Device and computer-assisted procedure for determining a control protocol for a microfluidic system
Patent Information
- Application Number
- ES2023702307T
- Authority / Receiving Office
- ES · ES
- Patent Type
- Patents
- Current Assignee / Owner
- Priority Date
- 2023-01-25
- Filing Date
- 2023-01-26
- Publication Date
- 2026-09-15
- Estimated Expiration
- 2043-01-26
Smart Images
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Abstract
Description
Device and computer-assisted procedure for determining a control protocol for a microfluidic system State of the art The description is based on a procedure for determining a control protocol for a microfluidic system. Microfluidic systems enable decentralized analysis of patient samples using modern molecular diagnostic procedures, for example, for performing PCR tests to detect pathogens. For highly reliable and fully automated execution of the biochemical processes and the specific thermal manipulation of the sample fluids in these systems, both proper structural design and accurate execution of the process steps are generally necessary to ensure the desired functionalities. US patent 2020 / 116398 A1 discloses a method for temperature control in a nucleic acid analysis system based on a thermal model. The publication by ZOU Q ET AL: "Microassembled multi-chamber thermal cycler for low-cost reaction chip thermal multiplexing", SENSORS AND ACTUATORS A: PHYSICAL, ELSEVIER BV, NL, vol. 102, No. 1-2, December 1, 2002 (2002-12-01), pages 114-121, XP004393667, ISSN: 0924-4247, DOI: 10.1016 / S0924-4247 (02) 00384-9 describes an optimization of a thermal model for a miniature thermal cycler. Description of the invention The desired functionality is achieved particularly efficiently through the object of the independent claims. According to the independent claims, a prediction is determined for a control protocol that achieves a predetermined temperature profile over time, i.e., a target temperature. Thanks to this prediction, the need for laborious experimental iterations, which would otherwise be difficult to perform in parallel, is avoided or at least reduced. This results in significantly fewer computational resources being required to determine the control protocol for microfluidic systems. This is achieved by means of a computer-aided procedure for determining a control protocol for a microfluidic system under thermal influences, wherein the microfluidic system comprises a first chamber for a fluid, and wherein the microfluidic system comprises a first heating element designed to influence the temperature of the fluid in the first chamber, wherein a parameterizable model with a set of parameters is provided, designed to determine the influence of a first temperature and the influence of a second temperature on the temperature evolution of the fluid in the microfluidic system when it is controlled according to the control protocol, wherein a target for the temperature evolution is established, wherein the set of parameters is determined in which a temperature evolution calculated with the model meets the target,and wherein the parameter set comprises at least one control protocol parameter that specifies the first and / or second temperature. In this way, the temperature in a chamber can alternate between two temperatures during the cycle. The control protocol may be provided to specify a third temperature or more than three temperatures in the cycle. The microfluidic system preferably comprises an analytical device that houses a cartridge for analyzing the fluid contained therein. The cartridge may form part of the microfluidic system and include the first chamber described above, while the analytical device preferably includes the first heating element. In this context, the cartridge may, depending on a specific configuration, include other passive components such as channels, chambers, controllable (diaphragm) valves, and pumping chambers, while the analytical device may include active components for processing the cartridge, such as heaters, pumps, compressed air reservoirs, and electronic components like a processor and memory.Thus, the control protocol determined by the procedure according to the invention is preferably configured to control the analytical device for processing the cartridge. The microfluidic system can be configured, for example, for molecular biology testing, particularly for isothermal or polymerase chain reaction-based amplification of nucleic acid fragments, for example, for performing a PCR test for the detection of pathogens. Preferably, the microfluidic system, preferably the cartridge, comprises a second chamber for the fluid; the microfluidic system, preferably the analysis device, comprises a second heating element designed to influence the temperature of the fluid in the second chamber; the model is designed to determine the influence of the first temperature of the first heating element and the influence of the second temperature of the second heating element on the temperature evolution of the fluid in the microfluidic system. In this way, the sample is evaluated in two chambers, preferably at different temperatures. The temperature evolution can be influenced by the choice of chamber, the temperature within the chamber, and the residence time of the fluid in the chamber. The time the fluid spends in one chamber is comparatively short compared to the time it takes to move from one chamber to another. The following parameters are particularly suitable for simulating the temperature effects that different control protocols exert on the fluid. For example, at least one parameter from the parameter set is determined, specifying in the control protocol a sequence in which the first and / or second chambers are used to influence the fluid temperature. These parameters indicate which chambers should be used in each case. For example, at least one parameter from the parameter set is determined that specifies, in the control protocol, a fluid residence time in the first chamber and / or a fluid residence time in the second chamber. The simulation can predict that several candidates for the control protocol will be identified, and the candidate that achieves the objective faster than at least one of the other candidates will be selected as the control protocol. For example, the candidate that reaches the objective most quickly is chosen. In this way, an optimum for the process duration is found considerably more reliably and quickly than through experimentation. To the extent that the geometry of the variable-geometry microfluidic system is modifiable, for example, of at least one chamber, it is stipulated that at least one parameter from the parameter set specified in the control protocol shall define the geometry of that at least one chamber of the variable-geometry microfluidic system. Variable geometry is understood, in particular, to mean a modifiable geometry of a chamber or channel, for example, by means of a wall that is at least partially movable, such as an extensible membrane, or a movable piston. In this way, the volume of the chamber or channel can be modified and / or a portion of the fluid within the chamber or channel can be displaced, which in turn can influence the thermal conditions, such as a change in the heat capacity or thermal resistance of the chamber or channel. To the extent that it is possible to modify the position of at least one heating element of the variable position microfluidic system, it is foreseen that at least one parameter of the parameter set will be determined in the control protocol, specifying the position of at least one heating element of the variable position microfluidic system. It can be expected that at least one parameter from the set of parameters used to determine the influence of temperature on the microfluidic system will be determined. This means that the behavior of the microfluidic system is also learned. To control the microfluidic system using the control protocol, the protocol is determined by the procedure, and the microfluidic system is then controlled with the control protocol. It is advantageous to first determine the control protocol and then operate the microfluidic system using that protocol. The control protocol can be advantageously determined in advance, i.e., offline, particularly using another device, and then the microfluidic system, preferably the analytical device, can be operated using the control protocol. The microfluidic system is controlled, for example, by the control protocol to adjust the first temperature in the first heating element and / or adjust the second temperature in the second heating element. The microfluidic system is controlled, for example, by the control protocol to pneumatically push or draw the fluid into at least one chamber. The microfluidic system is controlled, for example, by a control protocol to modify the geometry of at least one chamber and / or modify the position and / or temperature of at least one heating element. A device that achieves the same advantage comprises at least one processor and at least one memory, configured to execute the procedure. For example, the device could be a suitably programmed computer or part of a cloud computing solution. In one particular configuration, the device may correspond to the microfluidic system, specifically the analytical device described above. According to this variant, both the determination of the control protocol according to the invention and the execution of the control protocol can be carried out using the same device or, at least, using the same type of device. A computer program that achieves the same advantage comprises computer-readable instructions, during whose execution by a computer the procedure is carried out. Other advantageous embodiments can be derived from the following description and drawing. In the drawing: Fig. 1 shows a schematic representation of a microfluidic system, Fig. 2 shows a schematic representation of a device, Fig. 3 shows a flowchart with the steps of a procedure to determine a control protocol for the microfluidic system, Fig. 4 shows an evolution of the temperature in the fluid, Fig. 5 shows an exemplary process control, Fig. 6 shows an evolution of the fluid temperature in an example calculation, Fig. 7 shows an evolution of the temperatures and residence times of the fluid in the example calculation. In microfluidic structures, sequential processing steps are typically carried out in which a fluid, the sample fluid, is introduced into the microfluidic structure, processed, and passes through a predefined temperature cycle or several such cycles. To efficiently complete a prescribed temperature sequence within a given timeframe, the use of multiple chambers, i.e., dwelling chambers, located at different temperature levels has become widespread. Microfluidic structures are manufactured, for example, as cartridges with multiple chambers and under the influence of several heating elements, i.e., heaters. When using heating elements and multiple chambers, sequential process control can be implemented through a cycle. This cycle must be as efficient as possible, minimizing, for example, the cycle duration and ensuring an optimal biochemical reaction process. Manual experimentation is time-consuming and expensive, and does not guarantee an optimal result. The procedure described below provides a prediction of a control protocol for a given temperature profile over time without costly and impractical parallel experimental iterations, and finds an optimal result for the process duration—that is, the sequence duration—much more reliably and quickly than through experimentation. Figure 1 schematically represents a microfluidic system 100. The microfluidic system 100 comprises a first chamber 102 and a second chamber 104. It is possible to envisage the microfluidic system comprising more than two chambers. The microfluidic system represented by way of example in Figure 1 comprises a third chamber 106. The approach described below works with one chamber, with two chambers, and also with more than two chambers, in particular with three chambers, as described in the exemplary embodiment. The microfluidic system 100 comprises a first heating element 108, which is designed to influence the temperature of the fluid in the first chamber 102. The microfluidic system 100 comprises a second heating element 110, which is designed to influence the temperature of the fluid in the second chamber 104. The microfluidic system may be provided to comprise more than two heating elements. The microfluidic system represented by way of example in Figure 1 comprises a third heating element 112. According to a preferred configuration, the microfluidic system 100 comprises an analytical device in which a cartridge for analyzing the fluid contained therein can be housed, as described, for example, in publications DE 102016222075 A1 or DE 102016222072 A1.In this case, the analysis device may include heating elements 108, 110, 112, the processor and the memory, while the cartridge is preferably configured as a passive part for fluid processing and, during processing, heating elements 108, 110, 112 may come into contact with chambers 102, 104, 106. For example, the presented procedure is part of a procedure for carrying out an amplification of nucleic acid fragments, for example, a procedure for performing a PCR test, for example, for the detection of a pathogen in a sample. Between each pair of chambers, there is a fluid opening 114 in a wall 116 that separates the chambers. In this example, a first actuator 118 is configured to draw fluid into or expel it from the first chamber 102. In this example, a second actuator 120 is configured to draw fluid into or expel it from the second chamber 104. In this example, a third actuator 122 is configured to draw fluid into or expel it from the third chamber 106. In this context, one or more of the actuators 118, 120, and 122 can be configured to modify the geometry of the first chamber 102 or the second chamber 104 to perform the draw-in or expulsion, for example, by activating a stretchable membrane, which may be formed from one of the walls delimiting chambers 102 and 104. Figure 2 schematically represents a device 200. The device 200 comprises the microfluidic system 100. The device 200 comprises at least one processor 202 and at least one memory 204. A model 206 is provided in the at least one memory 204. The model 206 is defined by a set of parameters. In the example, the set of parameters is stored in memory 204. Depending on a particular configuration, the device 200 may comprise the microfluidic system 100 or be designed to control it. Alternatively, the device 200 described above is a suitably programmed computer that determines the control protocol subsequently used for the operation of the microfluidic system 100. In the example, a control connection 208 connects, at least temporarily, the at least one processor 202 to the microfluidic system 100.In the example, the at least one processor 202 and the at least one memory 204 communicate through a data connection 210. The device 200 is designed to execute the procedure described below. For example, a computer program comprising computer-readable instructions is provided, the execution of which by at least one processor 202, i.e., by a computer, results in the execution of the procedure. The following description describes a procedure using the first chamber 102 and the second chamber 104 as examples. The procedure is applicable to microfluidic systems with more than two chambers. The procedure is applicable to microfluidic systems with more than two heating elements. The procedure is designed to determine a control protocol for the microfluidic system 100 under temperature influences. Model 206 simulates the temperature influences on the fluid temperature in the chambers, caused by the execution of the control protocol, in order to determine said control protocol. Model 206 facilitates the application of new biochemical assays, in particular, by quantitatively leveraging a physically modeled relationship between the microfluidic system control 100 and a biochemically relevant fluid temperature to determine one or more control protocols. This quantitative, calculation-based solution uses an optimization procedure, as exemplified by this method. This allows for finding a better solution, particularly more quickly than is possible through experimental iterations. One reason for this is that both stochastic approaches, such as RandomSearch or Bayesian optimization, and technical control approaches, such as model predictive control, test non-intuitive and sometimes complex parameter combinations. Another reason is the parallelization capability of the procedure—that is, the ability to test many parameter sets simultaneously, allowing for more tests per unit of time. Preferably, the procedure is executed in essentially parallel fashion for several Model 206. The procedure for one Model 206 is described below. The microfluidic system 100 comprises, as described above in the example, a cartridge in which the fluid constitutes a sample. A computer-based procedure of this type does not require resources from the team that are needed for other development tasks. For example, an analyzer is not required. For the biochemical process to occur correctly in the cartridge, the temperature of the sample must evolve over time as prescribed. To achieve this, the procedure automatically determines the appropriate control protocol. In this example, a target result is known, and the necessary control protocol is determined to achieve it. In step 302, model 206 is provided, configurable by a set of parameters, in particular with a set of initial parameters. Model 206 is designed to determine a control protocol and the influence of a first temperature of the first heating element 108 and the influence of a second temperature of the second heating element 110 on the temperature evolution of the fluid in the microfluidic system 100, i.e., in the example of the cartridge, when it is controlled according to the control protocol. Step 304 establishes the target for the temperature evolution. This procedure may be repeated for different targets. The target is continuously improved, for example, based on validation data, or adapted to new cartridge designs or test types. In step 306, the set of parameters is determined in which a temperature evolution calculated with model 206 meets the objective. The parameter set comprises at least one control protocol parameter specifying the first and second temperatures. If other heating elements are planned or used, the example parameter set also includes the parameters for these. Additional parameters may also be specified. For example, a control protocol parameter is defined that specifies, within the parameter set, the order in which the first chamber 102 and the second chamber 104 are used to influence the fluid temperature. If other chambers are anticipated or used, the example parameter set also includes the parameters for these chambers. For example, a control protocol parameter is determined that specifies, in the parameter set, a fluid residence time in the first chamber 102 and a fluid residence time in the second chamber 104. According to the invention, a control protocol parameter is determined that specifies in the parameter set a geometry of at least one of the chambers of the microfluidic system 100 with variable geometry and / or at least a position of at least one of the heating elements of the microfluidic system with variable position. Optionally, several candidates for the control protocol are expected to be determined. Furthermore, it is possible to foresee the determination of at least one parameter of the set of parameters with which the influence of temperature on the microfluidic system 100 is determined. For example, a parameter of model 206 is determined that defines a differential equation or a weight of an artificial neural network, or an expected value or a variance of a statistical process, in particular a Gaussian process. For example, by repeating step 306, several control protocols are determined as candidates, and in an optional step 308, the candidate is selected from among the candidates as the control protocol that achieves the goal faster than at least one of the other candidates. In the process depicted in the example, step 306 is repeated after step 308. The control protocol is determined in the first iteration and stored as a candidate. In the example, the candidate stored in each iteration is compared with other candidates determined in subsequent iterations. The candidate selected in each iteration is then stored and used to compare it with a determined candidate in the next iteration. The procedure can be expected to end when the optimal candidate has been determined. In this case, the procedure is limited to simulation. The control protocol for the optimal candidate is stored, for example, for application in microfluidic system 100. Optionally, after step 308, the microfluidic system 100 is controlled in step 310 using this control protocol. For example, a control protocol for the microfluidic system 100 is determined using the described procedure, and the microfluidic system 100 is controlled using this control protocol. For example, the microfluidic system 100 is controlled using the control protocol to adjust the first temperature in the first heating element 108. For example, the microfluidic system 100 is controlled using the control protocol to adjust the second temperature in the second heating element 110. For example, the microfluidic system 100 is controlled using the control protocol to pneumatically pressurize or aspirate the fluid into the first chamber, in particular using the first actuator 118 and / or the second actuator 120.In the example, the control protocol specifies the time intervals during which the fluid remains in the first chamber 102. For example, the microfluidic system 100 is controlled by the control protocol to pneumatically pressurize or aspirate the fluid into the second chamber, in particular by means of the first actuator 118 and / or the second actuator 120. In the example, the control protocol specifies the time intervals during which the fluid remains in the second chamber 102. In one example, the control protocol specifies a sequence in which the fluid moves through these chambers. The parameter that determines the first temperature can be specified as constant during the control process by the control protocol. Alternatively, the parameter that determines the first temperature can be specified as changing during the control process by the control protocol. For example, respective first temperatures are specified for the different time intervals that the fluid must remain in the first chamber 102. Similarly, the parameter that determines the second temperature can be specified as changing during the control process by the control protocol. For example, respective second temperatures are specified for the different time periods that the fluid must remain in the second chamber 104. For a correct biochemical process in the case where only the first chamber 102 is provided, the first heating element 108 is controlled by the control protocol, which adjusts a change in the temperature regulation for the temporal evolution of the temperature required in the sample with the first heating element 108. If, in addition, a configuration of two chambers is provided, the corresponding procedure is followed, and a change in temperature regulation can be provided by moving the fluid alternately to each of the two chambers. In one example, the microfluidic system 100 is controlled by the control protocol to modify the geometry of at least one of the chambers. In one example, the microfluidic system 100 is controlled by the control protocol to modify the position of at least one of the heating elements. In the example, the control protocol specifies control signals for the heating elements and / or the actuators and / or the geometry and / or the position. These are determined, for example, by device 200 and sent to it for the control of the microfluidic system 100. Typical biochemical assays determine the objective function for the optimization problem as follows: specification of an initial denaturation temperature and the corresponding duration; specification of temperature intervals and durations for n cycles of denaturation, hybridization, and elongation, as required, for example, to carry out a polymerase chain reaction. The following table presents a sequence of steps in an exemplary assay. Step Temperature (°C) Time Initial denaturation, 95 2 min Denaturation I 95 30 s Hybridization I 63 40 s Elongation I 72 15 s Denaturation II 95 12 s Hybridization II 63 12 s Extension II 72 12 s Following the initial denaturation, in this example 15 cycles I are carried out, that is, denaturation I, hybridization I and elongation I. Then, in this example 25 cycles II are carried out, that is, denaturation II, hybridization II and elongation II. In the example, the temperature limits are 94 °C to 99 °C for denaturation, 62 °C to 64 °C for hybridization, and 71 °C to 73 °C for elongation. To achieve the objective function thus defined, in the example the following set of parameters is varied in the simulation until at least one solution is found for the optimization problem: temperature, selection of the cameras used, stay times in the selected cameras. A time sequence of these parameters constitutes a control protocol that must be executed in the microfluidic system 100, specifically in the cartridge. The control protocol is implemented, for example, directly in a controller of the microfluidic system 100 or the cartridge. The system controller is, for example, the device 200 or a separate microprocessor in which the control protocol has been or will be implemented. A set of parameters that defines a control protocol comprises specific values for those parameters. In this example, the variation of these parameters is carried out using a suitable algorithm. It is also possible to provide the user with the option, via a human-machine interface, to manually modify one or all of the parameters. For the system description—that is, the prediction of the temporal evolution of the temperature in the sample, which occurs when the microfluidic system 100 or the cartridge is controlled by the control protocol—model 206, which is parameterized with the set of parameters, is used. For the initial set of parameters, the example specifies the dwell times and temperatures, with the number of chambers corresponding to the number of different temperature levels in the test. If a fluid temperature prediction made by model 206 with these parameters shows deviations from the target, the example performs automatic or empirical manual corrections according to established rules. Deviations that occur, for example, due to differences in thermal behavior between model 206 and the modeled microfluidic system 100 or the target cartridge, are corrected in the example by varying the parameters. The rules for automatic correction or empirical correction are, for example, the following: If the temperature intervals are too short, for example, the dwell times are increased. If the target temperatures are not reached, for example, the dwell times are increased and the temperatures of the corresponding heaters are adjusted. Due to the physically determined coupling between fluid temperature and various parameters, a compensation or reciprocal reinforcement of parameter influences may occur. Additional corrections are therefore implemented. During several of these iterations, the corrections are successively reduced until convergence is reached. The 206 model may include at least one of the following models: Model 206 can be expected to include a 3D model designed to determine the initial temperature based on the control protocol and a time- and space-discretized calculation of heat and mass transport in the microfluidic system 100 or cartridge. The 3D model preferably represents the actual structure of the portion of the microfluidic system 100 to be modeled, specifically the structure and material properties of the chambers and heating elements. The control protocol is incorporated, for example, into the boundary and initial conditions of the 3D model. The temperature evolution is a result of the calculation obtained when the 3D model is computed. For the 3D model, the enthalpy equation (1) can be spatially discretized using finite volumes or finite elements, provided that the geometric features and physical gradients are solved. The same applies to the time discretization, which must allow tracking the temporal changes in the system. In this case, the Fourier thermal conductivity equation is solved for the temperature field T in the subvolumes i\i (subdivided by internal cutting surfaces iy, each with density , specific heat capacity cp, and thermal conductivity ): It should also be noted that the continuity of temperature and heat flows must be preserved at the physical boundaries of the domain (equation (2)). The heater temperatures are specified here as time-dependent Dirichlet boundary conditions. Model 206 can be expected to include a network model, also known as a thermal network model, designed to determine the initial temperature based on the control protocol using a combination of relevant states represented by simplified equations for heat and mass transport in the microfluidic system 100 or cartridge. For example, a 3D model abstraction based on physical principles is used as the network model, in which, as its name suggests, the specific relationships between thermal capacities and thermal resistances are organized in a network, leveraging the analogies between voltage and temperature, and between current and heat flow. The control protocol is incorporated, for example, into the activation signals and initial conditions of the network model. The temperature evolution is a result of the calculation obtained when the network model is computed.The mathematical form of the system of equations represented in the network model is given by equation (3): This is a system of first-order linear ordinary differential equations for the temperatures Ti at different points i of the microfluidic system 100 or in the cartridge. The parameters involved are the thermal capacities Ci = (cpV) i, with the specific heat capacity of the material in the vicinity of point i being the density of the material and the volume V associated with point i. In addition, the thermal resistances between points i, j, where l denotes the distance between points, ij the thermal conductivity of the material between points i, j and Aij the contact surface between the volumes associated with points i, j. The temperatures Tn+1 to Tm correspond to the specified heater temperatures. It can be anticipated that Model 206 comprises a data-driven model, trained using training data from the 3D model or experimental temperature measurements to predict the initial temperature based on the control protocol. The data-driven model is, for example, a feedforward artificial neural network. This model is designed, for instance, to predict, at any given time, the temporal variation from current temperature states. A combination of these models can also be foreseen. A combination of different models is used, for example, when a single model does not accurately represent all relevant relationships. In that case, each model represents parts of the relevant relationships. For example, fluid motions are determined using the 3D model, and thermal effects on the solid body of the microfluidic system 100 or the cartridge are determined using the network model. This reduces the computational load. It can be anticipated that the unknown parameters of the material will be determined by a data-based model, based on training data obtained from experiments, and used in one of the other models, i.e., the 3D model or the network model. The network model and the data-based model offer the advantage of requiring less computational effort for evaluation compared to the 3D model for the same calculation. This accelerates the analysis of described parameter variations. The network model can be created independently of the 3D model. However, the network model is less accurate than the 3D model. The accuracy of the data-based model depends on the accuracy of the data used for training. For example, the time series calculated by the 3D model and / or the network model are used, along with the control signals, for data-based system identification. Regarding thermal effects, for example, the time behavior of the temperature in the fluid, with a fixed fluid position, is described with sufficient accuracy by a linear time-invariant system of ordinary differential equations. A change in the retention chambers, that is, a movement of the fluid from one chamber to another, can be represented by time-dependent coefficients of this system or by a discrete or continuous change between several systems of equations. Each of these latter systems of equations corresponds to a position or configuration of the fluid. The calculated time series represents the training data. In the example, the training data is used to determine the coefficients of the linear differential equations by minimizing the error between a predicted system response and a system response described by the training data. This can be done, for example, by using one of the following options: the least squares procedure on all data points of the training data, or training a neural network that contains the coefficients of the system. Furthermore, it is possible to assess the success of the training, in addition to the reproduction of the training data, based on previously calculated system responses. The objective, in one example, is an objective function that defines target intervals, that is, nominal time intervals and nominal temperature intervals. The models described provide time series, for example, a fluid temperature as a function of time. In the example, these time series are compared with the objective function. For instance, the actual time intervals in which the fluid temperature falls within a nominal temperature interval assigned to the corresponding nominal time interval are determined. To do this, the time series is traversed from the beginning, and at each instant, it is checked whether the temperature falls within a nominal temperature interval and, if so, which one.If a temperature value falls within the temperature range assigned to a given nominal time interval, subsequent temperature values are added to the real-time interval as long as they remain within the same nominal temperature range; otherwise, the real-time interval ends. The duration of the real-time interval is determined by the difference between the start and end times of those temperature values that were within the nominal temperature range. In the example, the sequence of real-time intervals and their durations are compared to the nominal time intervals and their durations. If there is a discrepancy between the two, adjustments are made to the control protocol parameters. This process is repeated in the example. In this procedure, an expert or an algorithm determines the parameters with the aim of minimizing the deviation. By using appropriate objective functions, an optimal solution in terms of time can also be achieved, i.e., a fast process. Examples of the algorithm are provided below. The algorithms differ in terms of the solution space analyzed and / or the quality of the solution found. Random search: by randomly scanning the parameter space, a solution is found that meets a termination criterion, in the example, the set goal. Bayesian optimization: The influence of parameters on the objective function is explored and learned using a learning algorithm, and this information is used to propose the best solution. This involves generating new sets of parameters based on the learned probability distribution and a heuristic function, such as the acquisition function. For this purpose, the currently assumed optimal value is used, for example. Model-based predictive control: During the solution of a system of differential equations that includes model 206, the control parameters are dynamically adjusted by calculating a specific time interval in the future and evaluating the effect of the parameters based on this calculation.Figure 4 shows an exemplary temperature evolution of fluid 402 under the influence of a first temperature evolution of 404 in the first chamber 102, a second temperature evolution of 406 in the second chamber 104, and a third temperature evolution of 408 in the third chamber 106 over time, for example, for carrying out a polymerase chain reaction at three different temperature levels. Due to fluid movement between chambers as a consequence of the control protocol, the fluid is in different chambers over time. A first temperature of fluid 410 for the first chamber 102, predicted by model 206; a second temperature of fluid 412 for the second chamber 104, predicted by model 206; and a third temperature of fluid 414 for the third chamber 106, predicted by model 206, are represented in Figure 4 as curve segments, respectively.The fluid is heated during its stay in the first chamber 102 according to the first fluid temperature 410 foreseen and is cooled during its stay in the third chamber 106 according to the third fluid temperature 414 foreseen, since a temperature in the third chamber 106 in the. In this example, the temperature in the first chamber, 102, is lower than the temperature in the first chamber. The first temperature of fluid 410 predicted by model 206 deviates from the temperature evolution of 402 after reaching a maximum temperature in that evolution. The third temperature of fluid 414 predicted by model 206 deviates from the temperature evolution of 402 after reaching a minimum temperature in that evolution. In this example, the fluid also remains in the second chamber, 104, where the temperature in the second chamber is between the temperature in the third chamber, 106, and the temperature in the first chamber, 104. In this example, at the end of the temperature evolution of 402, while remaining in the second chamber, 104, the fluid reaches the second predicted temperature of fluid 412, which in this example is the temperature to which the second chamber, 104, is heated. Figure 5 schematically represents a process control 500 over time. In the example, process control 500 is carried out using the control protocol in which the fluid alternately resides in the first chamber 102 and the third chamber 106, as well as, in each case, in the second chamber 104 between them, so that, in the end, the fluid is in the second chamber 104 and the temperature evolution in fluid 402 is established. For a simulation time of 150 seconds, Figure 6 shows the temperature evolution of fluid 602 with a target temperature range 604 and a target 606 for an example calculation using Bayesian optimization. In the example, the target temperature range is determined by the temperature limits. Figure 7 shows the evolution of the fluid temperatures and residence times in the example calculation. The upper graph shows the evolution of the first temperature 702 of the first heating element 108, the evolution of the second temperature 704 of the second heating element 110, and the evolution of the third temperature 706 of the third heating element 112 over time. The lower graph indicates, with a value of 1, whether the fluid is in the first chamber 102, the second chamber 104, or the third chamber 106. A value of 0 means that the fluid is not in the corresponding chamber. The example calculation assumes that the temperatures of the heating elements are initially set randomly and remain constant throughout the calculation.In the example, the fluid's position changes within the 150-second simulation time, depending on whether the fluid temperature falls within the target corridor. The chamber change occurs as soon as the fluid has remained within the target temperature range for a sufficient amount of time—the time defined by the objective. The control protocol used to establish this temperature evolution is used, for example, in Lab-on-Chip microfluidic systems for medical diagnostics, particularly for the development of biochemical processes through temperature evolutions.
Claims
1. A computer-aided method for determining a control protocol for a microfluidic system (100) under thermal influences, wherein the microfluidic system (100) comprises a first chamber (102) for a fluid, the microfluidic system (100) comprises a first heating element (108) designed to influence the temperature of the fluid in the first chamber (102), a parameterizable model (206) with a set of parameters is provided (302), which is designed to determine the influence of a first temperature and the influence of a second temperature on the temperature evolution in the fluid of the microfluidic system when it is controlled according to the control protocol, a target for the temperature evolution is established (304), and the set of parameters with which the temperature evolution calculated by the model (206) meets the target is determined (306).and the parameter set comprises at least one control protocol parameter specifying the first temperature and / or the second temperature, characterized in that at least one parameter of the parameter set specifying in the control protocol the geometry of at least one chamber of the microfluidic system with variable geometry and / or at least one position of at least one heating element of the microfluidic system with variable position is determined (306).
2. Method according to claim 1, wherein the microfluidic system (100) comprises a second chamber (104) for the fluid, the microfluidic system (100) comprises a second heating element (110) that is designed to influence the temperature of the fluid in the second chamber (104),characterized in that the model (206) is designed to determine the influence of the first temperature of the first heating element (108) and the influence of the second temperature of the second heating element (110) on the temperature evolution in the fluid of the microfluidic system.
3. Method according to claim 2, characterized in that at least one parameter (306) is determined from the set of parameters which, in the control protocol, specifies a sequence in which the first chamber (102) and / or the second chamber (104) are used to influence the temperature of the fluid.
4. Method according to any one of claims 2 or 3, characterized in that at least one parameter (306) is determined from the set of parameters which specifies, in the control protocol, a residence time of the fluid in the first chamber (102) and / or a residence time of the fluid in the second chamber (104).
5. Method according to any one of the preceding claims,characterized in that a plurality of candidates for the control protocol is determined (306), the candidate from among the candidates being selected (308) as the control protocol with which the objective is achieved more rapidly than with at least one of the other candidates.
6. A method according to any of the preceding claims, characterized in that at least one parameter of the set of parameters with which the influence of temperature on the microfluidic system is determined (100) is determined (306).
7. A method for controlling a microfluidic system, characterized in that a control protocol for the microfluidic system is determined in a method according to any one of claims 1 to 6, the microfluidic system being controlled (310) by means of said control protocol.
8. A method according to claim 7,characterized in that the microfluidic system (100) is controlled (310) by the control protocol to adjust the first temperature in the first heating element and / or adjust the second temperature in the second heating element.
9. Method according to claim 7 or 8, characterized in that the microfluidic system (100) is controlled (310) by the control protocol to pneumatically pressurize or aspirate the fluid into at least one chamber.
10. Method according to any one of claims 6 to 8, characterized in that the microfluidic system (100) is controlled (310) by the control protocol to modify the geometry of at least one chamber and / or modify the position and / or temperature of at least one heating element.
11. Device (200), characterized in that the device comprises at least one processor (202) and at least one memory (204),that are configured to execute the procedure according to any one of claims 1 to 10.
12. Device (200) according to claim 11, characterized in that the device (200) comprises the microfluidic system (100).
13. Computer program, characterized in that the computer program comprises computer-readable instructions, in the execution of which by a computer the procedure is carried out according to any one of claims 1 to 10.