Ion channel analysis device

Abstract

To provide an ion channel analysis device capable of obtaining the number of clusters for analyzing a state of an ion channel.SOLUTION: An ion channel analysis device 1 includes: a detection unit 20 that detects ion current I flowing through an ion channel 10; and an analysis unit 30 that obtains a first analysis target histogram 80 corresponding to a current waveform 50 and indicating a frequency F for each value of the ion current I, based on the current waveform 50 indicating temporal change of the ion current I detected by the detection unit 20. There are a plurality of reference histograms 90 composed of Gaussian distributions for each cluster number N. The analysis unit 30 compares the first analysis target histogram 80 with the reference histogram 90 for each cluster number N. The analysis unit 30 determines the cluster number N of when the first analysis target histogram 80 is closest to the reference histogram 90 as the cluster number N of the first analysis target histogram 80.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to an ion channel analysis device.

Background Art

[0002] In the cell membrane of a living body, there are transmembrane proteins called ion channels (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] Based on a current waveform showing the time change of an ion current, a histogram showing the frequency for each value of the ion current can be obtained. Grasping the number of clusters in the histogram is effective for analyzing the state of ion channels.

[0006] However, it has been difficult to grasp the number of clusters in a histogram without relying on human visual inspection.

[0007] The present disclosure has been made in view of such circumstances, and an object thereof is to provide an ion channel analysis device capable of obtaining the number of clusters for analyzing the state of ion channels.

Means for Solving the Problem

[0008] The ion channel analysis device according to the present disclosure includes a detection unit that detects an ion current flowing through an ion channel, and an analysis unit that obtains a first analysis target histogram corresponding to the current waveform and indicating the frequency for each value of the ion current based on a current waveform indicating a temporal change of the ion current detected by the detection unit. There are a plurality of reference histograms each composed of a Gaussian distribution for each number of clusters, and the analysis unit compares the first analysis target histogram with the reference histograms for each number of clusters, and the analysis unit determines the number of clusters when the first analysis target histogram is closest to the reference histogram as the number of clusters of the first analysis target histogram.

[0009] According to such a configuration, by comparing the first analysis target histogram with the reference histograms for each number of clusters and obtaining the number of clusters when the first analysis target histogram is closest to the reference histogram, the number of clusters of the first analysis target histogram can be determined. The number of clusters is a factor effective for analyzing the state of the ion channel.

[0010] As described above, it is possible to provide an ion channel analysis device capable of obtaining the number of clusters for analyzing the state of an ion channel.

[0011] In one embodiment, when the number of clusters of the first analysis target histogram is 2 or more, the analysis unit determines that the ion channel is open or closed.

[0012] According to such a configuration, based on whether or not the number of clusters of the first analysis target histogram is 2 or more, it is possible to easily determine whether or not the ion channel is open or closed.

[0013] In one embodiment, the reference histogram is generated for each number of clusters by a mixture Gaussian model.

[0014] According to such a configuration, a reference histogram with high accuracy can be generated by a mixture Gaussian model.

[0015] In one embodiment, after determining the number of clusters of the first analysis target histogram, the analysis unit obtains a second analysis target histogram having a larger number of data than the first analysis target histogram based on the current waveform, and outputs mathematical parameters corresponding to the second analysis target histogram based on the second analysis target histogram and the determined number of clusters.

[0016] According to such a configuration, by using the first analysis target histogram with a small number of data, the calculation for determining the number of clusters can be speeded up. On the other hand, by using the second analysis target histogram with a large number of data, accurate mathematical parameters can be obtained.

[0017] In one embodiment, the ion channel analyzer is applied to the ion channel formed in the artificial cell membrane.

[0018] According to such a configuration, it is effective in conducting experiments using an artificial cell membrane.

Advantages of the Invention

[0019] According to the present disclosure, an ion channel analyzer capable of obtaining the number of clusters for analyzing the state of an ion channel can be provided.

Brief Description of the Drawings

[0020]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Embodiments for Carrying Out the Invention

[0021] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. The following description of the preferred embodiments is merely exemplary in nature and is in no way intended to limit the present disclosure, its applications, or its uses.

[0022] <First Embodiment> (Ion Channel Analyzer) The ion channel analyzer 1 according to the first embodiment will be described. The ion channel analyzer 1 is applied to the ion channel 10. The ion channel analyzer 1 analyzes the state of the ion channel 10. In this example, the ion channel 10 is formed in the artificial cell membrane 5.

[0023] FIG. 1 shows the ion channel 10 formed in the artificial cell membrane 5. The artificial cell membrane 5 mimics the cell membrane of a living body and is artificially formed. When water droplets 4 are dropped into an organic solvent (oil) in which lipid molecules 2 are dispersed, a monolayer of lipid molecules 2 is spontaneously formed around the water droplets 4. When two water droplets 4 are brought into contact, the monolayers of lipid molecules 2 overlap at the contact portion between the two water droplets 4. An artificial cell membrane 5 composed of a bilayer of lipid molecules 2 is formed between the two water droplets 4. 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.

[0024] The protein 3 that serves as the basis for the ion channel 10 is dispersed in the water droplet 4. When the protein 3 adheres to the artificial cell membrane 5, the ion channel 10 is formed.

[0025] 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.

[0026] Ion channel 10 opens and closes, or switches between open and closed states. 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.

[0027] Ion channel analyzer 1 includes a detection unit 20 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.

[0028] Analysis unit 30 is connected to detection unit 20. Analysis unit 30 is built into the main body of ion channel analyzer 1. 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.

[0029] Analysis unit 30 obtains first data 40 related to the ion current I detected by detection unit 20. Based on the first data 40 related to the ion current I, analysis unit 30 analyzes the state of ion channel 10. In particular, in this example, analysis unit 30 analyzes the open / closed state of ion channel 10.

[0030] (Current waveform) The first data 40 includes a current waveform 50. The current waveform 50 shows the time variation of the ion current I. FIG. 2 shows the current waveform 50 of the ion current I. In FIG. 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 of time t and data of the ion current I.

[0031] In the current waveform 50, the ion current I includes a leakage current value IL as a base current value and a 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 completely become zero, but leaks through the artificial cell membrane 5 and flows a little through the ion channel 10. Therefore, the leakage current value IL is slightly larger than zero.

[0032] The magnitude of the leakage current value 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 a negative potential, the leakage current value IL in the ion current I also becomes a negative value.

[0033] The peak current value IP is larger than the leakage current value IL in absolute value. The peak current value IP corresponds to the value of the ion current I when the ion channel 10 is open. When the ion channel 10 is open, the ion current I flows more through the ion channel 10. Therefore, the peak current value IP is larger than the leakage current value IL in absolute value.

[0034] According to FIG. 2, the ion current I repeats the leakage current value IL and the peak current value IP. The leakage current value IL is not completely constant and fluctuates randomly within the range of noise σ. The peak current value IP is not completely constant and fluctuates randomly within the range of noise σ.

[0035] When the ion channel 10 opens from the closed state, that is, when the ion current I reaches the peak current value IP from the leakage current value IL, it takes a time of the first time constant τo. When the ion channel 10 closes from the open state, that is, when the ion current I reaches the leakage current value IL from the peak current value IP, it takes a time of the second time constant τc.

[0036] The ion current I persists at the leakage current value IL for a closed duration Tc. The closed durations Tc at the leakage current values IL of the first to fourth valleys are denoted as Tc1, Tc2, Tc3, and Tc4. The total closed duration Tc in the target period is the sum of Tc1 to Tc4. That is, Tc = Tc1 + Tc2 + Tc3 + Tc4.

[0037] The ion current I persists at the 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 as To1, To2, To3, and To4. The total open duration To in the target period is the sum of To1 to To4. That is, To = To1 + To2 + To3 + To4.

[0038] As the open / closed state of the ion channel 10, there is an open probability Po. The open probability Po is the ratio of the open duration To during which the ion channel 10 is open to the target period. The open probability Po is approximately obtained by To / (Tc + To) (Po = To / (Tc + To)).

[0039] As the open / closed state of the ion channel 10, there is a closed probability Pc. The closed probability Pc is the ratio of the closed duration Tc during which the ion channel 10 is closed to the target period. The closed probability Pc is approximately obtained by Tc / (Tc + To) (Pc = Tc / (Tc + To)).

[0040] The sum of the open probability Po and the closed probability Pc is 1 (Po + Pc = 1). If one of the open probability Po and the closed probability Pc is determined, the other is also determined. Obtaining the open probability Po and obtaining the closed probability Pc are essentially the same. Hereinafter, the open probability Po will be described.

[0041] During the target period, in addition to the closed duration Tc and the open duration To, there are also a first time constant τo and a second time constant τc. When accurately obtaining the open probability Po, the first time constant τo and the second time constant τc must not be assigned to either the closed duration Tc or the open duration To.

[0042] For example, an appropriate threshold value IA is set for the ionic current I. It is conceivable to assign the time when the ionic current I is smaller than the threshold value IA to the closed duration Tc, and the time when the ionic current I is larger than the threshold value IA to the open duration To.

[0043] In the case of a human, for a given current waveform 50, the threshold value IA may be set visually each time in the middle of the leakage current value IL and the peak current value IP. Thus, in the case of a human, an accurate open probability Po considering the first time constant τo and the second time constant τc can be obtained.

[0044] However, it is difficult for a computer to automatically and accurately obtain the open probability Po based on the current waveform 50. In the first place, even if the current waveform 50 is given, the computer cannot recognize the leakage current value IL and the peak current value IP. For this reason, for a given current waveform 50, the computer cannot appropriately set the threshold value IA in the middle of the leakage current value IL and the peak current value IP each time. Also, the influence of the noise σ makes it difficult for the computer to recognize the leakage current value IL and the peak current value IP.

[0045] It is also conceivable to fix the threshold value IA in advance at an appropriate predetermined value. However, the form of the current waveform 50 varies each time depending on the type of the 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 increase or decrease (the entire current waveform 50 moves up or down). For this reason, when the threshold value IA is fixed in advance at an appropriate predetermined value, it is possible that both the leakage current value IL and the peak current value IP in the current waveform 50 become larger or smaller than the threshold value IA, and an accurate open probability Po cannot be obtained.

[0046] Thus, it is difficult for a computer to automatically and accurately obtain the opening probability Po based on the current waveform 50.

[0047] (Histogram) The first data 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 the ion current I. FIG. 3 shows the histogram 60 of the ion current I. In FIG. 2, the horizontal axis represents the ion current I, and the vertical axis represents the frequency F. The unit of the frequency F is dimensionless [-]. The frequency F is also a probability.

[0048] The histogram 60 is originally a bar graph, but by increasing the number of data points, it becomes a curve as shown in FIG. 3. In this example, the histogram 60 is composed of a Gaussian distribution. The Gaussian distribution is also called a normal distribution.

[0049] In the histogram 60, a closed-side peak 61 corresponding to the closed 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 duration To (peak current IP) is formed on the larger side (right side) of the ion current I. In the histogram 60, a skirt portion 63 corresponding to the first time constant τo, the second time constant τc, and the noise σ is formed between the closed-side peak 61 and the open-side peak 62.

[0050] The closed-side peak 61 has a closed area A1. The open-side peak 62 has an open area A2. The skirt portion 63 has a slight skirt area A3.

[0051] The opening probability Po of the ion channel 10 is approximately obtained by A2 / (A1 + A2) (Po = A2 / (A1 + A2)). The closing probability Pc of the ion channel 10 is approximately obtained by A1 / (A1 + A2) (Pc = A1 / (A1 + A2)).

[0052] As described above, obtaining the opening probability Po and obtaining the closing probability Pc are essentially the same. Therefore, the opening probability Po will be described below.

[0053] In accurately obtaining the opening probability Po, the base area A3 must be distributed to either the closed area A1 or the open area A2. For example, an appropriate threshold value IA is set for the ion current I. It is conceivable to distribute the area when the ion current I is smaller than the threshold value IA to the closed area A1, and distribute the area when the ion current I is larger than the threshold value IA to the open area A2.

[0054] In the case of a human, for the given histogram 60, the closed-side peak 61 and the open-side peak 62 can be visually identified, and the threshold value IA can be set each time at the middle (base part 63) between the two. In this way, a human can obtain an accurate opening probability Po that also takes into account the base area A3 of the base part 63.

[0055] However, it is difficult for a computer to automatically and accurately obtain the opening probability Po based on the histogram 60. In the first place, even if the histogram 60 is given, the computer cannot distinguish between the closed-side peak 61 and the open-side peak 62. For this reason, the computer cannot appropriately set the threshold value IA each time at the middle (base part 63) between the closed-side peak 61 and the open-side peak 62.

[0056] It is also conceivable to fix the threshold value IA in advance at an appropriate predetermined value. However, the form of the histogram 60 varies 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, when both the leak current value IL and the peak current value IP increase or decrease, the entire histogram 60 moves to the right or left. For this reason, when the threshold value IA is fixed in advance at an appropriate predetermined value, in the histogram 60, both the closed-side peak 61 and the open-side peak 62 may be larger or smaller than the threshold value IA (may be located on the right or left), and an accurate opening probability Po cannot be obtained.

[0057] Thus, it is difficult for a computer to automatically and accurately obtain the opening probability Po based on the histogram 60.

[0058] (Regression model) FIG. 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 in the external server 6. Specifically, the regression model M is stored in the external server 6. The analysis unit 30 accesses the regression model M in the external server 6, inputs data to the regression model M, and receives data output by the regression model M.

[0059] The regression model M is a supervised machine learning model. Specific examples of the regression model M include ridge regression, GDBT (Gradient Boosting Decision Tree), multi-layer perceptron (MLP), CNN (Convolutional Neural Network) model, Transformer model, and the like.

[0060] The regression model M learns as follows. The regression model M uses the known first data 40 and the known mathematical parameter 70 as teacher data. The known mathematical parameter 70 corresponds to the known first data 40.

[0061] During the learning of the regression model M, the known first data 40 is given as teacher data to the input layer of the regression model M, and the known mathematical parameter 70 is given as teacher data to the output layer of the regression model M.

[0062] For example, the regression model M uses the known current waveform 50 and the known mathematical parameter 70 as teacher data. At this time, the known mathematical parameter 70 corresponds to the known current waveform 50. Also, the known current waveform 50 is given as teacher data to the input layer of the regression model M, and the known mathematical parameter 70 is given as teacher data to the output layer of the regression model M.

[0063] Alternatively, the regression model M uses the known histogram 60 and the known mathematical parameter 70 as training data. At this time, the known mathematical parameter 70 corresponds to the known histogram 60. Also, the known histogram 60 is given as training data to the input layer of the regression model M, and the known mathematical parameter 70 is given as training data to the output layer of the regression model M.

[0064] 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. A number of mathematical parameters 70 are set while 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. As a result, a number of sets of the first data 40 (current waveform 50 and histogram 60) and the mathematical parameter 70 are prepared according to the conditions.

[0065] The mathematical parameter 70 includes information regarding the open / closed state of the ion channel 10. The open / closed state of the ion channel 10 refers to the state regarding the presence or absence of opening / closing of the ion channel 10, the degree of opening, and the degree of closing. Specifically, the mathematical parameter 70 includes information regarding the open probability Po as the open / closed state of the ion channel 10.

[0066] More specifically, the mathematical parameter 70 includes, as information regarding the open probability Po of the ion channel 10, the leak current value IL, the peak current value IP, the first time constant τo from the leak current value IL to reaching the peak current value IP, the second time constant τc from the peak current value IP to reaching the leak current value IL, the closed duration Tc at the leak current value IL, and the open duration To at the peak current value IP.

[0067] Also, the mathematical parameter 70 may include the open probability Po itself as information regarding the open probability Po of the ion channel 10.

[0068] Note that, regardless of whether the known current waveform 50 or the known histogram 60 is used as the teacher data given to the input layer, it is preferable to give these known mathematical parameters 70 (items not enclosed in parentheses in FIG. 4) to the output layer.

[0069] As information regarding the opening probability Po of the ion channel 10, the mathematical parameters 70 may include the number of opening / closing events D (the number of repetitions of the opening and closing of the ion channel 10 during the target period), the opening / closing timing E (the time when the ion channel 10 opens and closes), the closed duration Tc for each opening / closing event (e.g., Tc1 to Tc4, etc.), and the open duration To for each opening / closing event (e.g., To1 to To4, etc.).

[0070] Note that when the known current waveform 50 is used as the teacher data given to the input layer, it is preferable to give these known mathematical parameters 70 (items enclosed in parentheses in FIG. 4) to the output layer. However, when the known histogram 60 is used as the teacher data given to the input layer, it is not essential to give these known mathematical parameters 70 (items enclosed in parentheses in FIG. 4) to the output layer.

[0071] 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 the mathematical parameters 70 corresponding to the input first data 40. The analysis unit 30 accesses the regression model M in 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 acquires the mathematical parameters 70 (corresponding to the first data 40) output by the regression model M in the external server 6. The analysis unit 30 outputs the acquired mathematical parameters 70.

[0072] As the mathematical parameters 70 corresponding to the first data 40, information regarding the opening probability Po (open / closed state) of the ion channel 10 is output.

[0073] 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.

[0074] As the mathematical parameters 70 corresponding to the current waveform 50, a leakage current value IL, a peak current value IP, a first time constant τo, a second time constant τc, a closed duration Tc, an open duration To, and the open probability Po itself are output. Further, as the mathematical parameters 70 corresponding to the current waveform 50, the number of opening and closing events D, the opening and closing timing E, the closed duration Tc (e.g., Tc1 to Tc4, etc.) for each opening and closing event, and the open duration To (e.g., To1 to To4, etc.) for each opening and closing event are output.

[0075] 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.

[0076] As the mathematical parameters 70 corresponding to the histogram 60, a leakage current value IL, a peak current value IP, a first time constant τo, a second time constant τc, a closed duration Tc, an open duration To, and the open probability Po itself are output.

[0077] 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.

[0078] More specifically, the analysis unit 30 obtains the open probability Po of the ion channel 10 based on the output mathematical parameters 70. Since specific numerical values are given as the mathematical parameters 70, the analysis unit 30 can easily obtain the open probability Po.

[0079] For example, the analysis unit 30 obtains the opening probability Po based on the mathematical parameter 70 corresponding to the histogram 60 (see FIG. 3). In the mathematical parameter 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.

[0080] In the histogram 60, the analysis unit 30 distributes the area of the portion on the side (left side) where the ion current I is smaller than the threshold value IA to the closed area A1 of the closed-side peak portion 61, and distributes the area of the portion on the side (right side) where the ion current I is larger than the threshold value IA to the open area A2 of the open-side peak portion 62. At this time, the skirt area A3 of the skirt portion 63 is distributed to the closed area A1 and the open area A2. Thereby, the analysis unit 30 can easily obtain the opening probability Po by Po = A2 / (A1 + A2).

[0081] Also, in the mathematical parameter 70 corresponding to the histogram 60, the opening probability Po itself is given by the regression model M. The analysis unit 30 may directly apply the opening probability Po as the mathematical parameter 70 given by the regression model M.

[0082] For example, by comparing the opening probability Po obtained using the above-mentioned threshold value IA with the opening probability Po as the mathematical parameter 70 given by the regression model M, the validity (reliability) of the mathematical parameter 70 obtained from the regression model M may be confirmed.

[0083] Alternatively, the analysis unit 30 may obtain the opening probability Po based on the mathematical parameter 70 corresponding to the current waveform 50 (see FIG. 2). Note that when based on the mathematical parameter 70 corresponding to the current waveform 50, the number of items of the mathematical parameter 70 increases (see FIG. 4) compared to the case based on the mathematical parameter 70 corresponding to the histogram 60, so the calculation becomes complicated.

[0084] Even when based on the mathematical parameters 70 corresponding to the current waveform 50, the opening probability Po as the mathematical parameter 70 given by the regression model M may be applied as it is, similar to the case based on the mathematical parameters 70 corresponding to the histogram 60.

[0085] (Operation and Effect of the First Embodiment) In the ion channel analysis device 1 according to the present embodiment, the detection unit 20 detects the ion current I flowing through the ion channel 10, and the analysis unit 30 obtains the first data 40 detected by the detection unit 20.

[0086] The regression model M uses the known first data 40 and the known mathematical parameters 70 corresponding to the known first data 40 as teacher data. That is, the regression model M learns the correlation between the known first data 40 and the known mathematical parameters 70.

[0087] The analysis unit 30 inputs the first data 40 related to the ion current I detected by the detection unit 20 to the regression model M that reflects the correlation between the known first data 40 and the known mathematical parameters 70. Thereby, the analysis unit 30 can output the mathematical parameters 70 corresponding to (correlated with) the input first data 40.

[0088] Based on the output mathematical parameters 70, the state of the ion channel 10 can be analyzed.

[0089] As described above, it is possible to provide the ion channel analysis device 1 capable of obtaining the mathematical parameters 70 for analyzing the state of the ion channel 10.

[0090] The regression model M is not just a dataset that simply aggregates the known first data 40 and the known mathematical parameters 70, but a learning model. Since the regression model M is a learning model, even if the first data 40 input to the regression model M at the time of execution is in a complex form (for example, the noise σ of the current waveform 50 is large as shown in FIG. 2, or the tail part 63 of the histogram 60 is larger than zero as shown in FIG. 3), the regression model M with improved accuracy through learning outputs reliable and appropriate mathematical parameters 70.

[0091] By applying the current waveform 50 as the first data 40, the analysis unit 30 outputs the mathematical parameters 70 corresponding to the input current waveform 50. Thereby, the mathematical parameters 70 effective for analyzing the state of the ion channel 10 can be obtained.

[0092] By applying the histogram 60 as the first data 40, the analysis unit 30 outputs the mathematical parameters 70 corresponding to the input histogram 60. Thereby, similar to the case of the current waveform 50, the mathematical parameters 70 effective for analyzing the state of the ion channel 10 can be obtained.

[0093] In particular, when the histogram 60 is applied as the first data 40, the number of items of the mathematical parameters 70 required to analyze the state of the ion channel 10 can be reduced compared to the case where the current waveform 50 is applied as the first data 40 (see FIG. 4). This makes it easier to analyze the state of the ion channel 10 based on the mathematical parameters 70.

[0094] By the analysis unit 30 itself analyzing the state of the ion channel 10 based on the mathematical parameters 70, the operation of analyzing the state of the ion channel 10 can be automated.

[0095] Since the mathematical parameters 70 include information regarding the open / closed state of the ion channel 10, the open / closed state of the ion channel 10 can be grasped.

[0096] In particular, since the mathematical parameter 70 includes information regarding the opening probability Po which is the open / closed state of the ion channel 10, the opening probability Po of the ion channel 10 can be obtained.

[0097] As information regarding the opening probability Po, the mathematical parameter 70 includes a leakage current value IL, a peak current value IP, a first time constant τo, a second time constant τc, a closed duration Tc, and an open duration To. Thereby, based on the mathematical parameter 70, the opening probability Po of the ion channel 10 can be accurately obtained.

[0098] Since the ion channel analyzer 1 is applied to the ion channel 10 formed in the artificial cell membrane 5, it is effective for conducting experiments (such as those related to pharmaceuticals) using the artificial cell membrane 5.

[0099] (Modification of the First Embodiment) In the above embodiment, when obtaining the opening probability Po of the ion channel 10, the threshold value IA is used, but it is not limited thereto, and the opening probability Po may be obtained by other methods.

[0100] In the above embodiment, the opening probability Po of the ion channel 10 is obtained based on the mathematical parameter 70, but it is not limited thereto. Based on the mathematical parameter 70, the closing probability Pc may be obtained (obtaining the opening probability Po and obtaining the closing probability Pc are essentially the same), other open / closed states of the ion channel 10 (such as the degree of opening or closing of the ion channel 10) may be analyzed, or other states of the ion channel 10 (such as the conductance that can be calculated from the voltage applied to the ion channel 10 and the observed ion current in the ion channel 10) may be analyzed.

[0101] In the above embodiment, the analysis unit 30 analyzes the state of the ion channel 10 based on the output mathematical parameter 70, but it is not limited thereto. The analysis unit 30 may perform operations until the mathematical parameter 70 is output, and then a human may analyze the state of the ion channel 10 based on the mathematical parameter 70.

[0102] In the above-described embodiment, the current waveform 50 and the histogram 60 were exemplified as the first data 40, but the present invention is not limited thereto. For example, as the first data 40, a conductance (unit: Siemens) obtained by dividing the ion current I by the command voltage may be used.

[0103] The regression model M may be stored in the memory of the analysis unit 30 instead of being stored in the external server 6.

[0104] <Second Embodiment> (Mixture Gaussian Model) The ion channel analysis device 1 according to the second embodiment will be described. In the following description, the same components as those in the above embodiment may be denoted by the same reference numerals, and detailed description may be omitted. FIG. 5 shows a first analysis target histogram 80 and a reference histogram 90 according to the second embodiment.

[0105] The detection unit 20 detects the ion current I flowing through the ion channel 10. The analysis unit 30 obtains a first analysis target histogram 80 based on the current waveform 50 related to the ion current I detected by the detection unit 20. The current waveform 50 shows the time change of the ion current I (see FIG. 2).

[0106] The first analysis target histogram 80 is the same as the histogram 60 according to the first embodiment (see FIG. 3). However, although details will be described later, the first analysis target histogram 80 has a smaller number of data compared to the second analysis target histogram 85 described later.

[0107] The first analysis target histogram 80 corresponds to the current waveform 50. Specifically, the first analysis target histogram 80 is converted from the current waveform 50. The first analysis target histogram 80 shows the frequency F for each value of the ion current I.

[0108] The first histogram 80 to be analyzed is composed of a Gaussian distribution (normal distribution). In the first histogram 80 to be analyzed, a closed-side peak 81 corresponding to the closed duration Tc (leakage current IL) is formed on the side with a small ion current I (left side), and an open-side peak 82 corresponding to the open duration To (peak current IP) is formed on the side with a large ion current I (right side). In the first histogram 80 to be analyzed, a skirt portion 83 corresponding to the first time constant τo, the second time constant τc, and the noise σ is formed between the closed-side peak 81 and the open-side peak 82.

[0109] When the histogram is composed of a Gaussian distribution like the first histogram 80 to be analyzed, the number of peaks such as the closed-side peak 81 and the open-side peak 82 in the first histogram 80 to be analyzed is referred to as the number of clusters N. The number of clusters N of the first histogram 80 to be analyzed is 2 (N = 2).

[0110] In order to analyze the state (especially the open / closed state) of the ion channel 10, it is necessary to determine the number of clusters N of the histogram. For example, when the number of clusters N is 2, it can be seen that there are a cluster corresponding to when the ion channel 10 is closed (for example, the closed-side peak 81) and a cluster corresponding to when the ion channel 10 is open (for example, the open-side peak 82). If the number of clusters N is known, the state (especially the open / closed state) of the ion channel 10 can be known.

[0111] As can be seen from FIG. 5, in the case of a human, by visually observing the first histogram 80 to be analyzed, the closed-side peak 81 and the open-side peak 82 can be distinguished, so it is easy to determine that the number of clusters N of the first histogram 80 to be analyzed is 2.

[0112] However, even if the first histogram 80 to be analyzed is given to a computer, the computer cannot distinguish between the closed-side peak 81 and the open-side peak 82. For this reason, it is difficult for the computer to determine the number of clusters N of the first histogram 80 to be analyzed, and some kind of device is required.

[0113] The analysis unit 30 of the ion channel analysis device 1 uses the mixture Gaussian model G. The mixture Gaussian model G is in the external server 6. Specifically, the mixture Gaussian model G is stored in the external server 6. The analysis unit 30 accesses the mixture Gaussian model G in the external server 6, inputs data to the mixture Gaussian model G, and receives data output by the mixture Gaussian model G.

[0114] The mixture Gaussian model G is an unsupervised machine learning model. The mixture Gaussian model G is one of the clustering methods, and it is a method of analyzing by considering that data is generated from a plurality of Gaussian distributions (normal distributions).

[0115] The mixture Gaussian model G in the external server 6 has a plurality of reference histograms 90. The plurality of reference histograms 90 are generated by the mixture Gaussian model G. The reference histogram 90 is composed of Gaussian distributions for each number of clusters N by the mixture Gaussian model G. In other words, the reference histogram 90 is generated for each number of clusters N by the mixture Gaussian model G.

[0116] In this example, the mixture Gaussian model G generates five reference histograms 90 composed of Gaussian distributions with the number of clusters N from 1 to 5. When only one ion channel 10 exists, two numbers of clusters N are sufficient, but when two or more ion channels 10 may exist, three or more numbers of clusters N are required.

[0117] The analysis unit 30 inputs the first analysis target histogram 80 to the mixture Gaussian model G in the external server 6. The mixture Gaussian model G generates and outputs a plurality of reference histograms 90 corresponding to the input first analysis target histogram 80. The analysis unit 30 acquires the plurality of reference histograms 90 output by the mixture Gaussian model G.

[0118] The analysis unit 30 compares the first analysis target histogram 80 with the reference histogram 90 for each number of clusters N. Specifically, the analysis unit 30 compares the first analysis target histogram 80 with the reference histogram 90 when the number of clusters N is 1 (N = 1), the reference histogram 90 when the number of clusters N is 2 (N = 2), the reference histogram 90 when the number of clusters N is 3 (N = 3), the reference histogram 90 when the number of clusters N is 4 (N = 4), and the reference histogram 90 when the number of clusters N is 5 (N = 5) in sequence.

[0119] The analysis unit 30 determines the number of clusters N when the first analysis target histogram 80 is closest to the reference histogram 90 as the number of clusters N of the first analysis target histogram 80. For example, the analysis unit 30 obtains the degree of coincidence between the first analysis target histogram 80 and the reference histogram 90 for each number of clusters N, and determines the number of clusters N when the degree of coincidence is the highest (the degree of matching is the highest, the difference is the smallest, etc.).

[0120] In this example, the first analysis target histogram 80 is closest to the reference histogram 90 when the number of clusters N is 2 (N = 2). The analysis unit 30 determines that the number of clusters N of the first analysis target histogram 80 is 2 (N = 2).

[0121] Furthermore, the analysis unit 30 determines whether the number of clusters N of the first analysis target histogram 80 is 2 or more (N≥2?).

[0122] When the number of clusters N of the first analysis target histogram 80 is 2 or more (N≥2), the analysis unit 30 determines that the ion channel 10 is open and closed. This is because when the number of clusters is 2 or more, it can be seen that at least the cluster corresponding to when the ion channel 10 is closed (for example, the closed-side peak 81) and the cluster corresponding to when the ion channel 10 is open (for example, the open-side peak 82) exist. Note that "the ion channel 10 is open and closed" means that the ion channel 10 has two states, an open state and a closed state.

[0123] When the number of clusters N of the first analysis target histogram 80 is less than 2 (N < 2), that is, when the number of clusters N of the first analysis target histogram 80 is 1 (N = 1), the analysis unit 30 determines that the ion channel 10 is not opened or closed (the ion channel 10 is always open or always closed).

[0124] After determining the number of clusters N (= 2) of the first analysis target histogram 80, the analysis unit 30 obtains a second analysis target histogram 85 based on the current waveform 50. Specifically, the analysis unit 30 converts from the current waveform 50 to the second analysis target histogram 85. The second analysis target histogram 85 is the same as the histogram 60 according to the first embodiment (see FIG. 3).

[0125] The second analysis target histogram 85 corresponds to the current waveform 50. The second analysis target histogram 85 corresponds to the first analysis target histogram 80. The second analysis target histogram 85 has a larger number of data than the first analysis target histogram 80. In other words, the first analysis target histogram 80 has a smaller number of data than the second analysis target histogram 85.

[0126] Based on the second analysis target histogram 85 and the determined number of clusters N (= 2), the analysis unit 30 outputs a Gaussian distribution mathematical parameter 100 corresponding to the second analysis target histogram 85. The Gaussian distribution mathematical parameter 100 is information regarding the Gaussian distribution constituting the second analysis target histogram 85, and is, for example, the mean, variance, standard deviation, specific gravity, and the like.

[0127] Based on the output Gaussian distribution mathematical parameter 100, the analysis unit 30 may analyze the state of the ion channel 10 (for example, the opening probability Po, etc.).

[0128] FIG. 6 is a flowchart of the determination of the number of clusters N according to the second embodiment. Starting from the start, in the first step S1, the detection unit 20 detects the ion current I flowing through the ion channel 10.

[0129] In the second step S2, the analysis unit 30 obtains a first analysis target histogram 80 based on the current waveform 50 related to the ion current I detected by the detection unit 20. The number of data in the first analysis target histogram 80 is smaller than that in the second analysis target histogram 85.

[0130] In the third step S3, the analysis unit 30 inputs the first analysis target histogram 80 into the mixture Gaussian model G in the external server 6.

[0131] In the fourth step S4, the mixture Gaussian model G outputs a plurality of reference histograms 90 corresponding to the first analysis target histogram 80 for each number of clusters N.

[0132] In the fifth step S5, the analysis unit 30 acquires a plurality of reference histograms 90 for each number of clusters N output by the mixture Gaussian model G. Then, the analysis unit 30 compares the first analysis target histogram 80 with the reference histograms 90 for each number of clusters N.

[0133] In the sixth step S6, the analysis unit 30 determines the number of clusters N (= 1 to 5) when the first analysis target histogram 80 is closest to the reference histogram 90 as the number of clusters N of the first analysis target histogram 80. In this example, the number of clusters N of the first analysis target histogram 80 is determined to be 2 (N = 2).

[0134] In the seventh step S7, the analysis unit 30 determines whether the number of clusters N of the first analysis target histogram 80 is 2 or more (N ≧ 2?). If it is determined that the number of clusters N of the first analysis target histogram 80 is 2 or more (N ≧ 2), the process proceeds to the eighth step S8. If it is determined that the number of clusters N of the first analysis target histogram 80 is less than 2 (N < 2), that is, if it is determined that the number of clusters N of the first analysis target histogram 80 is 1 (N = 1), the process proceeds to the ninth step S9.

[0135] In the eighth step S8 (where the number of clusters N is 2 or more (N ≧ 2)), the analysis unit 30 determines that the ion channel 10 is open and closed. When the eighth step S8 ends, the process proceeds to the tenth step S10.

[0136] In the ninth step S9 (where the number of clusters N is less than 2 (N < 2)), the analysis unit 30 determines that the ion channel 10 is not open and closed (the ion channel 10 is either always open or always closed). When the ninth step S9 ends, the process proceeds to the tenth step S10.

[0137] In the tenth step S10, the analysis unit 30 obtains a second analysis target histogram 85 that has a larger number of data than the first analysis target histogram 80 based on the current waveform 50.

[0138] In the eleventh step S11, the analysis unit 30 outputs a Gaussian distribution mathematical parameter 100 corresponding to the second analysis target histogram 85 based on the second analysis target histogram 85 and the determined number of clusters N (= 2). Then, it reaches the end.

[0139] (Operation and Effect of the Second Embodiment) According to the ion channel analysis device 1 according to the present embodiment, by comparing the first analysis target histogram 80 with the reference histogram 90 for each number of clusters N and obtaining the number of clusters N when the first analysis target histogram 80 is closest to the reference histogram 90, the number of clusters N of the first analysis target histogram 80 can be determined.

[0140] Here, as described above, the number of clusters N is an effective factor for analyzing the state (particularly the open / closed state) of the ion channel 10.

[0141] As described above, it is possible to provide the ion channel analysis device 1 capable of obtaining the number of clusters N for analyzing the state of the ion channel 10.

[0142] It is possible to grasp the number of clusters N of the first analysis target histogram 80 without relying on human visual inspection.

[0143] Based on whether the number of clusters N of the first histogram 80 to be analyzed is 2 or more, it is possible to easily determine whether the ion channel 10 is open or closed.

[0144] A reference histogram 90 with high accuracy can be generated by the mixture Gaussian model G.

[0145] By using the first histogram 80 with a small number of data, the calculation for determining the number of clusters N can be speeded up. On the other hand, by using the second histogram 85 with a large number of data, accurate Gaussian distribution mathematical parameters 100 can be obtained.

[0146] Since the ion channel analyzer 1 is applied to the ion channel 10 formed in the artificial cell membrane 5, it is effective in conducting experiments using the artificial cell membrane 5 and the like.

[0147] (Modification of the Second Embodiment) As described above, when the number of clusters N of the first histogram 80 to be analyzed is less than 2 (N < 2), that is, when the number of clusters N of the first histogram 80 to be analyzed is 1 (N = 1), it is determined that the ion channel 10 is not open or closed, that is, the ion channel 10 is always open or always closed.

[0148] In order to determine whether the ion channel 10 is always open or always closed, a threshold value IA related to the ion current I is set for the first histogram 80 to be analyzed, and based on whether the ion current I is greater than or less than the threshold value IA, it may be determined whether the ion channel 10 is always open or always closed. In this case, if the ion current I is greater than the threshold value IA, it is determined that the ion channel 10 is always open, and if the ion current I is less than the threshold value IA, it is determined that the ion channel 10 is always closed.

[0149] In the above-described embodiment, the analysis unit 30 determines whether the ion channel 10 is open or closed based on whether the number of clusters N of the first analysis target histogram 80 is 2 or more. However, the present invention is not limited to this. The analysis unit 30 may only perform until determining the number of clusters N. Based on the number of clusters N determined by the analysis unit 30, a person may analyze the state of the ion channel 10.

[0150] The mixture Gaussian model G may be stored in the memory of the analysis unit 30 instead of being stored in the external server 6.

[0151] <Other Embodiments> As described above, the present disclosure has been described by way of preferred embodiments. However, such descriptions are not limiting matters, and of course, various modifications, substitutions, or combinations are possible.

[0152] The analysis unit 30 may be provided outside the main body of the ion channel analysis device 1 instead of being built into the main body of the ion channel analysis device 1.

[0153] In the above-described embodiment, the ion channel analysis device 1 is applied to the artificial cell membrane 5. However, the present invention is not limited to this, and for example, it may be applied to a biological cell membrane or the like.

[0154] In the above-described embodiment, the case where there is one ion channel 10 is illustrated. However, the present invention is not limited to this. There may be a plurality of ion channels 10. In this case, the current waveform 50 may be a superposition of currents I flowing through the plurality of ion channels 10.

Industrial Applicability

[0155] Since the present disclosure can be applied to the ion channel analysis device 1, it is extremely useful and has high industrial applicability.

Explanation of Reference Numerals

[0156] I Ion current IL Leak current value (base current value) IP Peak current value IA threshold Time t τo First time constant (time constant) τc Second time constant (time constant) To Open duration (duration) Tc Closed duration (duration) σ Noise Po Open probability Pc Closed probability F Frequency A1 Closed area A2 Open area A3 Skirt area D Number of opening and closing events E Opening and closing timing M Regression model G Mixture Gaussian model N Number of clusters 1 Ion channel analyzer 2 Lipid molecule 3 Organic solvent 4 Water droplet 5 Artificial cell membrane 6 External server 10 Ion channel 20 Detection unit 30 Analysis unit 40 First data 50 Current waveform 60 Histogram 61 Closed side peak 62 Open side peak 63 Skirt part 70 Mathematical parameter 80 First analysis target histogram 81 Closed side peak 82 Open side peak 83 Skirt part 85 Second analysis target histogram 90 Reference histogram 100 Gaussian distribution mathematical parameter (mathematical parameter)

Claims

1. A detection unit that detects an ion current flowing through an ion channel, An analysis unit that obtains a first analysis target histogram corresponding to the current waveform and showing the frequency for each value of the ion current based on a current waveform showing the temporal change of the ion current detected by the detection unit, There are a plurality of reference histograms composed of Gaussian distributions for each number of clusters, The analysis unit compares the first analysis target histogram with the reference histograms for each number of clusters, The analysis unit determines the number of clusters when the first analysis target histogram is closest to the reference histogram as the number of clusters of the first analysis target histogram, an ion channel analysis device.

2. The analysis unit determines that the ion channel is open or closed when the number of clusters of the first analysis target histogram is 2 or more, the ion channel analysis device according to claim 1.

3. The reference histogram is generated for each number of clusters by a mixture Gaussian model, the ion channel analysis device according to claim 1 or 2.

4. After the analysis unit determines the number of clusters of the first analysis target histogram, it obtains a second analysis target histogram having a larger number of data than the first analysis target histogram based on the current waveform, and outputs a mathematical parameter corresponding to the second analysis target histogram based on the second analysis target histogram and the determined number of clusters, the ion channel analysis device according to claim 1 or 2.

5. The ion channel analysis device according to claim 1 or 2, applied to the ion channel formed in an artificial cell membrane.

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