JP5102439B2 - Simulation system and computer program - Google Patents

Description

  The present invention relates to a simulation system for simulating a change in concentration of a target substance in a living body over time and a computer program for causing a computer to function as the simulation system.

  Descriptions of substance concentrations in living bodies, particularly blood glucose levels and blood insulin concentrations, have been attempted using numerical models for medical reasons represented by diabetes diagnosis.

  Examples of the model used here include Bergman's minimal model (see, for example, Non-Patent Document 1 and Non-Patent Document 2).

  In this minimal model, the blood glucose level, plasma insulin concentration, and the amount of insulin action at the insulin action point of peripheral tissues, that is, remote insulin are variables. Here, assuming that the blood glucose level at time t is G (t), the plasma insulin concentration is I (t), and the remote insulin is X (t), G (t), I (t) and X (t) are time It is described by the following differential equation with the differential on the left side.

dG (t) / dt = -p1 (G (t) -Gb) -X (t) ・ G (t)
dX (t) / dt = -p2 ・ X (t) + p3 (I (t) -Ib)
dI (t) / dt = -n (I (t) -Ib) + γ (G (t) -h) (G (t)> h)
= -n (I (t) -Ib) + γ (G (t) -h) (G (t) ≤h)
Where each parameter in the equation is
p1: Insulin-independent glucose metabolism rate
Gb: Blood glucose level
p2: Insulin uptake at the site of insulin action
p3: Insulin consumption rate for insulin-dependent glucose metabolism
Ib: Base value of insulin concentration
n: Insulin consumption per unit time γ: Insulin secretion sensitivity to glucose stimulation
h: blood glucose thresholds at which insulin secretion is initiated, and these can be set differently for each individual.

  In addition, as another blood glucose level reproduction method, there is a blood glucose level prediction method in a diabetic patient (for example, see Patent Document 1).

Bergman et al., American Journal of Physiology, 1979, Vol. 236, No. 6, p. E-667-77 Bergman et al., Journal of Clinical Investigation, 1981, Vol. 68, No. 6, p. 1456-67. Japanese Patent Laid-Open No. 11-296598

  Originally, in the living body, the pancreas secretes insulin in response to stimulation of blood glucose level, the liver takes in glucose from or releases glucose in blood depending on insulin concentration and blood glucose level, and distributes insulin to peripheral tissues Four blocks, the peripheral system that metabolizes glucose under the action of the circulatory system and insulin, control blood glucose levels in relation to each other. However, in the minimal model described above, the components of the model are abstract elements that do not correspond to the four blocks of the living body, and the simulation results of the blood sugar level fluctuation and the insulin concentration fluctuation of the living body are expressed as four blocks of the living body. It is difficult to think in relation. Further, with the method disclosed in Patent Document 1, it is possible to predict a blood glucose level, but it is impossible to know the state of an organ related to blood glucose control.

The present invention takes the above-described circumstances into consideration, and changes in the concentration of glucose and / or insulin , which are target substances of a living body, with time , pancreas, liver, and insulin dynamics among the components (organs) of the living body. Another object of the invention is to provide peripheral tissues and readily made to correspond to a simulation system system and computer capable of simulating a computer program to function as the simulation system.

A simulation system according to the present invention is a simulation system for simulating a temporal change in the concentration of glucose and / or insulin as a target substance in a living body using a computer having storage means and data input means ,
The storage means has the following differential equations in which pancreas, liver, insulin kinetics and peripheral tissue functions relating to the target substance are:
dY / dt = −α {Y (t) −β (BG (t) −h)} (where BG (t)> h)
= −α · Y (t) (However, BG (t) ≦ h)
dX / dt = −M · X (t) + Y (t)
SR (t) = M · X (t)
RGout (t) = P1 (Gb−BG (t)) − P2 · SR (t) · BG (t) + Goff (where BG (t) <Gb)
= −P2 · SR (t) · BG (t) + Goff (where BG (t) ≧ Gb)
SRpost (t) = K · SR (t)
dI1 (t) / dt = −A3 · I1 (t) + A5 · I2 (t) + A4 · I3 (t) + SRpost (t)
dI2 (t) / dt = A6 · I1 (t) −A5 · I2 (t)
dI3 (t) / dt = A2 · I1 (t) −A1 · I3 (t)
dBG (t) / dt = −K1 · BG (t) −K2 · I3 (t) · BG (t) + RG (t) + RGout (t)
(Where BG (t) is the blood glucose concentration, X (t) is the total amount of insulin that can be secreted from the pancreas, Y (t) is the insulin supply rate that is newly supplied in response to glucose stimulation, and SR (t) is Insulin secretion rate from the pancreas, RGout (t) is net glucose from the liver, SRpost (t) is insulin after passing through the liver, I1 (t) is blood insulin concentration, I2 (t) is in insulin-independent tissue Insulin concentration, I3 (t) is the insulin concentration in peripheral tissues, RG (t) is a variable representing glucose absorption from the gastrointestinal tract, h is a threshold of glucose concentration that can stimulate insulin supply, α is glucose stimulation , Β is the sensitivity to glucose stimulation, M is the secretion rate per unit concentration, Gb is the glucose concentration base value, and P1 is glucose below Gb Glucose production rate in response to pulmonary stimulation, P2 is unit insulin, liver glucose uptake rate per unit glucose, K is liver insulin uptake rate, Goff is glucose release rate relative to basal metabolism, A1 is insulin disappearance rate in peripheral tissues, A2 Is the rate of insulin distribution to the peripheral tissue, A3 is the rate of insulin distribution after passing through the liver, A4 is the rate of insulin efflux after passing through the peripheral tissue, A5 is the rate of insulin disappearance in the insulin-independent tissue, and A6 is the rate to the insulin-independent tissue insulin distribution rate, K1 is insulin independent glucose consumption rate in peripheral tissues, K2 is stored an application program including a parameter a is.) biological simulation model described in representing the insulin-dependent glucose consumption rate in peripheral tissues And
The data input means can input time series data of glucose concentration and / or insulin concentration obtained by oral glucose tolerance test ,
By executing the application program, the computer
Using the differential equation describing the biological simulation model stored in the storage means, a calculation means for simulating the activity of the biological organ and sequentially calculating the concentration of the target substance in the living body ; and
Wherein the set of parameter values of the differential equation generates a plurality, data of the concentration of the target substance to be calculated by the calculation means, a set of parameter values that best approximates the time series data input by said data input means, said to function as a acquisition means for acquiring from among the set of the plurality generated parameter values,
It is possible to simulate the temporal change of the concentration of the target substance in vivo by biological simulation model described by the differential equation of the parameter value was set that has been acquired by the acquisition means.

The computer program of the present invention is a computer program for causing a computer to function as the above-described simulation system. As a result, the biological simulation model in which the functions of the pancreas, liver, insulin and peripheral tissues are described in the above differential equations as the organs of the living body. It is possible to match and simulate the time change of the concentration of the target substance glucose and / or insulin.

According to the invention, the pancreas of biological actual, liver, it is possible to simulate the temporal change of the concentration of glucose and / or insulin, the subject material matched to the insulin kinetics and peripheral tissues per activity, It is possible to perform a simulation that is easy to understand medically.

  The present invention will be described in detail below based on the embodiments shown in the drawings. However, this does not limit the present invention.

  FIG. 1 is a block diagram showing a hardware configuration of a simulation system according to an embodiment of the present invention. The simulation system 100 according to the present embodiment is configured by a computer 100a mainly composed of a main body 110, a display 120, and an input device 130. The main body 110 mainly includes a CPU 110a, a ROM 110b, a RAM 110c, a hard disk 110d, a reading device 110e, an input / output interface 110f, a communication interface 110g, and an image output interface 110h. The CPU 110a, ROM 110b, and RAM 110c. The hard disk 110d, the reading device 110e, the input / output interface 110f, and the image output interface 110h are connected by a bus 110i so that data communication is possible.

  The CPU 110a can execute a computer program stored in the ROM 110b and a computer program loaded in the RAM 110c. Then, the CPU 110a executes an application program 140a as described later, thereby realizing each functional block as described later, and the computer 100a functions as the simulation system 100.

  The ROM 110b is configured by a mask ROM, PROM, EPROM, EEPROM, or the like, and stores a computer program executed by the CPU 110a, data used for the same, and the like.

  The RAM 110c is configured by SRAM, DRAM, or the like. The RAM 110c is used for reading computer programs recorded in the ROM 110b and the hard disk 110d. Further, when these computer programs are executed, they are used as a work area of the CPU 110a.

  The hard disk 110d is installed with various computer programs to be executed by the CPU 110a, such as an operating system and application programs, and data used for executing the computer programs. An application program 140a described later is also installed in the hard disk 110d.

  The reading device 110e is configured by a flexible disk drive, a CD-ROM drive, a DVD-ROM drive, or the like, and can read a computer program or data recorded on the portable recording medium 140. The portable recording medium 140 stores an application program 140a for causing the computer to function as a simulation system according to the present invention. The computer 100a downloads the application program 140a according to the present invention from the portable recording medium 140. It is possible to read and install the application program 140a in the hard disk 110d.

  The application program 140a is not only provided by the portable recording medium 140, but also from an external device that is communicably connected to the computer 100a via an electric communication line (whether wired or wireless). It is also possible to provide through. For example, the application program 140a is stored in the hard disk of a server computer on the Internet. The computer 100a can access the server computer, download the computer program, and install it on the hard disk 110d. is there.

  The hard disk 110d is installed with an operating system that provides a graphical user interface environment such as Windows (registered trademark) manufactured and sold by Microsoft Corporation. In the following description, the application program 140a according to the present embodiment is assumed to operate on the operating system.

  The input / output interface 110f includes, for example, a serial interface such as USB, IEEE1394, and RS-232C, a parallel interface such as SCSI, IDE, and IEEE1284, and an analog interface including a D / A converter and an A / D converter. ing. An input device 130 including a keyboard and a mouse is connected to the input / output interface 110f, and the user can input data to the computer 100a by using the input device 130.

  The image output interface 110h is connected to a display 120 configured by an LCD, a CRT, or the like, and outputs a video signal corresponding to image data given from the CPU 110a to the display 120. The display 120 displays an image (screen) according to the input video signal.

  FIG. 2 is a functional block diagram showing a conceptual configuration of the simulation system according to the embodiment of the present invention. As shown in FIG. 2, the biological simulation model 140b used in the simulation of the present invention includes a pancreas block 1, a liver block 2, an insulin dynamic block 3, and a peripheral tissue block 4, and each block has an input and an output. .

  The pancreas block 1 receives blood glucose concentration 6 as input and outputs insulin secretion rate 7 as output. The liver block 2 receives blood glucose concentration 6 and insulin secretion rate 7 as inputs, and outputs net glucose release 8 and insulin 9 after passing through the liver as outputs. The insulin dynamic block 3 receives the insulin 9 after passing through the liver as an input and outputs the insulin concentration 10 in the peripheral tissue as an output. The peripheral tissue block 4 inputs net glucose release 8, external glucose absorption 5, and insulin concentration 10 in the peripheral tissue, and outputs blood glucose concentration 6 as an output. The glucose absorption 5 is data given from the outside, and this function is realized by the user inputting test data or the like using the input device 130, for example. Each functional block 1 to 4 is realized by the application program 140a being executed by the CPU 110a.

  Details of each block in this embodiment will be described below.

The input / output relationship of the pancreas block 1 is described using the differential equation 1 shown below. Further, it can be expressed using a block diagram equivalent to the differential equation 1 shown in FIG.
<Differential equation 1>
dY / dt = -α {Y (t) -β (BG (t) -h)} (where BG (t)> h)
= -α ・ Y (t) (However, BG (t) ≦ h)
dX / dt = -M ・ X (t) + Y (t)
SR (t) = M ・ X (t)
<Variable>
BG (t): Blood glucose level
X (t): Total amount of insulin that can be secreted from the pancreas
Y (t): Newly supplied insulin supply rate for glucose stimulation
SR (t): rate of insulin secretion from the pancreas <parameter>
h: threshold of glucose concentration that can stimulate insulin supply α: follow-up to glucose stimulus β: sensitivity to glucose stimulus
M: Secretion rate per unit concentration Here, the blood glucose concentration 6 which is an input to the pancreas block in FIG. 2 corresponds to BG (t). The insulin secretion rate 7, which is the output of the pancreas block in FIG. 2, corresponds to SR (t).

  In the block diagram of FIG. 3, 11 is blood glucose concentration (blood glucose level) BG, 12 is a glucose concentration threshold value H that can stimulate insulin supply, 13 is sensitivity β to glucose stimulation, and 14 is followability to glucose stimulation. α, 15 is an integral element, 16 is an insulin supply rate Y newly supplied in response to glucose stimulation, 17 is an integral element, 18 is a total amount of insulin X that can be secreted from the pancreas, 19 is a secretion rate M per unit concentration, 20 indicates the insulin secretion rate SR from the pancreas.

The input / output relationship of the liver block 2 is described using the differential equation 2 shown below. It can also be expressed using the block diagram shown in FIG.
<Differential equation 2>
RGout (t) = P1 (Gb-BG (t))-P2 ・ SR (t) ・ BG (t) + Goff (However, BG (t) <Gb)
= -P2 ・ SR (t) ・ BG (t) + Goff (However, BG (t) ≧ Gb)
SRpost (t) = K ・ SR (t)
<Variable>
BG (t): Blood glucose level
SR (t): rate of insulin secretion from the pancreas
RGout (t): Net glucose from the liver
SRpost (t): Insulin after passing through liver <Parameter>
Gb: Base value of glucose concentration
Glucose production rate in response to glucose stimulation below P1: Gb
P2: Liver glucose uptake rate per unit insulin and unit glucose
K: Insulin uptake rate in the liver
Goff: Glucose release rate relative to basal metabolism Here, the blood glucose concentration 6 which is an input to the liver block in FIG. 2 corresponds to BG (t), and the insulin secretion rate 7 corresponds to SR (t). The net glucose release 8 which is the output of the liver block in FIG. 2 corresponds to RGout (t) and the post-liver insulin 9 corresponds to SRpost (t).

  In the block diagram of FIG. 4, 21 is the blood glucose concentration (blood glucose level) BG, 22 is the insulin secretion rate SR from the pancreas, 23 is the glucose concentration base value Gb, and 24 is the glucose production rate P1 for glucose stimulation below Gb. , 25 is the liver glucose uptake rate P2 per unit insulin unit glucose, 26 is the insulin uptake rate K in the liver, 27 is the glucose release rate Goff relative to the basal metabolism, 28 is the net glucose RGout from the liver, 29 is the post-liver passage Insulin SRpost is shown.

The input / output relationship of the insulin dynamic block 3 is described using the differential equation 3 shown below. It can also be expressed using a block diagram shown in FIG.
<Differential equation 3>
dI1 (t) / dt = -A3 ・ I1 (t) + A5 ・ I2 (t) + A4 ・ I3 (t) + SRpost (t)
dI2 (t) / dt = A6 ・ I1 (t) -A5 ・ I2 (t)
dI3 (t) / dt = A2 ・ I1 (t) -A1 ・ I3 (t)
<Variable>
SRpost (t): Insulin after passing through the liver
I1 (t): Blood insulin concentration
I2 (t): Insulin concentration in non-insulin dependent tissues
I3 (t): Insulin concentration in peripheral tissues <parameter>
A1: Insulin disappearance rate in peripheral tissues
A2: Insulin distribution rate to peripheral tissues
A3: Insulin distribution rate after passing through the liver
A4: Insulin flow rate after passing through peripheral tissues
A5: Insulin disappearance rate in non-insulin dependent tissues
A6: Insulin distribution rate to non-insulin-dependent tissue Here, the insulin 9 after passing through the liver, which is an input to the insulin dynamic block in FIG. 2, corresponds to SRpost (t). The insulin concentration 10 in the peripheral tissue, which is the output of the insulin kinetic block in FIG. 2, corresponds to I3 (t).

  In the block diagram of FIG. 5, 31 is insulin SRpost after passing through the liver, 32 is an integral element, 33 is insulin distribution rate A3 after passing through the liver, 34 and 35 are blood insulin concentrations I1, and 36 is insulin to peripheral tissues. Distribution rate A2, 37 is an integral factor, 38 and 39 are insulin concentrations I3 in peripheral tissues, 40 is an insulin disappearance rate A1 in peripheral tissues, 41 is an insulin outflow rate A4 after passing through peripheral tissues, and 42 is an insulin-independent tissue Insulin distribution ratios A6 and 43 are integral elements, 44 is an insulin concentration I2 in an insulin-independent tissue, and 45 is an insulin disappearance rate A5 in the insulin-independent tissue.

The input / output relationship of the peripheral tissue block 4 is described using the differential equation 4 shown below. It can also be expressed using a block diagram shown in FIG.
<Differential equation 4>
dBG (t) / dt = -K1 ・ BG (t) -K2 ・ I3 (t) ・ BG (t) + RG (t) + RGout (t)
<Variable>
BG (t): Blood glucose level
RG (t): Glucose absorption from the digestive tract
RGout (t): Net glucose from the liver
I3 (t): Insulin concentration in peripheral tissues <parameter>
K1: Insulin-independent glucose consumption rate in peripheral tissues
K2: Insulin-dependent glucose consumption rate in peripheral tissue Here, the insulin concentration 10 in the peripheral tissue, which is the input to the peripheral tissue block in FIG. 2, is I3 (t), and the net glucose 8 from the liver is RGout ( t) and glucose absorption 5 from the gastrointestinal tract correspond to RG (t). The blood glucose concentration 6, which is the output of the peripheral tissue block in FIG. 2, corresponds to BG (t).

  In the block diagram of FIG. 6, 51 is the net glucose RGout from the liver, 52 is the glucose absorption RG from the gastrointestinal tract, 53 is the integral element, 54 is the insulin-independent glucose consumption rate K1 in the peripheral tissue, 55 is the peripheral tissue Insulin concentrations I3 and 56 in FIG. 5 indicate insulin-dependent glucose consumption rates K2 and 57 in peripheral tissues, respectively, and blood glucose concentration BG.

  Next, the operation of the simulation system according to the embodiment of the present invention will be described. FIG. 7 is a flowchart showing an operation flow of the simulation system according to the embodiment of the present invention. First, the user performs a predetermined operation on the input device 130, such as double-clicking a corresponding icon, and instructs the computer 100a to start the application program 140a. As a result, the application program 140a is read from the hard disk 110d and loaded into the RAM 110c. After starting the application program 140a in this manner, the user can obtain time-series data of glucose absorption 5 (ie, glucose intake) from the digestive tract obtained by OGTT (oral glucose tolerance test) and the like, and at this time The glucose concentration (that is, blood glucose level) and the time series data of each insulin concentration are input to the computer 100a by the input device 130.

  The CPU 110a accepts input of these data (step S1) and executes parameter value generation processing (step S2). The parameter value generation process is a parameter value to be set for each parameter as described above of the biological simulation model 140b. The parameter value is set in the biological simulation model 140b, and the glucose absorption 5 received in step S1 is set. Time series data of glucose concentration and insulin concentration (hereinafter referred to as simulation result data) similar to the time series data of glucose concentration and insulin concentration (hereinafter referred to as reference data) received in step S1 when given to the biological simulation model 140b. Is a process of calculating a parameter value obtained by a simulation process S4 described later. Specifically, the parameter value generation processing S2 according to the present embodiment randomly generates a plurality of parameter value sets, and uses the genetic algorithm to approximate the reference data most closely from the parameter value sets. This is a process of calculating a set of parameter values from which the simulation result data can be obtained. In addition to the genetic algorithm, other algorithms such as a known least square method, steepest descent method, or simulated annealing may be used as a parameter value calculation method.

  Next, the CPU 110a sets the set of parameter values generated by the parameter value generation process S2 in this way in the biological simulation model 140b (step S3), and executes the simulation process (step S4). The simulation process S4 is a process for giving the glucose absorption 5 to the biological simulation model 140b and calculating the corresponding insulin concentration 10 and blood glucose concentration 6. As the glucose absorption 5, the one input by the user in step S <b> 1 can be used, or separately, the user can input the time series data of glucose absorption and use it. It is also possible to automatically generate and use glucose absorption time-series data by the CPU 110a. As shown in FIG. 2, since the input and output between the blocks constituting this system are connected to each other and feedback-controlled, by providing glucose absorption 5 from the digestive tract, the time series of blood glucose level and insulin concentration Changes can be calculated and simulated based on mathematical formulas. That is, when the time series data of glucose absorption 5 is sequentially given to the biological simulation model 140b, the insulin concentration 10 and blood glucose concentration 6 corresponding to the glucose absorption at each time are sequentially calculated, and as a result, the insulin concentration 10 and blood Time series data of medium glucose concentration (blood glucose level) 6 will be obtained.

  Next, the CPU 110a displays the time series data of the insulin concentration 10 and the blood glucose concentration 6 obtained by the simulation process S4 on the display 120 (step S5), and ends the process. At this time, in the simulation system 100 according to the present embodiment, as shown in FIG. 9 and FIG. 10, a graph with the vertical axis representing blood glucose level or insulin concentration and the horizontal axis representing time is generated. This is displayed on the display 120. However, the present invention is not limited to this. For example, the blood glucose level and insulin concentration at each time may be displayed side by side in time series, and the target to be displayed is not blood glucose level and insulin concentration but blood insulin. Other data handled by the biological simulation model 140b, such as the density 35, may be used.

  As described above, the blood glucose level and the insulin concentration calculated sequentially are displayed on the display 120. As a result, the user can easily confirm the result of simulating the living organ as described above. In addition, this system can be adopted as a subsystem that simulates a biological function in a medical system such as a diabetes care support system. In this case, the time series changes of the calculated blood glucose level and insulin concentration are transferred to other components of the medical system, thereby creating, for example, diabetes medical care support information, and the blood glucose level and insulin concentration calculated by this system. Based on this, it is possible to obtain highly reliable medical information.

  Further, for example, E-Cell (Keio University public software) and MatLab (Massworks products) can be used for the calculation of the differential equation in the simulation process S4. Other calculation systems may also be used.

  Next, an example of simulating time-series changes in blood glucose level and insulin concentration using this system is shown. At this time, the values shown in Table 1 were used as an example of the parameters of each block.

また、微分方程式を計算するにあたり、変数の初期値の一例として表２の値を用いた。

In calculating the differential equation, the values in Table 2 were used as an example of the initial values of the variables.

また、消化管からのグルコース吸収速度は、図８に示す値を用いた。

Moreover, the value shown in FIG. 8 was used for the rate of glucose absorption from the digestive tract.

  As a result of the simulation under the above conditions, the time series fluctuation of blood glucose level 6 is shown in FIG. 9, the time series fluctuation of insulin concentration 10 in the peripheral tissue is shown in FIG. 10, and the blood insulin concentration 35, that is, I3 (t) 34 time series fluctuation. Is shown in FIG. Moreover, the clinical measurement value of a blood glucose level is shown in FIG. By executing simulations using this system, results consistent with physiological fluctuations are reproduced.

  As described above, by using this system, it is possible to reproduce the time-series fluctuations of blood glucose level and insulin concentration accompanying glucose absorption in a form that closely approximates physiological fluctuations. In addition, the biological simulation model 140b used in this system includes blocks corresponding to pancreas, liver, insulin dynamics, and peripheral tissues, which are involved in blood glucose level control, as components, so medically understand its meaning. It is easy to do.

  In the present embodiment, the blocks corresponding to the organs (tissues) of the pancreas, pancreas, liver, insulin kinetics and peripheral tissues involved in glucose absorption, accumulation, metabolism and / or insulin secretion, transport and action. Although the configuration for simulating the temporal fluctuation of the glucose concentration and the insulin concentration using the biological simulation model 140b having 1 to 4 has been described, the present invention is not limited to this, and the biological simulation model is included in the other organs described above. It may be configured to have a corresponding block and simulate the time variation of other substance concentrations.

  The simulation system of the present invention uses a biological simulation model having, as a block, functions of a plurality of biological organs related to a target substance, and simulates time-dependent changes in the concentration of the target substance in accordance with the activity of each organ of the actual biological body. Simulation system that simulates changes in the concentration of the target substance in the living body, especially blood glucose level and blood insulin concentration. It is useful as a computer program for causing a computer to function as the simulation system.

It is a block diagram which shows the hardware constitutions of the simulation system which concerns on this Embodiment. It is a functional block diagram which shows the whole structure of the model which concerns on embodiment of this invention. It is a block diagram which shows the structure of the pancreas model shown in FIG. It is a block diagram which shows the structure of the liver model shown in FIG. FIG. 3 is a block diagram showing a configuration of an insulin dynamic model shown in FIG. 2. It is a block diagram which shows the structure of the peripheral tissue model shown in FIG. It is a flowchart which shows the flow of operation | movement of the simulation system which concerns on embodiment of this invention. It is a graph which shows an example of the glucose absorption rate used as input in embodiment of this invention. It is a graph which shows an example of the blood glucose level reproduced in the embodiment of the present invention. It is a graph which shows an example of the peripheral tissue insulin density | concentration reproduced in embodiment of this invention. It is a graph which shows an example of the blood insulin concentration reproduced in the embodiment of the present invention. It is a graph which shows an example of the clinical measurement value of a blood glucose level.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Pancreas block 2 Liver block 3 Insulin movement block 4 Peripheral tissue block 5 Glucose absorption 6 Blood glucose concentration 7 Insulin secretion rate 8 Net glucose release 9 Insulin after passing through liver 10 Insulin concentration 100 Simulation system 100a Computer 110 Main body 110 Main body 110a CPU
110b ROM
110c RAM
110d hard disk 110e reading device 110f input / output interface 110g communication interface 110h image output interface 110i bus 120 display 130 input device 140a application program 140b biological simulation model

Claims (2)

A simulation system for simulating a temporal change in the concentration of glucose and / or insulin as a target substance in a living body using a computer having a storage means and a data input means ,
The storage means has the following differential equations in terms of pancreatic, liver, insulin dynamics and peripheral tissue functions related to the target substance :
dY / dt = −α {Y (t) −β (BG (t) −h)} (where BG (t)> h)
= −α · Y (t) (However, BG (t) ≦ h)
dX / dt = −M · X (t) + Y (t)
SR (t) = M · X (t)
RGout (t) = P1 (Gb−BG (t)) − P2 · SR (t) · BG (t) + Goff (where BG (t) <Gb)
= −P2 · SR (t) · BG (t) + Goff (where BG (t) ≧ Gb)
SRpost (t) = K · SR (t)
dI1 (t) / dt = −A3 · I1 (t) + A5 · I2 (t) + A4 · I3 (t) + SRpost (t)
dI2 (t) / dt = A6 · I1 (t) −A5 · I2 (t)
dI3 (t) / dt = A2 · I1 (t) −A1 · I3 (t)
dBG (t) / dt = −K1 · BG (t) −K2 · I3 (t) · BG (t) + RG (t) + RGout (t)
(Where BG (t) is the blood glucose concentration, X (t) is the total amount of insulin that can be secreted from the pancreas, Y (t) is the insulin supply rate that is newly supplied in response to glucose stimulation, and SR (t) is Insulin secretion rate from the pancreas, RGout (t) is net glucose from the liver, SRpost (t) is insulin after passing through the liver, I1 (t) is blood insulin concentration, I2 (t) is in insulin-independent tissue Insulin concentration, I3 (t) is the insulin concentration in peripheral tissues, RG (t) is a variable representing glucose absorption from the gastrointestinal tract, h is a threshold of glucose concentration that can stimulate insulin supply, α is glucose stimulation , Β is the sensitivity to glucose stimulation, M is the secretion rate per unit concentration, Gb is the glucose concentration base value, and P1 is glucose below Gb Glucose production rate in response to pulmonary stimulation, P2 is unit insulin, liver glucose uptake rate per unit glucose, K is liver insulin uptake rate, Goff is glucose release rate relative to basal metabolism, A1 is insulin disappearance rate in peripheral tissues, A2 Is the rate of insulin distribution to the peripheral tissue, A3 is the rate of insulin distribution after passing through the liver, A4 is the rate of insulin efflux after passing through the peripheral tissue, A5 is the rate of insulin disappearance in the insulin-independent tissue, and A6 is the rate to the insulin-independent tissue insulin distribution rate, K1 is insulin independent glucose consumption rate in peripheral tissues, K2 is stored an application program including a parameter a is.) biological simulation model described in representing the insulin-dependent glucose consumption rate in peripheral tissues And
The data input means can input time series data of glucose concentration and / or insulin concentration obtained by oral glucose tolerance test ,
By executing the application program, the computer
Using the differential equation describing the biological simulation model stored in the storage means, a calculation means for simulating the activity of the biological organ and sequentially calculating the concentration of the target substance in the living body ; and
Wherein the set of parameter values of the differential equation generates a plurality, data of the concentration of the target substance to be calculated by the calculation means, a set of parameter values that best approximates the time series data input by said data input means, said to function as a acquisition means for acquiring from among the set of the plurality generated parameter values,
Simulation system characterized in that to simulate the temporal change of the concentration of the target substance in vivo by biological simulation model described respective parameter values acquired by the acquiring means in degrees Tsu Sorted said differential equation. A computer program for causing a computer to function as the simulation system according to claim 1 .

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