Biological information processing method and device, recording medium and program

• US 10,303,845 B2
• Filed: 06/28/2011
• Issued: 05/28/2019
• Est. Priority Date: 07/05/2010
• Status: Active Grant

• Current Assignee: Sony Corporation (Sony Group Corp.)
• Original Assignee: Sony Corporation (Sony Group Corp.)
• Inventors: Sakurada, Kazuhiro
• Primary Examiner(s): Negin, Russell S
• Application Number:
  US13/704,940
• Publication Number:
  US 20130110409A1
• Time in Patent Office: 2,891 Days
• Field of Search: None
• 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 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/48 Other medical applications
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/30 Unsupervised data analysis
• 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: G16B 99/00 Subject matter not provided for in other groups of this subclass
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

Provided is a biological information processing method and a device, a recording medium and a program that are able to predict and control changes in the state of an organism. The expression level of molecules in an organism is measured over a specific time interval; the measured time-series data is divided into a periodic component, an environmental stimulus response component and a baseline component; constant regions of the time-series data are identified from variations in the baseline component or from the amplitude or periodic variations of the periodic component; and causal relation between the identified constant regions is identified. The relation between the external environment and variations in the internal environment is identified and from the identified causal relation between the constant regions, changes in the state of the organism are inferred.

Claims

1. A biological information processing method comprising the steps of:

measuring an extracellular expression level of biological molecules in an organism over a predetermined time interval;

creating time-series data y(t) showing changes in the measured extracellular expression level over the predetermined time interval;

creating baseline data b(t) by extracting from the time-series data y(t) at least one of a periodic component of the measured extracellular expression level of the time series data y(t) and an environmental stimulus response component of the measured extracellular expression level of the time series data y(t);

dividing the baseline data b(t) into a plurality of constant regions based on changes in magnitude of the baseline data over the predetermined time interval;

creating a matrix of data having a p×

n matrix size, where n represents the constant regions for p types of biological molecules;

predicting onset of a disease based on a pattern of change across nodes of the matrix, the pattern determined from measured expression levels of biological molecules known to have the disease; and

treating for the predicted disease,

wherein the time-series data y(t) is expressed by the following Equation 1;

y(t)=s(t)+x(t)+b(t)+v(t) 

 

Equation 1,

where s(t) is the periodic component, x(t) is the environmental stimulus response component, b(t) is the baseline data, and v(t) is an observational error.

2. The biological information processing method according to claim 1, wherein the environmental stimulus response component includes one or more change in magnitude of the measured extracellular expression level of the time series data y(t) caused by an external environment.

3. The biological information processing method according to claim 1, wherein the measuring step comprises measuring a glucose level.

4. The biological information processing method according to claim 1, wherein the molecules are blood molecules of the organism.

5. The biological information processing method according to claim 1, wherein the molecules are molecules associated with metabolic syndrome.

6. The biological information processing method according to claim 1, wherein the molecules are molecules in a culture medium.

7. The biological information processing method according to claim 1, wherein the environmental stimulus response component is formulated using the following multilinear model expressed by Equation 2:

x(t)=F(t)×

(t−

1)+v_x(t) 

 

Equation 2,

where x(t) is the environmental stimulus response component, F(t) represents a conversion function of an output with respect to an environmental stimulus, and v_x(t) represents a change of an environmental stimulus.

8. The biological information processing method according to claim 1, wherein the baseline data is expressed by the following Equation 3:

b(t)=H(t,t−

1)b(t−

1)+V(t,t−

1) 

 

Equation 3,

where b(t) is the baseline data, H(t, t−

1) is an m×

m matrix, and V(t, t−

1) is a matrix associated with m-dimensional noise.

9. The biological information processing method according to claim 1, wherein the periodic component is formulated as a seasonal adjustment model as expressed in the following Equation 4:

10. The biological information processing method according to claim 1, wherein the periodic component is represented by a substantially constant amplitude and frequency of the measured extracellular expression level of the time series data y(t), and wherein the environmental stimulus response component is represented by a change in a substantially constant average magnitude or in a substantially constant average magnitude shift of the measured extracellular expression level of the time series data y(t).

11. The biological information processing method according to claim 1, wherein treating for the predicted disease includes providing a treatment option for the predicted disease.

12. A biological information processing method comprising the steps of:

creating time-series data y(t) showing changes in a measured extracellular expression level of biological molecules in an organism;

creating baseline data b(t) by extracting from the time-series data y(t) at least one of a periodic component of the measured extracellular expression level of the time series data y(t) and an environmental stimulus response component of the measured extracellular expression level of the time series data y(t);

dividing the baseline data b(t) into a plurality of constant regions based on changes in magnitude of the baseline data over the predetermined time interval;

creating a matrix of data having a p×

n matrix size, where n represents the constant regions for p types of biological molecules;

predicting onset of a disease based on a pattern of change across nodes of the matrix, the pattern determined from measured expression levels of biological molecules known to have the disease; and

treating for the predicted disease,

wherein the time-series data y(t) is expressed by the following Equation 1;

y(t)=s(t)+x(t)+b(t)+v(t) 

 

Equation 1,

where s(t) is the periodic component, x(t) is the environmental stimulus response component, b(t) is the baseline data, and v(t) is an observational error.

13. The biological information processing method according to claim 12, wherein the environmental stimulus response component includes one or more change in magnitude of the measured extracellular expression level of the time series data y(t) caused by an external environment.

14. The biological information processing method according to claim 12, wherein the periodic component is represented by a substantially constant amplitude and frequency of the measured extracellular expression level of the time series data y(t), and wherein the environmental stimulus response component is represented by a change in a substantially constant average magnitude or in a substantially constant average magnitude shift of the measured extracellular expression level of the time series data y(t).

15. The biological information processing method according to claim 12, wherein treating for the predicted disease includes providing a treatment option for the predicted disease.

16. A biological information processing method comprising the steps of:

creating time-series data y(t) showing changes in a measured extracellular expression level of biological molecules in an organism;

creating baseline data b(t) by extracting from the time-series data y(t) an environmental stimulus response component of the measured extracellular expression level of the time series data y(t);

dividing the baseline data b(t) into a plurality of constant regions based on changes in magnitude of the baseline data over the predetermined time interval;

creating a matrix of data having a p×

n matrix size, where n represents the constant regions for p types of biological molecules;

predicting onset of a disease based on a pattern of change across nodes of the matrix, the pattern determined from measured expression levels of biological molecules known to have the disease; and

treating for the predicted disease,

wherein the environmental stimulus response component is formulated using the following multilinear model expressed by Equation 2;

x(t)=F(t)×

(t−

1)+v_x(t) 

 

Equation 2,

where x(t) is the environmental stimulus response component, F(t) represents a conversion function of an output with respect to an environmental stimulus, and v_x(t) represents a change of an environmental stimulus.

17. The biological information processing method according to claim 16, wherein the environmental stimulus response component includes one or more change in magnitude of the measured extracellular expression level of the time series data y(t) caused by an external environment.

18. The biological information processing method according to claim 16, wherein the environmental stimulus response component is represented by a change in a substantially constant average magnitude or in a substantially constant average magnitude shift of the measured extracellular expression level of the time series data y(t).

19. The biological information processing method according to claim 16, which includes measuring the extracellular expression level of biological molecules in the organism over a predetermined time interval.

20. The biological information processing method according to claim 16, wherein creating the baseline data b(t) includes extracting from the time-series data y(t) a periodic component of the measured extracellular expression level of the time series data y(t).

21. The biological information processing method according to claim 16, wherein treating for the predicted disease includes providing a treatment option for the predicted disease.

22. A biological information processing method comprising the steps of:

creating time-series data y(t) showing changes in a measured extracellular expression level of biological molecules in an organism;

creating baseline data b(t) by extracting from the time-series data y(t) at least one of a periodic component of the measured extracellular expression level of the time series data y(t) and an environmental stimulus response component of the measured extracellular expression level of the time series data y(t);

dividing the baseline data b(t) into a plurality of constant regions based on changes in magnitude of the baseline data over the predetermined time interval;

creating a matrix of data having a p×

n matrix size, where n represents the constant regions for p types of biological molecules;

predicting onset of a disease based on a pattern of change across nodes of the matrix, the pattern determined from measured expression levels of biological molecules known to have the disease; and

treating for the predicted disease,

wherein the baseline data is expressed by the following Equation 3;

b(t)=H(t,t−

1)b(t−

1)+V(t,t−

1) 

 

Equation 3,

where b(t) is the baseline data, H(t, t−

1) is an m×

m matrix, and V(t, t−

1) is a matrix associated with m-dimensional noise.

23. The biological information processing method according to claim 22, wherein the environmental stimulus response component includes one or more change in magnitude of the measured extracellular expression level of the time series data y(t) caused by an external environment.

24. The biological information processing method according to claim 22, wherein the periodic component is represented by a substantially constant amplitude and frequency of the measured extracellular expression level of the time series data y(t), and wherein the environmental stimulus response component is represented by a change in a substantially constant average magnitude or in a substantially constant average magnitude shift of the measured extracellular expression level of the time series data y(t).

25. The biological information processing method according to claim 22, which includes measuring the extracellular expression level of biological molecules in the organism over a predetermined time interval.

26. The biological information processing method according to claim 22, wherein treating for the predicted disease includes providing a treatment option for the predicted disease.

27. A biological information processing method comprising the steps of:

creating time-series data y(t) showing changes in a measured extracellular expression level of biological molecules in an organism;

creating baseline data b(t) by extracting from the time-series data y(t) a periodic component of the measured extracellular expression level of the time series data y(t);

dividing the baseline data b(t) into a plurality of constant regions based on changes in magnitude of the baseline data over the predetermined time interval;

creating a matrix of data having a p×

n matrix size, where n represents the constant regions for p types of biological molecules;

predicting onset of a disease based on a pattern of change across nodes of the matrix, the pattern determined from measured expression levels of biological molecules known to have the disease; and

treating for the predicted disease,

wherein the periodic component is formulated as a seasonal adjustment model as expressed in the following Equation 4;

28. The biological information processing method according to claim 27, wherein creating the baseline data b(t) includes extracting from the time-series data y(t) an environmental stimulus response component of the measured extracellular expression level of the time series data y(t), wherein the environmental stimulus response component is represented by a change in a substantially constant average magnitude or in a substantially constant average magnitude shift of the measured extracellular expression level of the time series data y(t).

29. The biological information processing method according to claim 27, wherein the periodic component is represented by a substantially constant amplitude and frequency of the measured extracellular expression level of the time series data y(t).

30. The biological information processing method according to claim 27, which includes measuring the extracellular expression level of biological molecules in the organism over a predetermined time interval.

31. The biological information processing method according to claim 27, wherein treating for the predicted disease includes providing a treatment option for the predicted disease.