Ion current determination system, ion current determination method, and ion current determination program.

Abstract

The reliability of the first data used in the regression model and the mathematical parameters output from the regression model can be determined. [Solution] The ion current determination device 7 includes an acquisition unit 81 that acquires mathematical parameters 71 that are output in correspondence with the first data 41 from a regression model M to which first data 41 indicating a first current I1 which is at least one of an ion current and a non-ion current is input; a data generation unit 82 that generates second data 42 indicating a second current I2 which is a pseudo ion current based on the mathematical parameters 71; and a determination unit 83 that determines the reliability of at least one of the first data 41 and the mathematical parameters 71 based on the error between the first data 41 and the second data 42.

Description

Technical Field

[0007]

[0001] The present disclosure relates to an ion current determination system, an ion current determination method, and an ion current determination 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 carry 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 a mathematical parameter corresponding to the data, but it has been difficult to obtain such a mathematical parameter.

[0007] Therefore, it is conceivable to generate a regression model in which, when the first data obtained from ion current is input, mathematical parameters corresponding to the first data are output.

[0008] However, when a predetermined first data set was input into such a regression model, the correct mathematical parameters were sometimes not output.

[0009] This disclosure has been made in view of the above, and its purpose is to provide an ion current determination system, an ion current determination method, and an ion current determination program for determining the reliability of the first data used in the regression model and the mathematical parameters output from the regression model. [Means for solving the problem]

[0010] The ion current determination system according to this disclosure includes: an acquisition unit that acquires mathematical parameters output in correspondence with first data from a regression model to which first data representing a first current that is at least one of an ion current and a non-ion current is input; a data generation unit that generates second data representing a second current that is a pseudo ion current based on the mathematical parameters; and a determination unit that determines the reliability of at least one of the first data and the mathematical parameters based on the error between the first data and the second data. [Effects of the Invention]

[0011] According to this disclosure, it is possible to determine the reliability of the first data used in the regression model and the mathematical parameters output from the regression model. [Brief explanation of the drawing]

[0012] [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 embodiment. [Figure 5] Figure 5 shows a flowchart of the determination process of the ion current measurement system 7. [Modes for carrying out the invention]

[0013] Embodiments of the present 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 present disclosure, its applications, or its uses in any way.

[0014] The ion current determination method and ion current determination program described herein are implemented as a function of an ion current determination device (ion current determination system).

[0015] <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.

[0016] 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.

[0017] 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.

[0018] Ion channel 10 is a passage that penetrates artificial cell membrane 5. Ion channel 10 connects two water droplets 4 to each other. Ions passively pass through ion channel 10 between the two water droplets 4. Ion channel 10 is a passage for ions in artificial cell membrane 5. Ions have a charge. Examples of ions include potassium ions and sodium ions.

[0019] Ion channel 10 opens and closes. When ion channel 10 opens, the ion current I flowing through ion channel 10 between the two water droplets 4 increases. When ion channel 10 closes, the ion current I flowing through ion channel 10 between the two water droplets 4 decreases.

[0020] Ion channel analyzer 1 includes a detection unit 20 (measuring instrument) and an analysis unit 30. Detection unit 20 is a known ammeter. Detection unit 20 detects the ion current I flowing through ion channel 10. Detection unit 20 is connected in series to both ends of ion channel 10 (one water droplet 4 side and the other water droplet 4 side). Further, detection unit 20 has a function of applying a command voltage to ion channel 10.

[0021] Analysis unit 30 is connected to detection unit 20. Analysis unit 30 is built into the ion channel analyzer 1 main body. Analysis unit 30 is, for example, a computer. Analysis unit 30 includes, for example, a processor mounted on a substrate and a memory device that stores software for operating the processor. Analysis unit 30 performs the arithmetic processing described later.

[0022] Analysis unit 30 obtains first data 40 related to the ion current I detected by detection unit 20. Analysis unit 30 analyzes the state of ion channel 10 based on the first data 40 related to the ion current I. In particular, in this example, analysis unit 30 analyzes the open / closed state of ion channel 10.

[0023] (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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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)).

[0032] 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)).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] Thus, it is difficult for a computer to automatically determine the exact open probability Po based on the current waveform 50.

[0040] (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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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)).

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] Thus, it is difficult for a computer to automatically determine the exact open probability Po based on the histogram 60.

[0051] (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.

[0052] The regression model M is a supervised machine learning model. Specific examples of regression models M include ridge regression, GDBT (Gradient Boosting Decision Tree), multi-layer perceptron (MLP), CNN (Convolutional Neural Network) models, and Transformer models. In this embodiment, we will explain using the case where the regression model M is a supervised machine learning model as an example, but it is also applicable when the regression model M is an unsupervised machine learning model (e.g., an autoencoder).

[0053] 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.

[0054] 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.

[0055] For example, 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 regression model M, and the known mathematical parameters 70 are provided as training data to the output layer of regression model M. Alternatively, 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. The known histogram 60 is provided as training data to the input layer of regression model M, and the known mathematical parameters 70 are provided as training data to the output layer of 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 determination device) As shown in Figure 1, the ion current determination device 7 (ion current determination system) comprises an acquisition unit 81, a data generation unit 82, and a determination unit 83.

[0078] The acquisition unit 81 inputs the first data 41 related to the first current I1 into the regression model M. The acquisition unit 81 acquires the mathematical parameters 71 output from the regression model M.

[0079] Here, the first current I1 input to the regression model M is the current detected by the detection unit 20, but it is unknown whether it is an ionic current (current flowing through an ion channel) or a non-ionic current (current not flowing through an ion channel, for example, the current generated when a command voltage is applied to an artificial cell membrane). In other words, the first current I1 is at least one of either an ionic current or a non-ionic current.

[0080] Furthermore, mathematical parameter 71 includes parameters common to mathematical parameter 70. Specifically, mathematical parameter 71, like mathematical parameter 70, includes the average values ​​of the ion channel's open probability Po, leakage current value IL, peak current value IP, closed duration Tc, and open duration To. Mathematical parameter 71 also includes the command voltage for the ion channel, the ion current width (or conductance) in the ion channel, the sampling frequency of the ion current, the block size (sec) of the ion current, the standard deviation of the noise σ, and the cutoff frequency for the ion current.

[0081] The data generation unit 82 outputs second data 42 (second data) that represents a pseudo-ion current, the second current I2, according to the mathematical parameters 71 acquired by the acquisition unit 81.

[0082] For example, the data generation unit 82 generates noise-free second data 42 based on the open probability Po of the ion channel 10, the peak current value IP, the closed duration Tc, the open duration To, the command voltage of the ion current, the sampling frequency, and the block size, which are input as mathematical parameters 71.

[0083] Next, the data generation unit 82 generates second data 42, which includes leakage current and noise, based on the leakage current value IL, the first time constant τo, the second time constant τc, the standard deviation of noise in the ion current, and the cutoff frequency, which are input as mathematical parameters 71. In other words, the second data 42 is a pseudo-ion current (second current I2) generated from the mathematical parameters 71.

[0084] The determination unit 83 compares the first data 41 input to the regression model M by the acquisition unit 81 with the first data 42 generated by the data generation unit 82 to determine the reliability of the first data 41 and the mathematical parameters 71. Specifically, if the error between the first data 41 and the first data 42 is within a predetermined range, the determination unit 83 determines that the first data 41 is an ion current. Also, if the error between the first data 41 and the first data 42 is within a predetermined range, the determination unit 83 determines that the mathematical parameters 71 are normal.

[0085] Figure 5 is a flowchart of the determination process of the ion current measurement system 7.

[0086] The acquisition unit 81 acquires the first data 41 (step S1). The acquisition unit 81 inputs the acquired first data 41 into the regression model M (step S2). The acquisition unit 81 acquires the mathematical parameters 71 output from the regression model M (step S3).

[0087] The data generation unit 82 generates second data 42 according to the mathematical parameters 71 acquired by the acquisition unit 81 (step S4).

[0088] The determination unit 83 determines whether the error between the first data 41 acquired by the acquisition unit 81 and the second data 42 generated by the data generation unit 82 is within a predetermined range (step S5).

[0089] Specifically, if the determination unit 83 determines that the error between the first data 41 and the second data 42 is within a predetermined range (Yes in step S5), it determines that the ion current I1 corresponding to the first data 41 is an ion current (step S6). The determination unit 83 also determines that the mathematical parameter 71 is normal (step S7). In other words, if the determination unit 83 determines that the error between the first data 41 and the second data 42 is within a predetermined range (Yes in step S5), it determines that the reliability of the first data 41 and the mathematical parameter 71 is high.

[0090] On the other hand, if the determination unit 83 determines that the error between the first data 41 and the second data 42 is outside a predetermined range (No. in step S5), it determines that the ion current I1 corresponding to the first data 41 is a non-ion current (step S8). Also, the determination unit 83 determines that the mathematical parameter 71 is abnormal (step S9). In other words, if the determination unit 83 determines that the error between the first data 41 and the second data 42 is outside a predetermined range (No. in step S5), it determines that the reliability of the first data 41 and the mathematical parameter 71 is low.

[0091] In step S5, if the determination unit 83 determines that the error between the first data 41 and the second data 42 is within a predetermined range, the first data 41 may be used to train the regression model M.

[0092] (Effects of the embodiment) The ion current determination device 7 according to this embodiment includes an acquisition unit 81 that acquires mathematical parameters 71 that are output in correspondence with first data 41 from a regression model M to which first data 41 indicating a first current I1 which is at least one of an ion current and a non-ion current is input; a data generation unit 82 that generates second data 42 indicating a second current I2 which is a pseudo-ion current based on the mathematical parameters 71; and a determination unit 83 that determines the reliability of at least one of the first data 41 and the mathematical parameters 71 based on the error between the first data 41 and the second data 42.

[0093] If the error between the first data 41, which represents the first current I1 acquired by the acquisition unit 81, and the second data, which represents the second current I2, a pseudo-ion current generated by the data generation unit 82, is small (within a predetermined range), it can be said that the regression model M has correctly analyzed the first data 41, and therefore it can be determined that the first data 41 is an ion current and the mathematical parameter 71 is normal. On the other hand, if the error between the first data 41 and the second data 42 is large (outside the predetermined range), it can be said that the regression model M has not correctly analyzed the first data 41, and therefore it can be determined that the first data 41 is a non-ion current and the mathematical parameter 71 is abnormal. In other words, the reliability of the first data used in the regression model and the mathematical parameter output from the regression model can be determined based on the error between the first data 41 and the second data 42.

[0094] The determination unit 83 determines that the first current I1 is an ionic current if the error between the first data 41 and the second data 42 is within a predetermined range. This allows the determination of whether the first data 41 used in the regression model was obtained from an ionic current or a non-ionic current.

[0095] The determination unit 83 determines that the mathematical parameter 71 is normal if the error between the first data 41 and the second data 42 is within a predetermined range. This makes it possible to determine whether the mathematical parameter 71 is normal or abnormal.

[0096] In this embodiment, the case where the first current I1 is a current waveform was described as an example, but the reliability of the first data 41 and mathematical parameter 71 may be determined after converting the first current I1 into a histogram. In this case, after step S1, the current waveform in the first data 41 is converted into a histogram. Also, after step S4, the current waveform in the second data 42 is converted into a histogram. Then, in step S5, the determination unit 83 determines whether the error between the first data 41 converted into a histogram and the second data 42 converted into a histogram is within a predetermined range. In this case, the amount of data is reduced compared to when the first data 41 and the second data 42 are current waveforms, thus reducing the load on the ion current determination device.

[0097] <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.

[0098] 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.

[0099] The ion current determination device 7 and the server device 6 may be composed of one computer or multiple computers.

[0100] 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.

[0101] 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]

[0102] This disclosure is extremely useful and has high potential for industrial application because it can be applied to the ion current determination device 7 (ion channel analyzer 1). [Explanation of symbols]

[0103] 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 determination device 10 Ion Channels 20 Detection unit 30 Analysis Department 40,41 First Data 42. Data 2 50 Current waveform 60 histograms 61 Closed side mountain part 62 Open-sided mountain section 63 Hem 70,71 Mathematical Parameters 81 Acquisition Department 82 Data Generation Unit 83 Judgment section

Claims

1. An acquisition unit that acquires mathematical parameters output in correspondence with the first data from a regression model to which first data representing a first current that is at least one of an ionic current and a nonionic current is input, A data generation unit generates second data indicating a second current, which is a pseudo-ion current, based on the aforementioned mathematical parameters. An ion current determination system comprising: a determination unit that determines the reliability of at least one of the first data and the mathematical parameter based on the error between the first data and the second data.

2. The ion current determination system according to claim 1, wherein the determination unit determines that the first current is the ion current when the error between the first data and the second data is within a predetermined range.

3. The ion current determination system according to claim 1, wherein the determination unit determines that the mathematical parameter is normal if the error between the first data and the second data is within a predetermined range.

4. A step of obtaining mathematical parameters output in correspondence with first data from a regression model that receives first data representing a first current which is at least one of an ionic current and a nonionic current, The steps include generating second data representing a second current, which is a pseudo-ion current, based on the aforementioned mathematical parameters, A method for determining ion current, comprising the step of determining the reliability of either the first data or the mathematical parameter based on the error between the first data and the second data.

5. On the computer, A step of obtaining mathematical parameters output in correspondence with first data from a regression model that receives first data representing a first current which is at least one of an ionic current and a nonionic current, The steps include generating second data representing a second current, which is a pseudo-ion current, based on the aforementioned mathematical parameters, An ion current determination program that performs the step of determining the reliability of either the first data or the mathematical parameter based on the error between the first data and the second data.

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