primate, non-human primate, canine, feline, equine, bovine, porcine, rabbit, rodent, liver tissue, liver spheroids, primary hepatocytes, liver cell lines, and HepG2 cells.

102. The method according to claim 100, wherein the biological sample is selected from the group consisting of Arabidopsis, corn, wheat, barley, rye, legumes, mint, tobacco, tomatoes, rice, spinach, and peas.

103. The method according to claim 100, wherein the biological sample is selected from the group consisting of Magnaporthe, Candida, Mycosphaerella, Botrytis, Saccharomyces, Aspergillus, Puccinia, Erysiphe, Ustilago, Fursarium, Phytophthora and Penicillium.

104. The method according to claim 91, wherein said coherent data set is a model for a particular disease or disease stage.

105. The method according to claim 91, wherein said coherent data set is a model for the efficacy of a therapeutic program or exposure to a particular chemical.

106. The method according to claim 91, wherein the biological sample is selected from the group consisting of a healthy organism, a diseased organism, a drug-treated organism, and a genetically altered organism.

107. The method according to claim 91, wherein the biological sample is from an organism having received an environmental or chemical insult.

108. The method according to claim 91, wherein the common unit system is deviation from a standard.

109. The method according to claim 91, wherein said reduction of dimensionality is achieved by applying one of the group consisting of principle components analysis, correlation analysis, regression analysis, and pre-clustering of said common unit system data.

110. The method according to claim 91, wherein said transformation into a Gaussian distribution occurs by conversion of said numeric format data to a logarithmic scale.

111. A method for creating a coherent data set for modeling biological systems, comprising:

a) entering a unique identifier of at least one biological sample into a computer tracking system;

b) storing in said computer tracking system disparate data, wherein said disparate data comprise metabolite data and other data, and said disparate data are linked to said unique identifier;

c) converting said linked disparate data to a numeric format;

d) converting said numeric format data to a common unit system; and

e) reducing the dimensionality of said common unit system data, wherein said dimensionally reduced common unit system data are a coherent data set which serves as a model for a biological system.

112. The method according to claim 111, wherein said metabolite data comprise data from at least three processes.

113. The method according to claim 112, wherein said three processes are selected from the group consisting of liquid chromatography (LC), high-pressure liquid chromatography (HPLC), mass spectroscopy (MS), hyphenated mass spectroscopy (MS-MS), gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), Fourier transform-ion cyclotron resonance-mass spectrometer (FT-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transform InfraRed (FT-IR), and inductively coupled plasma mass spectrometry (ICP-MS).

114. The method according to claim 112, wherein two of said three processes are LC-MS and GC-MS.

115. The method according to claim 112, wherein said three processes are LC-MS, GC-MS, and FT-MS.

116. The method according to claim 112, wherein said three processes are LC-MS, GC-MS, and NMR.

117. The method according to claim 112, wherein said three processes are LC-MS, GC-MS, and ICP-MS.

118. The method according to claim 111, wherein said other data are selected from the group consisting of RNA data, DNA data, protein data, and phenotypic data.

119. The method according to claim 111, wherein the computer tracking system is a Laboratory Information Management System (LIMS).

120. The method according to claim 111, wherein the biological sample is selected from the group consisting of animalia, plantae, protista, monera, and fungi.

121. The method according to claim 120, wherein the biological sample is selected from the group consisting of human primate, non-human primate, canine, feline, equine, bovine, porcine, rabbit, rodent, liver tissue, liver spheroids, primary hepatocytes, liver cell lines, and HepG2 cells.

122. The method according to claim 120, wherein the biological sample is selected from the group consisting of Arabidopsis, corn, wheat, barley, rye, legumes, mint, tobacco, tomatoes, rice, spinach, and peas.

123. The method according to claim 120, wherein the biological sample is selected from the group consisting of Magnaporthe, Candida, Mycosphaerella, Botrytis, Saccharomyces, Aspergillus, Puccinia, Erysiphe, Ustilago, Fursarium, Phytophthora and Penicillium.

124. The method according to claim 111, wherein said coherent data set is a model for a particular disease or disease stage.

125. The method according to claim 111, wherein said coherent data set is a model for the efficacy of a therapeutic program or exposure to a particular chemical.

126. The method according to claim 111, wherein the biological sample is selected from the group consisting of a healthy organism, a diseased organism, a drug-treated organism, and a genetically altered organism.

127. The method according to claim 111, wherein the biological sample is from an organism having received an environmental or chemical insult.

128. The method according to claim 111, wherein the common unit system is deviation from a standard.

129. The method according to claim 111, wherein said reduction of dimensionality is achieved by applying one of the group consisting of principle components analysis, correlation analysis, regression analysis, and pre-clustering of said common unit system data.

130. A method for creating a coherent data set for modeling biological systems, comprising:

a) entering a unique identifier of at least one biological sample into a computer tracking system;

b) storing in said computer tracking system disparate data, wherein said disparate data comprise metabolite data and other data, and said disparate data are linked to said unique identifier;

c) converting said linked disparate data to a numeric format;

d) transforming said numeric format data into a Gaussian distribution; and

e) converting said Gaussian distribution data to a common unit system wherein said common unit system data are a coherent data set which serves as a model for a biological system.

131. The method according to claim 130, wherein said metabolite data comprise data from at least three processes.

132. The method according to claim 131, wherein said three processes are selected from the group consisting of liquid chromatography (LC), high-pressure liquid chromatography (HPLC), mass spectroscopy (MS), hyphenated mass spectroscopy (MS-MS), gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), Fourier transform-ion cyclotron resonance-mass spectrometer (FT-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transform InfraRed (FT-IR), and inductively coupled plasma mass spectrometry (ICP-MS).

133. The method according to claim 131, wherein two of said three processes are LC-MS and GC-MS.

134. The method according to claim 131, wherein said three processes are LC-MS, GC-MS, and FT-MS.

135. The method according to claim 131, wherein said three processes are LC-MS, GC-MS, and NMR.

136. The method according to claim 131, wherein said three processes are LC-MS, GC-MS, and ICP-MS.

137. The method according to claim 130, wherein said other data are selected from the group consisting of RNA data, DNA data, protein data, and phenotypic data.

138. The method according to claim 130, wherein the computer tracking system is a Laboratory Information Management System (LIMS).

139. The method according to claim 130, wherein the biological sample is selected from the group consisting of animalia, plantae, protista, monera, and fungi.

143. The method according to claim 130, wherein said coherent data set is a model for a particular disease or disease stage.

144. The method according to claim 130, wherein said coherent data set is a model for the efficacy of a therapeutic program or exposure to a particular chemical.

145. The method according to claim 130, wherein the biological sample is selected from the group consisting of a healthy organism, a diseased organism, a drug-treated organism, and a genetically altered organism.

146. The method according to claim 130, wherein the biological sample is from an organism having received an environmental or chemical insult.

147. The method according to claim 130, wherein the common unit system is deviation from a standard.

148. The method according to claim 130, wherein said transformation into a Gaussian distribution occurs by conversion of said numeric format data to a logarithmic scale.

140. The method according to claim 140, wherein the biological sample is selected from the group consisting of human primate, non-human primate, canine, feline, equine, bovine, porcine, rabbit, rodent, liver tissue, liver spheroids, primary hepatocytes, liver cell lines, and HepG2 cells.

141. The method according to claim 140, wherein the biological sample is selected from the group consisting of Arabidopsis, corn, wheat, barley, rye, legumes, mint, tobacco, tomatoes, rice, spinach, and peas.

142. The method according to claim 140, wherein the biological sample is selected from the group consisting of Magnaporthe, Candida, Mycosphaerella, Botrytis, Saccharomyces, Aspergillus, Puccinia, Erysiphe, Ustilago, Fursarium, Phytophthora and Penicillium.

149. A method for creating a coherent data set for modeling biological systems, comprising:

a) entering a unique identifier of at least one biological sample into a computer tracking system;

b) storing in said computer tracking system disparate data, wherein said disparate data comprise metabolite data and at least two other types of data, and said disparate data are linked to said unique identifier;

c) converting said linked disparate data to a numeric format; and

d) converting said numeric format data to a common unit system, wherein said common unit system data are a coherent data set which serves as a model for a biological system.

150. The method according to claim 149, wherein said metabolite data comprise data from at least three processes.

151. The method according to claim 150, wherein said three processes are selected from the group consisting of liquid chromatography (LC), high-pressure liquid chromatography (HPLC), mass spectroscopy (MS), hyphenated mass spectroscopy (MS-MS), gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), Fourier transform-ion cyclotron resonance-mass spectrometer (FT-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transform InfraRed (FT-IR), and inductively coupled plasma mass spectrometry (ICP-MS).

152. The method according to claim 150, wherein two of said three processes are LC-MS and GC-MS.

153. The method according to claim 150, wherein said three processes are LC-MS, GC-MS, and FT-MS.

154. The method according to claim 150, wherein said three processes are LC-MS, GC-MS, and NMR.

155. The method according to claim 150, wherein said three processes are LC-MS, GC-MS, and ICP-MS.

156. The method according to claim 149, wherein said two other types of data are selected from the group consisting of RNA data, DNA data, protein data, and phenotypic data.

157. The method according to claim 149, wherein the computer tracking system is a Laboratory Information Management System (LIMS).

158. The method according to claim 149, wherein the biological sample is selected from the group consisting of animalia, plantac, protista, monera, and fungi.

159. The method according to claim 158, wherein the biological sample is selected from the group consisting of human primate, non-human primate, canine, feline, equine, bovine, porcine, rabbit, rodent, liver tissue, liver spheroids, primary hepatocytes, liver cell lines, and HepG2 cells.

160. The method according to claim 158, wherein the biological sample is selected from the group consisting of Arabidopsis, corn, wheat, barley, rye, legumes, mint, tobacco, tomatoes, nice, spinach, and peas.

161. The method according to claim 158, wherein the biological sample is selected from the group consisting of Magnaporthe, Candida, Mycosphaerella, Botrytis, Saccharomyces, Aspergillus, Puccinia, Erysiphe, Ustilago, Fursarium, Phytophthora and Penicillium.

162. The method according to claim 149, wherein said coherent data set is a model for a particular disease or disease stage.

163. The method according to claim 149, wherein said coherent data set is a model for the efficacy of a therapeutic program or exposure to a particular chemical.

164. The method according to claim 149, wherein the biological sample is selected from the group consisting of a healthy organism, a diseased organism, a drug-treated organism, and a genetically altered organism.

165. The method according to claim 149, wherein the biological sample is from an organism having received an environmental or chemical insult.

166. The method according to claim 149, wherein the common unit system is deviation from a standard.

167. A method for creating a coherent data set for modeling biological systems, comprising:

a) entering a unique identifier of at least one biological sample into a computer tracking system;

b) storing in said computer tracking system disparate data, wherein said disparate data comprise metabolite data and at least two other types of data, and said disparate data are linked to said unique identifier;

c) converting said linked disparate data to a numeric format;

d) transforming said numeric format data into a Gaussian distribution;

e) converting said Gaussian distribution data to a common unit system; and

f) reducing the dimensionality of said common unit system data, wherein said dimensionally reduced common unit system data are a coherent data set which serves as a model for a biological system.

168. The method according to claim 167, wherein said metabolite data comprise data from at least three processes.

169. The method according to claim 168, wherein said three processes are selected from the group consisting of liquid chromatography (LC), high-pressure liquid chromatography (HPLC), mass spectroscopy (MS), hyphenated mass spectroscopy (MS-MS), gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), Fourier transform-ion cyclotron resonance-mass spectrometer (FT-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transform InfraRed (FT-IR), and inductively coupled plasma mass spectrometry (ICP-MS).

170. The method according to claim 168, wherein two of said three processes are LC-MS and GC-MS.

171. The method according to claim 168, wherein said three processes are LC-MS, GC-MS, and FT-MS.

172. The method according to claim 168, wherein said three processes are LC-MS, GC-MS, and NMR.

173. The method according to claim 168, wherein said three processes are LC-MS, GC-MS, and ICP-MS.

174. The method according to claim 167, wherein said two other types of data are selected from the group consisting of RNA data, DNA data, protein data, and phenotypic data.

175. The method according to claim 167, wherein the computer tracking system is a Laboratory Information Management System (LIMS).

176. The method according to claim 167, wherein the biological sample is selected from the group consisting of animalia, plantae, protista, monera, and fungi.

177. The method according to claim 176, wherein the biological sample is selected from the group consisting of human primate, non-human primate, canine, feline, equine, bovine, porcine, rabbit, rodent, liver tissue, liver spheroids, primary hepatocytes, liver cell lines, and HepG2 cells.

178. The method according to claim 176, wherein the biological sample is selected from the group consisting of Arabidopsis, corn, wheat, barley, rye, legumes, mint, tobacco, tomatoes, nice, spinach, and peas.

179. The method according to claim 176, wherein the biological sample is selected from the group consisting of Magnaporthe, Candida, Mycosphaerella, Botrytis, Saccharomyces, Aspergillus, Puccinia, Erysiphe, Ustilago, Fursarium, Phytophthora and Penicillium.

180. The method according to claim 167, wherein said coherent data set is a model for a particular disease or disease stage.

181. The method according to claim 167, wherein said coherent data set is a model for the efficacy of a therapeutic program or exposure to a particular chemical.

182. The method according to claim 167, wherein the biological sample is selected from the group consisting of a healthy organism, a diseased organism, a drug-treated organism, and a genetically altered organism.

183. The method according to claim 167, wherein the biological sample is from an organism having received an environmental or chemical insult.

184. The method according to claim 167, wherein the common unit system is deviation from a standard.

185. The method according to claim 167, wherein said reduction of dimensionality is achieved by applying one of the group consisting of principle components analysis, correlation analysis, regression analysis, and pre-clustering of said common unit system data.

186. The method according to claim 167, wherein said transformation into a Gaussian distribution occurs by conversion of said numeric format data to a logarithmic scale.

187. A method for creating a coherent data set for modeling biological systems, comprising:

a) entering a unique identifier of at least one biological sample into a computer tracking system;

b) storing in said computer tracking system disparate data, wherein said disparate data comprise metabolite data and at least two other types of data, and said disparate data are linked to said unique identifier;

c) converting said linked disparate data to a numeric format;

d) converting said numeric format data to a common unit system; and

e) reducing the dimensionality of said common unit system data, wherein said dimensionally reduced common unit system data are a coherent data set which serves as a model for a biological system.

188. The method according to claim 187, wherein said metabolite data comprise data from at least three processes.

189. The method according to claim 188, wherein said three processes are selected from the group consisting of liquid chromatography (LC), high-pressure liquid chromatography (HPLC), mass spectroscopy (MS), hyphenated mass spectroscopy (MS-MS), gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), Fourier transform-ion cyclotron resonance-mass spectrometer (FT-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transform InfraRed (FT-IR), and inductively coupled plasma mass spectrometry (ICP-MS).

190. The method according to claim 188, wherein two of said three processes are LC-MS and GC-MS.

191. The method according to claim 188, wherein said three processes are LC-MS, GC-MS, and FT-MS.

192. The method according to claim 188, wherein said three processes are LC-MS, GC-MS, and NMR.

193. The method according to claim 188, wherein said three processes are LC-MS, GC-MS, and ICP-MS.

194. The method according to claim 187, wherein said two other types of data are selected from the group consisting of RNA data, DNA data, protein data, and phenotypic data.

195. The method according to claim 187, wherein the computer tracking system is a Laboratory Information Management System (LIMS).

196. The method according to claim 187, wherein the biological sample is selected from the group consisting of animalia, plantae, protista, monera, and fungi.

197. The method according to claim 196, wherein the biological sample is selected from the group consisting of human primate, non-human primate, canine, feline, equine, bovine, porcine, rabbit, rodent, liver tissue, liver spheroids, primary hepatocytes, liver cell lines, and HepG2 cells.

198. The method according to claim 196, wherein the biological sample is selected from the group consisting of Arabidopsis, corn, wheat, barley, rye, legumes, mint, tobacco, tomatoes, rice, spinach, and peas.

199. The method according to claim 196, wherein the biological sample is selected from the group consisting of Magnaporthe, Candida, Mycosphaerella, Botrytis, Saccharomyces, Aspergillus, Puccinia, Erysiphe, Ustilago, Fursarium, Phytophthora and Penicillium.

200. The method according to claim 187, wherein said coherent data set is a model for a particular disease or disease stage.

201. The method according to claim 187, wherein said coherent data set is a model for the efficacy of a therapeutic program or exposure to a particular chemical.

202. The method according to claim 187, wherein the biological sample is selected from the group consisting of a healthy organism, a diseased organism, a drug-treated organism, and a genetically altered organism.

203. The method according to claim 187, wherein the biological sample is from an organism having received an environmental or chemical insult.

204. The method according to claim 187, wherein the common unit system is deviation from a standard.

205. The method according to claim 187, wherein said reduction of dimensionality is achieved by applying one of the group consisting of principle components analysis, correlation analysis, regression analysis, and pre-clustering of said common unit system data.

206. A method for creating a coherent data set for modeling biological systems, comprising:

a) entering a unique identifier of at least one biological sample into a computer tracking system;

b) storing in said computer tracking system disparate data, wherein said disparate data comprise metabolite data and at least two other types of data, and said disparate data are linked to said unique identifier;

c) converting said linked disparate data to a numeric format;

d) transforming said numeric format data into a Gaussian distribution; and

e) converting said Gaussian distribution data to a common unit system, wherein said common unit system data are a coherent data set which serves as a model for a biological system.

207. The method according to claim 206, wherein said metabolite data comprise data from at least three processes.

208. The method according to claim 207, wherein said three processes are selected from the group consisting of liquid chromatography (LC), high-pressure liquid chromatography (HPLC), mass spectroscopy (MS), hyphenated mass spectroscopy (MS-MS), gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), Fourier transform-ion cyclotron resonance-mass spectrometer (FT-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transform InfraRed (FT-IR), and inductively coupled plasma mass spectrometry (ICP-MS).

209. The method according to claim 207, wherein two of said three processes are LC-MS and GC-MS.

210. The method according to claim 207, wherein said three processes are LC-MS, GC-MS, and FT-MS.

211. The method according to claim 207, wherein said three processes are LC-MS, GC-MS, and NMR.

212. The method according to claim 207, wherein said three processes are LC-MS, GC-MS, and ICP-MS.

213. The method according to claim 206, wherein said two other types of data are selected from the group consisting of RNA data, DNA data, protein data, and phenotypic data.

214. The method according to claim 206, wherein the computer tracking system is a Laboratory Information Management System (LIMS).

215. The method according to claim 206, wherein the biological sample is selected from the group consisting of animalia, plantae, protista, monera, and fungi.

216. The method according to claim 215, wherein the biological sample is selected from the group consisting of human primate, non-human primate, canine, feline, equine, bovine, porcine, rabbit, rodent, liver tissue, liver spheroids, primary hepatocytes, liver cell lines, and HepG2 cells.

217. The method according to claim 215, wherein the biological sample is selected from the group consisting of Arabidopsis, corn, wheat, barley, rye, legumes, mint, tobacco, tomatoes, rice, spinach, and peas.

218. The method according to claim 215, wherein the biological sample is selected from the group consisting of Magnaporthe, Candida, Mycosphaerella, Botrytis, Saccharomyces, Aspergillus, Puccinia, Erysiphe, Ustilago, Fursarium, Phytophthora and Penicillium.

219. The method according to claim 206, wherein said coherent data set is a model for a particular disease or disease stage.

220. The method according to claim 206, wherein said coherent data set is a model for the efficacy of a therapeutic program or exposure to a particular chemical.

221. The method according to claim 206, wherein the biological sample is selected from the group consisting of a healthy organism, a diseased organism, a drug-treated organism, and a genetically altered organism.

222. The method according to claim 206, wherein the biological sample is from an organism having received an environmental or chemical insult.

223. The method according to claim 206, wherein the common unit system is deviation from a standard.

224. The method according to claim 206, wherein said transformation into a Gaussian distribution occurs by conversion of said numeric format data to a logarithmic scale.

225. A method for creating a coherent data set for modeling biological systems, comprising:

a) entering a unique identifier of at least one biological sample into a laboratory information management system (LIMS);

b) storing data comprising RNA data, phenotype data, and metabolite data in said LIMS, wherein said data are linked to said unique identifier;

c) converting said linked data to a numeric format;

d) transforming said numeric format data into a Gaussian distribution;

e) converting said Gaussian distribution data to a common unit system, wherein said common unit system is deviation from a baseline; and

f) reducing the dimensionality of said common unit system data, wherein said dimensionally reduced common unit system data are a coherent data set which serves as a model for a biological system.

226. The method according to claim 225, wherein said metabolite data comprise data from at least three processes.

227. The method according to claim 226, wherein said three processes are selected from the group consisting of liquid chromatography (LC), high-pressure liquid chromatography (HPLC), mass spectroscopy (MS), hyphenated mass spectroscopy (MS-MS), gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), Fourier transform-ion cyclotron resonance-mass spectrometer (FT-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transform InfraRed (FT-IR), and inductively coupled plasma mass spectrometry (ICP-MS).

228. The method according to claim 226, wherein two of said three processes are LC-MS and GC-MS.

229. The method according to claim 226, wherein said three processes are LC-MS, GC-MS, and FT-MS.

230. The method according to claim 226, wherein said three processes are LC-MS, GC-MS, and NMR.

231. The method according to claim 226, wherein said three processes are LC-MS, GC-MS, and ICP-MS.

232. The method according to claim 225, wherein the computer tracking system is a Laboratory Information Management System (LIMS).

233. The method according to claim 225, wherein the biological sample is selected from the group consisting of animalia, plantae, protista, monera, and fungi.

234. The method according to claim 233, wherein the biological sample is selected from the group consisting of human primate, non-human primate, canine, feline, equine, bovine, porcine, rabbit, rodent, liver tissue, liver spheroids, primary hepatocytes, liver cell lines, and HepG2 cells.

235. The method according to claim 233, wherein the biological sample is selected from the group consisting of Arabidopsis, corn, wheat, barley, rye, legumes, mint, tobacco, tomatoes, rice, spinach, and peas.

236. The method according to claim 233, wherein the biological sample is selected from the group consisting of Magnaporthe, Candida, Mycosphaerella, Botrytis, Saccharomyces, Aspergillus, Puccinia, Erysiphe, Ustilago, Fursarium, Phytophthora and Penicillium.

237. The method according to claim 225, wherein said coherent data set is a model for a particular disease or disease stage.

238. The method according to claim 225, wherein said coherent data set is a model for the efficacy of a therapeutic program or exposure to a particular chemical.

239. The method according to claim 225, wherein the biological sample is selected from the group consisting of a healthy organism, a diseased organism, a drug-treated organism, and a genetically altered organism.

240. The method according to claim 225, wherein the biological sample is from an organism having received an environmental or chemical insult.

241. The method according to claim 225, wherein the common unit system is deviation from a standard.

242. The method according to claim 225, wherein said reduction of dimensionality is achieved by applying one of the group consisting of principle components analysis, correlation analysis, regression analysis, and pre-clustering of said common unit system data.

243. The method according to claim 225, wherein said transformation into a Gaussian distribution occurs by conversion of said numeric format data to a logarithmic scale.

244. A method for creating a coherent data set for modeling biological systems, comprising:

a) entering a unique identifier of at least one biological sample into a laboratory information management system (LIMS);

b) storing data comprising RNA data, phenotype data, and metabolite data in said LIMS, wherein said data are linked to said unique identifier;

c) converting said linked data to a numeric format; and

d) converting said numeric format data to a common unit system, wherein said common unit system data are a coherent data set which serves as a model for a biological system.

245. The method according to claim 244, wherein said metabolite data comprise data from at least three processes.

246. The method according to claim 245, wherein said three processes are selected from the group consisting of liquid chromatography (LC), high-pressure liquid chromatography (HPLC), mass spectroscopy (MS), hyphenated mass spectroscopy (MS-MS), gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), Fourier transform-ion cyclotron resonance-mass spectrometer (FT-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transform InfraRed (FT-IR), and inductively coupled plasma mass spectrometry (ICP-MS).

247. The method according to claim 245, wherein two of said three processes are LC-MS and GC-MS.

248. The method according to claim 245, wherein said three processes are LC-MS, GC-MS, and FT-MS.

249. The method according to claim 245, wherein said three processes are LC-MS, GC-MS, and NMR.

250. The method according to claim 245, wherein said three processes are LC-MS, GC-MS, and ICP-MS.

251. The method according to claim 244, wherein the computer tracking system is a Laboratory Information Management System (LIMS).

252. The method according to claim 244, wherein the biological sample is selected from the group consisting of animalia, plantae, protista, monera, and fungi.

253. The method according to claim 252, wherein the biological sample is selected from the group consisting of human primate, non-human primate, canine, feline, equine, bovine, porcine, rabbit, rodent, liver tissue, liver spheroids, primary hepatocytes, liver cell lines, and HepG2 cells.

254. The method according to claim 252, wherein the biological sample is selected from the group consisting of Arabidopsis, corn, wheat, barley, rye, legumes, mint, tobacco, tomatoes, rice, spinach, and peas.

255. The method according to claim 252, wherein the biological sample is selected from the group consisting of Magnaporthe, Candida, Mycosphaerella, Botrytis, Saccharomyces, Aspergillus, Puccinia, Erysiphe, Ustilago, Fursarium, Phytophthora and Penicillium.

256. The method according to claim 244, wherein said coherent data set is a model for a particular disease or disease stage.

257. The method according to claim 244, wherein said coherent data set is a model for the efficacy of a therapeutic program or exposure to a particular chemical.

258. The method according to claim 244, wherein the biological sample is selected from the group consisting of a healthy organism, a diseased organism, a drug-treated organism, and a genetically altered organism.

259. The method according to claim 244, wherein the biological sample is from an organism having received an environmental or chemical insult.

260. The method according to claim 244, wherein the common unit system is deviation from a standard.

261. A method for creating a coherent data set for modeling biological systems, comprising:

a) entering a unique identifier of at least one biological sample into a laboratory information management system (LIMS);

b) storing data comprising RNA data, phenotype data, and metabolite data in said LIMS, wherein said data are linked to said unique identifier;

c) converting said linked data to a numeric format;

d) transforming said numeric format data into a Gaussian distribution; and

e) converting said Gaussian distribution data to a common unit system, wherein said common unit system data are a coherent data set which serves as a model for a biological system.

262. The method according to claim 261, wherein said metabolite data comprise data from at least three processes.

263. The method according to claim 262, wherein said three processes are selected from the group consisting of liquid chromatography (LC), high-pressure liquid chromatography (HPLC), mass spectroscopy (MS), hyphenated mass spectroscopy (MS-MS), gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), Fourier transform-ion cyclotron resonance-mass spectrometer (FT-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transform InfraRed (FT-IR), and inductively coupled plasma mass spectrometry (ICP-MS).

264. The method according to claim 262, wherein two of said three processes are LC-MS and GC-MS.

265. The method according to claim 262, wherein said three processes are LC-MS, GC-MS, and FT-MS.

266. The method according to claim 262, wherein said three processes are LC-MS, GC-MS, and NMR.

267. The method according to claim 262, wherein said three processes are LC-MS, GC-MS, and ICP-MS.

268. The method according to claim 261, wherein the computer tracking system is a Laboratory Information Management System (LIMS).

269. The method according to claim 261, wherein the biological sample is selected from the group consisting of animalia, plantae, protista, monera, and fungi.

270. The method according to claim 269, wherein the biological sample is selected from the group consisting of human primate, non-human primate, canine, feline, equine, bovine, porcine, rabbit, rodent, liver tissue, liver spheroids, primary hepatocytes, liver cell lines, and HepG2 cells.

271. The method according to claim 269, wherein the biological sample is selected from the group consisting of Arabidopsis, corn, wheat, barley, rye, legumes, mint, tobacco, tomatoes, rice, spinach, and peas.

272. The method according to claim 269, wherein the biological sample is selected from the group consisting of Magnaporthe, Candida, Mycosphaerella, Botrytis, Saccharomyces, Aspergillus, Puccinia, Erysiphe, Ustilago, Fursarium, Phytophthora and Penicillium.

273. The method according to claim 261, wherein said coherent data set is a model for a particular disease or disease stage.

274. The method according to claim 261, wherein said coherent data set is a model for the efficacy of a therapeutic program or exposure to a particular chemical.

275. The method according to claim 261, wherein the biological sample is selected from the group consisting of a healthy organism, a diseased organism, a drug-treated organism, and a genetically altered organism.

276. The method according to claim 261, wherein the biological sample is from an organism having received an environmental or chemical insult.

277. The method according to claim 261, wherein the common unit system is deviation from a standard.

278. The method according to claim 261, wherein said transformation into a Gaussian distribution occurs by conversion of said numeric format data to a logarithmic scale.

279. A method for creating a coherent data set for modeling biological systems, comprising:

a) entering a unique identifier of at least one biological sample into a laboratory information management system (LIMS);

b) storing data comprising RNA data, phenotype data, and metabolite data in said LIMS, wherein said data are linked to said unique identifier;

c) converting said linked data to a numeric format;

d) converting said numeric format data to a common unit system; and

e) reducing the dimensionality of said common unit system data, wherein said dimensionally reduced common unit system data are a coherent data set which serves as a model for a biological system.

280. The method according to claim 279, wherein said metabolite data comprise data from at least three processes.

281. The method according to claim 280, wherein said three processes are selected from the group consisting of liquid chromatography (LC), high-pressure liquid chromatography (HPLC), mass spectroscopy (MS), hyphenated mass spectroscopy (MS-MS), gas chromatography (GC), liquid chromatography/mass spectroscopy (LC-MS), gas chromatography/mass spectroscopy (GC-MS), Fourier transform-ion cyclotron resonance-mass spectrometer (FT-MS), nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), Fourier Transform InfraRed (FT-IR), and inductively coupled plasma mass spectrometry (ICP-MS).

282. The method according to claim 280, wherein two of said three processes are LC-MS and GC-MS.

283. The method according to claim 280, wherein said three processes are LC-MS, GC-MS, and FT-MS.

284. The method according to claim 280, wherein said three processes are LC-MS, GC-MS, and NMR.

285. The method according to claim 280, wherein said three processes are LC-MS, GC-MS, and ICP-MS.

286. The method according to claim 279, wherein the computer tracking system is a Laboratory Information Management System (LIMS).

287. The method according to claim 279, wherein the biological sample is selected from the group consisting of animalia, plantae, protista, monera, and fungi.

288. The method according to claim 287, wherein the biological sample is selected from the group consisting of human primate, non-human primate, canine, feline, equine, bovine, porcine, rabbit, rodent, liver tissue, liver spheroids, primary hepatocytes, liver cell lines, and HepG2 cells.

289. The method according to claim 287, wherein the biological sample is selected from the group consisting of Arabidopsis, corn, wheat, barley, rye, legumes, mint, tobacco, tomatoes, rice, spinach, and peas.

290. The method according to claim 287, wherein the biological sample is selected from the group consisting of Magnaporthe, Candida, Mycosphaerella, Botrytis, Saccharomyces, Aspergillus, Puccinia, Erysiphe, Ustilago, Fursarium, Phytophthora and Penicillium.

291. The method according to claim 279, wherein said coherent data set is a model for a particular disease or disease stage.

292. The method according to claim 279, wherein said coherent data set is a model for the efficacy of a therapeutic program or exposure to a particular chemical.

293. The method according to claim 279, wherein the biological sample is selected from the group consisting of a healthy organism, a diseased organism, a drug-treated organism, and a genetically altered organism.

294. The method according to claim 279, wherein the biological sample is from an organism having received an environmental or chemical insult.

295. The method according to claim 279, wherein the common unit system is deviation from a standard.

296. The method according to claim 279, wherein said reduction of dimensionality is achieved by applying one of the group consisting of principle components analysis, correlation analysis, regression analysis, and pre-clustering of said common unit system data.