Systems and methods regarding peak exploration and artificial tissue response including for contraction type classification of engineered tissue

A non-transitory computer readable medium storing computing instructions which, when executed by a device comprising one or more processors, cause the one or more processors to:

• obtain a first waveform comprising at least one contraction response and at least one relaxation response of an engineered tissue, the first waveform having a predetermined length corresponding to an expected length of a contraction-relaxation cycle of the engineered tissue;
• determine a predicted contraction type of a plurality of contraction types for the first waveform, wherein the plurality of contraction types include a single contraction type and a double contraction type;
• fit a model to the first waveform based on the predicted contraction type, wherein the model parameterizes growth of the at least one contraction response of the engineered tissue independently of growth of the at least one relaxation response of the engineered tissue;
• and
• generate a second waveform from the model fit to the first waveform such that the second waveform comprises a noise filtered representation of the first waveform.

A system comprising:

• one or more processors;
• and
• a memory storing computing instructions which, when executed by the one or more processors, cause the one or more processors to:
• obtain a first waveform comprising at least one contraction response and at least one relaxation response of an engineered tissue, the first waveform having a predetermined length corresponding to an expected length of a contraction-relaxation cycle of the engineered tissue;
• determine a predicted contraction type of a plurality of contraction types for the first waveform, wherein the plurality of contraction types include a single contraction type and a double contraction type;
• fit a model to the first waveform based on the predicted contraction type, wherein the model parameterizes growth of the at least one contraction response of the engineered tissue independently of growth of the at least one relaxation response of the engineered tissue;
• and
• generate a second waveform from the model fit to the first waveform such that the second waveform comprises a noise filtered representation of the first waveform.

A method for training a classifier to predict a tissue contraction type from a functional response waveform, the method comprising:

• obtaining, by one or more processors, a plurality of waveforms comprising functional responses of a one or more engineered tissues, each of the plurality of waveforms having a predetermined length corresponding to an expected length of a contraction-relaxation cycle of an engineered tissue;
• extracting, by the one or more processors, a first plurality of parameter sets from a first subset of the plurality of waveforms associated with a single contraction type, wherein a first parameter set of the first plurality of parameter sets characterizes a first waveform of the first subset of waveforms;
• extracting, by the one or more processors, a second plurality of parameter sets from a second subset of the plurality of waveforms associated with a double contraction type, wherein a second parameter set of the second plurality of parameter sets characterizes a second waveform of the second subset of waveforms;
• determining, by the one or more processors, a plurality of parameter set distributions, wherein the plurality of parameter set distributions comprise a first parameter set distribution determined from the first plurality of parameter sets and a second parameter set distribution determined from the second plurality of parameter sets;
• generating, by the one or more processors, a synthetic training data set, each element of the synthetic training data set comprising a synthetic waveform and a corresponding tissue contraction type associated with the synthetic waveform, wherein the synthetic waveform is generated using a parameter set distribution of the plurality of parameter set distributions associated with the corresponding tissue contraction type;
• and
• training, by the one or more processors, the classifier using the synthetic training data set, wherein the classifier trained using the synthetic training data set determines a predicted tissue contraction type for an input waveform.

A non-transitory computer readable medium storing computing instructions which, when executed by a device comprising one or more processors, cause the one or more processors to:

• obtain a plurality of waveforms comprising functional responses of a one or more engineered tissues, each of the plurality of waveforms having a predetermined length corresponding to an expected length of a contraction-relaxation cycle of an engineered tissue;
• extract a first plurality of parameter sets from a first subset of the plurality of waveforms associated with a single contraction type, wherein a first parameter set of the first plurality of parameter sets characterizes a first waveform of the first subset of waveforms;
• extract a second plurality of parameter sets from a second subset of the plurality of waveforms associated with a double contraction type, wherein a second parameter set of the second plurality of parameter sets characterizes a second waveform of the second subset of waveforms;
• determine a plurality of parameter set distributions, wherein the plurality of parameter set distributions comprise a first parameter set distribution determined from the first plurality of parameter sets and a second parameter set distribution determined from the second plurality of parameter sets;
• generate a synthetic training data set, each element of the synthetic training data set comprising a synthetic waveform and a corresponding tissue contraction type associated with the synthetic waveform, wherein the synthetic waveform is generated using a parameter set distribution of the plurality of parameter set distributions associated with the corresponding tissue contraction type;
• and
• train a classifier using the synthetic training data set, wherein the classifier trained using the synthetic training data set determines a predicted tissue contraction type for an input waveform.

A system comprising:

• one or more processors;
• and
• a memory storing computing instructions which, when executed by the one or more processors, cause the one or more processors to:
• obtain a plurality of waveforms comprising functional responses of a one or more engineered tissues, each of the plurality of waveforms having a predetermined length corresponding to an expected length of a contraction-relaxation cycle of an engineered tissue;
• extract a first plurality of parameter sets from a first subset of the plurality of waveforms associated with a single contraction type, wherein a first parameter set of the first plurality of parameter sets characterizes a first waveform of the first subset of waveforms;
• extract a second plurality of parameter sets from a second subset of the plurality of waveforms associated with a double contraction type, wherein a second parameter set of the second plurality of parameter sets characterizes a second waveform of the second subset of waveforms;
• determine a plurality of parameter set distributions, wherein the plurality of parameter set distributions comprise a first parameter set distribution determined from the first plurality of parameter sets and a second parameter set distribution determined from the second plurality of parameter sets;
• generate a synthetic training data set, each element of the synthetic training data set comprising a synthetic waveform and a corresponding tissue contraction type associated with the synthetic waveform, wherein the synthetic waveform is generated using a parameter set distribution of the plurality of parameter set distributions associated with the corresponding tissue contraction type;
• and
• train a classifier using the synthetic training data set, wherein the classifier trained using the synthetic training data set determines a predicted tissue contraction type for an input waveform.

A method for processing a functional response waveform the method comprising:

• obtaining, by one or more processors, a first waveform comprising a plurality of functional responses of an artificial tissue stimulated at a first frequency;
• convolving, by the one or more processors, the first waveform with a pulse-train to generate a convolved waveform, wherein the pulse-train is generated at the first frequency;
• identifying, by the one or more processors, a first location associated with a first maximum value of the convolved waveform, wherein the first location corresponds to an expected location of a first contraction-relaxation cycle;
• and
• extracting, by the one or more processors and from the first location of the first waveform, a second waveform comprising the first contraction-relaxation cycle, wherein the second waveform has a first duration proportional to the first frequency.

A non-transitory computer readable medium storing computing instructions which, when executed by a device comprising one or more processors, cause the one or more processors to:

• obtain a first waveform comprising a plurality of functional responses of an artificial tissue stimulated at a first frequency;
• convolve the first waveform with a pulse-train to generate a convolved waveform, wherein the pulse-train is generated at the first frequency;
• identify a first location associated with a first maximum value of the convolved waveform, wherein the first location corresponds to an expected location of a first contraction-relaxation cycle;
• and
• extract, from the first location of the first waveform, a second waveform comprising the first contraction-relaxation cycle, wherein the second waveform has a first duration proportional to the first frequency.

A system comprising:

• one or more processors;
• and
• a memory storing computing instructions which, when executed by the one or more processors, cause the one or more processors to:
• obtain a first waveform comprising a plurality of functional responses of an artificial tissue stimulated at a first frequency;
• convolve the first waveform with a pulse-train to generate a convolved waveform, wherein the pulse-train is generated at the first frequency;
• identify a first location associated with a first maximum value of the convolved waveform, wherein the first location corresponds to an expected location of a first contraction-relaxation cycle;
• and
• extract, from the first location of the first waveform, a second waveform comprising the first contraction-relaxation cycle, wherein the second waveform has a first duration proportional to the first frequency.

A method for predicting a perturbation effect, the method comprising:

• obtaining, by one or more processors, a plurality of signals comprising a baseline signal and a perturbation signal, wherein the baseline signal comprises a first plurality of functional responses of an engineered tissue under control conditions and the perturbation signal comprises a second plurality of functional responses of the engineered tissue under a first set of perturbation conditions;
• splitting, by the one or more processors, the plurality of signals into a first plurality of waveforms, each waveform of the first plurality of waveforms having a predetermined length and comprising at least one contraction response and at least one relaxation response of the engineered tissue, wherein the predetermined length corresponds to an expected length of a contraction-relaxation cycle of the engineered tissue;
• determining, by the one or more processors, a predicted contraction type of a plurality of contraction types for each of the first plurality of waveforms, wherein the plurality of contraction types include a single contraction type and a double contraction type;
• fitting, by the one or more processors, a model to each waveform of the first plurality of waveforms based on a corresponding predicted contraction type, wherein the model parameterizes growth of the at least one contraction response independently of growth of the at least one relaxation response of the engineered tissue;
• generating, by the one or more processors, a second plurality of waveforms from the model fit to each waveform of the first plurality of waveforms, wherein the second plurality of waveforms comprise a plurality of filtered baseline waveforms associated with the baseline signal and a plurality of filtered perturbation waveforms associated with the perturbation signal;
• extracting, by the one or more processors, a first feature value of a first feature from the plurality of filtered baseline waveforms;
• extracting, by the one or more processors, a second feature value of the first feature from the plurality of filtered perturbation waveforms;
• and
• determining, by the one or more processors, an effect associated with the first set of perturbation conditions based on a comparison of the first feature value and the second feature value.

A non-transitory computer readable medium storing computing instructions which, when executed by a device comprising one or more processors, cause the one or more processors to:

• obtain a plurality of signals comprising a baseline signal and a perturbation signal, wherein the baseline signal comprises a first plurality of functional responses of an engineered tissue under control conditions and the perturbation signal comprises a second plurality of functional responses of the engineered tissue under a first set of perturbation conditions;
• split the plurality of signals into a first plurality of waveforms, each waveform of the first plurality of waveforms having a predetermined length and comprising at least one contraction response and at least one relaxation response of the engineered tissue, wherein the predetermined length corresponds to an expected length of a contraction-relaxation cycle of the engineered tissue;
• determine a predicted contraction type of a plurality of contraction types for each of the first plurality of waveforms, wherein the plurality of contraction types include a single contraction type and a double contraction type;
• fit a model to each waveform of the first plurality of waveforms based on a corresponding predicted contraction type, wherein the model parameterizes growth of the at least one contraction response independently of growth of the at least one relaxation response of the engineered tissue;
• generate a second plurality of waveforms from the model fit to each waveform of the first plurality of waveforms, wherein the second plurality of waveforms comprise a plurality of filtered baseline waveforms associated with the baseline signal and a plurality of filtered perturbation waveforms associated with the perturbation signal;
• extract a first feature value of a first feature from the plurality of filtered baseline waveforms;
• extract a second feature value of the first feature from the plurality of filtered perturbation waveforms;
• and
• determine an effect associated with the first set of perturbation conditions based on a comparison of the first feature value and the second feature value.

A system comprising:

• one or more processors;
• and
• a memory storing computing instructions which, when executed by the one or more processors, cause the one or more processors to:
• obtain a plurality of signals comprising a baseline signal and a perturbation signal, wherein the baseline signal comprises a first plurality of functional responses of an engineered tissue under control conditions and the perturbation signal comprises a second plurality of functional responses of the engineered tissue under a first set of perturbation conditions;
• split the plurality of signals into a first plurality of waveforms, each waveform of the first plurality of waveforms having a predetermined length and comprising at least one contraction response and at least one relaxation response of the engineered tissue, wherein the predetermined length corresponds to an expected length of a contraction-relaxation cycle of the engineered tissue;
• determine a predicted contraction type of a plurality of contraction types for each of the first plurality of waveforms, wherein the plurality of contraction types include a single contraction type and a double contraction type;
• fit a model to each waveform of the first plurality of waveforms based on a corresponding predicted contraction type, wherein the model parameterizes growth of the at least one contraction response independently of growth of the at least one relaxation response of the engineered tissue;
• generate a second plurality of waveforms from the model fit to each waveform of the first plurality of waveforms, wherein the second plurality of waveforms comprise a plurality of filtered baseline waveforms associated with the baseline signal and a plurality of filtered perturbation waveforms associated with the perturbation signal;
• extract a first feature value of a first feature from the plurality of filtered baseline waveforms;
• extract a second feature value of the first feature from the plurality of filtered perturbation waveforms;
• and
• determine an effect associated with the first set of perturbation conditions based on a comparison of the first feature value and the second feature value.

A method for training a model using synthetic training data, the method comprising:

• obtaining, by one or more processors, a plurality of waveforms comprising functional responses of one or more artificial tissues, wherein the plurality of waveforms all comprise a common contraction type of a predetermined plurality of contraction types;
• each of the plurality of waveforms having a predetermined length corresponding to an expected length of a contraction-relaxation cycle of an artificial tissue, of the plurality of waveforms have a same contraction type;
• extracting, by the one or more processors, a plurality of parameter sets from the plurality of waveforms, wherein a parameter set of the plurality of parameter sets characterizes a corresponding waveform of the plurality of waveforms;
• determining, by the one or more processors, a parameter set distribution from the plurality of parameter sets;
• generating, by the one or more processors, a synthetic training data set, each element of the synthetic training data set comprising a synthetic waveform and a corresponding parameter set used to generate the synthetic waveform, wherein the corresponding parameter set is obtained from the parameter set distribution;
• and
• training, by the one or more processors, a parameter estimation model using the synthetic training data set, wherein the parameter estimation model is trained to estimate an output parameter set from an input waveform.

A non-transitory computer readable medium storing computing instructions which, when executed by a device comprising one or more processors, cause the one or more processors to:

• obtain a plurality of waveforms comprising functional responses of one or more artificial tissues during a single contraction-relaxation cycle;
• extract a plurality of parameter sets from the plurality of waveforms, wherein a parameter set of the plurality of parameter sets characterizes a corresponding waveform of the plurality of waveforms;
• determine a parameter set distribution from the plurality of parameter sets;
• generate a synthetic training data set, each element of the synthetic training data set comprising a synthetic waveform and a corresponding parameter set used to generate the synthetic waveform, wherein the corresponding parameter set is obtained from the parameter set distribution;
• and
• train a parameter estimation model using the synthetic training data set, wherein the parameter estimation model is trained to estimate an output parameter set from an input waveform.

A system comprising:

• one or more processors;
• and
• a memory storing computing instructions which, when executed by the one or more processors, cause the one or more processors to:
• obtain a plurality of waveforms comprising functional responses of one or more artificial tissues during a single contraction-relaxation cycle;
• extract a plurality of parameter sets from the plurality of waveforms, wherein a parameter set of the plurality of parameter sets characterizes a corresponding waveform of the plurality of waveforms;
• determine a parameter set distribution from the plurality of parameter sets;
• generate a synthetic training data set, each element of the synthetic training data set comprising a synthetic waveform and a corresponding parameter set used to generate the synthetic waveform, wherein the corresponding parameter set is obtained from the parameter set distribution;
• and
• train a parameter estimation model using the synthetic training data set, wherein the parameter estimation model is trained to estimate an output parameter set from an input waveform.