Patent Application
20170169183
The present disclosure relates in general to the identification and selection of appropriate drug therapies. More specifically, the present disclosure relates to systems and methodologies for improving the identification and selection of a drug therapy by making quantitative assessments of the relevance of candidate drug therapies to patient specific disease conditions.
Patients suffering from rapid-onset diseases such as some forms of cancer would benefit from clinicians having the ability to make a rapid evaluation of a given drug's potential therapeutic relevance to that patient's specific condition. There are, however, significant obstacles to efficiently and effectively creating a personalized drug regimen for treatment of a particular patient's disease state. Specifically, the relevancy of a given drug to a given disease depends on possibly hundreds or thousands of interrelated variables. For example, because biological pathways are the basic mechanism for genes to communicate and/or interact with one another, a drug's relevancy to particular patient disease state may be influenced by all possible pathways that participate in the emergence of the disease state, as well as how these possible pathways are altered by changes in gene activity of any member of the pathway.
It would be beneficial to provide systems and methodologies to allow clinicians to effectively and efficiently filter and/or sort the hundreds or thousands of potential drugs available to treat a particular disease in a specific patient to create a highly personalized drug treatment regiment, which may in some cases include drugs that are not be generally known to be part of the diagnostic and treatment process for a certain type of patient, illness or clinical circumstance.
Embodiments are directed to a computer implemented method of assessing a relevancy of a drug to a disease state of a patient. The method includes assessing an impact of the drug on driver genes (DGs) of the disease state of the patient, assessing an impact of the drug on druggable target genes (DTs) of the drug, and assessing the relationship between the DGs and DTs that are in one of a plurality of biological pathways of the disease state of the patient. The method further includes combining the impact of the drug on the DGs, the impact of the drug on the DTs, and the relationship between the DGs and DTs that are in the one of the biological pathways, wherein the combining results in an assessment of the relevancy of the drug to the disease state of the patient.
Embodiments are further directed to a computer system for assessing a relevancy of a drug to a disease state of a patient. The system includes a memory and a processor system communicatively coupled to the memory. The processor system configured to perform a method including assessing an impact of the drug on DGs of the disease state of the patient, assessing an impact of the drug on druggable target genes DTs of the drug, and assessing the relationship between the DGs and DTs that are in one of a plurality of biological pathways of the disease state of the patient. The method further including combining the impact of the drug on the DGs, the impact of the drug on the DTs, and the relationship between the DGs and DTs that are in the one of the biological pathway, wherein the combining results in an assessment of the relevancy of the drug to the disease state of the patient.
Embodiments are further directed to a computer program product for assessing a relevancy of a drug to a disease state of a patient. The computer program product includes a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, and the program instructions are readable by a processor system to cause the processor system to perform a method. The method includes assessing an impact of the drug on DGs of the disease state of the patient, assessing an impact of the drug on DTs of the drug, and assessing the relationship between the DGs and DTs that are in one of a plurality of biological pathways of the disease state of the patient. The method further includes combining the impact of the drug on the DGs, the impact of the drug on the DTs, and the relationship between the DGs and DTs that are in the one of the biological pathways, wherein the combining results in an assessment of the relevancy of the drug to the disease state of the patient.
Additional features and advantages are realized through the techniques described herein. Other embodiments and aspects are described in detail herein. For a better understanding, refer to the description and to the drawings.
The subject matter which is regarded as the present disclosure is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other features and advantages are apparent from the following detailed description taken in conjunction with the accompanying drawings in which:
In the accompanying figures and following detailed description of the disclosed embodiments, the various elements illustrated in the figures are provided with three or four digit reference numbers. The leftmost digit(s) of each reference number corresponds to the figure in which its element is first illustrated.
Various embodiments of the present disclosure will now be described with reference to the related drawings. Alternate embodiments may be devised without departing from the scope of this disclosure. It is noted that various connections are set forth between elements in the following description and in the drawings. These connections, unless specified otherwise, may be direct or indirect, and the present disclosure is not intended to be limiting in this respect. Accordingly, a coupling of entities may refer to either a direct or an indirect connection.
The present disclosure provide systems and methodologies for improving the identification and selection of a drug therapy by making quantitative assessments of the relevance of candidate drug therapies to patient specific disease conditions. The present disclosure combines information from both literature (community consensus information) and an in-depth analysis of a patient's genomic profile. Specifically, from the genomic profile analysis, the present disclosure scores a drug's potential efficacy based on an analysis of its molecular target's relationship to genes promoting patient disease. The resulting score thereby reflects patient specific conditions and community consensus information to enable the ranking and filtering of potential drug therapies
A component of the disclosed methodology for measuring drug relevancy with respect to a disease is an evaluation of the biological relationship between the drug's targets (DTs) and a set of genes referred to herein as druggable genes or driver genes (DGs). DGs are defined as genes that are causally linked to the formation and development of a disease. In general, for purposes of the present disclosure, the term DG is used to refer to a gene that advances (or drives) a biological pathway that is involved in some kind of disease. There are many DGs that drive many pathways that can result in a given disease. Biological relationships are based on biological interactions that lead from a DG to a DT in the context of known biological pathways. Typically, these genes and their products are behaving aberrantly. Some example sources of these aberrations include, but not limited to, gene mutations (a change in DNA sequence that makes up a gene) and over or under expression of the gene as detected by RNA expression or copy number variation. Thus, in the present disclosure the determination that a given drug is relevant to the treatment of a selected disease is based at least in part on the identification of the DGs in a biological pathway that advance the pathway toward the selected disease state, as well as the relationship of the drug to actionable DTs also present in the pathway.
As background, a general description of biological pathways will now be provided. A biological pathway is a series of actions among molecules in a cell that leads to a certain product or a change in a cell. Such pathways can trigger the assembly of new molecules, such as a fat or a protein. Pathways can also turn genes on and off, or spur a cell to move. Thus, pathways constantly transport signals or cues to cells from both inside and outside the body, which are prompted by such things as injury, infection, stress or even food. To react and adjust to these cues, cells also send signals and cues through biological pathways. The molecules that make up biological pathways interact with signals, as well as with each other, to carry out their designated tasks. Biological pathways can act over short or long distances. For example, some cells send out signals to nearby cells to repair localized damage, such as a scratch on your knee. Other cells produce substances, such as hormones, that travel through your blood to distant target cells. Biological pathways can also produce small or large outcomes. For example, some pathways subtly affect how the body processes drugs, while others play a major role in how a fertilized egg develops into a baby.
There are many types of biological pathways. Some of the most common are involved in metabolism, the regulation of genes and the transmission of signals. Metabolic pathways make possible the chemical reactions that occur in our bodies. An example of a metabolic pathway is the process by which human cells break down food into energy molecules that can be stored for later use. Other metabolic pathways actually help to build molecules. Gene regulation pathways turn genes on and off. Such action is vital because genes produce proteins, which are the key component needed to carry out nearly every task in our bodies. Proteins make up our muscles and organs, and help our bodies move and defend us against germs. Signal transduction pathways move a signal from a cell's exterior to its interior. Different cells are able to receive specific signals through structures on their surface, called receptors. After interacting with a receptor, the signal travels through the cell where its message is transmitted by specialized proteins that trigger a specific action in the cell. For example, a chemical signal from outside the cell might be turned into a protein signal inside the cell. In turn, that protein signal may be converted into a signal that prompts the cell to move.
Biological pathways do not always work properly. When something goes wrong in a pathway, the result can be a disease such as cancer or diabetes. Researchers often learn about human disease from studying biological pathways. Identifying what genes, proteins and other molecules are involved in a biological pathway can provide clues about what goes wrong when a disease strikes. For example, researchers may compare certain biological pathways in a healthy person to the same pathways in a person with a disease to assist in discovering the roots of the disorder.
The identification of relevant pathways from a set of genes is often the first step in implementing this research philosophy. However, determining the pathways that are most relevant for a set of diseased genes is challenging, and is often made even more difficult by differences in pathway composition and topology that are present between different pathway repositories. For example, problems in any number of steps along a biological pathway can often lead to the same disease. Genetic mutations also complicate the identification of relevant pathways for disease state. For example, cancer is a genomic disease associated with a plethora of gene mutations. Among these mutated genes, driver genes are defined as being causally linked to the formation and development of tumors, while passenger genes are thought to be irrelevant for cancer development. Different genetic mutations can lead to the same cancer in different patients. Instead of attempting to discover ways to attack one well-defined genetic enemy, this complex view can be simplified by identifying and focusing on the biological pathways that are disrupted by the genetic mutations. Rather than designing dozens of drugs to target dozens of mutations, drug developers could focus their attentions on just two or three biological pathways. Patients could then receive the one or two drugs most likely to work for them based on the pathways affected in their particular tumors.
The accurate identification of pathways that are involved in a disease, and of the steps of the identified pathways that are affected in each patient, may lead to more personalized strategies for diagnosing, treating and preventing disease. Researchers currently are using information about biological pathways to develop new and better drugs. Additionally, pathway information may also be used to more effectively choose and combine existing drugs. With increasing numbers of large scale genomic datasets available, integrating these genomic data to identify driver genes from aberration regions of cancer genomes becomes an important goal of cancer genome analysis and investigations into mechanisms responsible for cancer development.
Turning now to the drawings in greater detail, wherein like reference numerals indicate like elements,
Computer system 100 includes one or more processors, such as processor 102. Processor 102 is connected to a communication infrastructure 104 (e.g., a communications bus, cross-over bar, or network). Computer system 100 can include a display interface 106 that forwards graphics, text, and other data from communication infrastructure 104 (or from a frame buffer not shown) for display on a display unit 108. Computer system 100 also includes a main memory 110, preferably random access memory (RAM), and may also include a secondary memory 112. Secondary memory 112 may include, for example, a hard disk drive 114 and/or a removable storage drive 116, representing, for example, a floppy disk drive, a magnetic tape drive, or an optical disk drive. Removable storage drive 116 reads from and/or writes to a removable storage unit 118 in a manner well known to those having ordinary skill in the art. Removable storage unit 118 represents, for example, a floppy disk, a compact disc, a magnetic tape, or an optical disk, etc. which is read by and written to by removable storage drive 116. As will be appreciated, removable storage unit 118 includes a computer readable medium having stored therein computer software and/or data.
In alternative embodiments, secondary memory 112 may include other similar means for allowing computer programs or other instructions to be loaded into the computer system. Such means may include, for example, a removable storage unit 120 and an interface 122. Examples of such means may include a program package and package interface (such as that found in video game devices), a removable memory chip (such as an EPROM, or PROM) and associated socket, and other removable storage units 120 and interfaces 122 which allow software and data to be transferred from the removable storage unit 120 to computer system 100.
Computer system 100 may also include a communications interface 124. Communications interface 124 allows software and data to be transferred between the computer system and external devices. Examples of communications interface 124 may include a modem, a network interface (such as an Ethernet card), a communications port, or a PCM-CIA slot and card, etcetera. Software and data transferred via communications interface 124 are in the form of signals which may be, for example, electronic, electromagnetic, optical, or other signals capable of being received by communications interface 124. These signals are provided to communications interface 124 via communication path (i.e., channel) 126. Communication path 126 carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular phone link, an RF link, and/or other communications channels.
In the present disclosure, the terms “computer program medium,” “computer usable medium,” and “computer readable medium” are used to generally refer to media such as main memory 110 and secondary memory 112, removable storage drive 116, and a hard disk installed in hard disk drive 114. Computer programs (also called computer control logic) are stored in main memory 110 and/or secondary memory 112. Computer programs may also be received via communications interface 124. Such computer programs, when run, enable the computer system to perform the features of the present disclosure as discussed herein. In particular, the computer programs, when run, enable processor 102 to perform the features of the computer system. Accordingly, such computer programs represent controllers of the computer system.
In its overall operation, drug assessment system 200 ranks the relevance of a drug to a particular patient disease condition based on a quantitative determination of the likelihood that the drug can impact the development of the particular patient disease condition. The determination that a given drug is involved in the development of or relevant to the treatment of, a particular patient disease condition is based at least in part on the identification of genes in the pathway that advance the pathway toward the patient's disease state.
A GM is a change in DNA sequence that makes up a gene. GMs complicate the identification of relevant pathways for disease state. For example, cancer is a genomic disease associated with a plethora of GMs. Among these GMs, DGs are defined as the GMs that are causally linked to the formation and development of tumors, while passenger genes are GMs thought to be irrelevant for cancer development. Different DGs can lead to the same cancer in different patients. In general, for purposes of the present disclosure, the term DG is used to refer to a GM that advances (or drives) a pathway that is involved in some kind of disease. There are many DGs that drive many pathways that can result in a given disease. For a given disease, there are typically a certain number of DGs (e.g., 15 to 20) that are most significant.
As shown in
A disease of interest is selected based on the needs and interests of the individual user. Once a disease of interest (disease of interest input 208) is identified, the most significant genes and gene mutations (GMs inputs 204) involved in the selected disease can then be identified. In general, GMs inputs 204 may be compiled from existing literature or databases. Additionally, known computational and informatics methods for inferring the most significant GMs may also be utilized. The method for determining the pool of GMs is dependent on the application, e.g. a clinical treatment or a basic research application.
Once a disease of interest (disease of interest input 208) is identified, the druggable target genes (targetable genes 206) associated with the disease of interest can also be identified from drug information databases (drug database 202). For example, druggable target genes may be compiled from existing literature or databases. Computational and informatics methods may also be used to infer druggable targets. The particular method chosen for determining a pool of druggable target genes is dependent on the application, e.g. a clinical treatment or a basic research application.
Drug database 202 is also used by drug citation score module 222 to determine if the relevant drugs identified through drug database 202 have applications broader than the specific diseases the drugs have been approved to treat. For example, a drug that has been approved for heart disease or colon cancer may also have been demonstrated to have efficacy for breast or liver cancer. In one or more embodiments, drug citation score 222 looks at the literature and determines whether a particular drug has been studied in the context of a particular patient's disease (i.e., patient specific disease context). In effect, a citation score may be developed by searching medical literature for the co-occurrence of the mention of a drug name with either a broad term like cancer treatment or a more specific term for a particular form of cancer relevant to a particular patient. Module 218 combines the outputs of the assessments made by modules 222, 212, 214 and 216 and determines a relevancy of each drug of interest as described in greater detail later in this disclosure.
A pathway score mix function module 210 receives inputs from drug database inputs 202, GMs inputs 204, targetable genes inputs 206, disease of interest inputs 208 and pathway inputs 209, and selectively provides them to modules 212, 214 and 216 according to a general multiplex functionality. Module 312 calculates a weighted value of the DGs. In one or more embodiments, module 312 calculates the weighted value of the DGs according to an A-term, shown at Equation (3) in
Continuing with the A-term shown in
Accordingly, the A-term provides a representation of the relevancy of a current drug of interest versus all other drugs considered. If the “a” values for all drugs of interest are equal, then the A-term will represent the sensitivity of that drug to capturing the set of GMs. When the “a” values for all drugs of interest are not equally weighted, then the A-term will represent the relevancy with respect to the considerations that support the calculation of “a.” In other words, the A-term represents the percentage of the initial GM pool that are DGs, and therefore most likely to advance a pathway that results in a particular disease of interest. The higher the A-term, the greater impact the drug is expected to have on the identified DGs in the pathway.
Module 214 calculates a weighted value of the TG. In one or more embodiments, module 314 calculates the weighted value of the TG according to a B-term, shown at Equation (3) in
Alternatively, the B-term may also be given by an alternative B-term equation shown in
Accordingly, the B-term provides a representation of a relevancy of a current drug of interest versus all other drugs considered. If the “b” values for all drugs of interest are equal, then the B-term merely represents the sensitivity of that drug to capturing the set of druggable target genes. When the “b” values for all drugs of interest are not equally weighted, then the B-term represents the relevancy with respect to the considerations that support the calculation of “b.”
Module 216 calculates a topology of the pathway from source to target. In one or more embodiments, module 216 calculates the topology of the pathway from source to target according to a C-term, shown at Equation (3) in
Thus, in modules 212, 214, 216 of drug assessment system 200, the A-term, the B-term and the alternative B-term bring into the pathway assessment portion of system 200 a weighting factor that allows a quantification of the importance of a DG or druggable target gene. In addition, the C-term bring into the pathway assessment portion of system 200 a way to take into consideration the actual topology of the current pathway of interest. In other words, the C-term allows a consideration of the connections from the source gene (e.g., GM) to the druggable target. Thus, the weighting applied by the A and B terms is dictated by whether or not there is a path between the DG and target, as well as by the distance of the path between the DG and target. The topology can be further accounted for by including additional information regarding the expression of each gene along that path. In this way, instead of following the path's route along a flat surface (e.g., in a “no gene expression” scenario), the route along a contoured surface may be traced, wherein the contour heights are dictated by the expression level. A score may then be determined to minimize the total distance that accounts for those contours. Additionally, the topology can be accounted for by evaluating the number and variety of possible routes between the DG and the target.
Module 218 combines the outputs of the assessments made by modules 222, 212, 214 and 216 and determines a relevancy of each drug of interest. In one or more embodiments, the relevancy of the current pathway of interest may be implemented as a computed drug assessment/effectiveness score, shown at Equation (1) in
The overall drug assessment/effectiveness measure shown in Equation (1) considers two primary features, namely the drug effectiveness as determined by disease-related citations and the drug effectiveness determined by pathway analysis. These two features are each given a quantitative score and combined into a single measure. In Equation (1), a and b are numbers [0-1] that weight the relative importance of the pathway analysis score and citation score, respectively. These weights can be learned through testing with a set of known recommended drugs for a particular patient sample and maximizing drug scores for therapeutically effective drugs. Both the pathAnalysisScore and citationScore of Equation (1) are values between 0 and 1, and are described greater detail below. Equation (1) enables the rapid sorting and potential filtering of drugs to more efficiently review potential therapeutic agents in a patient specific manner. Drug filtering is an important feature of the present disclosure as the space of potentially effective drugs can be large. This filtering can be accomplished by determining a threshold by which to remove low scoring drugs. There are several ways to compute this threshold, including machine learning, a feedback system, fitting distributions, etc. In an example provided later in this disclosure, an example of such a threshold is provided, wherein the threshold is determined by the simple fitting of multiple distributions.
An example citationScore in the context of an application to cancer is shown at Equation (2) of
Equation (3) depicts an example implementation of the PathAnalysisScore, A more detailed example of how the PathAnalysisScore may be implemented is described earlier herein in connection with the description of modules 212, 214 and 216 of
The DriverGeneEffectivess score considers the space of all DGs provided to as an input to system 200 (shown in
The DrugTargetEffectiveness score considers the space of all of a drug's known targets that are present in any biological pathway analyzed. The DrugTargetEffectiveness score develops a total possible score if DGs were able to reach all the available drug targets in all pathways. The DrugTargetEffectiveness score then calculates the observed score. The drug targets can be weighted by a number of factors, including the drug efficacy with respect to that target or target gene activity. In the most basic formulation, gene activity level will be used when the data is available. If unavailable, then the activity level will be 1. An example implementation of the DrugTargetEffectiveness score is shown at Equation (5) in
The PathsScore considers all found paths between the DGs and the DTs that are available in the pathways. The PathsScore calculates the total possible score if there were a path in both directions between all DGs and DTs. The PathsScore then totals the observed value. Each path, or DG-DT pair, is scored as a value (i.e., from 0 to 1), which is described herein as a pairScore. An example implementation of the PathsScore is shown at Equation (6) in
A more detailed example of the pairScore(d,t) of Equation (6) is shown by the unAdjustedPairScore of Equation (7). The pairScore(d,t) is an adjusted score for the weighted path between the driver d and drug target t. The unadjusted pairScore is the total weighted distance along the length of the path, which is scaled from 0 to 1. This score represents an overall strength into the path. Equation (7) shows an example implementation of the unAdjustedPairScore(d,t), wherein e is edge, or interaction, along the path between d and t. The weightedDist(e) is the weighted distance of the edge, which is provided to system 200 as an input. An example of this weighted distance can be the average expression of the genes that the edge directly connects, or a discrete categorization based on expression and edge interaction type. By using gene expression as the weight on the path, a shortest distance method would inherently account for the effects of changes in gene activity on the path's effectiveness. The weightedDistMax and the weightedDistMin are the maximum and minimum, respectively, weighted distance values possible. The dist(d,t) is the unweighted distance between d and t, and at its most basic iteration is the number of edges in the path.
To better distinguish categories of pairs, the scores are adjusted according to three rules. First, if t is downstream of d, pairs are weighted by the distance, i.e. number of links, between d and t. Here shorter paths are given higher scores. Second, if it is upstream of d, pairs are weighted as above but with an additional weight u that is equal to or greater than the maximum downstream distance. This ensures that upstream pairs are always scored lower than downstream pairs. Third, if t is a complex with d, pairs are weighted as above but with an additional weight c that is equal to or greater than the maximum upstream distance. This ensures the complex pairs are always scored lower than the upstream pairs. Accordingly, an example implementation of the pairScore(d,t) is shown at Equation (8), wherein: u is ≧maximum downstream distance if t is upstream of d and 0 otherwise; c is ≧the sum of the maximum downstream distance and the maximum upstream distance if d and t are in a complex; and the sum of the maximum downstream distance and the maximum upstream distance if d and t are not in a complex is otherwise set at 0.
For drug Y, “i” is equal to, sequentially, one (1) to four (4), the second and fourth GMs are not in the pathway that leads to a disease. The second and fourth druggable target genes are not upstream or downstream of any druggable gene in the pathway, “a” is one (1) in all cases, “b” is one (1) in all cases, ∂ij is the inverse of the path distance, and cij is one (1) for the potential pair “i,j.” Drug Y is slightly different from drug X in that the first GM has an addition and more direct pathway to the first druggable gene. As shown in
In the example analysis shown in
Thus it can be seen from the foregoing detailed description that the present disclosure provides a number of technical benefits, including the generation of a quantitative metric to rank a drug based at least in part on the likelihood that the drug is relevant to the treatment of a selected disease in a specific patient sample. The relevancy of a given drug to a given disease depends on possibly hundreds of DGs, thousands of different DTs across thousands of drugs, and the gene activity level of approximately 20,000 genes. Because biological pathways are the basic mechanism for understanding DG/DT relationships, a drug's relevancy may be influenced by all possible paths within a dense network from DGs to DTs, as well as how these possible paths are altered by changes in gene activity of any member of the path.
The present disclosure provides clinicians with the ability to quickly sort and filter the hundreds or thousands of potential drugs available to treat cancer and other diseases in a specific patient. Using the drug assessment/effectiveness systems and methodologies of the present disclosure, a clinician can create a highly personalized disease treatment, which may in some cases include administering drugs that are not be generally known to be part of the diagnostic and treatment process for a certain type of patient, illness or clinical circumstance. The computational approach of the disclosed systems and methodologies can account for multiple factors and quickly score a drug's relative importance with respect to other available drugs. The disclosed drug assessment systems and methodologies measure the biological relationships between DT's and DG's, and then combine this measure with a score that describes a drug's published relevancy to the patient's disease. These features enable the disclosed systems and methodologies to capture drugs having mechanisms with respect to a DG that are less well characterized but nonetheless may in fact be important to the treatment of the patient's disease.
Accordingly, the operation of a computer system implementing one or more of the disclosed embodiments can be improved.
Referring now to
The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.
The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, element components, and/or groups thereof.
The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the disclosure in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the disclosure. The embodiment was chosen and described in order to best explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various embodiments with various modifications as are suited to the particular use contemplated.
It will be understood that those skilled in the art, both now and in the future, may make various improvements and enhancements which fall within the scope of the claims which follow.
