Digital modeling of enzymatic function in biochemical reactions within cardiac cells

Abstract

A computational system and method for digital modeling of biochemical reactions within cardiac cells are disclosed. The system comprises processors and a computer-readable storage device with instructions to maintain biological data related to cardiac cell reactions and receive user selections via a user interface to configure a simulation. The user can select a cardiac cell class, and functions to load cellular components, generate enzyme activity, load specific enzymes, and perform enzymatic reactions. The system performs in silico experiments, predicts new biological data, and compiles this into configuration data. An enzyme activity model is generated and simulated under various conditions, including user-defined enzymatic dysregulation, to replicate and study cardiac physiology and pathology. The results, such as changes in energy metabolism parameters like glucose consumption and ATP usage, are outputted. The system provides a detailed and interactive platform for research and education in cardiac electrophysiology and disease.

Description

[0001] DIGITAL MODELING OF ENZYMATIC FUNCTION IN BIOCHEMICAL REACTIONS WITHIN CARDIAC CELLS

[0002] FIELD OF THE INVENTION

[0003] The present invention relates generally to the field of cell modeling and more specifically to computational simulation of enzymatic functions in biochemical reactions within cardiac cells.

[0004] BACKGROUND OF THE INVENTION

[0005] Digital models of human cells refer to computer-based representations and simulations of biological cells. These models are used in various fields, including biology, medicine, and biotechnology, to study and understand cellular processes, test hypotheses, and make predictions. Digital models of human cells play a crucial role in advancing the understanding of biology and medicine, and they continue to evolve as the knowledge of cellular processes grows. Researchers use these models to make predictions, conduct virtual experiments, and test new therapies, contributing to the fields of personalized medicine and systems biology.

[0006] Many digital cell models use mathematical equations and computational simulations to describe and predict cellular behaviors. These models can range from simple differential equations to complex systems of equations.

[0007] Models can vary in scale, from single-cell models to multicellular systems. They can also vary in complexity, from simplified representations to highly detailed, data-driven models. Many models incorporate experimental data, such as gene expression data, protein-protein interaction data, and metabolomics data, to make their predictions more biologically relevant.

[0008] Creating accurate and predictive digital models of human cells is challenging due to the complexity of biological systems. Gathering precise data and accounting for variability in biological systems are ongoing challenges.

[0009] There are collaborative efforts worldwide, such as the Physiome Project and the Virtual Physiological Human, aimed at developing comprehensive digital models of human physiology, including cell-level models. Modeling accessory pathway detection and ablation for educational / training purposes involves simulating of the realistic tissue behavior, which means that simulating the electrical and mechanical properties of cardiac tissue accurately is crucial. Realistic representation of tissue behavior, including conductivity, tissue heterogeneity, and response to energy delivery is impossible without a highly detailed model of cardiac cells as a physiological base for any effort in this field.

[0010] Therefore, there still remains a long felt unmet need to provide a highly detailed model of cardiac cells to enhance the realism and effectiveness of training simulations in electrophysiology procedures.

[0011] SUMMARY OF THE INVENTION

[0012] The present invention provides a solution to the aforementioned need by disclosing a computational system and method for the digital modeling of biochemical reactions within cardiac cells.

[0013] An object of the present invention is to provide a computational system comprising one or more processors and a computer-readable storage device. The storage device holds instructions that, when executed, cause the processors to perform a series of operations for modeling enzymatic function. These operations begin with maintaining a repository of biological data related to a plurality of biochemical reactions within cardiac cells.

[0014] The system is configured to receive, via one or more user interfaces, a selection of various components and functions to build and run a simulation. These selections include: a class configured to represent a cardiac cell; a function to load organic components of cellular biochemistry; a function to generate enzyme activity; a function to load a specific enzyme from a plurality of enzymes involved in cellular metabolism; a function to perform an enzymatic reaction in the cardiac cell; a function to calculate energy metabolism-related parameters; a function to calculate the electrochemical gradient; and a function to set the performance level of active enzymes. This last function is particularly important as it allows for the simulation of enzymatic dysregulation.

[0015] Once configured, the system performs in silico experiments under various predefined or user- selected conditions. It uses the results of these experiments to predict additional biological data. The initial biological data and the predicted data are then compiled into configuration data. This configuration data, along with a software core engine, is used to generate an enzyme activity model that replicates enzymatic function in biochemical reactions within cardiac cells. The generated enzyme activity model can then be simulated by executing it under various conditions, and the results are outputted to a data repository for analysis.

[0016] In some embodiments, the enzymatic reactions modeled by the system include key steps in glycolysis, glycogenesis, glycogenolysis, and the pentose phosphate pathway. These reactions may include, for example, the conversion of dihydroxyacetone phosphate (DHAP) into glyceraldehyde- 3 -phosphate, the conversion of glucose into glycogen, and pyruvate decarboxylation, among others.

[0017] In further embodiments, the calculated energy metabolism-related parameters include critical indicators such as glucose consumption and ATP usage by the cardiac cell, providing insight into the cell's metabolic state.

[0018] A significant aspect of the invention is its ability to simulate enzymatic dysregulation associated with various cardiac diseases. By allowing a user to manually or automatically alter the performance level of specific enzymes, the system can model conditions such as heart failure, myocardial infarction, and hypertension, providing a powerful tool for understanding disease mechanisms and exploring potential therapeutic interventions.

[0019] The invention also extends to a computer-implemented method for performing these modeling steps and a non-transitory computer-readable storage medium containing the instructions to carry out the method.

[0020] In one aspect of the invention, a computational system for digital modeling of biochemical reactions within cardiac cells, said system comprising: a. one or more processors; and b. a computer-readable storage device coupled to said one or more processors and having instructions stored thereon which, when executed by said one or more processors, cause said one or more processors to perform operations comprising: i. maintaining biological data, related to a plurality of biochemical reactions within cardiac cells, in a data repository; ii. receiving, via one or more user interfaces, a selection of:

[0021] 1) a class configured to represent a cardiac cell;

[0022] 2) a function that loads organic components of cellular biochemistry;

[0023] 3) a function that generates an enzyme activity; 4) a function that loads an enzyme selected from a plurality of enzymes involved in cellular metabolism;

[0024] 5) a function that performs an enzymatic reaction in said cardiac cell;

[0025] 6) a function that calculates the value of energy metabolism-related parameters in said cardiac cell;

[0026] 7) a function that calculates electrochemical gradient in said cardiac cell; and

[0027] 8) a function that sets the performance level of currently active enzymes in said cardiac cell; iii. performing in silico experiments under various conditions that are predefined or selected for analysis; iv. predicting at least some other biological data using said performed in silico experiments; v. compiling said biological data with said predicted at least some other biological data into configuration data; vi. generating an enzyme activity model using said configuration data and a software core engine to replicate enzymatic function in biochemical reactions within cardiac cells; vii. simulating said enzyme activity model by executing said model under various conditions; and viii. outputting one or more results from executing said enzyme activity model in a data repository.

[0028] In another aspect of the invention, the computational system above is provided, wherein said enzymatic reaction is selected from the group consisting of: a. converting dihydroxyacetone phosphate (DHAP) into glyceraldehyde-3 -phosphate; b. converting dihydroxyacetone phosphate (DHAP) into pyruvic acid; c. converting glucose into glycogen; d. converting glucose-6-phosphate into glyceraldehyde-3 -phosphate and dihydroxyacetone phosphate (DHAP); e. converting fructose 6-phosphate into glyceraldehyde-3 -phosphate and dihydroxyacetone phosphate (DHAP); f. converting glyceraldehyde-3 -phosphate into pyruvic acid; g. converting glyceraldehyde-3 -phosphate into glucose-6-phosphate; h. converting glucose-6-phosphate into pentoses; i. converting glucose-6-phosphate into fructose-6-phosphate; j . converting glucose-6-phosphate into glycogen; k. converting glucose into glucose-6-phosphate; l. converting glycogen into glucose-6-phosphate; m. converting glycogen into glucose; and n. pyruvate decarboxylation.

[0029] In another aspect of the invention, the computational system as defined in any of above is provided, wherein said energy metabolism-related parameters are selected from the group consisting of glucose consumption and adenosine triphosphate (ATP) usage by said cardiac cell.

[0030] In another aspect of the invention, the computational system as defined in any of above is provided, further wherein said system simulates enzymatic dysregulation in cardiac diseases.

[0031] In another aspect of the invention, the computational system as defined in any of above is provided, wherein simulating enzymatic dysregulation comprises receiving, via said one or more user interfaces, a manual input to alter the performance level of at least one of said currently active enzymes.

[0032] In another aspect of the invention, the computational system as defined in any of above is provided, wherein said one or more user interfaces comprise a graphical user interface displaying a network of said plurality of biochemical reactions, wherein individual enzymes are represented as selectable elements.

[0033] In one aspect of the invention, a computer-implemented method for digital modeling of biochemical reactions within cardiac cells, the method comprising steps of: a. maintaining, by one or more processors, biological data related to a plurality of biochemical reactions within cardiac cells in a data repository; b. receiving, by said one or more processors via one or more user interfaces, a selection of: i. a class configured to represent a cardiac cell; ii. a function that loads organic components of cellular biochemistry; iii. a function that generates an enzyme activity; iv. a function that loads an enzyme selected from a plurality of enzymes involved in cellular metabolism; v. a function that performs an enzymatic reaction in said cardiac cell; vi. a function that calculates a value of one or more energy metabolism-related parameters in said cardiac cell; vii. a function that calculates an electrochemical gradient in said cardiac cell; and viii. a function that sets a performance level of currently active enzymes in said cardiac cell; c. performing, by said one or more processors, in silico experiments under one or more conditions that are predefined or selected for analysis; d. predicting, by said one or more processors, at least some other biological data using said performed in silico experiments; e. compiling, by said one or more processors, said biological data with said predicted at least some other biological data into configuration data; f. generating, by said one or more processors, an enzyme activity model using said configuration data and a software core engine to replicate enzymatic function in biochemical reactions within cardiac cells; g. simulating, by said one or more processors, said enzyme activity model by executing said model under various conditions; and h. outputting, by said one or more processors, one or more results from executing said enzyme activity model in a data repository.

[0034] In another aspect of the invention, the computer-implemented method above is provided, wherein said enzymatic reaction is selected from the group consisting of: a. converting dihydroxyacetone phosphate (DHAP) into glyceraldehyde-3 -phosphate; b. converting dihydroxyacetone phosphate (DHAP) into pyruvic acid; c. converting glucose into glycogen; d. converting glucose-6-phosphate into glyceraldehyde-3 -phosphate and dihydroxyacetone phosphate (DHAP); e. converting fructose 6-phosphate into glyceraldehyde-3 -phosphate and dihydroxyacetone phosphate (DHAP); f. converting glyceraldehyde-3 -phosphate into pyruvic acid; g. converting glyceraldehyde-3 -phosphate into glucose-6-phosphate; h. converting glucose-6-phosphate into pentoses; i. converting glucose-6-phosphate into fructose-6-phosphate; j . converting glucose-6-phosphate into glycogen; k. converting glucose into glucose-6-phosphate; l. converting glycogen into glucose-6-phosphate; and m. converting glycogen into glucose; and n. pyruvate decarboxylation. In another aspect of the invention, the computer-implemented method as defined in any of above is provided, wherein said energy metabolism-related parameters are selected from the group consisting of glucose consumption and adenosine triphosphate (ATP) usage by said cardiac cell.

[0035] In another aspect of the invention, the computer-implemented method as defined in any of above is provided, further comprising simulating enzymatic dysregulation in cardiac diseases by receiving a user input to alter the performance level of at least one of said currently active enzymes.

[0036] In another aspect of the invention, the computer-implemented method as defined in any of above is provided, wherein simulating enzymatic dysregulation models a condition selected from the group consisting of heart failure, myocardial infarction, hypertension, atherosclerosis, dilated cardiomyopathy, cardiac hypertrophy, arrhythmia, and diabetic cardiomyopathy.

[0037] In one aspect of the invention, a non-transitory computer-readable storage medium having instructions stored thereon which, when executed by one or more processors, cause said one or more processors to perform a method for digital modeling of biochemical reactions within cardiac cells, the method comprising steps of: a. maintaining biological data related to a plurality of biochemical reactions within cardiac cells in a data repository; b. receiving, via one or more user interfaces, a selection of a class and a plurality of functions for configuring a simulation of a cardiac cell, wherein the plurality of functions includes a function to set a performance level of one or more active enzymes in said cardiac cell; c. performing in silico experiments based on the selection; d. generating an enzyme activity model based on results from said in silico experiments; e. simulating said enzyme activity model by executing said model under various conditions, including conditions of user-defined enzyme performance levels to model enzymatic dysregulation; and f. outputting one or more results from executing said enzyme activity model.

[0038] In another aspect of the invention, the non-transitory computer-readable storage medium above is provided, wherein the results include values for glucose consumption and adenosine triphosphate (ATP) usage by said cardiac cell. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present invention will be better understood in view of the following non-limiting figures, in which:

[0040] Figure 1 discloses a schematic representation of the process of generating computational simulations of enzymatic function and basic biochemical processes, in accordance with an embodiment of the present disclosure.

[0041] Figure 2 shows an exemplary graphical user-interface presentation of a network of biochemical reactions modeled in the system of the present invention, with grey rectangular sections marked by “E” representing the enzymes; clicking on the E-marked squares opens enzyme windows, in accordance with an embodiment of the present disclosure.

[0042] Figure 3 shows an exemplary user-interface window for the presentation of modeled reactions converting dihydroxyacetone phosphate (DHAP) into glyceraldehyde-3 -phosphate by Triosophosphateisomerase enzyme, in accordance with an embodiment of the present disclosure.

[0043] Figure 4 shows an exemplary user-interface presentation of modeled activity of Pyruvate kinase, in accordance with an embodiment of the present disclosure.

[0044] Figure 5 shows an exemplary user-interface presentation of modeled activity of glucose-6- phosphateisom erase, in accordance with an embodiment of the present disclosure.

[0045] Figure 6 shows an exemplary user-interface presentation of modeled activity of phosphofructokinase, in accordance with an embodiment of the present disclosure.

[0046] Figure 7 shows an exemplary user-interface presentation of modeled activity of fructosodiphosphate- adolase, in accordance with an embodiment of the present disclosure.

[0047] Figure 8 shows an exemplary user-interface presentation of modeled activity of dehydrogenase, in accordance with an embodiment of the present disclosure.

[0048] Figure 9 shows an exemplary user-interface presentation of modeled activity of gluconol actol ase, in accordance with an embodiment of the present disclosure.

[0049] Figure 10 shows an exemplary user-interface presentation of modeled activity of phosphogluconatdehydrognase, in accordance with an embodiment of the present disclosure. Figure 11 shows an exemplary user-interface presentation of modeled activity of riboso-5- phosphateisom erase, in accordance with an embodiment of the present disclosure.

[0050] Figure 12 shows an exemplary user-interface presentation of modeled activity of phosphoglucomutase, in accordance with an embodiment of the present disclosure.

[0051] Figure 13 shows an exemplary user-interface presentation of modeled activity of glucose-6- phosphatase, in accordance with an embodiment of the present disclosure.

[0052] Figure 14 shows an exemplary user-interface presentation of modeled activity of pyruvate carboxylase, in accordance with an embodiment of the present disclosure.

[0053] Figure 15 shows an exemplary user-interface presentation of modeled reaction of H transport- ATP - synthase, in accordance with an embodiment of the present disclosure.

[0054] Figure 16 shows an example of a graphical user interface allowing how a user can manually change quantitative indicators of the activity of an enzyme’s activity, thus altering the course of the corresponding biochemical process to simulate dysregulation, in accordance with an embodiment of the present disclosure. In that diagram, the small bolded rectangular frame shows the window of the quantitative indicator of a given enzyme’s performance (with up / down buttons for performance dysregulation), and the bolded circle marks the field of the automatic mode checkbox (that needs to be unchecked to allow dysregulation of the enzyme function).

[0055] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0056] For the purposes of promoting an understanding of the principles of the invention, reference will now be made to the embodiments illustrated in the figures and specific language will be used to describe the same. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without limitation of the scope of the disclosed embodiments. Any further applications of the principles described herein are contemplated as would normally occur to one skilled in the art.

[0057] This disclosure employs open-ended permissive language, indicating for example, that some embodiments “may” employ, involve, or include specific features. The use of the term “may”, and other open-ended terminology is intended to indicate that although not every embodiment may employ the specific disclosed feature, at least one embodiment employs the specific disclosed feature. In the following description, it is to be understood that the present disclosure may be practiced without one or more of the following details. Reference will now be made in detail to non-limiting examples of this disclosure, examples of which are illustrated in the accompanying figures. The examples are described below by referring to the figures, wherein reference numerals refer to like elements. When similar reference numerals are shown, corresponding description(s) are not repeated, and the interested reader is referred to the previously discussed figure(s) for a description of the like element(s).

[0058] Various embodiments are described herein with reference to a system(s) and method(s). It is intended that the disclosure of one is a disclosure of all. For example, it is to be understood that disclosure of a system described herein also constitutes a disclosure of the method implemented by the system, via, for example, one or more processors. It is to be understood that this form of disclosure is for ease of discussion only, and one or more aspects of one embodiment herein may be combined with one or more aspects of other embodiments herein, within the intended scope of this disclosure.

[0059] TERMS AND DEFINITIONS

[0060] In the context of computational modeling of the heart, the term CLASS represents various components of the heart such as chambers, valves, tissues, etc. Each class would typically include attributes that describe the properties of that component (such as size, shape, conductivity, etc.) In a specific context of the present invention, the term CLASS refers to a cardiac cell.

[0061] The term FUNCTIONS refers to blocks of code that carry out specific tasks. They can accept input parameters, perform operations, and return results. In the context of the present invention, functions might represent mathematical equations, numerical methods, or algorithms used to simulate enzymatic activity in biochemical reactions occurring on the cellular level, and the intracellular- extracellular exchange.

[0062] The present invention provides a system and method for the digital modeling of enzymatic function in biochemical reactions within cardiac cells. Digital modeling of enzymatic function in biochemical reactions within cardiac cells, according to the present invention, involves applying computational methods to understand the specific enzymatic processes and signaling pathways relevant to the heart. The cardiac system is highly complex, and enzymes play crucial roles in various biochemical reactions that regulate processes such as contraction, relaxation, and energy metabolism. The system integrates experimental data, bioinformatics, and computational simulations to provide a comprehensive understanding of the molecular processes governing cardiac physiology and pathology.

[0063] The following are some aspects of digital modeling in the context of enzymatic function in cardiac cells:

[0064] 1. Ion Channels and Transporters: Enzymes are involved in regulating ion channels and transporters in cardiac cells, influencing the electrical excitability and contractility of the heart. Computational models can simulate the behavior of these ion channels, helping to understand how enzymatic activity affects membrane potential and action potential propagation.

[0065] 2. Calcium Handling: Enzymes play a vital role in the regulation of intracellular calcium levels, which is essential for cardiac contraction and relaxation. Digital modeling can explore the dynamics of calcium release and uptake by the sarcoplasmic reticulum, as well as the impact of enzymes on these processes.

[0066] 3. Energy Metabolism: Enzymes are central to energy metabolism in cardiac cells, including processes like glycolysis, the citric acid cycle, and oxidative phosphorylation. Computational models can simulate these metabolic pathways to understand how enzymatic activity influences ATP production and energy utilization in the heart.

[0067] 4. Phosphorylation Cascades: Enzymatic phosphorylation and dephosphorylation events, often mediated by kinases and phosphatases, are crucial for regulating protein activity in cardiac cells. Computational models can simulate these cascades, providing insights into how enzymatic signaling modulates cellular function and contributes to cardiac health or disease.

[0068] 5. Disease Modeling: Digital modeling can be applied to simulate enzymatic dysregulation in cardiac diseases, such as hypertrophy, heart failure, and arrhythmias. This approach aids in identifying potential therapeutic targets and understanding the molecular mechanisms underlying cardiac pathologies.

[0069] Digital modeling of enzymatic function in cardiac cells integrates experimental data, bioinformatics, and computational simulations to provide a comprehensive understanding of the molecular processes governing cardiac physiology and pathology. This knowledge contributes to the development of targeted interventions for cardiac diseases and the optimization of therapeutic strategies. System Architecture and Operation

[0070] The system operates on a computational platform comprising one or more processors and a computer-readable storage medium. The process, schematically represented in FIG. 1, begins with maintaining biological data, such as experimental data and bioinformatics data related to cardiac cell reactions, in a data repository. This data may include known reaction pathways, enzyme kinetics, substrate and product concentrations, and other relevant biochemical parameters.

[0071] A user interacts with the system via one or more user interfaces. Through these interfaces, the user makes selections to configure a simulation by selecting functions representing biochemical processes. In an embodiment, a user first selects a Class configured to represent a cardiac cell. The user then selects various Functions, which are blocks of code that carry out specific tasks. Based on the user's configuration, the system performs in silico experiments under various predefined or user-selected conditions. The system then uses the results of these experiments to predict other biological data, such as downstream metabolite concentrations or changes in cellular energy state. The initial and predicted data are compiled into configuration data, which is then used with a software core engine to generate a comprehensive enzyme activity model. This model is then simulated by executing it under various conditions, generating computational simulations of enzymatic function. The results of the simulation are outputted to a data repository for analysis and visualization.

[0072] Reference is now made to Figure 1, which shows a schematic representation of the high-level process flow of the invention, in accordance with an embodiment of the present disclosure. It illustrates that inputs, such as "Experimental data" and "Bioinformatics" data, are used to define the "Functions representing biochemical processes occurring on the cellular level and intracellular-extracellular exchange." These functions, in turn, are used to generate the "Computational simulations of Enzymatic Function and Basic Biochemical Processes," which are the output of the system.

[0073] Figure 2 shows an exemplary graphical user interface for the system, specifically displaying a window for "Carbohydrates metabolism" in the heart, in accordance with an embodiment of the present disclosure. This interface presents a complex network diagram of interconnected biochemical pathways, including glycolysis, the pentose phosphate pathway, and glycogen metabolism. Metabolites such as "Glucose," "Pyruvate," and "Glycogen" are shown as nodes, and the grey rectangular boxes marked with an "E" represent the enzymes catalyzing the reactions. These "E" boxes are interactive elements that a user can select to view and modify the properties of a specific enzyme. Figure 3 provides a detailed view of the user interface after a user has selected an enzyme, in accordance with an embodiment of the present disclosure. In this example, the enzyme for the conversion of Dihydroxy acetone-phosphate to Glyceraldehyde-3 -phosphate has been selected. An overlay window titled "Enzyme" appears, showing a hierarchical list of enzyme classifications. The user has navigated to and selected "[5.3.1.1] Triosophosphateisomerase." Below the list, a box displays the enzyme's name, a numerical field representing its performance or activity level, and an "Automatic mode" checkbox.

[0074] Figure 4 illustrates another example of an enzyme selection window, this time for "Pyruvatekinase" ([2.7.1.40]), in accordance with an embodiment of the present disclosure. Similar to FIG. 3, a user has selected the corresponding "E" box on the main pathway map, which has opened a detailed window allowing the user to view and potentially adjust the activity of Pyruvate kinase, a key enzyme in glycolysis.

[0075] Figure 5 shows the user interface with the selection window for "Glucose-6-phosphateisomerase" ([5.3.1.9]), in accordance with an embodiment of the present disclosure. This enzyme catalyzes the conversion of Glucose-6-phosphate to Fructose-6-phosphate. The window provides the same controls as previously described: enzyme selection from a list, a numerical activity indicator, and an automatic mode toggle.

[0076] Figure 6 displays the user interface for "6-Phosphofructokinase" ([2.7.1.11]), a critical regulatory enzyme in the glycolytic pathway, in accordance with an embodiment of the present disclosure. The pop-up window allows for the inspection and modification of this enzyme's activity, enabling simulation of its regulatory effects on the overall metabolic flux.

[0077] Figure 7 shows the user interface for "Fructosodiphosphate-aldolase" ([4.1.2.13]), the enzyme responsible for cleaving Fructose- 1,6-bisphosphate into two three-carbon sugars, in accordance with an embodiment of the present disclosure. The overlay window provides detailed information and control over this specific enzymatic step.

[0078] Figure 8 shows the user interface for a dehydrogenase, specifically "Glucose-6-phosphate-l- dehydrogenase" ([1.1.1.49]), which is the rate-limiting enzyme of the pentose phosphate pathway, in accordance with an embodiment of the present disclosure. The interface allows a user to simulate the effects of altering the activity of this key branch point in glucose metabolism.

[0079] Figure 9 displays the user interface for "Gluconolactonase" ([3.1.1.17]), an enzyme involved in the pentose phosphate pathway, in accordance with an embodiment of the present disclosure. The pop- up window demonstrates the user's ability to select and interact with this specific enzyme from the larger metabolic map.

[0080] Figure 10 shows the user interface for "Phosphogluconatdehydrogenase" ([1.1.1.44]), another key enzyme in the pentose phosphate pathway, in accordance with an embodiment of the present disclosure. The selection window provides detailed control, allowing for the simulation of this part of the metabolic network.

[0081] Figure 11 illustrates the user interface for "Riboso-5-phosphateisomerase" ([5.3.1.6]), which catalyzes an interconversion step in the non-oxidative phase of the pentose phosphate pathway, in accordance with an embodiment of the present disclosure. The detailed window allows for focused analysis of this reaction.

[0082] Figure 12 shows the user interface for "Phosphoglucomutase" ([5.4.2.2]), an enzyme that interconverts Glucose- 1 -phosphate and Glucose-6-phosphate, linking glycogen metabolism with glycolysis, in accordance with an embodiment of the present disclosure. The pop-up window provides specific controls for this enzyme.

[0083] Figure 13 shows the user interface for "Glucose-6-phosphatase" ([3.1.3.9]). This enzyme is crucial for releasing free glucose from Glucose-6-phosphate, in accordance with an embodiment of the present disclosure. The interface allows for the simulation of this process, and in this example, the activity is set to zero, which could represent a tissue-specific condition.

[0084] Figure 14 displays the user interface for "Pyruvate carboxylase" ([6.4.1.1]), an enzyme that plays an anaplerotic role by converting pyruvate to oxaloacetate, in accordance with an embodiment of the present disclosure. The interface allows the user to model the activity of this enzyme, which is important for replenishing citric acid cycle intermediates.

[0085] Figure 15 shows the user interface for "H+ transport-ATP-synthase" ([3.6.1.34]), a key component of oxidative phosphorylation, in accordance with an embodiment of the present disclosure. The interface displays a diagram of the electron transport chain and ATP synthesis, with the pop-up window providing controls for the ATP synthase enzyme complex.

[0086] Figure 16 provides a clear example of how a user can manually alter an enzyme's activity to simulate dysregulation, in accordance with an embodiment of the present disclosure. The figure highlights two key user interface elements. The small bolded rectangular frame contains the numerical indicator of the enzyme's performance level, along with up and down buttons for manual adjustment. The bolded circle marks the "Automatic mode" checkbox. To simulate dysregulation, a user unchecks this box, which unlocks the numerical field and allows the user to manually increase or decrease the enzyme's performance, thereby directly altering the course of the corresponding biochemical process in the simulation.

[0087] Modeling Enzymatic Dysregulation in Cardiac Disease

[0088] A powerful application of the system is the ability to model enzymatic dysregulation in cardiac diseases. The function SetEnzymeOnMove sets the performance level of currently active enzymes. As shown in Figure 16, the user interface provides a means for a user to manually alter the quantitative indicators of an enzyme's activity. By dysregulating the performance of specific enzymes, a user can simulate the pathophysiology of various cardiac diseases. This capability makes the system an invaluable tool for researchers studying disease mechanisms and for training medical professionals by allowing them to conduct virtual experiments and observe the biochemical consequences of enzymatic dysfunction in a controlled, simulated environment.

[0089] In some embodiments, the computational system is implemented on a standard desktop computer, server, or cloud computing platform comprising the one or more processors and the computer- readable storage device. The one or more user interfaces may be accessed locally on the computer or remotely via a network connection.

[0090] In other embodiments, the biological data maintained in the data repository includes pre-existing kinetic parameters for enzymes, standard metabolite concentrations for cardiac cells, and known biochemical pathway information derived from experimental data and bioinformatics databases, as suggested by the process flow in Figure 1.

[0091] In further embodiments, the one or more user interfaces present a graphical representation of the plurality of biochemical reactions as a network diagram, as shown in Figure 2, wherein metabolites are represented as nodes and enzymatic reactions are represented as edges connecting the nodes. The enzymes themselves are represented by selectable elements, such as the grey boxes marked "E."

[0092] In some embodiments, upon a user selecting one of the selectable elements representing an enzyme from the network diagram, the system displays an overlay window, such as those shown in Figures 3-15. This window provides detailed information about the selected enzyme, including its name and Enzyme Commission (EC) number, and includes controls for its activity. In other embodiments, the function that sets the performance level of currently active enzymes is controlled by a user de-selecting an "Automatic mode" checkbox, as shown in Figure 16. This action enables a numerical input field, allowing the user to manually increase or decrease the quantitative indicator of the enzyme’s performance using associated up / down buttons.

[0093] In further embodiments, the simulation of enzymatic dysregulation is used to model the pathophysiology of heart failure. A user may achieve this by manually reducing the performance level of enzymes critical to oxidative phosphorylation, such as H+ transport-ATP-synthase (Figure 15), to simulate the energy -deprived state of a failing heart.

[0094] In some embodiments, the system is configured to simulate conditions of myocardial infarction by allowing a user to increase the activity level of caspases, which are proteases involved in apoptosis, to observe the downstream biochemical consequences of increased cardiomyocyte cell death.

[0095] In other embodiments, the system models hypertension by simulating a reduction in the activity of endothelial Nitric Oxide Synthase (eNOS), leading to predicted changes in signaling pathways related to vasodilation.

[0096] In further embodiments, the function that performs an enzymatic reaction specifically models key regulatory steps of the glycolytic pathway. This includes simulating the activity of phosphofructokinase (Figure 6) and pyruvate kinase (Figure 4), allowing a user to investigate the effects of their dysregulation on the overall metabolic flux from glucose to pyruvate.

[0097] In some embodiments, the system models the pentose phosphate pathway by simulating reactions catalyzed by enzymes such as Glucose-6-phosphate-l -dehydrogenase (Figure 8) and Ribose-5- phosphateisom erase (Figure 11), and outputs the resulting changes in the production of NADPH and pentose sugars.

[0098] In other embodiments, the function that calculates the value of energy metabolism-related parameters specifically computes the net ATP usage and glucose consumption over a simulated time period. These results are then outputted to the data repository and may be presented to the user numerically or graphically.

[0099] In further embodiments, the function that calculates the electrochemical gradient utilizes the Nernst equation, as described by the PotencialCell function. This calculation is used to model the effects of metabolic changes on the electrical properties and excitability of the cardiac cell.

[0100] In some embodiments, the generation of the enzyme activity model involves the software core engine integrating the compiled configuration data, which includes the user-defined enzyme performance levels, to create a set of mathematical equations that describe the dynamic state of the biochemical network.

[0101] In other embodiments, the one or more results outputted to the data repository include time-course data for the concentrations of key metabolites, such as Glucose-6-phosphate, Pyruvate, ATP, and Lactate, allowing for a dynamic analysis of the cell's metabolic response to simulated conditions.

[0102] In further embodiments, the function that loads an enzyme presents the user with a hierarchical, selectable list of enzymes organized by Enzyme Commission (EC) numbers, as depicted in the overlay windows of Figures 3-15, allowing for precise and unambiguous selection of the enzyme to be modeled or modified.

[0103] In some embodiments, a user first manually reduces the performance level of an enzyme associated with the citric acid cycle, such as oxoglutarate dehydrogenase as shown in Figure 16, and the system then simulates the model to output the resulting decrease in NADH production and subsequent impact on ATP synthesis.

[0104] In other embodiments, the system is utilized as an educational tool for training medical professionals in cardiac biochemistry and pathology, allowing them to perform virtual experiments on enzymatic dysregulation and observe the consequences in a safe, simulated environment.

[0105] In further embodiments, the system is used in a research context to test hypotheses about the molecular mechanisms of cardiac pathologies, such as diabetic cardiomyopathy, by simulating the effects of increased advanced glycation end-products (AGEs) on enzymatic functions.

[0106] In some embodiments, the computer-implemented method further comprises displaying the outputted results as a graphical plot showing metabolite concentrations over time or as a color- coded overlay on the network diagram of Figure 2 to visually indicate changes in metabolic flux.

[0107] In other embodiments, the non-transitory computer-readable storage medium stores instructions for a software core engine that is configured to dynamically update the enzyme activity model in realtime in response to user inputs that alter enzyme performance levels, providing immediate feedback on the simulated biochemical system.

[0108] In further embodiments, the system simulates the processes of glycogenesis and glycogenolysis by modeling the enzymatic reactions that convert glucose to glycogen via Glucose- 1 -phosphate and UDP-glucose, and the reaction that converts glycogen back into Glucose-6-phosphate via the activity of phosphoglucomutase (Figure 12). In some embodiments, the simulation of enzymatic dysregulation is used to model dilated cardiomyopathy by altering the performance level of calcium-dependent proteases like calpains, allowing a user to investigate the resulting impact on cytoskeletal protein models and cellular integrity.

[0109] The following classes and functions represent the model of enzymatic function and basic biochemical processes, according to the present invention.

[0110] EXAMPLE 1

[0111] Basic instructions

[0112] CLASSES: Cell

[0113] This class describes the cell. It represents biochemical processes occurring on the cellular level, and the intracellular-extracellular exchange.

[0114] FUNCTIONS:

[0115] 1. Cell

[0116] The Cell function loads substances in strict accordance with normal inorganic plasma concentrations; it loads organic components of cellular biochemistry.

[0117] 2. CreateEnzyme

[0118] The CreateEnzyme function generates enzyme activity.

[0119] 3. LoadCellEnzyme

[0120] The LoadCellEnzyme function loads enzymes that are involved in cellular metabolism.

[0121] EXAMPLE 2

[0122] Enzymatic activity-simulating functions

[0123] 4. DiHyAceton3PhTransGliceraldegid3P

[0124] The function DiHyAceton3PhTransGliceraldegid3P converts dihydroxyacetone phosphate (DHAP) into glyceraldehyde-3-phosphate. The parameters uProcentBhRest - the percentage of the residue.

[0125] 5. FractPsgBhCellBloodGetGlucose

[0126] The function FractPsgBhCellBloodGetGlucose calculates the glucose to be consumed by the cells. 6. F ractP sgB hDiHy Aceton3 PhT oPy ruvat

[0127] The function FractPsgBhDiHyAceton3PhToPyruvat converts dihydroxyacetone phosphate (DHAP) into pyruvic acid. Parameters uProcentBhRest - the residue percentage, Cx23DPG -the concentration of 2, 3 -diphosphoglycerate.

[0128] 7. FractPsgBhGetGlucose

[0129] The function FractPsgBhGetGlucose calculates the amount of glucose that the cells, under the action of insulin, require at the moment. Parameters dZatrataMusculusKcallOSec - ATP consumption, pnNum - the organ number, pCarb24h - by carbohydrates per day, pFat24h - by fats per day, pDueCaptureFaAcGr - glucose capture required, pSettingl - setting, pSetting2 - setting.

[0130] 8. FractPsgBhGlucoseToGlycogen

[0131] The function FractPsgBhGlucoseToGlycogen turns glucose into glycogen.

[0132] 9. FractPsgBhRashodATP

[0133] The function FractPsgBhRashodATP calculates ATP intended for use in cells. The parameters uNum is the organ number.

[0134] 10. FractPsgBhUseGlucose

[0135] The function FractPsgBhUseGlucose converts glucose-6-phosphate into glyceraldehyde-3 -phosphate and dihydroxyacetone phosphate (DHAP). Parameters uProcentPentos - the pentose percentage, uProcentBhRest is the residue percentage.

[0136] 11. Fruct6PTransDiHyAceton3PhAndGlicd3P

[0137] The function Fruct6PTransDiHyAceton3PhAndGlicd3P converts fructose 6-phosphate into glyceraldehyde-3 -phosphate and dihydroxyacetone phosphate (DHAP). Parameters uProcentBhRest - the percentage of the residue.

[0138] 12. Glicd3PTransPyruvat

[0139] The function Glicd3PTransPyruvat converts glyceraldehyde-3 -phosphate into pyruvic acid. Parameters uProcentBhRest - the residue percentage, Cx23DPG - the concentration of 2,3- diphosphoglycerate.

[0140] 13. Gliceraldegid3PTransGluc6P

[0141] The Gliceraldegid3PTransGluc6P function converts glyceraldehyde-3 -phosphate into glucose-6- phosphate. 14. Gluc6PhToPentose

[0142] The function Gluc6PhToPentose converts glucose-6-phosphate into pentoses. Parameters uProcentPentos - the percentage of pentoses, uProcentBhRest - the percentage of residue.

[0143] 15. Glucose6PhTransFruct6P

[0144] The Glucose6PhTransFruct6P function converts glucose-6-phosphate into fructose-6-phosphate. Parameters uProcentBhRest - the residue percentage.

[0145] 16. Glucose6PTransGly cogen

[0146] The Glucose6PTransGlycogen function converts glucose-6-phosphate into glycogen.

[0147] 17. GlucoseTransGluc6Ph

[0148] The GlucoseTransGluc6Ph function converts glucose into glucose-6-phosphate. Parameters uProcentBhRest - the residue percentage

[0149] 18. GlycogenTransGluc6P

[0150] The function GlycogenTransGluc6P converts glycogen into glucose-6-phosphate. The mol parameters - the number of mol.

[0151] 19. GlycogenTransGlucose

[0152] The function GlycogenTransGlucose turns glycogen into glucose. Parameters LeaveGlucosaGr - the output glucose in grams.

[0153] 20. O2ControlNADH

[0154] The O2ControlNADH function describes pyruvate decarboxylation.

[0155] 21. PotencialCell

[0156] The function PotencialCell calculates the electrochemical gradient using the Nernst equation. Parameters Temper - temperature, valentnosti - ion valence, ICellMmolLitr - mmoll concentration, ECellMolLitr - mmoll concentration.

[0157] 22. PyruvatTransAcetylCoA

[0158] The PyruvatTransAcetylCoA function converts pyruvic acid into acetyl-CoA (acetyl -coenzyme- A). Parameters dDelenieCO2 - the correction of CO2.

[0159] 23. PyruvatTransLactat

[0160] The PyruvatTransLactat function converts pyruvic acid into lactic acid. Parameters bO2NoSet - no oxygen. 24. SetEnzymeOnMove

[0161] The SetEnzymeOnMove function sets the performance level of currently active enzymes. The parameters MolActivEnzyme - the number of moles of the active enzyme, pEnzymeOnMove is the currently active enzyme, nSizaArray is the index of the enzyme in the array.

[0162] EXAMPLE 3

[0163] Enzyme dysregulation involved in heart diseases

[0164] The system, according to the present invention, involves applying computational methods to understand the specific enzymatic processes and signaling pathways relevant to the heart, including heart disease.

[0165] Examples for enzymatic dysregulation that play a significant role in the pathophysiology of various cardiac diseases are:

[0166] 1. Heart Failure

[0167] Matrix Metalloproteinases (MMPs): These enzymes degrade extracellular matrix proteins. In heart failure, dysregulation of MMPs leads to excessive remodeling and degradation of the cardiac extracellular matrix, contributing to cardiac dilatation and dysfunction.

[0168] Angiotensin-Converting Enzyme (ACE): Overactivity of ACE increases levels of angiotensin II, leading to vasoconstriction, increased blood pressure, and adverse cardiac remodeling.

[0169] 2. Myocardial Infarction

[0170] Caspases: These enzymes play a role in the apoptosis of cardiomyocytes following ischemia and reperfusion injury. Dysregulation leads to increased cell death, exacerbating tissue damage.

[0171] Lysosomal Proteases (Cathepsins): Involved in the degradation of cellular components post- myocardial infarction. Dysregulation can lead to enhanced proteolysis and further myocardial damage.

[0172] 3. Hypertension

[0173] Nitric Oxide Synthase (NOS): In hypertension, there is often reduced activity of endothelial NOS (eNOS), leading to decreased nitric oxide production, impaired vasodilation, and increased vascular resistance. NADPH Oxidase: Overactivity produces excessive reactive oxygen species (ROS), contributing to oxidative stress and endothelial dysfunction, which are key factors in hypertension.

[0174] 4. Atherosclerosis

[0175] Lipoxygenases: These enzymes oxidize low-density lipoprotein (LDL) particles. Dysregulation leads to the formation of oxidized LDL, a key player in the development of atherosclerotic plaques.

[0176] Proprotein Convertase Subtilisin / Kexin Type 9 (PCSK9): This enzyme regulates LDL receptor degradation. Overactivity leads to reduced LDL receptor levels and higher plasma LDL cholesterol, promoting atherosclerosis.

[0177] 5. Dilated Cardiomyopathy

[0178] Calpains: These calcium-dependent proteases are involved in the cleavage of cytoskeletal proteins. Dysregulation in dilated cardiomyopathy leads to cytoskeletal damage and cardiomyocyte death.

[0179] Phospholamban: Mutations or dysregulation affect its inhibition of the sarcoplasmic reticulum Ca2+- ATPase (SERCA), impairing calcium reuptake and leading to contractile dysfunction.

[0180] 6. Cardiac Hypertrophy

[0181] Protein Kinase C (PKC): Dysregulation contributes to pathological cardiac hypertrophy by altering signaling pathways involved in cell growth and contractility.

[0182] Histone Deacetylases (HDACs): These enzymes modify chromatin structure and gene expression. Dysregulation leads to altered expression of genes involved in hypertrophic growth.

[0183] 7. Arrhythmias

[0184] Ca2+ / Calmodulin-Dependent Protein Kinase II (CaMKII): Overactivity can lead to abnormal calcium handling and electrical disturbances, contributing to arrhythmias.

[0185] Sodium-Potassium ATPase: Dysregulation affects ion homeostasis, leading to altered cardiac excitability and arrhythmic conditions.

[0186] 8. Diabetic Cardiomyopathy

[0187] Glycogen Synthase Kinase-3p (GSK-3P): Dysregulation in diabetes affects cardiac metabolism and contractility, contributing to cardiomyopathy.

[0188] Advanced Glycation End-products (AGEs) and Receptor for AGEs (RAGE): Increased activity leads to cross-linking of proteins and stiffening of cardiac tissues. 9. Pulmonary Hypertension

[0189] Endothelin-Converting Enzyme (ECE): Overactivity leads to increased levels of endothelin-1, a potent vasoconstrictor, contributing to pulmonary hypertension.

[0190] Phosphodiesterase-5 (PDE5): Dysregulation affects nitric oxide signaling and cyclic GMP levels, influencing vascular tone and pulmonary pressures.

[0191] These examples highlight the diverse roles enzymes play in cardiac diseases, where their dysregulation can lead to significant pathophysiological changes and clinical manifestations. Understanding these mechanisms is crucial for developing targeted therapies.

Claims

1. CLAIMS1. A computational system for digital modeling of biochemical reactions within cardiac cells, said system comprising: a. one or more processors; and b. a computer-readable storage device coupled to said one or more processors and having instructions stored thereon which, when executed by said one or more processors, cause said one or more processors to perform operations comprising: i. maintaining biological data, related to a plurality of biochemical reactions within cardiac cells, in a data repository; ii. receiving, via one or more user interfaces, a selection of:1) a class configured to represent a cardiac cell;2) a function that loads organic components of cellular biochemistry;3) a function that generates an enzyme activity;4) a function that loads an enzyme selected from a plurality of enzymes involved in cellular metabolism;5) a function that performs an enzymatic reaction in said cardiac cell;6) a function that calculates the value of energy metabolism-related parameters in said cardiac cell;7) a function that calculates electrochemical gradient in said cardiac cell; and8) a function that sets the performance level of currently active enzymes in said cardiac cell; iii. performing in silico experiments under various conditions that are predefined or selected for analysis; iv. predicting at least some other biological data using said performed in silico experiments; v. compiling said biological data with said predicted at least some other biological data into configuration data; vi. generating an enzyme activity model using said configuration data and a software core engine to replicate enzymatic function in biochemical reactions within cardiac cells; vii. simulating said enzyme activity model by executing said model under various conditions; andviii. outputting one or more results from executing said enzyme activity model in a data repository.

2. The system according to claim 1, wherein said enzymatic reaction is selected from the group consisting of: a. converting dihydroxyacetone phosphate (DHAP) into glyceraldehyde-3 -phosphate; b. converting dihydroxyacetone phosphate (DHAP) into pyruvic acid; c. converting glucose into glycogen; d. converting glucose-6-phosphate into glyceraldehyde-3 -phosphate and dihydroxyacetone phosphate (DHAP); e. converting fructose 6-phosphate into glyceraldehyde-3 -phosphate and dihydroxyacetone phosphate (DHAP); f. converting glyceraldehyde-3 -phosphate into pyruvic acid; g. converting glyceraldehyde-3 -phosphate into glucose-6-phosphate; h. converting glucose-6-phosphate into pentoses; i. converting glucose-6-phosphate into fructose-6-phosphate; j . converting glucose-6-phosphate into glycogen; k. converting glucose into glucose-6-phosphate; l. converting glycogen into glucose-6-phosphate; m. converting glycogen into glucose; and n. pyruvate decarboxylation.

3. The system according to claim 1, wherein said energy metabolism -related parameters are selected from the group consisting of glucose consumption and adenosine triphosphate (ATP) usage by said cardiac cell.

4. The system according to claim 1, further wherein said system simulates enzymatic dysregulation in cardiac diseases.

5. The system according to claim 4, wherein simulating enzymatic dysregulation comprises receiving, via said one or more user interfaces, a manual input to alter the performance level of at least one of said currently active enzymes.

6. The system according to claim 1, wherein said one or more user interfaces comprise a graphical user interface displaying a network of said plurality of biochemical reactions, wherein individual enzymes are represented as selectable elements.

7. A computer-implemented method for digital modeling of biochemical reactions within cardiac cells, the method comprising steps of: a. maintaining, by one or more processors, biological data related to a plurality of biochemical reactions within cardiac cells in a data repository; b. receiving, by said one or more processors via one or more user interfaces, a selection of: i. a class configured to represent a cardiac cell; ii. a function that loads organic components of cellular biochemistry; iii. a function that generates an enzyme activity; iv. a function that loads an enzyme selected from a plurality of enzymes involved in cellular metabolism; v. a function that performs an enzymatic reaction in said cardiac cell; vi. a function that calculates a value of one or more energy metabolism-related parameters in said cardiac cell; vii. a function that calculates an electrochemical gradient in said cardiac cell; and viii. a function that sets a performance level of currently active enzymes in said cardiac cell; c. performing, by said one or more processors, in silico experiments under one or more conditions that are predefined or selected for analysis; d. predicting, by said one or more processors, at least some other biological data using said performed in silico experiments; e. compiling, by said one or more processors, said biological data with said predicted at least some other biological data into configuration data; f. generating, by said one or more processors, an enzyme activity model using said configuration data and a software core engine to replicate enzymatic function in biochemical reactions within cardiac cells; g. simulating, by said one or more processors, said enzyme activity model by executing said model under various conditions; and h. outputting, by said one or more processors, one or more results from executing said enzyme activity model in a data repository. The method according to claim 7, wherein said enzymatic reaction is selected from the group consisting of: a. converting dihydroxyacetone phosphate (DHAP) into glyceraldehyde-3 -phosphate; b. converting dihydroxyacetone phosphate (DHAP) into pyruvic acid;c. converting glucose into glycogen; d. converting glucose-6-phosphate into glyceraldehyde-3 -phosphate and dihydroxyacetone phosphate (DHAP); e. converting fructose 6-phosphate into glyceraldehyde-3 -phosphate and dihydroxyacetone phosphate (DHAP); f. converting glyceraldehyde-3 -phosphate into pyruvic acid; g. converting glyceraldehyde-3 -phosphate into glucose-6-phosphate; h. converting glucose-6-phosphate into pentoses; i. converting glucose-6-phosphate into fructose-6-phosphate; j . converting glucose-6-phosphate into glycogen; k. converting glucose into glucose-6-phosphate; l. converting glycogen into glucose-6-phosphate; and m. converting glycogen into glucose; and n. pyruvate decarboxylation. The method according to claim 7, wherein said energy metabolism-related parameters are selected from the group consisting of glucose consumption and adenosine triphosphate (ATP) usage by said cardiac cell. The method according to claim 7, further comprising simulating enzymatic dysregulation in cardiac diseases by receiving a user input to alter the performance level of at least one of said currently active enzymes. The method according to claim 10, wherein simulating enzymatic dysregulation models a condition selected from the group consisting of heart failure, myocardial infarction, hypertension, atherosclerosis, dilated cardiomyopathy, cardiac hypertrophy, arrhythmia, and diabetic cardiomyopathy. A non-transitory computer-readable storage medium having instructions stored thereon which, when executed by one or more processors, cause said one or more processors to perform a method for digital modeling of biochemical reactions within cardiac cells, the method comprising steps of: a. maintaining biological data related to a plurality of biochemical reactions within cardiac cells in a data repository; b. receiving, via one or more user interfaces, a selection of a class and a plurality of functions for configuring a simulation of a cardiac cell, wherein the plurality of functions includes a function to set a performance level of one or more active enzymes in said cardiac cell;c. performing in silico experiments based on the selection; d. generating an enzyme activity model based on results from said in silico experiments; e. simulating said enzyme activity model by executing said model under various conditions, including conditions of user-defined enzyme performance levels to model enzymatic dysregulation; and f. outputting one or more results from executing said enzyme activity model.

13. The non-transitory computer-readable storage medium of claim 12, wherein the results include values for glucose consumption and adenosine triphosphate (ATP) usage by said cardiac cell.

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