Ion current data generation device, ion current data generation method, and ion current data generation program.

Abstract

Mathematical parameters can be obtained to analyze the state of ion channels. [Solution] The ion current data generation device 7 includes a data generation unit 80 that generates first data 41 indicating the ion current I based on mathematical parameters 71 set as data indicating the ion current I flowing through the ion channel 10 and the frequency characteristics of a detection unit 20 that measures the ion current I.

Description

Technical Field

[0001] The present disclosure relates to an ion current data generation device, an ion current data generation method, and an ion current data generation program.

Background Art

[0002] There are transmembrane proteins called ion channels in the cell membrane of a living body (for example, Patent Document 1). Ion channels serve as passageways for ions in the cell membrane. Ions have electric charges. In the cell membrane, ions are passively permeated through ion channels. Ion channels open and close.

[0003] When an ion channel opens, the ion current flowing through the ion channel increases. When the ion channel closes, the ion current flowing through the ion channel decreases.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] By the way, it is conceivable to analyze the state of an ion channel based on first data (such as a current waveform or a histogram) obtained from the ion current flowing through the ion channel.

[0006] In order to analyze the state of an ion channel based on data obtained from the ion current flowing through the ion channel, it is necessary to obtain mathematical parameters corresponding to the data, but it has been difficult to obtain such mathematical parameters.

[0007] Therefore, it is conceivable to generate a regression model in which, when the first data obtained from ion currents is input, mathematical parameters corresponding to the first data are output. To generate such a regression model, a large number of first data are required as training data.

[0008] This disclosure has been made in view of the above, and its purpose is to provide an ion current data generation device, an ion current data generation method, and an ion current data generation program that can obtain first data which will serve as training data for a regression model that outputs mathematical parameters. [Means for solving the problem]

[0009] The ion current data generation device according to this disclosure includes a data generation unit that generates first data indicating the ion current based on mathematical parameters set as data indicating the ion current flowing through an ion channel and the frequency characteristics of a measuring instrument that measures the ion current. [Effects of the Invention]

[0010] According to this disclosure, it is possible to obtain first data that will serve as training data for a regression model from which mathematical parameters are output. [Brief explanation of the drawing]

[0011] [Figure 1] Figure 1 shows the ion channels formed in the artificial cell membrane. [Figure 2] Figure 2 shows the current waveform of the ion current. [Figure 3] Figure 3 shows the histogram of ion current. [Figure 4] Figure 4 shows the regression model M according to the first embodiment. [Figure 5] Figure 5 is a diagram illustrating the generation process of the first data 41. [Modes for carrying out the invention]

[0012] The embodiments of this disclosure will be described in detail below with reference to the drawings. The following description of preferred embodiments is illustrative in nature and is not intended to limit the disclosure, its applications or uses in any way.

[0013] Furthermore, the ion current data generation method and ion current data generation program described herein are implemented as a function of an ion current data generation device.

[0014] <Embodiment> (Ion channel analyzer) Ion channel analyzer 1 will now be described. Ion channel analyzer 1 is applied to ion channel 10. Ion channel analyzer 1 analyzes the state of ion channel 10. In this example, ion channel 10 is formed on artificial cell membrane 5.

[0015] Figure 1 shows the ion channels 10 formed on the artificial cell membrane 5. The artificial cell membrane 5 is artificially formed to mimic the cell membrane of a living organism. When a water droplet 4 is dropped into an organic solvent (oil) in which lipid molecules 2 are dispersed, a monolayer of lipid molecules 2 spontaneously forms around the water droplet 4. When two water droplets 4 are brought into contact, the monolayers of lipid molecules 2 overlap at the contact point between the two water droplets 4. Between the two water droplets 4, an artificial cell membrane 5 composed of a bilayer of lipid molecules 2 is formed. One of the two water droplets 4 mimics the inside of a living cell, and the other of the two water droplets 4 mimics the outside of a living cell.

[0016] The water droplet 4 contains dispersed protein 3, which is the precursor to ion channel 10. When protein 3 attaches to the artificial cell membrane 5, ion channel 10 is formed.

[0017] The ion channel 10 is a passage that penetrates the artificial cell membrane 5. The ion channel 10 connects two water droplets 4 to each other. Ions passively pass through the ion channel 10 between the two water droplets 4. The ion channel 10 is a passage for ions in the artificial cell membrane 5. Ions have a charge. Examples of ions include potassium ions and sodium ions.

[0018] The ion channel 10 opens and closes. When the ion channel 10 opens, the ion current I flowing through the ion channel 10 between the two water droplets 4 increases. When the ion channel 10 closes, the ion current I flowing through the ion channel 10 between the two water droplets 4 decreases.

[0019] The ion channel analyzer 1 includes a detection unit 20 (measuring instrument) and an analysis unit 30. The detection unit 20 is a known ammeter. The detection unit 20 detects the ion current I flowing through the ion channel 10. The detection unit 20 is connected in series to both ends of the ion channel 10 (one water droplet 4 side and the other water droplet 4 side). In addition, the detection unit 20 has a function of applying a command voltage to the ion channel 10.

[0020] The analysis unit 30 is connected to the detection unit 20. The analysis unit 30 is built into the ion channel analyzer 1 main body. The analysis unit 30 is, for example, a computer. The analysis unit 30 includes, for example, a processor mounted on a substrate and a memory device that stores software for operating the processor. The analysis unit 30 performs the arithmetic processing described later.

[0021] The analysis unit 30 obtains first data 40 related to the ion current I detected by the detection unit 20. The analysis unit 30 analyzes the state of the ion channel 10 based on the first data 40 related to the ion current I. In particular, in this example, the analysis unit 30 analyzes the open / closed state of the ion channel 10.

[0022] (Current waveform) The first data set 40 includes a current waveform 50. The current waveform 50 shows the time variation of the ion current I. Figure 2 shows the current waveform 50 of the ion current I. In Figure 2, the horizontal axis represents time t, and the vertical axis represents the value of the ion current I. The unit of time is, for example, [s]. The unit of the ion current I is, for example, [A]. The current waveform 50 is a collection of sets of data for time t and data for the ion current I.

[0023] In the current waveform 50, the ion current I includes the leakage current value IL as the base current value and the peak current value IP. The leakage current value IL corresponds to the value of the ion current I when the ion channel 10 is closed. Even when the ion channel 10 is closed, the ion current I does not become completely zero, but flows a small amount through the ion channel 10 by leaking through the artificial cell membrane 5. For this reason, the leakage current value IL is slightly greater than zero.

[0024] The magnitude of the leakage current IL in the ion current I is determined in proportion to the magnitude of the command voltage applied to the ion channel 10. If the applied command voltage is at a negative potential, the leakage current IL in the ion current I will also be a negative value.

[0025] The peak current value IP is greater than the leakage current value IL in absolute terms. The peak current value IP corresponds to the ion current I when ion channel 10 is open. When ion channel 10 is open, a large amount of ion current I flows through ion channel 10. Therefore, the peak current value IP is greater than the leakage current value IL in absolute terms.

[0026] As shown in Figure 2, the ion current I fluctuates between a leakage current value IL and a peak current value IP. The leakage current value IL is not perfectly constant, but fluctuates randomly within the range of noise σ. The peak current value IP is not perfectly constant, but fluctuates randomly within the range of noise σ.

[0027] When the ion channel 10 changes from closed to open, that is, it takes time for the ion current I to go from the leakage current value IL to the peak current value IP by a first time constant τo. When the ion channel 10 changes from open to closed, that is, it takes time for the ion current I to go from the peak current value IP to the leakage current value IL by a second time constant τc.

[0028] The ionic current I persists at the leakage current value IL for a closed duration Tc. The closed durations Tc at the leakage current values ​​IL for the first to fourth valleys are denoted by Tc1, Tc2, Tc3, and Tc4. The total closed duration Tc for the period in question is the sum of Tc1 to Tc4. That is, Tc = Tc1 + Tc2 + Tc3 + Tc4.

[0029] The ion current I is maintained at a peak current value IP for an open duration To. The open durations To at the peak current values ​​IP of the first to fourth peaks are denoted by To1, To2, To3, and To4. The total open duration To for the period is the sum of To1 to To4. That is, To = To1 + To2 + To3 + To4.

[0030] The open probability Po represents the open state of ion channel 10. The open probability Po is the ratio of the open duration To of ion channel 10 to the given period. The open probability Po can be roughly obtained as To / (Tc+To) (Po=To / (Tc+To)).

[0031] The closure probability Pc represents the open / closed state of ion channel 10. The closure probability Pc is the ratio of the closed duration Tc of ion channel 10 to the given period. The closure probability Pc can be roughly obtained as Tc / (Tc+To) (Pc=Tc / (Tc+To)).

[0032] The sum of the open probability Po and the closed probability Pc is 1 (Po + Pc = 1). If one of the open probability Po or closed probability Pc is determined, the other is also determined. Finding the open probability Po and finding the closed probability Pc are essentially the same thing. The open probability Po will be explained below.

[0033] The period in question includes not only the closed duration Tc and the open duration To, but also the first time constant τo and the second time constant τc. In order to accurately determine the open probability Po, the first time constant τo and the second time constant τc must be allocated to either the closed duration Tc or the open duration To.

[0034] For example, a suitable threshold IA can be set for the ion current I. The time when the ion current I is less than the threshold IA can be allocated to the closed duration Tc, and the time when the ion current I is greater than the threshold IA can be allocated to the open duration To.

[0035] For a human, a threshold IA can be set visually each time, midway between the leakage current value IL and the peak current value IP, for a given current waveform 50. This allows a human to accurately determine the open probability Po, taking into account the first time constant τo and the second time constant τc.

[0036] However, it is difficult for a computer to automatically determine the accurate open probability Po based on the current waveform 50. A computer cannot even recognize the leakage current value IL and the peak current value IP, even if the current waveform 50 is provided. Therefore, the computer cannot appropriately set a threshold IA between the leakage current value IL and the peak current value IP for each given current waveform 50. Furthermore, the influence of noise σ also makes it difficult for the computer to recognize the leakage current value IL and the peak current value IP.

[0037] While it is conceivable to pre-fix the threshold value IA to an appropriate predetermined value, the characteristics of the current waveform 50 vary each time depending on the type of ion channel 10 and the magnitude of the command voltage applied to the ion channel 10. For example, both the leakage current value IL and the peak current value IP may increase or decrease (the entire current waveform 50 may shift upward or downward). Therefore, if the threshold value IA is pre-fixed to an appropriate predetermined value, both the leakage current value IL and the peak current value IP in the current waveform 50 may increase or decrease beyond the threshold value IA, making it impossible to determine an accurate open probability Po.

[0038] Thus, it is difficult for a computer to automatically determine the exact open probability Po based on the current waveform 50.

[0039] (histogram) The first data set 40 includes a histogram 60. The histogram 60 corresponds to the current waveform 50. Specifically, the histogram 60 is converted from the current waveform 50. The histogram 60 shows the frequency F for each value of ion current I. Figure 3 shows the histogram 60 for ion current I. In Figure 2, the horizontal axis represents the ion current I, and the vertical axis represents the frequency F. The unit of frequency F is dimensionless [-]. Frequency F is also a probability.

[0040] Histogram 60 is inherently a bar graph, but by increasing the number of data points, it becomes a curve as shown in Figure 3. In this example, histogram 60 is constructed using a Gaussian distribution. A Gaussian distribution is also called a normal distribution.

[0041] In the histogram 60, a closed-side peak 61 corresponding to the closed-side duration Tc (leakage current IL) is formed on the smaller side (left side) of the ion current I, and an open-side peak 62 corresponding to the open-side duration To (peak current IP) is formed on the larger side (right side) of the ion current I. In the histogram 60, a tail 63 corresponding to the first time constant τo, the second time constant τc, and noise σ is formed between the closed-side peak 61 and the open-side peak 62.

[0042] The closed side peak 61 has a closed area A1. The open side peak 62 has an open area A2. The base 63 has a small base area A3.

[0043] The open probability Po of ion channel 10 is roughly given by A2 / (A1+A2) (Po=A2 / (A1+A2)). The closed probability Pc of ion channel 10 is roughly given by A1 / (A1+A2) (Pc=A1 / (A1+A2)).

[0044] As mentioned above, finding the open probability Po and finding the closed probability Pc are essentially the same thing, so we will explain the open probability Po below.

[0045] To accurately determine the open probability Po, the tail area A3 must be allocated to either the closed area A1 or the open area A2. For example, a suitable threshold IA can be set for the ion current I. When the ion current I is less than the threshold IA, the area can be allocated to the closed area A1, and when the ion current I is greater than the threshold IA, the area can be allocated to the open area A2.

[0046] A human can visually identify the closed-side peaks 61 and the open-side peaks 62 in a given histogram 60 and set a threshold IA in the middle (tail 63) between them each time. This allows a human to accurately determine the open probability Po, taking into account the tail area A3 of the tail 63.

[0047] However, it is difficult for a computer to automatically determine the accurate open probability Po based on the histogram 60. Even with the histogram 60 provided, a computer cannot distinguish between the closed-side peak 61 and the open-side peak 62. Therefore, the computer cannot appropriately set a threshold IA in the intermediate area (tail 63) between the closed-side peak 61 and the open-side peak 62 each time.

[0048] It is conceivable to pre-fix the threshold IA to an appropriate predetermined value, but the appearance of the histogram 60 will vary each time depending on the type of ion channel 10 and the magnitude of the command voltage applied to the ion channel 10. For example, if both the leakage current value IL and the peak current value IP increase or decrease, the entire histogram 60 will shift to the right or to the left. Therefore, if the threshold IA is pre-fixed to an appropriate predetermined value, both the closed-side peak 61 and the open-side peak 62 in the histogram 60 may become larger or smaller than the threshold IA (they may be located to the right or to the left), making it impossible to determine the accurate open probability Po.

[0049] Thus, it is difficult for a computer to automatically determine the exact open probability Po based on the histogram 60.

[0050] (Regression model) Figure 4 shows a regression model M according to the first embodiment. The analysis unit 30 of the ion channel analyzer 1 uses the regression model M. The regression model M is located on an external server 6. Specifically, the regression model M is stored on the external server 6. The analysis unit 30 accesses the regression model M on the external server 6 to input data into the regression model M and to receive data output by the regression model M.

[0051] A regression model M is a supervised machine learning model. Specific examples of regression models M include ridge regression, GDBT (Gradient Boosting Decision Tree), multilayer perceptron (MLP), CNN (Convolutional Neural Network) models, and Transformer models.

[0052] The regression model M is trained as follows: The regression model M uses known first data 40 and known mathematical parameters 70 as training data. The known mathematical parameters 70 correspond to the known first data 40.

[0053] During the training of the regression model M, the known first data 40 is provided as training data to the input layer of the regression model M, and the known mathematical parameters 70 are provided as training data to the output layer of the regression model M.

[0054] For example, a regression model M uses a known current waveform 50 and known mathematical parameters 70 as training data. In this case, the known mathematical parameters 70 correspond to the known current waveform 50. The known current waveform 50 is provided as training data to the input layer of the regression model M, and the known mathematical parameters 70 are provided as training data to the output layer of the regression model M.

[0055] Alternatively, the regression model M uses a known histogram 60 and known mathematical parameters 70 as training data. In this case, the known mathematical parameters 70 correspond to the known histogram 60. Furthermore, the known histogram 60 is provided as training data to the input layer of the regression model M, and the known mathematical parameters 70 are provided as training data to the output layer of the regression model M.

[0056] The mathematical parameter 70 is an input variable corresponding to the first data 40 (current waveform 50 and histogram 60). The known first data 40 (current waveform 50 and histogram 60) is generated by numerical simulation based on the known mathematical parameter 70. Numerous mathematical parameters 70 are set by changing the conditions of the mathematical parameter 70. For each mathematical parameter 70 with different conditions, the corresponding first data 40 (current waveform 50 and histogram 60) is generated. This results in a large number of sets of first data 40 (current waveform 50 and histogram 60) and mathematical parameter 70 being prepared according to the conditions.

[0057] The mathematical parameter 70 contains information about the open / closed state of the ion channel 10. The open / closed state of the ion channel 10 refers to the state of whether the ion channel 10 is open or closed, and the degree to which it is open or closed. Specifically, the mathematical parameter 70 contains information about the open probability Po as the open / closed state of the ion channel 10.

[0058] More specifically, the mathematical parameter 70 includes, as information about the open probability Po of the ion channel 10, the leakage current value IL, the peak current value IP, the first time constant τo from the leakage current value IL to the peak current value IP, the second time constant τc from the peak current value IP to the leakage current value IL, the closed duration Tc at the leakage current value IL, the open duration To at the peak current value IP, the standard deviation of the noise σ, and the cutoff frequency.

[0059] Furthermore, the mathematical parameter 70 may include the open probability Po itself as information about the open probability Po of the ion channel 10.

[0060] Furthermore, regardless of whether known current waveforms 50 or known histograms 60 are used as training data for the input layer, it is preferable to provide these known mathematical parameters 70 (items not in parentheses in Figure 4) to the output layer.

[0061] The mathematical parameter 70 may include, as information regarding the opening probability Po of the ion channel 10, the number of opening / closing events D (the number of times the ion channel 10 opens and closes during the target period), the opening / closing timing E (the time when the ion channel 10 opens and closes), the closing duration Tc for each opening / closing event (e.g., Tc1 to Tc4), and the opening duration To for each opening / closing event (e.g., To1 to To4).

[0062] When using known current waveforms 50 as training data for the input layer, it is preferable to provide these known mathematical parameters 70 (items in parentheses in Figure 4) to the output layer. However, when using known histograms 60 as training data for the input layer, it is not essential to provide these known mathematical parameters 70 (items in parentheses in Figure 4) to the output layer.

[0063] The analysis unit 30 executes the regression model M as follows: The analysis unit 30 inputs the first data 40 related to the ion current I detected by the detection unit 20 into the regression model M, and outputs mathematical parameters 70 corresponding to the input first data 40. The analysis unit 30 accesses the regression model M on the external server 6 and inputs the first data 40 related to the detected ion current I into the regression model M. The analysis unit 30 obtains the mathematical parameters 70 (corresponding to the first data 40) output by the regression model M on the external server 6. The analysis unit 30 outputs the obtained mathematical parameters 70.

[0064] As the mathematical parameter 70 corresponding to the first data 40, information regarding the open probability Po (open / closed state) of the ion channel 10 is output.

[0065] For example, the analysis unit 30 outputs mathematical parameters 70 corresponding to the input current waveform 50 by inputting the current waveform 50 related to the ion current I detected by the detection unit 20 into the regression model M.

[0066] The mathematical parameters 70 corresponding to the current waveform 50 are output as follows: leakage current value IL, peak current value IP, first time constant τo, second time constant τc, closed duration Tc, open duration To, open probability Po itself, standard deviation of noise σ, and cutoff frequency. Furthermore, the number of switching events D, switching timing E, closed duration Tc for each switching event (e.g., Tc1 to Tc4), and open duration To for each switching event (e.g., To1 to To4) are output as mathematical parameters 70 corresponding to the current waveform 50.

[0067] Alternatively, the analysis unit 30 outputs mathematical parameters 70 corresponding to the input histogram 60 by inputting the histogram 60 related to the ion current I detected by the detection unit 20 into the regression model M.

[0068] The mathematical parameters 70 corresponding to the histogram 60 are output as follows: leakage current value IL, peak current value IP, first time constant τo, second time constant τc, closed duration Tc, open duration To, open probability Po itself, standard deviation of noise σ, and cutoff frequency.

[0069] The analysis unit 30 analyzes the state of the ion channel 10 based on the output mathematical parameters 70. Specifically, the analysis unit 30 analyzes the open / closed state of the ion channel 10 based on the output mathematical parameters 70.

[0070] More specifically, the analysis unit 30 determines the open probability Po of the ion channel 10 based on the output mathematical parameters 70. Since specific numerical values ​​are given as mathematical parameters 70, the analysis unit 30 can easily determine the open probability Po.

[0071] For example, the analysis unit 30 calculates the open probability Po based on mathematical parameters 70 corresponding to the histogram 60 (see Figure 3). In the mathematical parameters 70 corresponding to the histogram 60, the leakage current value IL and the peak current value IP are given as specific numerical values. The analysis unit 30 calculates the intermediate value between the leakage current value IL and the peak current value IP and sets this intermediate value as the threshold value IA.

[0072] The analysis unit 30 allocates the area of ​​the portion on the left side (where the ion current I is smaller than the threshold IA) of the histogram 60 to the closed area A1 of the closed peak 61, and the area of ​​the portion on the right side (where the ion current I is larger than the threshold IA) to the open area A2 of the open peak 62. At this time, the tail area A3 of the tail portion 63 is allocated to the closed area A1 and the open area A2. As a result, the analysis unit 30 can easily determine the open probability Po using the formula Po = A2 / (A1+A2).

[0073] Furthermore, the mathematical parameter 70 corresponding to the histogram 60 is given by the regression model M, which provides the open probability Po itself. The analysis unit 30 may directly apply the open probability Po, which is given by the regression model M as the mathematical parameter 70.

[0074] For example, the validity (reliability) of the mathematical parameter 70 obtained from the regression model M can be confirmed by comparing the open probability Po obtained using the threshold IA described above with the open probability Po given as a mathematical parameter 70 by the regression model M.

[0075] Alternatively, the analysis unit 30 may determine the open probability Po based on mathematical parameters 70 corresponding to the current waveform 50 (see Figure 2). Note that when using mathematical parameters 70 corresponding to the current waveform 50, the number of mathematical parameters 70 increases compared to when using mathematical parameters 70 corresponding to the histogram 60 (see Figure 4), making the calculation more complex.

[0076] In the case based on mathematical parameters 70 corresponding to the current waveform 50, as in the case based on mathematical parameters 70 corresponding to the histogram 60, the open probability Po given by the regression model M as mathematical parameter 70 may be applied directly.

[0077] (Ion current data generation device) As shown in Figure 1, the ion current data generation device 7 is connected to the server device 6. The ion current data generation device 7 is, for example, a computer. The ion current data generation device 7 includes, for example, a processor mounted on a substrate and a memory device that stores software for operating the processor.

[0078] The ion current data generation device 7 includes a data generation unit 80. The data generation unit 80 outputs first data 41 related to the ion current I according to the set mathematical parameters 71. In this embodiment, the case in which the first data 41 generated by the data generation unit 80 is a current waveform will be described as an example. The first data 41 will serve as the training data for the regression model M as known first data 40. In addition, some or all of the mathematical parameters 71 will serve as the training data for the regression model M as known mathematical parameters 70. The mathematical parameters 71 include parameters common to mathematical parameters 70. Specifically, the mathematical parameters 71, like the mathematical parameters 70, include the average values ​​of the opening probability Po of the ion channel 10, the leakage current value IL, the closed duration Tc, and the open duration To. Furthermore, the mathematical parameters 71 include the command voltage for the ion channel 10, the ion current width (or conductance) in the ion channel 10, the sampling frequency of the first data 41, the block size (sec) of the first data 41, the standard deviation of the noise σ of the first data 41, the cutoff frequency of the first data 41, and the frequency characteristics (transfer function) of the detection unit 20. The frequency characteristics of the detection unit 20 are obtained by fitting the amplitude response (dB) and phase response (degrees) data of the detection unit 20 as a transfer function.

[0079] Although a detailed explanation will be omitted, each parameter included in the mathematical parameter 71 is set via an input device connected to the ion current data generation device 7.

[0080] Figure 5 is a diagram illustrating the generation process of the first data 41.

[0081] First, as shown in Figure 5(a), the data generation unit 80 generates noise-free first data 41 based on the mathematical parameters 71 set as the open probability Po of the ion channel 10, the peak current value IP, the average values ​​of the closed duration Tc and open duration To, the command voltage for the ion channel 10, the sampling frequency of the first data 41, and the block size of the first data.

[0082] Next, as shown in Figure 5(b), the data generation unit 80 generates first data 41 including leakage current and noise based on the leakage current value IL and the standard deviation of noise σ, which are set as mathematical parameters 71.

[0083] Then, as shown in Figure 5(c), the data generation unit 80 generates first data 41 corresponding to the frequency characteristics of the detection unit 20, based on the cutoff frequency set as mathematical parameter 71 and the frequency characteristics (transfer function) of the detection unit 20. This first data 41 becomes the known first data 40 and serves as the training data for the regression model M. Note that some or all of the set mathematical parameters 71 become known mathematical parameters 70 and serve as the training data for the regression model M.

[0084] Furthermore, after Figure 5(c), the data generation unit 80 may convert the first data 41 generated as a current waveform into a histogram. The first data 41 converted into a histogram may be used as training data for the regression model M.

[0085] (Effects of the embodiment) The ion current data generation device 7 according to this embodiment includes a data generation unit 80 that generates first data 41 representing the ion current I based on mathematical parameters 71 set as data representing the ion current I flowing through the ion channel 10 and the frequency characteristics (transfer function) of a detection unit 20 (measuring device) that measures the ion current I.

[0086] As described above, obtaining mathematical parameters corresponding to ion currents has been difficult. Therefore, it is conceivable to generate a regression model M, such as the ion channel analyzer according to this embodiment, in which a mathematical parameter corresponding to a first data point obtained from ion currents is output when the first data point is input. To generate such a regression model M, a large number of first data points are required as training data.

[0087] When acquiring numerous initial data points using actual ion currents, it is necessary to determine the mathematical parameters corresponding to each ion current, which takes time to prepare the training data. Furthermore, when using actual ion currents, there is a bias in the mathematical parameters, making it difficult to acquire ion currents corresponding to the desired mathematical parameters. In addition, if the ion current measuring instrument changes, the ion currents must be acquired again, which is time-consuming.

[0088] In contrast, in this embodiment, the data generation unit 80 artificially generates first data 41 representing the ion current I based on the set mathematical parameters 71 and the frequency characteristics of the detection unit 20 that measures the ion current I, thus eliminating the need to use the actual ion current. Furthermore, since the first data 40 representing the ion current I is artificially generated based on the frequency characteristics of the detection unit 20, it is possible to accommodate cases where the measuring instrument for measuring the ion current is changed. Therefore, first data that will serve as training data for a regression model that outputs mathematical parameters can be obtained.

[0089] Furthermore, the first data set 41 is generated multiple times as training data for the regression model M (machine learning). This allows the data generation unit 80 to generate a large number of training data sets (first data sets) used in the regression model.

[0090] <Other Embodiments> Although this disclosure has been described above with reference to preferred embodiments, this description is not limiting, and various modifications, substitutions, or combinations are, of course, possible.

[0091] The analysis unit 30 may be provided outside the ion channel analyzer 1, rather than being built into the ion channel analyzer 1 main body.

[0092] The ion current data generation device 7 and the server device 6 may be configured by a single computer.

[0093] In the above embodiment, the ion channel analyzer 1 was applied to an artificial cell membrane 5, but it is not limited to this and may be applied to, for example, a biological cell membrane.

[0094] In the above embodiment, a case with one ion channel 10 was illustrated, but the embodiment is not limited to this. There may be multiple ion channels 10. In this case, the current waveform 50 may be a superposition of the currents I flowing through multiple ion channels 10. [Industrial applicability]

[0095] This disclosure is extremely useful and has high potential for industrial application because it can be applied to the ion current data generation device 7 (ion channel analyzer 1). [Explanation of Symbols]

[0096] Ionic current IL Leakage Current Value (Base Current Value) IP Peak Current Value IA threshold t time τo First time constant (time constant) τc Second time constant (time constant) To Open Duration (Duration) Tc Closure duration (duration) σ Noise Po Open Probability Pc closed probability F frequency A1 closed area A2 open area A3 Hem area D Number of opening / closing events E Opening and closing timing 1. Ion channel analyzer 2 Lipid molecules 3 Organic solvents 4 water drops 5 Artificial cell membrane 6. External Servers 7. Ion Current Data Generator 10 Ion Channels 20. Detection unit (measuring device) 30 Analysis Department 40,41 First Data 50 Current waveform 60 histograms 61 Closed side mountain part 62 Open-sided mountain section 63 Hem 70,71 Mathematical Parameters 80 Data Generation Unit

Claims

1. An ion current data generation device comprising a data generation unit that generates first data representing the ion current based on mathematical parameters set as data representing the ion current flowing through an ion channel and the frequency characteristics of a measuring instrument that measures the ion current.

2. The first data is generated multiple times as training data for machine learning using an ion current data generation device.

3. The steps include setting mathematical parameters that indicate the ion current flowing through the ion channel, A method for generating ion current data, comprising the step of generating first data indicating the ion current based on the set mathematical parameters and the frequency characteristics of a measuring instrument for measuring the ion current.

4. On the computer, The steps include setting mathematical parameters that indicate the ion current flowing through the ion channel, An ion current data generation program that performs the step of generating first data indicating the ion current based on the set mathematical parameters and the frequency characteristics of the measuring instrument that measures the ion current.

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