JP4940433B2 - Simulation apparatus and program - Google Patents

Description

  The present invention relates to a simulation apparatus or the like that can perform a simulation related to insulin secretion of pancreatic β cells or predict a pharmacological action of a SU agent on pancreatic β cells.

Insulin is one of the hormones secreted into the blood from the pancreatic islets of Langerhans. Further, when the blood glucose level rises, pancreatic β cells take in glucose into the cell via the glucose transporter and produce ATP in the mitochondria. As the ATP concentration increases, the _KATP channel closes and the cell membrane depolarizes. This depolarization opens a voltage-gated Ca ²⁺ channel and Ca ²⁺ flows into the cell, causing insulin secretion. When insulin binds to an insulin receptor on the surface of each cell in the body, the cell takes in glucose in the blood and lowers the blood glucose level. With the above operation, insulin lowers the blood glucose level.

In addition, the SU agent is an agent that binds to the SU receptor of pancreatic β cells and promotes insulin secretion by closing the _KATP channel, thereby lowering the blood glucose level.

  FIG. 10 is a conceptual diagram illustrating the concept from glucose reception to insulin secretion.

As a conventional simulation model for simulating the above-described operation in β cells, there is a model in which time transitions of ions such as intracellular ATP, ADP, and Ca ²⁺ dependent on pancreatic β extracellular glucose concentration are expressed numerically (non-patented). Reference 1).

There is also a model in which the intracellular volume ratio of the Ca ²⁺ -uniporter module, the endoplasmic reticulum module, the mitochondria, and the endoplasmic reticulum is formulated (see Non-Patent Document 2).

  Furthermore, there is a hypothesis that qualitatively estimates the position / activation information of insulin granules in pancreatic β cells (see Non-Patent Document 3).

Note that the Kd value of the SU agent is described in Non-Patent Document 4. Moreover, there exists a nonpatent literature 5 in related literature.
Magnus G., et al, Model of β-cell mitochondrial calcium handling and electrical activity.I. Cytoplasmic variables., Am. J. Physiol., USA, 1998, 274, C1158-C1173 CHRISTOPHER P. FALL, et al, Mitochondrial Modulation of Intracellular Ca2 + Signaling, J. Theor. Biol., USA, 2001, 210, 151-165 Rorsman P., The pancreatic beta-cell as a fuel sensor: an electrophysiologist's viewpoint, Diabetologia, USA, 1997, 40, 487-495 Schmid-Antomarchi, et al., The receptor for antidiabetic sulfonylureas controls the activity of the ATP-modulated K + channel in insulin-secreting cells., THE JOURNAL OF BIOLOGICAL CHEMISTRY, 1987, 262, 15840-15844 Detimary P., et al., Concentration dependence and time course of the effects of glucose on adenine and guanine nucleotides in mouse pancreatic islets., THE JOURNAL OF BIOLOGICAL CHEMISTRY, 1996, 271, 20559-20565

  However, the conventional simulation model has a problem that insulin secretion cannot be simulated from glucose reception.

  Specifically, the techniques disclosed in Non-Patent Documents 1, 2, and 4 do not deal with insulin secretion.

  In the technique disclosed in Non-Patent Document 3, the hypothesis is qualitative and cannot be used for simulation.

  In addition, there is a problem that insulin secretion due to the introduction of the SU agent cannot be simulated.

  Furthermore, there has been a problem that it is impossible to simulate a side effect due to the introduction of the SU agent.

  The simulation apparatus according to the first aspect of the present invention includes an accepting unit that accepts glucose information that is information about glucose, and an insulin generator that generates insulin information that is information about secreted insulin based on the glucose information accepted by the accepting unit And an output unit that outputs the insulin information generated by the insulin generation unit.

  With this configuration, insulin secretion can be simulated from glucose reception.

Further, in the simulation device of the second invention, in contrast to the first invention, the insulin generation unit flows into the cell with ATP information acquisition means for acquiring ATP information, which is information related to the produced ATP. Calcium ion information acquisition means for acquiring calcium ion information that is information related to Ca ²⁺ , information on insulin secreted near the Golgi apparatus based on the ATP information, the calcium ion information, and the glucose information. Golgi vicinity insulin information generation means for generating one insulin information, second insulin information that is information about insulin in the vicinity of the cell membrane is generated based on the ATP information, the calcium ion information, and the first insulin information A cell membrane vicinity insulin information generating means, the ATP information, Based on the calcium ion information and the second insulin information, phosphorylated insulin information generating means for generating third insulin information that is information related to phosphorylated insulin, the calcium ion information, and the third insulin information And an activated insulin information generating means for generating insulin information, which is information related to the activated insulin, based on the above.

With this configuration, the insulin generation unit can be modeled, and as a result, insulin secretion from glucose reception can be simulated with high accuracy. This model is, for example, a novel model in which insulin secretion is quantified by dividing the position and state of insulin granules in pancreatic β cells into four states and formulating these relationships. Further, in this model, for example, the speed between the positions and states of insulin granules depends on the intracellular ATP and Ca ²⁺ concentrations.

  Further, the simulation device of the third invention relates to the activated insulin information generating means, which is activated based on the calcium ion information and the third insulin information. It is a simulation device that generates fourth insulin information that is information, and generates insulin information that is information related to the insulin secretion rate based on the fourth insulin information.

  With this configuration, it is possible to simulate insulin secretion from glucose reception and output an important simulation result of insulin secretion rate.

  Further, in the simulation device of the fourth aspect of the invention, in contrast to the first aspect, the accepting unit accepts the glucose information and SU agent information that is information relating to the SU agent to be added, and the insulin generating unit is It is a simulation apparatus which produces | generates the insulin information which is the information regarding the secreted insulin based on the said glucose information and the said SU agent information.

  With this configuration, it is possible to simulate insulin secretion due to the introduction of the SU agent.

Further, in the simulation device of the fifth invention, in contrast to the fourth invention, the insulin generator generates potassium ion information that is information on K ⁺ flowing into the cell based on the SU agent information. Based on the potassium ion information, based on the potassium ion information, calcium ion generation means for generating calcium ion information that is information on Ca ²⁺ flowing into the cell, and ATP information that is information on ATP produced is acquired. ATP information acquisition means, Golgi vicinity insulin information generation that generates first insulin information that is information related to insulin secreted near the Golgi body based on the ATP information, the calcium ion information, and the glucose information Means, the ATP information, the calcium ion information, and the first Based on the insulin information, on the basis of the insulin information generating means near the cell membrane for generating the second insulin information, which is information on the insulin in the vicinity of the cell membrane, the ATP information, the calcium ion information, and the second insulin information, Phosphorylated insulin information generating means for generating third insulin information that is information relating to phosphorylated insulin, insulin information that is information relating to insulin activated based on the calcium ion information and the third insulin information And an activated insulin information generating means for generating.

  With this configuration, the insulin generation unit can be modeled, and as a result, the insulin secretion due to the introduction of the SU agent can be simulated with high accuracy.

  In addition, the simulation device of the sixth invention is a simulation device in which the output unit outputs side effect information, which is information about side effects on the introduction of the SU agent, with respect to any of the fourth and fifth inventions.

  With this configuration, it is possible to simulate the side effects caused by the introduction of the SU agent.

  In the simulation device of the seventh aspect of the invention, in contrast to the sixth aspect of the invention, the side effect information includes SU agent-corresponding insulin amount information that is information indicating the intracellular insulin amount corresponding to the SU agent exposure time. This is a simulation device.

  With this configuration, it is possible to simulate the side effects caused by the introduction of the SU agent and output important information such as the amount of insulin information corresponding to the SU agent.

  According to the simulation apparatus of the present invention, insulin secretion can be simulated.

Hereinafter, embodiments of a simulation apparatus and the like will be described with reference to the drawings. In addition, since the component which attached | subjected the same code | symbol in embodiment performs the same operation | movement, description may be abbreviate | omitted again.
(Embodiment 1)

  FIG. 1 is a block diagram of a simulation apparatus according to the present embodiment. The simulation apparatus includes a reception unit 11, an insulin generation unit 12, and an output unit 13.

  The insulin generator 12 includes an ATP information acquisition unit 121, a calcium ion information acquisition unit 122, a Golgi vicinity insulin information generation unit 123, a cell membrane vicinity insulin information generation unit 124, a phosphorylated insulin information generation unit 125, and an activated insulin information generation unit. 126.

  The reception unit 11 receives glucose information that is information about glucose. The glucose information is, for example, the extracellular glucose concentration, which is the concentration of extracellular glucose, the amount of glucose input into the cell, the amount or concentration of glucose present in the cell, and the like. The glucose information input means may be anything such as a numeric keypad, keyboard, mouse or menu screen. The receiving unit 11 can be realized by a device driver for input means such as a numeric keypad or a keyboard, control software for a menu screen, and the like.

  The insulin generation unit 12 generates insulin information that is information related to secreted insulin based on the glucose information received by the reception unit 11. The insulin information is, for example, an insulin secretion rate, an insulin secretion amount, and the like. A specific example of an algorithm by which the insulin generator 12 generates insulin information will be described later. The insulin generator 12 can be usually realized by an MPU, a memory, or the like. The processing procedure of the insulin generation unit is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The ATP information acquisition unit 121 acquires ATP information that is information related to ATP to be produced. The ATP information is, for example, an intracellular ATP concentration that is the concentration of ATP in the cell, an ATP generation rate, an amount of ATP in the cell, and the like. The ATP information acquisition unit 121 can usually be realized by an MPU, a memory, or the like. The processing procedure of the ATP information acquisition unit 121 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

The calcium ion information acquisition unit 122 acquires calcium ion information that is information regarding Ca ²⁺ flowing into the cell. The calcium ion information is information such as the concentration of intracellular calcium ions, the inflow rate of calcium ions into cells, and the amount of intracellular calcium ions. The calcium ion information acquisition unit 122 can be usually realized by an MPU, a memory, or the like. The processing procedure of the calcium ion information acquisition means is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  Based on the ATP information, calcium ion information, and glucose information, the Golgi vicinity insulin information generation means 123 generates first insulin information that is information related to insulin secreted in the vicinity of the Golgi body. An example of an algorithm by which the Golgi vicinity insulin information generating means 123 generates the first insulin information will be described later. The Golgi vicinity insulin information generating means 123 can be normally realized by an MPU, a memory, or the like. The processing procedure of the Golgi vicinity insulin information generating means 123 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  Based on the ATP information, the calcium ion information, and the first insulin information, the cell membrane vicinity insulin information generating unit 124 generates second insulin information that is information related to insulin in the vicinity of the cell membrane. An example of an algorithm by which the cell membrane vicinity insulin information generating unit 124 generates the second insulin information will be described later. The cell membrane vicinity insulin information generating means 124 can be usually realized by an MPU, a memory, or the like. The processing procedure of the cell membrane vicinity insulin information generating means 124 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The phosphorylated insulin information generating unit 125 generates third insulin information that is information regarding phosphorylated insulin based on the ATP information, the calcium ion information, and the second insulin information. An example of an algorithm by which the phosphorylated insulin information generation unit 125 generates the third insulin information will be described later. The phosphorylated insulin information generating unit 125 can be usually realized by an MPU, a memory, or the like. The processing procedure of the phosphorylated insulin information generating means 125 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The activated insulin information generating means 126 generates insulin information that is information about the activated insulin based on the calcium ion information and the third insulin information. The activated insulin information generating means 126 generates fourth insulin information that is information related to the activated insulin based on the calcium ion information and the third insulin information, and insulin secretion based on the fourth insulin information. Insulin information that is information about the speed may be generated. The activated insulin information generating means 126 can be usually realized by an MPU, a memory or the like. The processing procedure of the activated insulin information generating means 126 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The output unit 13 outputs the insulin information generated by the insulin generation unit 12. Here, output is a concept including display on a display, printing on a printer, sound output, transmission to an external device, and the like. An output example of insulin information from the output unit 13 will be described later. The output unit 13 may be considered as including or not including an output device such as a display or a speaker. The output unit 13 can be realized by output device driver software, or output device driver software and an output device.

  Next, the operation of the simulation apparatus will be described using the flowchart of FIG.

  (Step S201) The reception unit 11 determines whether glucose information has been received. If glucose information is accepted, the process goes to step S202. If glucose information is not accepted, the process returns to step S201.

  (Step S202) The ATP information acquisition unit 121 acquires ATP information. The technique for acquiring ATP information is disclosed in Non-Patent Document 1 described above and is a known technique, and therefore details thereof are omitted.

  (Step S203) The calcium ion information acquisition means 122 acquires calcium ion information. The technique for acquiring calcium ion information is disclosed in Non-Patent Document 1 described above and is a known technique, and therefore details thereof are omitted.

  (Step S204) The near-Golgi body insulin information generating means 123 generates insulin biosynthesis rate information, which is information indicating the insulin biosynthesis rate, based on the glucose information received in step S201.

  (Step S205) Golgi vicinity insulin information generating means 123, ATP information acquired in step S202, calcium ion information acquired in step S203, glucose information received in step S201, insulin biosynthesis generated in step S204 Based on the speed information, first insulin information that is information related to insulin secreted in the vicinity of the Golgi apparatus is generated.

  (Step S206) Cell membrane vicinity insulin information generation means 124 is based on the ATP information acquired in step S202, the calcium ion information acquired in step S203, and the first insulin information generated in step S205, and The second insulin information, which is information regarding, is generated.

  (Step S207) The phosphorylated insulin information generation means 125 is a phosphorylated insulin based on the ATP information acquired in Step S202, the calcium ion information acquired in Step S203, and the second insulin information generated in Step S206. 3rd insulin information which is information about is generated.

  (Step S208) The activated insulin information generation means 126 obtains fourth insulin information, which is information related to activated insulin, based on the calcium ion information acquired in step S203 and the third insulin information generated in step S207. Generate.

  (Step S209) The activated insulin information generation unit 126 generates insulin information that is information related to the insulin secretion rate based on the fourth insulin information generated in Step S208.

  (Step S210) The output unit 13 outputs the insulin information generated in step S209.

  In the flowchart of FIG. 2, for example, the processing from step S205 to step S208 may be calculated at a time.

  Further, in the flowchart of FIG. 2, the output insulin information may not be information regarding the insulin secretion rate. The insulin information to be output may be, for example, an insulin secretion amount. In the flowchart of FIG. 2, the output unit 13 may output information other than insulin information.

  Hereinafter, a specific operation of the simulation apparatus according to the present embodiment will be described.

  First, the reception unit 11 receives glucose information. Here, the glucose information is, for example, extracellular glucose concentration (glu) [unit: mM].

Next, the ATP information acquisition unit 121 acquires the ATP information using the glucose information received by the receiving unit 11 and temporarily stores it in a predetermined memory (variable). Here, the ATP information is the intracellular ATP concentration (ATP _i ). A technique for obtaining the intracellular ATP concentration (ATP _i ) is disclosed in Non-Patent Document 1.

Next, the calcium ion information acquisition unit 122 acquires the calcium ion information using the glucose information received by the receiving unit 11 and temporarily stores it in a predetermined memory (variable). Here, the calcium ion information is the intracellular calcium ion concentration (Ca _i ). A technique for obtaining the intracellular calcium ion concentration (Ca _i ) is disclosed in Non-Patent Document 1.

Next, the near-Golgi insulin information generating means 123 calculates the extracellular glucose concentration (glu), the maximum insulin biosynthesis rate (V _max ), the insulin biosynthesis Michaelis constant (K _m ), and the insulin biosynthesis Hill coefficient (h). Insulin biosynthesis rate information (V _synthesis ) is generated as a parameter. Specifically, the Golgi vicinity insulin information generating unit 123 reads the extracellular glucose concentration (glu) received by the receiving unit 11. In addition, the Golgi vicinity insulin information generating means 123 reads the constants of insulin biosynthesis maximum (V _max ), insulin biosynthesis Michaelis constant (K _m ), and insulin biosynthesis Hill coefficient (h). Then, the Golgi vicinity insulin information generating means 123 adds the read extracellular glucose concentration (glu), insulin biosynthesis maximum to the calculation formula (Formula 1 below) of the stored insulin biosynthesis rate information (V _synthesis ). Substituting the rate (V _max ), the insulin biosynthesis Michaelis constant (K _m ), and the insulin biosynthesis Hill coefficient (h), the insulin biosynthesis rate information (V _synthesis ) is calculated.

Here, the unit of the insulin biosynthesis rate information (V _synthesis ) is [ng / islet / msec]. The maximum insulin biosynthesis rate (V _max ) is, for example, “2.43 × 10 ⁻⁵ (ng / islet / msec)”. The insulin biosynthesis Michaelis constant (K _m ) is, for example, “20 (mM)”, and the insulin biosynthesis Hill coefficient (h) is, for example, “3.03”.

Next, the near-Golgi insulin information generating means 123 is acquired by the ATP information acquiring means 121, the intracellular ATP concentration (ATP _i ) temporarily stored in the memory, and the calcium ion information acquiring means 122 is acquired. The intracellular calcium ion concentration (Ca _i ) temporarily stored in the memory is read out. The intracellular ATP concentration (ATP _i ), the intracellular calcium ion concentration (Ca _i ), the extracellular glucose concentration (glu) received by the receiving unit 11, and the insulin biosynthesis rate information generated by Equation 1 above. Based on (V _synthesis ), first insulin information that is information on insulin secreted in the vicinity of the Golgi apparatus is generated. Here, the first insulin information is, for example, insulin granule amount D [unit: ng / iselet] near the Golgi apparatus. The Golgi vicinity insulin information generating means 123 temporarily stores the insulin granule amount D near the Golgi body in the memory.

In addition, the cell membrane vicinity insulin information generating means 124 includes the intracellular ATP concentration (ATP _i ) temporarily stored in the memory, the intracellular calcium ion concentration (Ca _i ) temporarily stored in the memory, and the Golgi apparatus. The nearby insulin granule amount D (existing in the memory) is read out, and using these as parameters, second insulin information, which is information related to insulin in the vicinity of the cell membrane, is generated. Here, the second insulin information is, for example, the amount of insulin granules S [unit: ng / iselet] near the cell membrane. The cell membrane vicinity insulin information generation means 124 temporarily stores the amount of insulin granules S near the cell membrane in the memory.

The phosphorylated insulin information generating means 125 reads the intracellular ATP concentration (ATP _i ) on the memory, the intracellular calcium ion concentration (Ca _i ) on the memory, and the amount of insulin granules S near the cell membrane on the memory. The third insulin information, which is information about phosphorylated insulin, is generated using these as parameters. Here, the third insulin information is, for example, phosphorylated insulin granule amount P [unit: ng / iselet]. The phosphorylated insulin information generating means 125 temporarily stores the phosphorylated insulin granule amount P in the memory.

The activated insulin information generating means 126 reads the intracellular calcium ion concentration (Ca _i ) on the memory and the phosphorylated insulin granule amount P on the memory, and relates to the activated insulin using these as parameters. The fourth insulin information which is information is generated. Here, the fourth insulin information is, for example, the activated insulin granule amount A [unit: ng / iselet]. The activated insulin information generating means 126 temporarily stores the activated insulin granule amount A on the memory.

In addition, the amount of insulin granules D near the Golgi body, the amount S of insulin granules near the cell membrane, the amount P of phosphorylated insulin, and the amount A of activated insulin A are expressed by Equation 2, Equation 3, and Equation 4, respectively. , Can be calculated by the differential equation of Formula 5. That is, the insulin generator 12 stores information indicating the differential equations of the following Equation 2, Equation 3, Equation 4, and Equation 5, the Golgi vicinity insulin information generation means 123, the cell membrane vicinity insulin information generation means 124, The phosphorylated insulin information generating means 125 and the activated insulin information generating means 126 respectively read the information of the following differential equation, substitute the acquired parameters into the differential equation, and corresponding information (insulin granule amount D, S, P, A) are calculated and temporarily stored in the memory. Since the technique for solving the differential equation is a known technique, detailed description thereof is omitted.

In the above formula, α ₁ and α ₂ are ATP · calcium-dependent migration rate constants. Preferably, α ₁ is “5.14 × 10 ⁻⁴ (1 / msec / mM ATP ² / mM Ca ^1.4 )”. Preferably, α ₂ is “1.05 × 10 ⁻⁴ (1 / msec / mM ATP / mM Ca)”.

Β ₁ is an ATP-dependent binding rate constant. Preferably, β ₁ is “6.70 × 10 ⁻³ (1 / msec / mM ATP)”.

Β ₂ is a dissociation rate constant. Preferably, β ₂ is “5.00 × 10 ⁻² (1 / msec)”.

Γ ₁ is a calcium-dependent binding rate constant. Preferably, γ ₁ is “2.31 × 10 ⁻¹ (1 / msec / mM Ca)”.

Further, γ ₂ is a dissociation rate constant. Preferably, γ ₂ is “2.86 × 10 ⁻⁴ (1 / msec)”.

In addition, the α ₁ , α ₂ , β ₁ , β ₂ , γ ₁ , γ ₂ are stored in advance by the insulin generator 12, and the Golgi vicinity insulin information generation unit 123, the cell membrane vicinity insulin information generation unit 124, The phosphorylated insulin information generating unit 125 and the activated insulin information generating unit 126 read out appropriately and substitute the differential equations for use.

More specifically, the insulin generator 12 performs processing by a computer as follows. The insulin generator 12 stores in advance values of D (D ₀ ), S (S ₀ ), P (P ₀ ), and A (A ₀ ) when t = 0. Then, for example, t is changed every “0.01 seconds”, and dD (D increase amount), dS (S increase amount), dP (P increase amount), dA (A increase amount) are calculated. To do. For example, insulin production unit 12, "t = 0.01" and obtains the first dD _{"(V synthesis -α 1 · D 0} · ATP i 2 · Ca i 1.4 + α 2 · S 0 · ATP _i · Ca _i ) × 0.01 ”. Then, the insulin generator 12 calculates D (D ₁ ) for 0.01 seconds from “D ₀ + dD”. Further, the second dD (dD in the case of “t = 0.02”) is expressed as “(V _synthesis− α ₁ , D ₁ , ATP _i ² , Ca _i ^1.4 + α ₂ , S ₁ , ATP _i , Ca _i ) × 0.01 (dt) ”. In this case, S ₁ has already been obtained by the processing of Equation 3. Then, the insulin generator 12 calculates D (D ₂ ) of 0.02 seconds from “D ₁ + dD”. The insulin generator 12 while increasing the t by 0.01 to obtain the dD one after _{another, D 1, D 2, D} 3, is calculated up to ... and _{D n.} In other words, insulin generator 12 while increasing the t by a predetermined value, in a loop process _{"(V synthesis -α 1 · D n} -1 · ATP i 2 · Ca i 1.4 + α 2 · S n-1 ATP _i · Ca _i ) × predetermined value ”(n is the number of times of loop processing) is executed, dD is calculated, and D _n is calculated by“ D _n−1 + dD ”. In such a case, V _synthesis , α ₁ , D _n-1 , ATP _i , Ca _i , α ₂ , S _n-1 are temporarily stored in the memory before the above calculation, so that they are read out and the above formula Substitute into and execute the operation. The insulin generator 12 calculates S, P, and A by computer processing similar to that for calculating D.

In addition, the calculation process in the computer of a differential equation is performed as follows. That is, in the case of (dX / dt = arithmetic expression), initial values of parameters for calculating the arithmetic expression (α ₁ , D ₀ , S _{0, and the} like in Expression 2) are stored in advance. Then, the processing unit (for example, the insulin generation unit 12) reads the initial value of the parameter, acquires dt (for example, 0.01) (dt is determined in advance), and calculates dX. Then, the stored X ₀ is read, and the next (when t = 0.01) X (X ₁ ) is calculated by “X ₁ = X ₀ + dX”. Similarly, the processing unit calculates the next dX using the value of the previous parameter in the arithmetic expression, adds the next dX and the previous X, and calculates the next X. By repeating the above processing, the value of X every 0.01 (seconds) can be obtained. The differential equations described below can also be obtained in the computer by the above loop processing.

Next, the activated insulin information generating means 126 generates insulin information that is information relating to the insulin secretion rate, using the activated insulin granule amount A as a parameter. Here, the insulin is an insulin secretion rate _{(V secretion).} The unit of insulin secretion rate is (ng / islet / msec). And, specifically, insulin secretion rates _{(V Secretion)} can be calculated by Equation 6. The activated insulin information generating means 126 stores the information of the arithmetic expression shown in Equation 6, reads the information of the arithmetic expression, activates the activated insulin granule amount A on the memory, and the previously stored δ _Is used to calculate the insulin secretion rate (V section).

Note that δ is an insulin secretion rate constant. Preferably, δ is “1.00 × 10 ⁻³ (1 / msec)”.

Then, the output unit 13 outputs the insulin secretion rate _{(V Secretion)} or the like. It is an example of the insulin secretion rate _{(V Secretion)} or the like shown in FIG. In FIG. 3, the received glucose information is the extracellular glucose concentration “8.3 mmol / L”, and shows the result of a simulation in which this simulation apparatus was operated for 5 minutes. In FIG. 3, the horizontal axis represents time, and the vertical axis represents insulin granule amount or insulin secretion rate. 3, (a) is the cumulative insulin secretion amount, (b) is the insulin secretion rate, (c) is the insulin granule amount near the cell membrane, (d) is the activated insulin granule amount, (E) is the amount of insulin granules near the Golgi apparatus, and (f) is the amount of phosphorylated insulin granules.

The output unit 13, the insulin secretion rate _{(V Secretion)} may output information other than. An output example in such a case is shown in FIG. In FIG. 4, cell membrane potential, ion current, channel opening probability, Ca ²⁺ concentration in cytoplasm, ATP concentration, ADP concentration, insulin secretion, Ca ²⁺ concentration in mitochondria and endoplasmic reticulum, and insulin secretion rate are output. In FIG. 4, information other than insulin information such as cell membrane potential is information that can be simulated in Non-Patent Document 1, and detailed description of the output method is omitted.

  As described above, according to the present embodiment, insulin secretion can be simulated from glucose reception.

  In addition, according to this Embodiment, although said numerical formula is a suitable numerical formula, it cannot be overemphasized that the other numerical formula which can simulate insulin secretion may be sufficient. That is, the simulation apparatus includes a reception unit that receives glucose information that is information about glucose, an insulin generation unit that generates insulin information that is information about secreted insulin based on the glucose information received by the reception unit, What is necessary is just a simulation apparatus provided with the output part which outputs the insulin information which the said insulin production | generation part produced | generated. For example, some of the parameters in the above calculation formula may be different.

Further, according to the present embodiment, the insulin generator includes the above-mentioned Golgi vicinity insulin information generation means, cell membrane vicinity insulin information generation means, phosphorylated insulin information generation means, and activated insulin information generation means. The structure which does is suitable. That is, this simulation apparatus can simulate the insulin secretion with high accuracy by modeling the states of four insulins. This model is shown in FIG. In FIG. 5, the state of insulin changes between insulin near the Golgi apparatus (D), insulin near the cell membrane (S), phosphorylated insulin (P), and activated insulin (A). In FIG. 5, parameters such as α ₁ are the same as the parameters in the above formula.

  Furthermore, the processing in the present embodiment may be realized by software. Then, this software may be distributed by software download or the like. Further, this software may be recorded and distributed on a recording medium such as a CD-ROM. This also applies to other embodiments in this specification. The software that realizes the simulation apparatus according to the present embodiment is the following program. That is, this program has a reception step for receiving glucose information that is information about glucose in a computer, and an insulin generation step for generating insulin information that is information about secreted insulin based on the glucose information received in the reception step. And a program for executing an output step of outputting the insulin information generated in the insulin generation step.

Moreover, the said insulin production | generation step of the said program is the calcium ion information which acquires the calcium ion information which acquires the ATP information acquisition step which acquires ATP information which is the information regarding ATP produced, and the information about Ca2 ⁺ which flows into a cell A Golgi vicinity insulin information generation step for generating first insulin information that is information related to insulin secreted in the vicinity of the Golgi body based on the obtaining step, the ATP information, the calcium ion information, and the glucose information; Based on the ATP information, the calcium ion information, and the first insulin information, a cell membrane vicinity insulin information generation step for generating second insulin information that is information related to insulin in the vicinity of the cell membrane, the ATP information, Calcium ion Based on the information and the second insulin information, a phosphorylated insulin information generation step for generating third insulin information that is information regarding phosphorylated insulin, the calcium ion information, and the third insulin information And an activated insulin information generating step for generating insulin information, which is information relating to the activated insulin.

Furthermore, the activated insulin information generation step generates fourth insulin information that is information related to the activated insulin based on the calcium ion information and the third insulin information, and based on the fourth insulin information. Thus, it is preferable to generate insulin information that is information relating to the insulin secretion rate.
(Embodiment 2)

  FIG. 6 is a block diagram of the simulation apparatus in the present embodiment. The simulation apparatus includes a reception unit 61, an insulin generation unit 62, and an output unit 63.

  The insulin generator 62 includes a potassium ion generator 621, a calcium ion generator 622, an ATP information acquisition unit 121, a calcium ion information acquisition unit 623, a Golgi vicinity insulin information generation unit 123, a cell membrane vicinity insulin information generation unit 124, and a phosphorylation. Insulin information generating means 125 and activated insulin information generating means 126 are provided.

The reception unit 61 receives glucose information and SU agent information that is information related to the SU agent. The SU agent information is, for example, the dissociation constant (K _d ) of the SU agent with respect to the SUR, the extracellular concentration of the SU agent, the name or ID of the SU agent. The receiving unit 61 may use any input means such as a numeric keypad, a keyboard, a mouse, or a menu screen. The accepting unit 61 can be realized by a device driver for input means such as a numeric keypad or a keyboard, control software for a menu screen, and the like. An example of the SU agent is tolbutamide.

  The insulin generation unit 62 generates insulin information, which is information related to secreted insulin, based on the glucose information received by the reception unit 61 and the SU agent information. The insulin generator 62 can be usually realized by an MPU, a memory, or the like. The processing procedure of the insulin generation unit is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

Based on the SU agent information received by the receiving unit 61, the potassium ion generating unit 621 generates potassium ion information that is information regarding K ⁺ flowing into the cell. The potassium ion generating means 621 can be usually realized by an MPU, a memory, or the like. The processing procedure of the potassium ion generation means 621 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

Based on the potassium ion information generated by the potassium ion generation unit 621, the calcium ion generation unit 622 generates calcium ion information that is information regarding Ca ²⁺ flowing into the cell. The calcium ion generating means 622 can be usually realized by an MPU, a memory, or the like. The processing procedure of the calcium ion generating means is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

The calcium ion information acquisition unit 623 acquires calcium ion information that is information regarding Ca ²⁺ flowing into the cell. The calcium ion information acquisition unit 623 can be usually realized by an MPU, a memory, or the like. The processing procedure of the calcium ion information acquisition unit 623 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The output unit 63 outputs the insulin information generated by the insulin generation unit 62. In addition, the output unit 63 outputs side effect information that is information regarding side effects on the injection of the SU agent. The side effect information is information regarding side effects with respect to the injection of the SU agent. The side effect information includes, for example, SU agent-corresponding insulin amount information that is information indicating the intracellular insulin amount corresponding to the SU agent exposure time. Here, output is a concept including display on a display, printing on a printer, sound output, transmission to an external device, and the like. The output unit 63 may or may not include an output device such as a display or a speaker. The output unit may be realized by output device driver software, or output device driver software and an output device.

  Next, the operation of the simulation apparatus will be described using the flowchart of FIG.

  (Step S701) The reception unit 61 determines whether glucose information and SU agent information have been received. If glucose information etc. are received, it will go to step S702, and if glucose information etc. are not received, it will return to step S701.

(Step S702) The potassium ion generation means 621 generates potassium ion information that is information regarding K ⁺ flowing into the cell, based on the SU agent information received in step S701.

(Step S703) The calcium ion generation means 622 generates calcium ion information, which is information regarding Ca ²⁺ flowing into the cell, based on the potassium ion information generated in step S702.

  (Step S704) The calcium ion information acquisition means 623 acquires the calcium ion information produced | generated by step S703.

  (Step S705) The output unit 63 acquires side effect information (for example, information on the amount of insulin corresponding to SU agent) that is information on the side effect with respect to the injection of the SU agent.

  (Step S706) The output unit 63 outputs the side effect information acquired in step S705. The process ends.

  In the flowchart of FIG. 7, there are various possible ways of outputting the side effect information. A specific example of the output mode of the side effect information will be described later.

  In the flowchart of FIG. 7, the output insulin information may not be information regarding the insulin secretion rate. The insulin information to be output may be, for example, an insulin secretion amount. In the flowchart of FIG. 7, the output unit 63 may output information other than insulin information and side effect information.

  Hereinafter, a specific operation of the simulation apparatus according to the present embodiment will be described.

First, the reception unit 61 receives glucose information and SU agent information. Here, for example, the glucose information is the extracellular glucose concentration (glu) [unit: mM]. In addition, for example, the SU agent information is a dissociation constant (K _d ) for the SUR of the SU agent and an extracellular SU concentration (C) [unit: nM]. The unit of the dissociation constant (K _d ) is [unit: nM].

Next, the potassium ion production | generation means 621 produces | generates the potassium ion information which is the information regarding K ^<+> which flows in into a cell based on the received dissociation constant ( _Kd ). Specifically, for example, the potassium ion generation unit 621 generates potassium ion information based on the calculation formula of Formula 7. Here, potassium ion information is the _KATP channel permeability (E) of intracellular potassium. That is, the potassium ion generating means 621 stores information on the arithmetic expression of Expression 7. Moreover, the potassium ion production | generation means 621 acquires the dissociation constant ( _Kd ) which the reception part 61 received. In addition, the potassium ion generation means 621 includes the stored intracellular potassium K _ATP channel maximum permeability “E _max ”, intracellular potassium K _ATP channel permeability Hill coefficient “m”, and extracellular SU agent concentration “C”. , “K _d ” and “K _0.5 ” conversion coefficients “a” and “K _d ” and “K _0.5 ” conversion coefficients “b” are read out, respectively. And the potassium ion production | generation means 621 reads the information of a computing equation, substitutes the acquired value ( _Kd , _Emax , m, C, a, b) for the said computing equation, and K _{ATP of} intracellular potassium The channel transmittance (E) is calculated. It is a known technique that stores information on an arithmetic expression, reads information on the arithmetic expression, assigns a value to a variable of the arithmetic expression, and obtains a result.

In Equation 7, “E _max ” is the K _ATP channel maximum transmittance of intracellular potassium. Specifically, “E _max ” is “1”, for example.

In Equation 7, “m” is the _KATP channel permeability Hill coefficient of intracellular potassium. Specifically, m is, for example, “1”.

  In Expression 7, “C” is the extracellular SU agent concentration. The unit of C is [nM].

In Equation 7, “a” is a conversion coefficient between “K _d ” and “K _0.5 ”, and the value is “0.828”, for example.

Furthermore, “b” is a conversion coefficient between “K _d ” and “K _0.5 ”, and the value is “0.725”, for example.

Next, the calcium ion production | generation means 622 produces | generates the calcium ion information which is the information regarding Ca2 ⁺ which flows in into a cell based on the produced | generated _KATP channel permeability _| transmittance (E). Here, the calcium ion information is intracellular calcium concentration (Ca _i ). Specifically, for example, the calcium ion generation means 622 calculates the intracellular calcium concentration (Ca _i ) by the following formulas 8 to 16. The calcium ion generating means 622 stores the _KATP channel maximum conductance in advance. The calcium ion-generating unit 622 _reads the value of a _{K ATP} channel maximum conductance, the value of the read _{K ATP} channel maximum conductance, resulting _{K ATP} channel transmission rate (E), are substituted into Equation 8, K Obtain _ATP channel conductance.

In addition, the calcium ion production | generation means 622 stores the information of the arithmetic expression of Formula 8-Formula 16 previously, reads these suitably, and performs a calculation.

In Equation 8, the unit of K _ATP channel conductance and K _ATP channel maximum conductance is [ns], and the K _ATP channel maximum conductance is, for example, a constant “70 [ns]”.

Note that in Equation _{9, I KATP are K ATP} channel transparency current. O _KATP is the K _ATP channel opening probability. V is the cell membrane potential. Furthermore, V _K is the reversal potential of the K ⁺ ion of the potassium channel.

Equation 9 shows the following phenomenon. That is, there at the moment (the time T), the _{K ATP} channel conductance is reduced by the SU agent, _{K ATP} channel transmission current _{I KATP} decreases by Equation 9. The calcium ion generation means 622 stores the K _ATP channel opening probability (O _KATP ), the K ⁺ ion reversal potential (V _K ) of the potassium channel, and reads out these values. And the calcium ion production | generation means 622 calculates cell membrane potential (V) by the following Numerical formula 10. The calcium ion generation means 622 substitutes the calculated K _ATP channel conductance, cell membrane potential (V), and read O _KATP , V _K into the calculation formula of Formula 9, and obtains the K _ATP channel permeation current (I _KATP ). In addition, the calcium ion production | generation means 622 reads the information of the arithmetic formula of Numerical formula 9, and performs a calculation.

In Equation 10, C is a constant and conductance. I _Kdr is a delayed rectification K ion current. Also, I _Caf is the “fast” potential-dependent Ca channel transmission current. I _Cas is the “slow” potential-dependent Ca channel transmission current. Furthermore, I _NS is a non-selective cation current. Calcium ion generating unit 622, _{"dV = (- (I Kdr +} I KATP + I Caf + I Cas + I NS) × dt) / C " is calculated (change in V) dV by. Calcium ion generating unit 622, when calculating the dV, before _{I Kdr, I KATP, I Caf} , I Cas, by using the _{I NS,} the conductance (C) that contains, substituted into the arithmetic expression To do. Then, the next V is calculated by “V = previous V + dV”.

Further, I _Kdr and I _NS are variables that change according to V, and the calculation method thereof is described in Non-Patent Document 1.

Equation 10 shows the following phenomenon. That is, when I _KATP decreases, the membrane potential change rate dV / dt increases. Note that when the membrane potential change rate dV / dt increases, depolarization occurs earlier.

At the next moment (time T + dt), the membrane potential V becomes V + dV, and V itself increases.

From Equations 11 and 12, as V increases, the “fast” potential-dependent Ca channel transmission current I _Caf and the “slow” potential-dependent Ca channel transmission current I _Cas increase.

In Equations 11 and 12, (g _ca bar) is Ca channel conductance, F is a Faraday constant, V is a cell membrane potential, R is a gas constant, and T is an absolute temperature.

In Formula 13, f _i is the fraction of unbound Ca in the cytoplasm. α is a conversion coefficient from Ca current to Ca film permeation rate. γ ₂ is a conversion factor from the mitochondrial concentration to the cytoplasmic concentration. J _uni is the rate of Ca transfer from the cytoplasm to the mitochondria by the uniporter. _{JNA + / Ca2 +} is the rate of Ca migration from the mitochondria to the cytoplasm by Na / Ca exchange. k _Ca is the cytoplasmic Ca removal rate constant.

As I _Caf and I _Cas increase according to Equation 13, the intracellular Ca concentration change rate d [Ca ²⁺ ] _i / dt increases.

Furthermore, at the next moment (time T + 2dt), the intracellular Ca concentration [Ca ²⁺ ] _i becomes [Ca ²⁺ ] _i + d [Ca ²⁺ ] _i and [Ca ²⁺ ] _i itself increases. Note that "i" (i of _{f i} and ^[Ca _{2+] i)} subscript refers intracellularly (internal).

Further, depends on "open (open)" probability _{O f} is "closed (closed)" probability C and "inactive (Ca-bound)" probability B for "fast" voltage-dependent Ca channels in Equation 11 . Furthermore, O _s in Equation 12 is the “open” probability of the “slow” potential-dependent Ca channel.

Here, C varies with changes in the membrane potential V from Equations 14 and 15. B changes according to the change of the membrane potential V from Equations 15 and 16. Further, Os in Equation 12 depends on the membrane potential V and the “slow” potential-dependent Ca channel inactivation rate J as shown in Equation 17. J changes with changes in the membrane potential V from Equations 18, 19, and 20.

  Note that Equation 9 to Equation 19 are described in Non-Patent Document 1 above.

The above K ₋₁ is a transition rate constant from the probability Of to the probability C. K _{+ 1} is a transition rate constant from probability C to probability Of. K _-2 are calcium dissociation rate constant for calcium-binding probability _{O f.} K ₊₂ is calcium dissociation rate constant for the probability _{O f.} K _-3 is a transition rate constants from the probability B to the calcium-binding probability _{O f.} K ₊₃ is a transition rate constants from calcium-binding probability _{O f} the probability B.

The above formula 20 is a known calculation formula. The above T _J and T _min are the maximum and minimum inactivation times of the “slow” voltage-dependent Ca channel, respectively. The numerical value is, for example, “T _J = 50000 msec” or “T _min = 1500 msec”.

Next, the calcium ion information acquisition unit 623 acquires the calculated intracellular calcium concentration (Ca _i ).

Next, the ATP information acquisition unit 121 acquires ATP information. Here, the ATP information is the intracellular ATP concentration (ATP _i ).

Next, the near-Golgi insulin information generating means 123 calculates insulin biosynthesis rate information (V _synthesis ) according to the above mathematical formula 1.

  Next, the Golgi vicinity insulin information generation unit 123, the cell membrane vicinity insulin information generation unit 124, the phosphorylated insulin information generation unit 125, and the activated insulin information generation unit 126 are differentials of Formula 2, Formula 3, Formula 4, and Formula 5. The amount of insulin granules in each state is calculated using an equation.

Next, activate the insulin information generating unit 126, the activated insulin granules amount A as a parameter, generates the insulin secretion rate _{(V secretion).} Specifically, activation insulin information generating unit 126, insulin secretion rate _{(V Secretion),} calculated by Equation 6.

Then, the output unit 63 outputs the insulin secretion rate _{(V secretion).} It is an example of the insulin secretion rate _{(V Secretion)} shown in FIG. In FIG. 3, the horizontal axis represents time.

The output unit 63, the insulin secretion rate _{(V Secretion)} may output information other than. An output example in such a case is shown in FIG.

  Furthermore, the output unit 63 acquires side effect information that is information regarding side effects on the introduction of the SU agent. Here, the side effect information is time-series information of the amount of intracellular insulin changing with time.

  Then, the output unit 63 outputs the acquired side effect information. Examples of output of side effect information are shown in FIGS. FIG. 8 shows images obtained by simulating the dependency of the insulin secretion rate on the insulin secretion rate with various SU agents. FIG. 8 shows that when the dissociation constant (Kd) for the SUR of the SU agent is known, it is possible to predict the insulin secretion promoting action according to the extracellular SU concentration. FIG. 9 shows the time course of insulin secretion rate and intracellular insulin content during exposure to the SU agent. In FIG. 9, when the SU agent is exposed for a long time, the insulin secretion is initially promoted, but there is a possibility that a side effect may occur such that the insulin secretion rate starts to decrease as the intracellular insulin content decreases. Show.

  As described above, according to the present embodiment, it is possible to simulate insulin secretion due to the introduction of the SU agent. Moreover, it can simulate about the side effect by injection | throwing-in of a SU agent.

  Moreover, according to the present embodiment, if SU agent information (for example, concentration of SU agent) and glucose information (for example, dissociation constant of SU agent for receptor) are input, various intracellular ion changes and insulin It is possible to simulate the effect of the SU agent on the state transition of the granules and the extracellular insulin secretion. This makes it possible to predict in advance side effects such as the reduction in the number of pharmacological tests and insulin depletion caused by the SU drug, which can be a very effective tool for the development of the SU drug.

  In addition, according to the present embodiment, it is useful to determine the therapeutic strategy for diabetes caused by drugs, such as the effect of SU drug administration to diabetic patients in clinical settings and the effective administration method of SU drug. Information can be provided.

  In addition, according to this Embodiment, the SU agent information which the reception part 61 receives was the dissociation constant with respect to SUR of an SU agent, and extracellular SU density | concentration.

  Moreover, the arithmetic expressions of the above formulas 7 to 20 in the present embodiment are suitable examples, and other arithmetic expressions may be used as long as the insulin secretion due to the injection of the SU agent can be simulated. The other arithmetic expressions are, for example, expressions obtained by slightly correcting the parameters of the arithmetic expressions of Expression 7 to Expression 20. That is, in the present embodiment, the simulation apparatus is configured to receive glucose information, an accepting unit that receives SU agent information that is information relating to the SU agent to be added, insulin that is secreted based on the glucose information, and the SU agent information. What is necessary is just a simulation apparatus provided with the insulin production | generation part which produces | generates the insulin information which is the information regarding, and the output part which outputs the insulin information which the said insulin production | generation part produced | generated.

  Furthermore, according to the present embodiment, it goes without saying that the output mode in the output unit is not limited to FIGS. 3, 4, 8, and 9.

  Furthermore, the processing in the present embodiment may be realized by software. Then, this software may be distributed by software download or the like. Further, this software may be recorded and distributed on a recording medium such as a CD-ROM. This also applies to other embodiments in this specification. Note that the software that implements the information processing apparatus according to the present embodiment is the following program. In other words, this program is a computer that accepts glucose information and SU agent information that is information about the SU agent to be added to the computer, information about insulin secreted based on the glucose information, and the SU agent information. It is a program for executing an insulin generation step for generating certain insulin information and an output step for outputting the insulin information generated in the insulin generation step.

Moreover, in the said program, an insulin production | generation step is based on the potassium ion production | generation step which produces | generates the potassium ion information which is the information regarding K ^<+> which flows into a cell based on the said SU agent information, and the said potassium ion information, A calcium ion generation step for generating calcium ion information that is information about Ca ²⁺ flowing into the cell, an ATP information acquisition step for acquiring ATP information that is information about ATP to be produced, the ATP information, and the calcium ion Based on the information and the glucose information, the Golgi vicinity insulin information generation step for generating the first insulin information that is information related to insulin secreted in the vicinity of the Golgi body, the ATP information, the calcium ion information, and the In the first insulin information Subsequently, phosphorylation is performed on the basis of the near-cell-membrane insulin information generation step for generating second-insulin information, which is information related to insulin in the vicinity of the cell membrane, the ATP information, the calcium ion information, and the second insulin information. Insulin information that is information about activated insulin is generated based on the phosphorylated insulin information generation step that generates third insulin information that is information related to the insulin, the calcium ion information, and the third insulin information. It is preferable to comprise an activated insulin information generation step.

Further, in the above program, it is preferable that the output step outputs side effect information that is information regarding side effects on the introduction of the SU agent. The side effect information includes, for example, SU agent-corresponding insulin amount information which is information indicating the intracellular insulin amount corresponding to the SU agent exposure time.
(Embodiment 3)

  FIG. 1 is a block diagram of a simulation apparatus according to the present embodiment. The results of a specific simulation performed in this simulation apparatus will be described below.

  First, it is assumed that the reception unit 11 sequentially receives information on an extracellular glucose concentration (an example of glucose information) that changes between 0 to 20 mmol / L for each predetermined value (for example, 1 mmol / L).

  Next, the insulin generator 12 generates insulin information, which is information related to secreted insulin, based on the information on the extracellular glucose concentration received by the receiver 11. Here, the insulin information is an insulin secretion rate.

  Hereinafter, the details of the processing until the information on the extracellular glucose concentration is received and the insulin secretion rate is acquired will be described.

  First, the ATP information acquisition unit 121 acquires the intracellular ATP concentration (an example of ATP information) and temporarily stores it in a predetermined memory (variable). Further, it is assumed that the ADP concentration is also acquired by means not shown and temporarily stored in a predetermined memory (variable). The technique for obtaining the ADP concentration is a known technique (described in Non-Patent Document 1). In this embodiment, the intracellular ADP concentration is obtained by the approximate expression described in Non-Patent Document 1 ([Intracellular ATP concentration] = 2 (mmol / L) − [Intracellular ADP concentration]). ing.

Next, the calcium ion information acquisition unit 122 acquires a calcium ion concentration (an example of calcium ion information) that is the concentration of Ca ²⁺ flowing into the cell, and temporarily stores it in a predetermined memory (variable).

  Next, as described in Embodiment 1, the Golgi vicinity insulin information generating unit 123 generates the insulin biosynthesis rate information based on the extracellular glucose concentration using Formula 1. Then, as described in Embodiment 1, the Golgi vicinity insulin information generating means 123 uses the intracellular ATP concentration, calcium ion concentration, extracellular glucose concentration, and insulin biosynthesis rate information to secrete near the Golgi body. First insulin information, which is information about the insulin to be performed, is generated.

  Next, as described in the first embodiment, the cell membrane vicinity insulin information generation unit 124 uses the intracellular ATP concentration, the calcium ion concentration, and the first insulin information, and the second information that is information related to insulin in the vicinity of the cell membrane. Generate insulin information.

  Next, as described in the first embodiment, the phosphorylated insulin information generation unit 125 uses the intracellular ATP concentration, the calcium ion concentration, and the second insulin information, and is third information that is information regarding phosphorylated insulin. Generate insulin information.

  Next, as described in the first embodiment, the activated insulin information generation unit 126 generates fourth insulin information that is information about activated insulin using the calcium ion concentration and the third insulin information. .

  Next, as described in the first embodiment, the activated insulin information generation unit 126 obtains the insulin secretion rate based on the fourth insulin information.

  Then, the output unit 13 calculates the intracellular ATP concentration acquired by the ATP information acquisition unit 121, the ADP concentration acquired by a unit (not shown), the calcium ion concentration acquired by the calcium ion information acquisition unit 122, and the insulin secretion rate. A graph is output in association with the glucose concentration. FIG. 11 shows the graph output.

  11 (a) and 11 (b), the horizontal axis represents the extracellular glucose concentration (mmol / L). In FIG. 11A, the horizontal axis represents the intracellular ATP concentration, the intracellular ADP concentration, and the insulin secretion rate. In FIG. 11 (b), the horizontal axis represents the intracellular calcium ion concentration and the insulin secretion rate.

  In FIG. 11A, the intracellular ATP concentration and the intracellular ADP concentration are indicated by a solid line, and the insulin secretion rate is indicated by a broken line. In FIG. 11B, the intracellular calcium ion concentration is indicated by a solid line, and the insulin secretion rate is indicated by a broken line.

According to FIG. 11, it can be seen that the insulin secretion rate changes at a glucose concentration of about 6 mmol / L. The simulation result reproduces the literature data of Non-Patent Document 5 well. In addition, according to FIG. 11, at low concentrations of glucose conditions insulin secretion is dependent on ATP and Ca ^2+, at high concentrations it is estimated that Ca ²⁺ dependency increases.

As described above, according to the present embodiment, it is considered that glucose-dependent insulin secretion of the pancreatic β cell model realized by the simulation apparatus is appropriate.
(Embodiment 4)

  FIG. 6 is a block diagram of the simulation apparatus in the present embodiment. The results of a specific simulation performed in this simulation apparatus will be described below.

  First, it is assumed that the reception unit 61 receives 10 mmol / L extracellular glucose concentration information (an example of glucose information) and 50 μmol / L tolbutamide concentration (an example of SU agent information).

Next, the potassium ion generation means 621 performs potassium ion information (here, cell information) that is information on K ⁺ flowing into the cell by the processing described in Embodiment 2 based on the tolbutamide concentration (50 μmol / L). _KATP channel permeability (E) of the inner potassium.

Next, calcium ion generation unit 622, using the potassium ion information, calcium ion information is information relating to Ca ²⁺ (herein, intracellular calcium concentration (Ca _i)) flowing into the cell to produce a.

Next, the calcium ion information acquisition means 623 acquires the generated calcium ion information (intracellular calcium concentration (Ca _i )).

Then, the ATP information acquisition unit 121 acquires ATP information (here, intracellular ATP concentration (ATP _i )).

Next, the Golgi vicinity insulin information generation means 123 generates insulin biosynthesis rate information (V _synthesis ) which is information indicating the insulin biosynthesis rate based on the extracellular glucose concentration by the above mathematical formula 1.

  Next, the Golgi vicinity insulin information generation means 123 performs ATP information (intracellular ATP concentration), calcium ion information (calcium ion concentration), and glucose information (extracellular glucose concentration) by the processing described in the first embodiment. ) And first insulin information (insulin granule amount D in the vicinity of the Golgi apparatus) that is information relating to insulin secreted in the vicinity of the Golgi apparatus is generated based on the insulin biosynthesis rate information.

  Next, the cell membrane vicinity insulin information generating means 124 uses the intracellular ATP concentration, the calcium ion concentration, and the first insulin information (insulin granule amount D near the Golgi body) by the processing described in the first embodiment. Second insulin information that is information related to insulin in the vicinity is generated.

  Next, the phosphorylated insulin information generation unit 125 uses the processing described in Embodiment 1, and the third information is information relating to the phosphorylated insulin using the intracellular ATP concentration, the calcium ion concentration, and the second insulin information. Generate insulin information.

  Next, the activated insulin information generation unit 126 generates fourth insulin information, which is information about the activated insulin, using the calcium ion concentration and the third insulin information by the processing described in the first embodiment. .

  Next, the activated insulin information generation unit 126 obtains the insulin secretion rate based on the fourth insulin information by the processing described in the first embodiment.

  And the output part 63 outputs the graph of Fig.12 (a), (b), (c), (d), for example.

  The graph of “when tolbutamide is added” in FIG. 12A shows the case where the extracellular glucose concentration is 10 mmol / L and SU agent information (tolbutamide is 50 μmol / L) is present (the information is received by the receiving unit 61). It is a graph which shows the change in 20 hours of the insulin secretion rate (A). In addition, the “control” graph in FIG. 12A shows the insulin secretion rate when the accepting unit 61 accepts the extracellular glucose concentration “10 mmol / L” and the SU agent information (tolbutamide is 0 μmol / L). It is a graph which shows the change in 20 hours of (A).

  The graph of “when tolbutamide is added” in FIG. 12B shows the case where the extracellular glucose concentration is 10 mmol / L and SU agent information (tolbutamide is 50 μmol / L) is present (the information is received by the receiving unit 61). It is a graph which shows the change in 20 hours of intracellular insulin amount (B) of a case. In addition, the “control” graph of FIG. 12 (a) shows the intracellular state when the accepting unit 61 accepts the extracellular glucose concentration “10 mmol / L” and the SU agent information (tolbutamide is 0 μmol / L). It is a graph which shows the change in 20 hours of the amount of insulin (B).

  The graph of “tolbutamide pretreatment” in FIG. 12 (c) shows that when 20 hours value is the initial value, tolubutamide in SU drug information is set to 0 μmol / L, and the extracellular glucose concentration is set to 0 to 20 mmol / L (acceptance). It is a graph which shows the change of the insulin secretion rate (C) when the part 61 receives the value between 0-20 sequentially). In the graph of “control” in FIG. 12C, when the 20-hour value is an initial value, tolubutamide in SU drug information is set to 0 μmol / L, and the extracellular glucose concentration is set to 0 to 20 mmol / L (accepting unit 61). It is a graph which shows the change of the insulin secretion rate (C) when the value between 0-20 is received sequentially.

  The graph of “tolbutamide pretreatment” and “control” in FIG. 12D shows the insulin secretion rate (C) of “tolbutamide pretreatment” and “control” in FIG. It is a graph corrected by (20 hours value of B).

  Note that the output unit 63 may acquire and output side effect information (for example, information on the amount of insulin corresponding to SU agent) that is information related to the side effect on the introduction of the SU agent.

  As described above, according to the simulation results of the present embodiment, compared to the absence of tolbutamide, the presence of pancreatic islets of Langerhans (La Islet) has an initial rapid increase in insulin secretion rate and a gradual decrease thereafter. It was found that the amount of intracellular insulin also tends to decrease gradually. In addition, regarding the amount of intracellular insulin 20 hours after the start of the simulation, the experimental data (data obtained separately) was reproduced well (FIG. 12B). Next, the insulin secretion rate was simulated when the intracellular insulin amount after 20 hours was set to the initial value, the tolbutamide concentration was set to 0 μmol / L, and the glucose concentration was set to 0 to 20 mmol / L (FIG. 12 (c) ( d)). FIG. 12C shows the insulin secretion rate itself, and FIG. 12D shows a value obtained by correcting the insulin secretion rate with the initial value. As a result, it was shown that the glucose concentration-dependent insulin secretion decreases as a whole by tolbutamide treatment, but the pattern in which the secretion rate changes at about 6 mmol / L glucose is almost the same (FIG. 12). (C)). It was also found that these secretion rates are proportional to the amount of intracellular insulin (FIG. 12 (d)). For these simulation results, the experimental data (data obtained separately) could be reproduced well.

  As described above, according to the present embodiment, it was possible to quantitatively understand the time change of the insulin secretion rate promotion and the intracellular insulin amount decrease by tolbutamide, which was unknown only by the experimental data, using the pancreatic β cell model (FIG. 12 ( a) (b)). Further, since the experimental result that the glucose concentration-dependent insulin secretion rate at the time of tolbutamide removal was proportional to the amount of intracellular insulin could be reproduced well by simulation (FIGS. 12 (c) and 12 (d)), The validity and usefulness of the simulator were confirmed.

  In each of the above embodiments, each process (each function) may be realized by centralized processing by a single device (system), or by distributed processing by a plurality of devices. May be.

  In each of the above-described embodiments, as one aspect in the case where the simulation apparatus is distributedly processed by a plurality of apparatuses, there is a case where it is realized by a server apparatus and a client apparatus. The client device transmits glucose information input by the user or glucose information and SU agent information to the server device, the server device receives (accepts) such information, the server device performs a simulation, and the result is sent to the client. This is a mode in which the client device receives (displays) the result of the simulation and displays it.

  FIG. 13 shows the external appearance of a computer that executes the programs described in this specification to realize the simulation apparatuses of the various embodiments described above. The above-described embodiments can be realized by computer hardware and a computer program executed thereon. FIG. 13 is an overview diagram of the computer system 340, and FIG. 14 is a block diagram of the computer system 340.

  In FIG. 13, the computer system 340 includes a computer 341 including a FD (Flexible Disk) drive and a CD-ROM (Compact Disk Read Only Memory) drive, a keyboard 342, a mouse 343, and a monitor 344.

  In FIG. 14, in addition to the FD drive 3411 and the CD-ROM drive 3412, the computer 341 includes a CPU (Central Processing Unit) 3413, a bus 3414 connected to the CPU 3413, the CD-ROM drive 3412, and the FD drive 3411, and a boot. A ROM (Read-Only Memory) 3415 for storing a program such as an up program, and a RAM (Random Access Memory) connected to the CPU 3413 for temporarily storing instructions of an application program and providing a temporary storage space 3416 and a hard disk 3417 for storing application programs, system programs, and data. Although not shown here, the computer 341 may further include a network card that provides connection to the LAN.

  A program that causes the computer system 340 to execute the functions of the simulation apparatus according to the above-described embodiment is stored in the CD-ROM 3501 or FD 3502, inserted into the CD-ROM drive 3412 or FD drive 3411, and further transferred to the hard disk 3417. May be. Alternatively, the program may be transmitted to the computer 341 via a network (not shown) and stored in the hard disk 3417. The program is loaded into the RAM 3416 at the time of execution. The program may be loaded directly from the CD-ROM 3501, the FD 3502, or the network.

  The program does not necessarily include an operating system (OS) or a third-party program that causes the computer 341 to execute the functions of the simulation apparatus according to the above-described embodiment. The program only needs to include an instruction portion that calls an appropriate function (module) in a controlled manner and obtains a desired result. How the computer system 340 operates is well known and will not be described in detail.

  Further, the computer that executes the program may be singular or plural. That is, centralized processing may be performed, or distributed processing may be performed.

  The present invention is not limited to the above-described embodiments, and various modifications are possible, and it goes without saying that these are also included in the scope of the present invention.

  As described above, the simulation device and the like according to the present invention have an effect that insulin secretion can be simulated from glucose reception, and are useful as a simulation device and the like.

Block diagram of simulation apparatus according to Embodiment 1 Flow chart explaining operation of the simulation apparatus Figure showing the same output example Figure showing the same output example The figure which shows the model of state change of the same insulin Block diagram of a simulation apparatus according to the second embodiment Flow chart explaining operation of the simulation apparatus Figure showing the same output example Figure showing the same output example Conceptual diagram explaining the concept from glucose reception to insulin secretion The figure which shows the example of an output in Embodiment 3. The figure which shows the example of an output in Embodiment 4. Overview of the computer system that composes the simulation device Block diagram of a computer constituting the simulation apparatus

Explanation of symbols

DESCRIPTION OF SYMBOLS 11, 61 Reception part 12, 62 Insulin production | generation part 13, 63 Output part 121 ATP information acquisition means 122,623 Calcium ion information acquisition means 123 Golgi vicinity insulin information generation means 124 Cell membrane vicinity insulin information generation means 125 Phosphorylated insulin information generation Means 126 Activated insulin information generation means 621 Potassium ion generation means 622 Calcium ion generation means

Claims (8)

A reception unit that accepts glucose information that is information about glucose;
An insulin generator that generates insulin information, which is information related to secreted insulin, based on the glucose information received by the reception unit;
An output unit that outputs insulin information generated by the insulin generation unit;
The insulin generator is
ATP information acquisition means for acquiring ATP information, which is information about ATP to be produced;
Calcium ion information acquisition means for acquiring calcium ion information, which is information relating to Ca ²⁺ flowing into the cell;
Based on the ATP information, the calcium ion information, and the glucose information, Golgi vicinity insulin information generation means for generating first insulin information that is information related to insulin generated in the vicinity of the Golgi body,
Based on the ATP information, the calcium ion information, and the first insulin information, a cell membrane vicinity insulin information generating unit that generates second insulin information that is information related to insulin in the vicinity of the cell membrane;
Phosphorylated insulin information generating means for generating third insulin information that is information related to phosphorylated insulin based on the ATP information, the calcium ion information, and the second insulin information;
Activated insulin information generating means for generating insulin information, which is information related to activated insulin, based on the calcium ion information and the third insulin information;
A simulation apparatus comprising: The activated insulin information generating means includes
Based on the calcium ion information and the third insulin information, fourth insulin information that is information about activated insulin is generated, and based on the fourth insulin information, insulin information that is information about the insulin secretion rate The simulation apparatus according to claim 1, wherein A reception unit that accepts glucose information that is information about glucose;
An insulin generator that generates insulin information, which is information related to secreted insulin, based on the glucose information received by the reception unit;
An output unit that outputs insulin information generated by the insulin generation unit;
The reception unit
Accepting the glucose information and SU agent information that is information about the SU agent to be introduced,
The insulin generator is
Based on the glucose information and the SU agent information, a simulation device that generates insulin information that is information related to secreted insulin ,
The insulin generator is
Based on the SU agent information, potassium ion generating means for generating potassium ion information that is information on K ⁺ flowing into the cell ,
Based on the potassium ion information, calcium ion generation means for generating calcium ion information, which is information related to Ca ²⁺ flowing into cells ,
ATP information acquisition means for acquiring ATP information, which is information about ATP to be produced;
Based on the ATP information, the calcium ion information, and the glucose information, Golgi vicinity insulin information generating means for generating first insulin information that is information related to insulin secreted near the Golgi body,
Based on the ATP information, the calcium ion information, and the first insulin information, a cell membrane vicinity insulin information generating unit that generates second insulin information that is information related to insulin in the vicinity of the cell membrane;
Phosphorylated insulin information generating means for generating third insulin information that is information related to phosphorylated insulin based on the ATP information, the calcium ion information, and the second insulin information;
Activated insulin information generating means for generating insulin information, which is information related to activated insulin, based on the calcium ion information and the third insulin information;
A simulation apparatus comprising: The output unit is
4. The simulation apparatus according to claim 3, wherein side effect information, which is information on side effects with respect to the injection of the SU agent, is output. The side effect information is
5. The simulation apparatus according to claim 4 , further comprising SU agent-corresponding insulin amount information, which is information indicating an intracellular insulin amount corresponding to the SU agent exposure time. On the computer,
An accepting step for accepting glucose information which is information on glucose;
An insulin generation step for generating insulin information, which is information related to secreted insulin, based on the glucose information received in the reception step;
A program for executing an output step of outputting the insulin information generated in the insulin generation step,
The insulin generation step includes
An ATP information acquisition step of acquiring ATP information, which is information about ATP to be produced;
A calcium ion information acquisition step of acquiring calcium ion information which is information relating to Ca ²⁺ flowing into the cell;
Based on the ATP information, the calcium ion information, and the glucose information, a Golgi vicinity insulin information generation step for generating first insulin information that is information related to insulin secreted in the vicinity of the Golgi body,
Based on the ATP information, the calcium ion information, and the first insulin information, a cell membrane vicinity insulin information generation step for generating second insulin information that is information related to insulin in the vicinity of the cell membrane;
A phosphorylated insulin information generating step for generating third insulin information, which is information related to phosphorylated insulin, based on the ATP information, the calcium ion information, and the second insulin information;
A program comprising: an activated insulin information generation step for generating insulin information that is information relating to activated insulin based on the calcium ion information and the third insulin information. On the computer,
An accepting step for accepting glucose information which is information on glucose;
An insulin generation step for generating insulin information, which is information related to secreted insulin, based on the glucose information received in the reception step;
A program for executing an output step of outputting the insulin information generated in the insulin generation step,
The reception step includes
Accepting the glucose information and SU agent information that is information about the SU agent to be introduced,
The insulin generation step includes
Based on the glucose information and the SU agent information, a program for generating insulin information that is information related to secreted insulin ,
The insulin production step
Based on the SU agent information, a potassium ion generation step for generating potassium ion information that is information on K ⁺ flowing into the cells ;
Based on the potassium ion information, a calcium ion generation step of generating calcium ion information that is information on Ca ²⁺ flowing into the cell ;
An ATP information acquisition step of acquiring ATP information, which is information about ATP to be produced;
Based on the ATP information, the calcium ion information, and the glucose information, a Golgi vicinity insulin information generation step for generating first insulin information that is information related to insulin generated in the vicinity of the Golgi body;
Based on the ATP information, the calcium ion information, and the first insulin information, a cell membrane vicinity insulin information generation step for generating second insulin information that is information related to insulin in the vicinity of the cell membrane;
A phosphorylated insulin information generating step for generating third insulin information, which is information related to phosphorylated insulin, based on the ATP information, the calcium ion information, and the second insulin information;
A program comprising: an activated insulin information generation step for generating insulin information that is information relating to activated insulin based on the calcium ion information and the third insulin information . The output step includes
8. The program according to claim 7, which outputs side effect information, which is information about side effects on the injection of SU agent.