HUMAN CANCER VIRTUAL SIMULATION SYSTEM

Abstract: A series of mathematical algorithms and descriptive process applies information from molecular biology and medical science to simulate the occurrence and metastases of cancer in the human body. The human cancer virtual simulation or HCVS, engine is a series of software program modules (5), run on a computer, containing the necessary medical information needed for a simulation of the occurence and metastases of cancer in the human body. These programs allow the user to enter individual patient information (2) into them producing data results in the form of statistical and predictive reports along applicable time lines into the future or of the past. This configuration allows selection of modules, subroutines parameters and patient input (2) to be entered into the engine selectively. Depending on the selection the user (3) can move forward or backward in time to generate simulated human cancer results (biological information, etc.).

Description: HUMAN CANCER VIRTUAL SIMULATION SYSTEM BACKGROUND OF THE INVENTION Field of the Invention This invention generally relates to computer-implemented simulation systems using mathematical and descriptive algorithms and data, and more specifically relates to a computer-implemented system that simulates events of cancer in the human body The invention uses computer-generated simulations of biochemical and morphological human cellular transformation from normal cells to metastatic tumors and provides where, when and how the distant metastases of cancer can take place. The invention uses information from molecular biology and medical science to model and predict cell to cancerous tumor to metastatic occurrence usmg parameters related to living organisms Background and Prior Art The simulation of human biological and medical processes, and m particular human cancer processes from cell to tumor to distant metastatic sites would provide many advantages to students, researchers, physicians and patients. Currently, no mathematical and descriptive approach exists that can provide practical results to students, researchers, physicians and their patients about the origin and future behavior of cancer in the human body. At present, cancer is diagnosed and treated in real-time and in the real world based on the best clinical information available to health care providmg teams The present invention improves this situation by utilizing a mathematical and descriptive process to develop vital information for a human cancer virtual simulation, thus providmg a computer based and interactive environment m which to examine oπgms, current characteristics and future outcomes of the human cancer process. One or two models currently are under development to simulate normal cellular processes and describe biologic properties of normal cells, not human cancer cells as proposed m this invention Some models are under construction based on scanned images or construction of tumors m a 3- dimensional computer simulation These are representational models and differ from the present invention in that they do not contain predictive, interactive or retroactive analytical subroutines capable of modification of the program output m real time. No descriptive or mathematical engines have been developed to depict a cell undergoing transformation from a normal cell and distant cancer sites withm the human body usmg specific molecular biological cellular changes for human cancer The simulation engme that is provided by the present invention has the specific ability to simulate human cancer behavior and m an interactive environment that has practical diagnostic, research and treatment approaches regarding the origin of human cancer and possible future behavior of cancer m the human body SUMMARY OF THE INVENTION The present invention provides a model that simulates human cancer cell behavior for medical and physiological functions has unique educational value. It provides medical students the ability to repeat a lesson. The invention reduces the use of animals and the concomitant uncertainty with animal information extrapolated to human bemgs. The invention can improve medical education by offering a dynamic flexible learning environment never before possible due to the small number and availabdity of real live patients with cancer cases at all stages of disease It allows students to make some therapeutic choices and examine the efficacy and results in a virtual environment It links probability to the medical learning process m a hands on way. No model can replace the live patient experience, however the invention augments it and ethically provides a realistic, yet forgiving environment for learning It provides students opportunities to make choices and to learn from them For researchers, the interactive human cancer model according to the present invention provides descπptive simulations useful for the study of new and current pharmaceutical applications m cancer treatment, and can reduce costs and improve therapeutic interventions. A computer based mathematical and descπptive engme of the molecular to metastases process can provide simulated outcomes m a non-clinical environment to help make decisions when experimental clinical applications are bemg considered. Some of the interfaces envisioned m the invention will allow drug response to be modeled at the molecular, cellular, tissue, and metastatic levels, with simulated results The results will be generated around biochemical parameters of importance for a drug's effectiveness or to help identify the patient's individual characteπstics that could benefit from a drug's treatment The invention has the ability examine timing cycles for drug delivery to improve the effectiveness of anti-cancer action, enable the modeling of a drug's effects on a tumor at multiple tunes usmg the invention under a vaπety of conditions enhancing the statistical and probable confidence of outcome. The human cancer virtual simulation engme is capable of continual refinement and improvement m predictive accuracy through medical research by comparing and contrasting results from the engme with results from the actual clinical settings. This allows testing, modification and improvement of the engme 's mathematical algoπthms as new medical and scientific discoveπes are uncovered The invention, because it is a computer based mathematical and descπptive engme model of the human cancer process, provides a researcher with a powerful tool because it can be experimented with and modified usmg information from past research and clinical studies This allows a researcher to test new and current hypotheses and algoπthmic expressions of the human cancer process The invention thus provides a means to test ideas and improve scientific knowledge m a clinical or non-clinical environment usmg retrospective information before clinical tπals on real patients begm. If new and unproved assumptions add new algoπthms, based on molecular biology and medical science, the invention can be modified and improved m its ability to provide useful information to students, researchers and physicians in understanding and predicting the path of human cancer m the human body. For treatment of the patient by a physician, the simulation system according to the invention will allow mput of clinical findings specific to an individual cancer patient to be entered mto the engme The function of allowmg mput will allow the physician flexibility to make a range of judgments of medical importance to be entered mto the engme and then the engme will develop a probable and statistical prediction of several possible clinical outcomes The possible clinical outcomes will mclude where or when high concentrations of cancerous emboh will cause metastases to appear m the human body. Once a tumor and its location is discovered, the engme will allow a simulation backwards m tune to determine the possible range of times for cancer or tumor ongin, cellular biochemical changes that possibly gave πse to carcinogenesis, vascular formation withm the tumor and micro metastatic behavior. The engme's results and reports will be of importance m understanding the etiology of cancer m an individual patient and examining treatment options and evaluating prognosis The reports from the mvention will potentially allow diagnostic testing to be performed m localized areas, looking for smaller cancer presence, mcreasmg the likelihood that earlier, less expensive, less mvasive treatment can be performed, thus improving the quality of treatment outcomes The mvention will provide useful information to assist the physician with short term and long term patient follow-up and to compare and contrast the patient's response to treatment m a real clinical setting with simulations developed by the human cancer virtual simulation engme The engme will allow interface and msertion of new information at any time along a time lme permitting the module's m the engme to be modified to conform to new assumptions on a daily, weekly, monthly or yearly basis The engme would become another tool in the arsenal of the oncologist for examining possible outcomes of treatment as they develop treatment regimes The simulation system is progressive and is capable of bemg modified and unproved m conjunction with new advances m molecular biology and medical science The continual updating of databases to improve the calculation abilities of the engme and its subroutines, descnbed later m this application, is one way to accomplish this Another dynamic aspect of the mvention is that the descπptions generated from some parts, specifically the molecular dπven portions of the metastatic, tissue, and tumor modules will provide estimations of tumor size, and location m the human body and other cancer tumor morphology characteπstics that can be coupled with visualization, display and imaging technology As descnbed heremafter, the mvention descπbes processes mvolved in cellular transformation from a normal cell then to a cancerous tumor and descnbe probable metastases elsewhere m the human body The mvention also has the capability to provide projections about the ongm and future of human cancer manifestations The mvention synthesizes fundamental molecular biology and medical knowledge mto a simulation system The system allows questions to be asked and provides answers of practical value to the human cancer process It provides a virtual descπption beyond the present tense, such as possible future metastatic sites and past ongm of the human cancer process As new discoveπes are made certain algoπthms can and will be modified and improved, but the fundamental workings of the engme will remain the same The principal object of the present mvention is to provide a descnptive and mathematical engme for a human cancer virtual simulation system that applies information from molecular biology and medical science to simulate the occurrence and metastases of cancer m the human body The mvention uses a computer as an information-input apparatus and a visual momtor for output A seπes of software program modules employmg a system of specially wπtten programs and databases are employed which allow the user to enter mto the programs individual patient information thereby producmg information, results and reports about simulated human cancer The mvention has two major configurations, medical and educational applications The medical application configuration generates human cancer simulation information, reports and statistical and predictive results along applicable time lmes of the future or the past This configuration allows selection of modules, subroutines, parameters, and patient mput to be entered mto the engme selectively Dependmg on the selection, the user can move forward or backward m time to generate simulated human cancer results and reports The system provides the capability to predict future courses of human cancer to descnbe the possible ongm of human cancer m a patient The results from the engme is virtual in that it produces simulated descπptions of human cancer m the body m the present, the past and the future The educational configuration will use pre-programmed information, but allows limited interaction for medical student educational purposes The medical applications configuration will allow diagnostic, treatment and research human cancer simulations to be performed Brief Description of The Drawings Fig 1 is a block diagram illustrating the operator's relationship to the engme mcludmg data flow, Fig 2 is a block diagram illustrating the operator's relationship as descnbed above with the engme configurations, Fig 3 is a block diagram of the overall engme module structure accordmg to one preferred embodiment of the mvention, Figs 4-10 are flow diagrams illustrating the vanous subroutines for each of the modules of the simulation engme accordmg to the present mvention, Fig 11 is a block diagram illustrating data flow mteractions of manual user mput with the vanous modules of Fig 3 and a patient information database, Fig 12 is a block diagram illustrating data flow mteractions between the vanous modules of Fig 3, Fig 13 is a block diagram of a system for receivmg data from external data sources, and analyzing and distπbuting the received data mto vanous data types for incorporation mto model algonthms accordmg to one prefeπed embodiment of the mvention, Fig 14 is a flow diagram detail of the cell cycle routine shown m Fig 3 accordmg to one preferred embodiment of the mvention, and Fig 15 is a flow diagram of an algoπthm for simulating cell protem production withm a cell life cycle, accordmg to one preferred embodiment of the mvention Detailed Description of the Preferred Embodiments Description of Databases The Human Cancer Virtual Simulation (HCVS) Tumor Databases hereafter will be refeπed to as the databases The HCVS system of databases is defined as the organized accumulation of information needed for the numencal subroutines to perform then functions The databases all operate m the following manner Based on mput or instructions from the user module interfaces the connection module m the HCVS system will access databases, provide information to numencal solution modules and their subroutines to generate results and reports relevant to the human cancer virtual simulation under study The databases descnbed m this version are the DNA (genetic) database, biomarker (molecular) database, statistical database of information from actual patients, and a metastatic database Genetic Database The DNA database mcludes information concerning genes of human cells as part of the carcrnogenesis The information will mclude cellular genes that are mutated and or deleted. This information will mclude why, when, how, and other parameters of need for the simulation system's subroutines. Molecular Database The molecular database mcludes information that provides biochemical evidence of human carcrnogenesis This database will mclude human normal cell and cancer cellular molecular information, for example the phenotype of the mutated genes from the genetic database. This information will mclude biochemical mechanisms, protem functions and possible biological significant compounds Statistical Database The statistical database mcludes information from studies performed for the specific forms of human cancer the engme is asked to examine. The statistical database will also mclude lifestyle issues related to specific cancers. This information is useful for all of the applications of the mvention. Examples mclude information on the average age of a cancer patient at death, physiological information concemmg the average size of an adenoma stage 1 tumor, etc. Metastatic Database The metastatic database mcludes all types of previously mentioned information specific to metastasis. This metastatic information will support the metastatic module of the HCVS System Overview and Operation The mvention is implemented on a computer, mcludmg an information mput device, such as a keyboard, and a visual momtor or printer for output. The simulation system mcludes a senes of software program modules each employmg a system of specially wπtten programs m conjunction with the above-descπbed databases that utilizes a computer to perform its operations and generate results. These programs allow the user to enter individual patient information, if desired, or to use pre-programmed information producmg information results m the form of reports about simulated human cancer As shown m Fig. 1, the operator interface (mcludmg keyboard, mouse and display device m operative connection with a central processmg unit) sends data and instructions to the engme, where they are processed, and processed data and instructions are sent by the engme back to the operator interface for output to the operator m the form of graphical displays, textual displays, or printed reports. As illustrated by Fig. 2, the mvention has two major configurations, educational and medical applications The medical applications configuration allows diagnostic, treatment and research human cancer simulations to be performed The educational configuration uses pre-programmed information, but allows limited mteraction for the medical student's or training professional's educational purposes. The medical application configuration generates human cancer simulation information. This information can be m the form of reports of present information, and/or statistical and predictive information coπelated to applicable time lmes m the future or from the past This configuration allows selection of modules and patient mput to be entered mto the engme selectively Dependmg on the module selection, the user can move mto the future or backward m time to generate simulated human cancer results The system provides the capability to predict future courses of human cancer, and to descnbe the possible ongm sight and initial biological traits of human cancer m a patient. The result from the engme is virtual m that it produces simulated descπptions of human cancer m the body m the present, the past, and the future. The biological process for a cell's transformation from a normal cell to a cancerous cell and then to metastatic activity is descnbed withm six modules m this mvention. As shown m Fig. 3, each section (biological stage and view) is organized mto a module. The six modules are the tumor ongm, cellular, colony, tissue, tumor and metastatic modules. Each module contains one or more subroutines. These subroutines carry out smaller descπptive and mathematical processes needed to simulate human cancer biology. Each of the subroutines will produce results m forms needed by the user to descnbe the biological process the subroutine simulates There are fourteen different subroutines withm the system of the mvention. The subroutines are as follows: The genetic mutation subroutine and diagnostic subroutine of the tumor ongm module, The cell cycle subroutine and the physical properties subroutine of the cellular module, The interaction between cells subroutine and the structure subroutine of the colony module, The interaction between cells subroutine and the tissue structure subroutine of the tissue module, The interaction between cells subroutine, the tissue structure subroutine, and the physical properties of the tumor subroutine of the tumor module, and The statistical and clinical outcome subroutine, the molecular biological subroutine, and the cancer origin/run forward subroutine of the metastatic module. The results are m the form of reports, generated by the subroutines of the modules, and placed mto data sets. The data sets can then be viewed m the interface coπesponding to the module that the reports came from As illustrated m Fig. 3, the mvention has six module user mterfaces, the molecular, cellular, cellular expansion, pre-neoplastic, neoplastic and metastatic mterfaces. These mterfaces each coπespond to a respective module and act as information mput and output points. This is where the human user receives instructions or requests, where choices about subroutines or their information output reports are chosen and where mput information about a patient is entered to allow the programs to operate. It is at the user interface where the reports, that the subroutine generated and places mto data sets, will be viewed. Thus the mput information goes m through the user mterfaces and the output information sets come back out of the user interface (see Fig 1) A general example of the entire process that the simulation system uses is descnbed in Fig 4. In operation, the user selects a particular module for operation. Each module and its subroutines then follows a process of operating the associated interface usmg the computer momtor and keyboard. The module then initiates activation of the central processmg unit through the software programs with the connection module connecting the module to the databases of information needed for the subroutmes. The modules then engage the numeπcal solutions module and the subroutines associated with the numencal solutions module and finally provide the user with reported solutions to user requests through the momtor and vanous other displays. All of the operations take place m the basic computer hardware and software previously descnbed. The connection module is activated whenever the mput from the user is completed and the user initiates the subroutines. The connection module processes the mput from the user, gathers information from the vanous databases necessary for the execution of the mathematical and descπptive algonthms withm the vanous numencal solution modules and transmit it to its proper location. If the program is stopped at the user interface along any aspect of the time lme, and mput is modified, the connection module will automatically engage to transmit to the numencal solutions module the new information. The numeπcal solutions module will then generate the sequential processmg. If the program is paused, the connector module will disengage The numencal solutions module subroutines carry out the task of generating the results for the reports. Once started they will generate the previously mentioned results and arrange them mto reports. These reports will be sent to the user interface for viewmg on the momtor or for printing. If continual output is chosen and the user stops the program, the numeπcal module (s) will store results generated up to that pomt m time. The user may at any time request reports. If the program is restarted it will begm where it left off, mclude the changed parameters in the next sequential report, continue calculations with modified parameters and produce a new report. If paused, the numeπcal module will resume its calculations when the pause is ended, generate its results, report back to the user interface and the process is completed. Detailed descπptions of the vanous subroutines of each module will now be descnbed with reference to Figs. 5-10. TUMOR ORIGIN MODULE Referring to Fig. 5, the HCVS Tumor Ongm Module has two purposes The first purpose is to use mathematical calculations to go back m time to the oπgmal site of carcrnogenesis. This will mclude the ongmal genetic mutations found to be the initiatory step of the disease. The initiatory step is the first mutation of a normal cell that leads to the phenotype of the specified cancer. The second purpose of the Tumor Ongm Module is to diagnose cancer at the genetic level. This will be the cutting edge of the early detection technology For example, m colon cancer there are set genetic steps to carcmogemcity. If the patient had a biopsy of a bemgn polyp and the molecular (i.e. genetic) information was extracted, it could be entered mto the HCVS and the Tumor Ongm Module would diagnose the probability of a future malignancy. The operator enters information about the patient and their current tumor mto the system through the operator interface. This will mclude the age, weight, and other relevant physiological information about the patient This information is then run through the tumor ongm subroutines m the Tumor Ongm Module. This information will then result m a report(s) of information about the location and molecular properties of the tumor's ongm. Prostate cancer is a good example of the function of this module. In prostate cancer the disease's initiatory step is not fully understood. The mam reason for the complexity of prostate cancer is the heterogemcity of the tumors. In the prostate there are several primary tumors at detection Withm the detected prostate tumors there are several different genotypes. This complexity makes early detection of prostate cancer for a pathologist difficult The tumor ongm module of the HCVS will be able to trace the tumors' progress from the state of the detected tumor back m time usmg the databases and calculations of the subroutines. The product of the subroutine executions will be molecular information showmg the probability of which mutation or senes of mutations could be the mitiatory step. The display of the resultant information is descnbed m the molecular interface. Genetic Mutation Subroutine of the Tumor Origin Module The purpose of the genetic subroutine of the tumor ongm module is to calculate the different mutations that can occur m cellular genetic code mvolvmg cell transformation steps leading to, and resulting m, a cancerous state. The genetic mutations can occur by three different methods (deletion, insertion, and substitution) these three methods lead to codon mutations (chain termination mutation, framesmft mutation, missence mutation, and nonsense mutations). Other somatic mutations also exist, these mutations only occur m somatic cells. By the pathways of mutation bemg limited we can categorize a mutation to have a specific phenotype. The genetic mutation will then effect the cell by the expression or non-expression of the gene's protem. For example, m order to inactivate the APC (Adenmatous Polyposis Coh) gene both alleles must be lost. In the situation of Familial Adenomatous Polyposis Syndrome a recessive APC allele is inhented. At this pomt only one mutation (i.e. a somatic mutation) needs to occur m order to lose APC function. (See Human Cancer Virtual Simulation Cellular Module to see the results of an APC deletion) By simulating these relationships, the tumor ongm module will be able to examme the existing situation (disease) and estimate back to the tumors ongm. These genetic mutations and functional relationships will be mathematically operated m the genetic subroutine of the ongm module Diagnosis from Molecular Information Subroutine of the Tumor Origin Module The purpose of the diagnosis from the molecular information subroutine m the tumor ongm module is to improve early detection of cancer. For example, m colon cancer, the accepted mitiatory step is the mutation of APC If, as descnbed earlier, a user had the molecular information for a bemgn polyp of a patient and this information was entered mto the HCVS the diagnosis from molecular information subroutine would calculate the probable timing and initial intensity of a future malignancy. Tumor Origin Module Information Sets Both of the tumor ongm module subroutines, genetic mutation and diagnosis from molecular information will have information outputs that will be inputted mto the molecular interface. Genetic Mutation Subroutine •The type and location of the genetic mutation. Usmg the information from the genetic database the genetic mutation subroutine will show the type and location of the genetic mutation that has occurred •The phenotypic result of the genetic mutation Usmg the genetic database as well as the molecular database the genetic mutation subroutine will express the phenotypic results of the mutated genes This will mclude protem expression, protem reactions, and etc •The gene products lost m the mutation Usmg the molecular database the genetic mutation subroutine will determine the gene products that are lost due to the genetic mutation The operator will be able to activate and deactivate genes therefore effecting the gene products This information will then be available to the operator to view and mampulate Diagnosis from Molecular Information Subroutine 'The possible mitiatory step and the probability it has occurred Usmg the genetic and the molecular databases, the diagnosis from molecular information subroutine will calculate a list of possible mitiatory steps and their probability This information will be displayed on the molecular interface, and the operator will be able to mampulate the scenanos usmg this interface •The mutations that have occurred and follow the carcinogenic path and the mutations along that same path that have not occurred Usmg the genetic and the molecular databases, the diagnosis from molecular information subroutine will determine the most likely genetic pathway From the most probable carcinogenic pathway, the diagnosis from molecular information subroutine will determine the genetic vaπations that have occurred, and the genetic vaπations that will occur, if the selected pathway is followed 'The probability and proposed carcinogenic pathway Usmg the genetic and molecular databases, the diagnosis from molecular information subroutine will determine all the possible carcinogenic pathways and their coπesponding probability This information will be displayed and the operator will be able to select a pathway and the engme will analyze it Molecular Interface The molecular interface will display and allow user mteraction with the module for the information on the actual mteraction of the genes and molecules The reports will mclude chemical mechamsms, bond strengths, timmg of mutations, possible alternative mutations, etc The cell bondmg strength is an example of the information mcluded m the molecular level In the cell bondmg the APC product must undergo homo-ohgomenzation to produce the beta-catemn that is essential for the production of the cell bondmg cadherm In the situation where an APC deletion would occur, the cell bond strength would be reduced The information will be available concemmg the reaction mechanism, time of reaction, conditions of reaction, and etc CELLULAR MODULE As shown m Fig 6, the Cellular Module is the portion of the HCVS engme that controls the cellular information The purpose of the Cellular Module is to use mathematical calculations to descnbe the behavior of an individual cell and its surrounding environment The Cellular Module will accomplish its tasks by mathematically calculating the properties of the cells This is descnbed m the subroutines of this module The control of an individual cell and it's environment is shown by an example of p53 gene loss The p53 gene is a tumor suppressor gene The p53 gene activates the p21 protem The p21 protem has the function of arresting cell cycle progression so the cell can either undergo repair or apoptosis This is accomplished by inhibiting cychn cdk complexes When p53 is mutated the p21 protem doesn't activate at the levels necessary and the cell continues through a complete cycle to produce two mutated daughter cells This process, and many others not descnbed, are part of carcrnogenesis, and will be controlled through said mathematical calculations m the cellular module The display of the resultant mformation is descnbed m the cellular level interface Cell Cycle Subroutine of the Cellular Module The purpose of the cell cycle subroutine of the cellular module is to simulate the aspects of a cell life cycle These aspects will mclude the timmg of the cell cycle, control of the stage of the cell cycle, etc The required information will be m the molecular database The cell cycle subroutine will use statistical and chemical information to run its calculations The control of the cells cycle properties is shown m the p53 example (see Cellular Module) The effects of the mutation of the p53 gene will affect the timing of mitosis This effect is an mcrease m speed because the cell doesn't stop the process of cell cycle progression to repair the mutation Physical Properties of the Cell Subroutine of the Cellular Module The purpose of the physical properties of the cell subroutine of the cellular module is to descnbe mathematically the size, shape and structure of the cell changes throughout carcrnogenesis For example, m the shape of the cells m large cell undifferentiated carcinoma there is a classification of cells named clear cell This nomenclature came about due to the cytoplasm of a cell turning clear during carcrnogenesis This state occurs because of large deposits of glycogen m the cytoplasm Cellular Module Information Sets Both of the cellular module subroutines, cell cycle and physical properties of the cell will generate mformation from its calculations and return this information to the cellular level interface Cell Cvcle Subroutine •Rate of mitosis Usmg the molecular and the statistical databases the cell cycle database will produce mformation on the rate of mitosis m the form of time/cell cycle This information will be useful m determining the speed of the diseases' progression. The operator will be able to adjust the cycle m order to view hypothetical situations, but the system will always default back to the calculated "real life" rate. •Survival rate of the cells Usmg the molecular and statistical database the cell cycle subroutine will determine the mean and standard deviation/time of the survival rates of cells This subroutine will also determine the number of cells that mduce apoptosis as a normal function. As descnbed m the cellular module many mutated cells will mduce apoptosis to deter a genotype or phenotypic mutation from progressing. In carcrnogenesis one of the factors is the situation that the gene products that are responsible for apoptosis are themselves mutated. This information will be displayed m the cellular level interface. Physical Properties of the Cell Subroutine 'Physical properties of the cell. Usmg the molecular and statistical databases the physical properties of the cell subroutine will determine the size, shape, physical makeup, and etc. This mformation will be displayed on the cellular level interface and will be used as mformation by other modules. Cellular Interface The cellular level of mformation will display the rate of mitosis, the rate and level of mutation, the size and shape of the cell, etc. generated by the Cellular Module of the HCVS engme. The rate of mitosis is an example of unportant mformation at the cellular level. The HCVS will be able to give the mformation concemmg the shape size and rate of replication (mitosis). The time and stage of the disease will reference this mformation from the cell. The operator will be able to select the information set accordmg to time to see the size and shape of the cells at vanous times m the past, present or future. The other vanables will be available for adjustment as well. The operator will have tailored the HCVS for the specific case and will then enter the size and shape of the cells they are looking at The operator wdl then enter that mformation and the HCVS will generate the mformation for the probable time and stage of the disease. COLONY MODULE Referring to Fig. 7, the purpose of the Colony Module is to mathematically descnbe small cellular populations. The importance of this can be shown by nutntion consumption. When a pre-neoplastic growth occurs, the nutntion consumption of the growth mcreases from its standard rate of consumption. This happens for many reasons, but an example is cellular growth rate. The pre-neoplastic growth occurred partly because of an increased growth rate. This mcreased growth rate needs more nutntion from the body This unbalance will lead to the cells around the growth to give up some of their needed nutntion to the growth; the colony module through mathematical calculations will determine this dynamic relationship. The display of the resultant mformation is descnbed m the cellular expansion interface Interaction between Cells Subroutine of the Colony Module The purpose of the mteraction between cells subroutine of the colony module is to control the mteraction of simulated cells through mathematical calculations. The nutntional consumption descnbed m the colony module is good example of this relationship Other modules m the mvention will use the mformation from this subroutine Structure Subroutine of the Colony Module The purpose of the structure subroutine of the colony module is to show the structure of the cells together m this small population. The structure subroutine will mclude the physical charactenstics of the individual cells but will also mclude the structural relationship of the cells together. In large cell undifferentiated carcinoma of the lung, the physical properties of the cell along with this lntracellular structure are the keys to diagnosis and classification Colony Module Information Sets Both of the subroutines of the colony module, mteraction between cells and structure, will produce mformation and return it to the cellular expansion interface. Interaction between Cells Subroutine •Nutntion Consumption Usmg the molecular and statistical databases, the mteraction between cells subroutine will produce information m the measurement of moles/unit This information will mclude all standard compounds that are biochemically necessary, but also will mclude a section to mput a new compound along with its chemical properties and the mteraction between cells subroutine will calculate the mvolvement. •Cell Bonding Usmg the molecular database the mteraction between cells subroutine will calculate the bond strength between cells. Some of the contemplated modes of measurement are kcal/mol necessary to break the bond and etc This mformation will be displayed on the cellular expansion interface, but will also go mto the information mput area to fuel other modules. Structure Subroutine •Physical Properties of the Individual Cells Usmg the molecular and statistical databases the physical properties of the cell subroutine will determine the size, shape, physical makeup, and etc. This mformation will be displayed on the cellular level interface and will be used as mformation by other modules. •Intracellular Structure Usmg the molecular and statistical database the structure subroutine will produce mformation for the structure of the tissue. This structure will take on many forms and functions. This mformation will be displayed on the cellular expansion interface. Cellular Expansion Interface The cellular expansion interface wdl be where the information form the subroutines of the Colony Module (concemmg small populations of cells and their mteractions) will be shown and descnbed to the user The operator will be able to adjust the vanables such as oxygen distnbution m order to see the mteraction between the simulated cells descnbed by the subroutine programs. The value of the information displayed here to the user is that these relationships would be difficult to see m the later mterfaces TISSUE MODULE Referring to Fig. 8, the purpose of the Tissue Module is to descnbe with mathematical calculations a large population of different cells interacting m a normal and a malignant environment. Withm the tissue of every organ there are several different types of cells. The different types of cells have different functions and therefore different locations The Tissue Module will descnbe small abnormal growths. The mteractions between the small growth and the outer levels of the tissue, are unportant to descπbmg and understanding the path of carcrnogenesis m practical terms. These mteractions are descnbed mathematically by the tissue module. The display of the resultant mformation is descnbed m the pre-neoplastic interface. Interaction between Cells Subroutine of the Tissue Module The purpose of the mteraction between cells subroutine of the tissue module is to mathematically descnbe the intracellular mteraction among large populations of simulated cells withm the Tissue Module. The nutnuonal consumption descnbed m the Colony Module is a good example of this relationship. Other modules m the mvention will use the information from this subroutine. Tissue Structure Subroutine of the Tissue Module The purpose of the tissue structure subroutine of the Tissue Module is to mathematically control the intracellular structure among large populations of simulated cells withm the Tissue Module. As descnbed m the Structure Subroutine of the Colony Module, the structure of the intracellular relationship is unportant not only m diagnosis but m cellular classification. In the Tissue Module, this subroutine will also begm to generate the different layers of the tissue cross section. In all tissues there are several levels of tissue withm the tissue sample. This subroutine will generate the information needed to represent these levels and their mteraction with each other. Tissue Module Information Set Both of the subroutines of the tissue module, mteraction between cells and tissue structure, will produce information and return it to the pre-neoplastic interface. Interaction between Cells Subroutine 'Nutntion Consumption Usmg the molecular and statistical databases, this subroutine will produce mformation m the measurement of tissue level nutπtional consumption. The simulation will generate mformation m chemical terms (i.e. moles) relevant to the user. The unit may vary dependent on the organ simulation. This information will mclude all standard compounds that are biochemically necessary, but also will mclude a section to mput a new compound along with its chemical properties and the mteraction between cells subroutine The subroutine will then calculate the chemical mteraction of the new compounds at the tissue level and display the information at the pre-neoplastic interface. •Cell Bondmg Usmg the molecular database the mteraction between cells subroutine will calculate the bond strength between simulated cells m the tissue module. One of the contemplated modes of measurement are kcal/mol necessary to break the bond among others not descnbed. This information will be displayed on the pre-neoplastic interface, but will also go mto the information mput area to fuel other modules. Tissue Structure Subroutme 'Physical Properties of the Individual Cells Usmg the molecular and statistical databases the physical properties of the cell subroutine will determine the size, shape, physical makeup, and etc. of the simulated cells withm the tissue module. This mformation will be displayed on the pre-neoplastic interface and will be used as mformation by other modules. •Intracellular Structure Usmg the molecular and statistical database the structure subroutine will produce mformation for the structure of the simulated tissue. This structure will take on many forms and functions. This mformation wdl be displayed on the pre-neoplastic interface •Tissue Levels Usmg the molecular and statistical database the tissue structure subroutine will generate mformation concemmg the development, size, shape, and etc. of the tissue. This information will be presented on the pre- neoplastic interface. Pre-neoplastic Interface The pre-neoplastic interface will display information from the Tissue Module mformation set, such as nutntional consumption and the other information sets descnbed which are generated by the Tissue Module subroutines This level will show mformation from the earliest stages of a simulated primary tumor or tumors This information will also be available for the operator to mampulate as m the previous levels TUMOR MODULE Referring to Fig 9, the purpose of the Tumor Module is to control complex relationships of simulated tumor growth through mathematical calculations for the majoπty of tumongenesis This process needs its own module because of the complexity of the mteractions between the disease and the suπoundmg cells The development of blood vessels is a good example of the complexity of the systems of the Tumor Module The primary tumor will continue to grow at an accelerated rate as long as the nutnents are available for the growth (When the threshold is reached where the nutnents are limited, this coπesponds to the weak cell bonds discussed earlier ) This weakness allows for the nutnent carrying blood to reach the tumor and form small blood vessels This relationship, along with many others, will be controlled mathematically by the Tumor Module The display of the resultant mformation is descnbed m the neoplastic interface Interaction between Cells Subroutine of the Tumor Module The purpose of the mteraction between cells subroutine of the Tissue Module is to mathematically run the intracellular mteraction The nutntional consumption descnbed m the Colony Module is a good example of this relationship Other modules m the mvention will use the mformation from this subroutine Tissue Structure Subroutine of the Tumor Module The purpose of the tissue structure subroutine of the Tumor Module is to mathematically control the intracellular structure As descnbed m the Structure Subroutine of the Colony Module, the structure of the intracellular relationship important not only m diagnosis but in classification In the Tissue Module this subroutine will also begm to generate the different layers of the tissue cross section As m all tissues there are several levels of tissue with m the tissue sample This subroutine will generate the mformation needed to represent these levels and theu mteraction with each other Physical Properties of the Tumor Subroutine of the Tumor Module The purpose of the physical properties of the tumor subroutine of the Tumor Module is to calculate the behavior of the virtual disease This subroutine will handle information such as tumor mass, growth rate, cell structure, etc The mformation descnbmg that charactenstic wdl be generated m the physical properties of the tumor subroutine An example of the physical properties of a tumor is prostate cancer, as mentioned previously (see Tumor Ongm Module) where there are multiple primary tumors Tumor Module Information Sets Each of the three subroutines; mteraction between cells, tissue structure, and physical properties of the tumor subroutine, will generate mformation for the neoplastic mterface. The first two subsections have already been descnbed and are referenced. The last of the three subroutines' results is descnbed below Physical Properties of the Tumor •Tumor Mass The physical properties of the tumor will descnbe the size, shape, and overall mass of the primary tumor. This mformation will be shown m a metnc scale and percentage of total area This information will also be sent to the mput mformation area to help run other modules. •Tumor Growth Rate The physical properties of the tumor, generate tumor growth information concemmg the growth of a tumor over time. This information will also mclude the major factors dnving the growth. This will be sent to the mput mformation area, as was the tumor mass information. 'Genetic Mutations Present Usmg all the databases except the metastatic database, the physical properties of the tumor subroutine will determme the genetic changes that have occuπed and will occur during the simulation. •Vascular Construction Usmg the molecular and statistical database the physical properties of the tumor database will generate information for concemmg vascular construction. This mformation will also come from the tissue module. This mformation will be sent to the information-input area to help run the metastatic module. Neoplastic Interface The neoplastic mterface will display the information from the Tumor Module. The complex relationships will be shown and available for adjustment. This mterface will show the most mformation concemmg the primary growth. After entering all the mformation concerning the patient and the disease, the operator will then be able to adjust the size of the tumor to determme the stage that the patient is m. The operator will also be able to adjust the information to see the coπespondmg information, the order of mutations that have occuπed and see the information for the mutations that have yet to occur. METASTATIC MODULE Referring to Fig. 10, the Metastatic Module is a human cancer predictive and prognostic engme used to simulate and generate simulations and reports about cancer behavior m the human body. It uses mformation supplied to its vanous subroutine computer software programs or usmg pre-programmed instructions to accomplish this. The purpose of the Metastatic Module is to provide useful information from mathematical and descnptive algoπthms to help forecast and predict the course of cancer progression m the human body A second use of the module is to examme the possible effects of treatment intervention steps usmg mput supplied to its programs. The mput supplied to the programs withm the Metastatic Module may be specific to an individual or the program can examme possible futures based on assumptions withm the program. This module has significant diagnostic and therapeutic applications for human cancer patients and the physicians that treat them Due to the importance of this module and the predictive and prognostic applications, its operation wdl be descnbed m more detail and serve as a fuller example of what the mvention is mtended to do The Metastatic Module Subroutines, Their Scientific Assumptions and Mode of Operation There are three metastatic cancer predictive and prognostic subroutines that appear on the user mterface once the Metastatic Model is selected. The first would be a clinical and statistical outcome, the second would be molecular biological, and the third would be cancer ongin/run forward. The Statistical and Clinical Outcome Subroutine of the Metastatic Module The Statistical and Clinical Outcome subroutine will use epidemiological information (morbidity, mortality, height, weight, treatment steps such as chemotherapy, surgery and radiation; when treatments were administered, patient response to treatment or estimates thereof, initial tumor size and volume, tumor geometπc shape, tumor location, patient age, health conditions of relevance to patient such as HIV status, immune system strength through secondary biomarkers such as white blood cell and T-cell count, family history of relevance to cancer epidemiology, other complicating factors or diseases, local and distant metastases location, biomarkers of human cancer such as estrogen receptivity m breast cancer and others, mitosis rate, cell diploidy, etc ) assembled mto databases previously descnbed within the mvention and gathered from credible scientific sources from real human cancer patients relating to the course of human cancer The purpose and resulting mformation from this subroutine is to predict the future course of a patient's human cancer by relating user mputted mformation related to the database descnbed and usmg linear mathematics withm the subroutine coupled with mputted information and assumptions to predict possible clinical disease outcomes for the patient/physician. The underlying assumption is that future cancer behavior will be similar to that documented from the past and the engme information will prove useful for predictive and prognostic purpose Molecular Biological Subroutine of the Metastatic Module The molecular biological subroutine will use mformation known about a cancer or tumor (pathology report mformation such as tumor size and volume, tumor geometnc shape, type of cancer, tumor location, level of vasculanty withm the tumor, location to veins and artery m target organ, mitosis rate, biomarkers of relevance, cell differentiation, cell diploidy etc.) m addition to patient charactenstics of importance to the engme (height, weight, treatment steps such as chemotherapy, surgery and radiation; when treatments were administered, patient response to treatment or estimates thereof, patient age, health conditions of relevance to patient such as HIV status, immune system strength through secondary biomarkers such as white blood cell and T-cell count, family history of relevance to cancer epidemiology, other complicating factors or diseases, local and distant metastases location, etc.) and generate mformation related to micro metastasis rate and behavior of cancer cells or emboli (clusters of cancer cells) and their ability to move to new sites developmg possible and probable scenaπos of human cancer reoccuπence and growth withm the body. The underlying assumption m this subroutine is to provide populations of individual cells with instructions, based on what is known about the patient and their cancer's molecular biological behavior, that will influence cancer cell growth, death, and other disease charactenstics withm the body. The molecular biological subroutine will then begm functioning, usmg programs and assumptions mputted by the physician about a patient to provide mformation useful for prognosis and prediction of human cancer. Cancer Origin/Run Forward Subroutine of the Metastatic Module The cancer ongin/run forward subroutine is a sequential operation of the Cancer Ongm module which will be a reverse operation of the subroutines withm the tumor, tissue, colony, cellular and finally tumor ongm modules and then followed by the molecular biological subroutine. The underlying assumption of the cancer ongm module is beginning at a pomt m time when the cancerous tumor is discovered a simulation with the HCVS engme can be developed running backward m time usmg the vanous modules to reach the ongm of cancer development. The HCVS engme can then be run forward through the present and mto the future. It is envisioned this will provide useful information for prognosis and prediction of human cancer behavior. One advantage of the cancer ongin/run forward subroutine is that it can test engme mput for efficacy When mformation from this subroutine generates mformation that closely resembles the patient's disease condition m the present, that result may be used to decide parameters to dnve simulations m the future. Additional mformation that could be provided by usmg this technique would be additional micro metastasis not seen at the primary site of the tumor. Metastatic Module Information Set Each of the three Metastatic Module subroutines, statistical and clinical outcome , molecular biological and cancer ongin/run forward will contain numeπcal solution modules to perform the following estimations and descnptions and generate reports back to the user mterface and can be selected by the user: a) metastatic occuπence, target organ and percentage possibility, b) percentage disease free survival, c) micro metastatic and metastases volume, d) projected cancer cellular mitosis phase table, e) projected blood biomarker concentration over time. Considering now that the system herem descnbed will have 15 different numeπcal solution modules descnbed m the diagram for this part of the mvention m greater detail, each numeπcal solution module withm the 3 Metastatic Module subroutines will be descnbed as to what it will do beyond the general function of a numeπcal solution module above and how it will perform its said function: The Statistical and Clinical Outcome Numencal Solution Module Output Subroutines •Metastatic occuπence, target organ and percentage possibility Usmg information from the epidemiological database and from other databases withm the mvention an algoπthm will be constructed that will make use of medical and personal mput about an individual patient's present condition to determme the probable organ sites of distant metastatic human cancer projected m a future time frame The algonthms will use linear and curvilinear analysis and other mathematical means to achieve this endpomt Where possible the algonthm will use statistical analysis to determme the confidence of its predictions •Percentage disease free survival Usmg mformation from the epidemiological database and from other databases withm the mvention, an algonthm wdl be constructed that will make use of medical and personal mput about an individual patient's present condition to determme the morbidity and mortality of distant metastatic human, cancer projected m a future time frame to generate predictions of disease free survival The algoπthms will use linear and curvilinear analysis and other mathematical means to achieve this endpomt Where possible, the algonthm will use statistical analysis to determme the confidence of its predictions "Micro metastatic and metastases volume Usmg mformation from the epidemiological database and from other databases withm the mvention an algonthm wdl be constructed that will make use of medical and personal mput about an individual patient's present condition to determme the micro metastatic and metastases volume of distant metastatic human cancer projected m a future time frame The algoπthms will use lmear and curvilinear analysis and other mathematical means to achieve this endpomt It is envisioned that this descnption will encompass assumptions about micro metastatic behavior not visibly seen but that can be inferred from the scientific literature Where possible, the algonthm will use statistical analysis to determine the confidence of its predictions •Projected cancer cellular mitosis phase table Usmg information from the epidemiological database and from other databases withm the mvention, an algoπthm will be constructed that will make use of medical and personal mput about an individual patient's present condition to determme the projected cancer cellular mitosis phase table of metastatic human cancer, projected m a future time frame to generate predictions about cancer cell populations and their behavior It is expected that this program will generate information about cellular functions of mterest, such as S-phase, mitosis or m-phase doublmg rate and many others to be specified The algoπthms will use linear and curvilinear analysis and other mathematical means to achieve this endpomt Where possible the algoπthm will use statistical analysis to determme the confidence of its predictions •Projected blood biomarker concentration over time Usmg information from the epidemiological database and from other databases withm the mvention an algonthm will be constructed that wdl make use of medical and personal mput about an individual patient's present condition to determme the projected blood biomarker concentration over time of metastatic human cancer projected m a future time frame It is expected that this program will generate mformation about chemical behavior of cancer cells, mcludmg biomarkers but not limited to them, chemical metabolic factors associated with cancer may be mcluded It is envisioned that numeπcal solution program will be mathematically linked to predictions generated by the micro metastatic and metastases volume, projected cancer cellular mitosis phase table and the metastatic occuπence, target organ and percentage numencal module subroutines, or then predictions, withm this module These numencal solution modules will probably mclude mathematical expression to determme uptake by vanous organs m the body and other mathematical modeling expressions to provide a close to reality prediction of the concentration of chemicals of mterest m the bloodstream The algonthms will use lmear and curvilinear analysis and other mathematical means to achieve this endpomt Where possible, the algonthm will use statistical analysis to determine the confidence of its predictions Molecular Biological Numencal Solution Module Output Subroutmes •Metastatic occuπence, target organ and percentage possibdity Usmg information from the GENETIC database and from other databases withm the mvention an algonthm will be constructed that will make use of medical and personal mput about an individual patient's present condition to determme the probable organ sites of distant metastatic human cancer projected m a future time frame The algonthms will use lmear, curvilinear, geometnc, algebraic functions and other mathematical means to achieve this endpomt The algonthm (s) withm this numencal solution module wdl extend and improve upon the mathematical expressions of the HCVS engme modules that will simulate genetic changes of a cell to cancerous tumor and cellular overgrowth to mclude descnptions of breakout or micro metastatic shed rate, new blood vessel formation; blood, tissue and lymph vessel mvasion, survival m the bloodstream, tissue transport and new colony formation at secondary or tertiary sites It is envisioned that mformation useful for motion, shape, path and particle behavior of cancer m the body will be generated Where possible, the algoπthm will use statistical analysis to determme the confidence of its predictions •Percentage disease free survival Usmg information from the GENETIC database and from other databases withm the mvention an algonthm will be constructed that will make use of medical and personal mput about an individual patient's present condition to determme the percentage of disease free survival from distant metastatic human cancer projected m a future time frame The algoπthms will use lmear, curvilinear, geometπc, algebraic functions and other mathematical means to achieve this endpomt The algoπthm (s) withm this numeπcal solution module will extend and improve upon the mathematical expressions of the HCVS engme modules that will simulate genetic changes of a cell to cancerous tumor and cellular overgrowth to mclude descπptions of breakout or micro metastatic shed rate, new blood vessel formation; blood, tissue and lymph vessel mvasion, survival m the bloodstream, tissue transport and new colony formation at secondary or tertiary sites It is envisioned that information useful for motion, shape, path and particle behavior of cancer m the body will be generated Where possible, the algoπthm will use statistical analysis to determine the confidence of its predictions. •Micro metastatic and metastases volume Usmg information from the GENETIC database and from other databases withm the mvention an algoπthm will be constructed that will make use of medical and personal mput about an individual patient's present condition to determme the micro metastatic and metastases volume of distant metastatic human cancer projected m a future time frame The algonthms will use lmear, curvilinear, geometnc, algebraic functions and other mathematical means to achieve this endpomt The algonthm (s) withm this numeπcal solution module will extend and improve upon the mathematical expressions of the HCVS engme modules that wdl simulate genetic changes of a cell to cancerous tumor and cellular overgrowth to mclude descπptions of breakout or micro metastatic shed rate, new blood vessel formation; blood, tissue and lymph vessel mvasion, survival m the bloodstream, tissue transport and new colony formation at secondary or tertiary sites. It is envisioned that information useful for motion, shape, path and particle behavior of cancer m the body will be generated. Where possible, the algoπthm will use statistical analysis to determine the confidence of its predictions. •Projected cancer cellular mitosis phase table. Usmg information from the genetic database and from other databases withm the mvention an algoπthm will be constructed that will make use of medical and personal mput about an individual patient's present condition to determme the projected cancer cellular mitosis phase table of distant metastatic human cancer projected m a future time frame. The algoπthms wdl use lmear, curvilinear, geometnc, algebraic functions and other mathematical means to achieve this endpomt. The algoπthm (s) withm this numeπcal solution module will extend and improve upon the mathematical expressions of the HCVS engme modules that will simulate genetic changes of a cell to cancerous tumor and cellular overgrowth to mclude descnptions of breakout or micro metastatic shed rate, new blood vessel formation; blood, tissue and lymph vessel mvasion, survival m the bloodstream, tissue transport and new colony formation at secondary or tertiary sites. It is envisioned that information useful for motion, shape, path and particle behavior of cancer m the body will be generated Where possible, the algonthm will use statistical analysis to determme the confidence of its predictions. • Projected blood biomarker concentration over time. Usmg information from the genetic database and from other databases withm the mvention, an algoπthm wdl be constructed that will make use of medical and personal mput about an individual patient's present condition to determme the projected blood biomarker concentration, over time, of distant metastatic human cancer projected m a future time frame. The algonthms will use lmear, curvilinear, geometπc, algebraic functions and other mathematical means to achieve this endpomt The algonthm (s) withm this numencal solution module will extend and improve upon the mathematical expressions of the HCVS engme modules that will simulate genetic changes of a cell to cancerous tumor and cellular overgrowth to mclude descnptions of breakout or micro metastatic shed rate, new blood vessel formation; blood, tissue and lymph vessel mvasion, survival m the bloodstream, tissue transport and new colony formation at secondary or tertiary sites. It is expected that this program will generate mformation about chemical behavior of cancer cells, mcludmg biomarkers, but not limited to them. Chemical metabolic factors associated with cancer may be mcluded. It is envisioned that numeπcal solution program will be mathematically linked to predictions generate by the micro metastatic and metastases volume, projected cancer cellular mitosis phase table and the metastatic occuπence, target organ and percentage numeπcal module subroutines, or then predictions, withm this module. This numeπcal solution module will probably mclude mathematical expressions to determme uptake by vanous organs m the body and other mathematical modelmg expressions to provide a close to reality prediction of the concentration of chemicals of mterest m the bloodstream Where possible, the algonthm will use statistical analysis to determine the confidence of its predictions. Cancer Ongin/Run Forward Numencal Solution Module Output Subroutines 'Metastatic occuπence, target organ and percentage possibdity Usmg information from the genetic database, the molecular database and from other databases withm the mvention, an algonthm will be constructed that will make use of medical and personal mput about an mdividual patient's present condition to determme the probable organ sites of distant metastatic human cancer projected m a future time frame The algonthm will reverse the process to the ongm of cancer and then move forward through the present to future time frames. The algonthms will use lmear, curvilinear, geometnc, algebraic functions and other mathematical means to achieve this endpomt The algoπthm (s) withm this numeπcal solution module will extend and improve upon the mathematical expressions of the Cancer Ongm and HCVS engme modules that will simulate genetic changes of a cell to cancerous tumor and cellular overgrowth to mclude descnptions of breakout or micro metastatic shed rate, new blood vessel formation; blood, tissue and lymph vessel mvasion, survival m the bloodstream, tissue transport and new colony formation at secondary or tertiary sites It is envisioned that information useful for motion, shape, path and particle behavior of cancer m the body will be generated. Where possible, the algoπthm will use statistical analysis to determine the confidence of its predictions •Percentage disease free survival Usmg information from the cancer ongm database, the molecular database and from other databases withm the mvention, an algonthm will be constructed that will make use of medical and personal mput about an mdividual patient's present condition to determme the percentage of disease free survival from distant metastatic human cancer projected in a future time frame. The algonthm will reverse the process to the ongm of cancer and then move forward through the present to future time frames. The algoπthms wdl use lmear, curvilinear, geometπc, algebraic functions and other mathematical means to achieve this endpomt The algoπthm (s) withm this numeπcal solution module will extend and improve upon the mathematical expressions of the cellular module to the tumor module that will simulate genetic changes of a cell to cancerous tumor and cellular overgrowth to mclude descπptions of breakout or micro metastatic shed rate, new blood vessel formation; blood, tissue and lymph vessel mvasion, survival m the bloodstream, tissue transport and new colony formation at secondary or tertiary sites. It is envisioned that information useful for motion, shape, path and particle behavior of cancer m the body wdl be generated. Where possible, the algoπthm will use statistical analysis to determine the confidence of its predictions. •Micro metastatic and metastases volume Usmg mformation from the cancer ongm, the genetic database and from other databases withm the mvention, an algoπthm will be constructed that will make use of medical and personal mput about an mdividual patient's present condition to determme the micro metastatic and metastases volume of distant metastatic human cancer projected m a future time frame The algonthm will reverse the process to the ongm of cancer and then move forward through the present to future time frames. The algoπthms will use lmear, curvilinear, geometπc, algebraic functions and other mathematical means to achieve this endpomt. The algoπthm (s) withm this numeπcal solution module will extend and improve upon the mathematical expressions of the Cancer Ongm and HCVS engme modules that will simulate genetic changes of a cell to cancerous tumor and cellular overgrowth to mclude descπptions of breakout or micro metastatic shed rate, new blood vessel formation; blood, tissue and lymph vessel mvasion, survival m the bloodstream, tissue transport and new colony formation at secondary or tertiary sites. It is envisioned that information useful for motion, shape, path and particle behavior of cancer m the body will be generated. Where possible, the algonthm will use statistical analysis to determine the confidence of its predictions. •Projected cancer cellular mitosis phase table Usmg information from the cancer ongm, the statistical and metastatic database and from other databases withm the mvention, an algonthm will be constructed that will make use of medical and personal mput about an mdividual patient's present condition to determme the probable organ sites of distant metastatic human cancer projected m a future time frame. The algonthm will reverse the process to the ongm of cancer and then move forward through the present to future time frames. The algonthms will use lmear, curvilinear, geometnc, algebraic functions and other mathematical means to achieve this endpomt The algonthm (s) withm this numencal solution module will extend and improve upon the mathematical expressions of the Cancer Ongm and HCVS engme modules that will simulate genetic changes of a cell to cancerous tumor and cellular overgrowth to mclude descπptions of breakout or micro metastatic shed rate, new blood vessel formation; blood, tissue and lymph vessel mvasion, survival m the bloodstream, tissue transport and new colony formation at secondary or tertiary sites. It is envisioned that mformation useful for motion, shape, path and particle behavior of cancer m the body will be generated. Where possible, the algoπthm will use statistical analysis to determine the confidence of its predictions •Projected blood biomarker concentration over time. Usmg information from the cancer ongm, the statistical and metastatic database and from other databases withm the mvention, an algoπthm will be constructed that will make use of medical and personal mput about an mdividual patient's present condition to determme the projected blood biomarker concentration over time of distant metastatic human cancer projected m a future time frame. The algoπthm wdl reverse the process to the ongm of cancer and then move forward through the present to future time frames. The algonthms will use lmear, curvdmear, geometπc, algebraic functions and other mathematical means to achieve this endpomt. The algoπthm (s) withm this numencal solution module will extend and improve upon the mathematical expressions of the HCVS engme modules that will simulate genetic changes of a cell to cancerous tumor and cellular overgrowth to mclude descnptions of breakout or micro metastatic shed rate, new blood vessel formation; blood, tissue and lymph vessel mvasion, survival m the bloodstream, tissue transport and new colony formation at secondary or tertiary sites It is expected that this program will generate mformation about chemical behavior of cancer cells, mcludmg biomarkers but not limited to them. Chemical metabolic factors associated with cancer may be mcluded. It is envisioned that numeπcal solution program will be mathematically linked to predictions generate by the micro metastatic and metastases volume, projected cancer cellular mitosis phase table and the metastatic occuπence, target organ and percentage numeπcal module subroutines, or their predictions, withm this module. This numeπcal solution module will probably mclude mathematical expressions to determine uptake by vanous organs m the body and other mathematical modelmg expressions to provide a close to reality prediction of the concentration of chemicals of mterest m the bloodstream. Where possible the algoπthm will use statistical analysis to determine the confidence of its predictions. Metastatic Interface In operation, the Metastatic Module and its subprograms follow the system descnbed m this mvention of engagmg a user mterface usmg a standard computer monitor and keyboard The Metastatic Module then activates the central processmg unit through the software programs with the connector module connecting to databases of information needed for the numencal and descnptive processmg. The Metastatic Module then engages the numencal solution module and subroutines and finally provides the user with reported solutions to their requests through the momtor and vanous displays. All of the operations take place m the basic software descnbed m figure 3-10. Similar to other modules descnbed m this mvention, the first phase of the Metastatic Module mvolves a fact gathering process for mformation mput and the selection of programs and patient information to be entered to enable operation of the subprograms selected through a keyboard. Assuming the software is loaded and operational through conventional means, the user will be prompted to select modules of operation When the Metastatic Module is selected, a menu with instructions is provided for all of the subroutines. Patient mformation is entered through the keyboard and when completed a prompt is given indicating mformation entry is finished and to run the engme. At this pomt the software engme(s) selected engages the central processmg unit and begins operations Metastatic Interface Communication to Connector Module The user mterface activates the connection module program and uses mput received from the user to make decisions and gather information from vanous databases to execute programs selected by the user m the numencal solution module Reports and mformation from other modules m the mvention, tumor ongm, cellular module, colony module, tissue module, tumor module will be retπeved from then mterfaces if needed automatically as part of the program It is envisioned that information and report links will exist between the molecular, cellular, cellular expansion, pre-neoplastic , neoplastic mterfaces and the numeπcal solution module subroutines m the metastatic module to facilitate exchange from other parts of the mvention so calculations and descnptions can be performed Modification and Customization of Variables in the Metastatic Module Subroutines All of the Metastatic Module's subroutines may be stopped and mput parameters modified at points along the time lme of operation accordmg to the instructions at the user mterface, and then allowed to continue provide human cancer simulation reports with a record of when and what modifications were made to mput assumptions Default Parameters/Pre-Programmed Inputs in the Metastatic Module Subroutines As with other modules descnbed m the mvention, the metastatic module subroutines and the algonthms withm them will have pre-programmed default parameters m the event that mformation supphed by the user is mcomplete This default parameter will allow the subroutines to run and/or instruct the user saymg that the program subroutine cannot operate Connection Module Communication to Numerical Solution Module of the Metastatic Module The connection module retπeves mformation from the databases, other module output reports as necessary and synthesizes it with the patient mput from the user so subroutines withm the numencal solution module can begm generating mformation relating to the future course of human cancer withm the human body Connection Module/Database Operation in the Metastatic Module Subroutines The connection module will be engaged whenever the mput from the user is completed and the subroutines are designated to start The connection module will process the mput from the user and gather information from the vanous databases necessary for the execution of the mathematical and descnptive algonthms withm the vanous numencal solution modules and transmit it to its proper location If the program is stopped at the user mterface along any aspect of the time lme, and mput is modified, the connection module will agam automatically be engaged and then the new mformation will be gathered and transmitted to the numeπcal solution module for sequential processmg If the program is paused, the connector module will not be engaged Numerical Solution Module(s) in the Metastatic Module Subroutines The numeπcal solutions module(s) carnes out the generation of results and mformation for the reports Once started they wdl complete their calculations, generate results and their reports and return them to the user interface for viewing on the momtor or for printing as per the computer hardware description. If continual output is chosen and the user stops the program the numerical solutions module (s) will store results generated up to that point in time or report if requested. If the program is restarted it will begin where it left off, include the changed parameters in the next sequential report, continue calculations with modified parameters and produce the report. If paused the numerical module will resume its calculations when the pause is ended and generate its results and report. Results and Reports/User Interface in the Metastatic Module Output Subroutines The final result of Metastatic Module and its subroutines will be a series of informational reports from the numerical solution modules and the information gathered from the databases to assist in running the mathematical and descriptive algorithms, continuously reported or sequentially reported, summarizing results or providing information at continuous or pre-selected discrete time intervals by request of the user along the time line of the human cancer virtual simulation engine's operation domain through the user interface. Similarly the reports can be requested by the user at various points in the future, along the time line of the operation of the module so that information generated by these subroutines can be reported. If the Metastatic Modules subroutines are stopped and restarted the report wdl contain the sequence of information generated by the numerical solution modules in the order of the instructions received and containing the modifications for ease of understanding by the user. Specific Operation Example of the Metastatic Module and its Subroutines As an example of use, imagine a physician who wishes to examine the possible metastatic behavior of a patient's breast cancer through the human cancer virtual simulation information engine. Information wdl be generated by the engine to help answer two questions, is there an optimal time to administer the adjuvant therapy? What kind of reoccurrence scenarios on the microscopic (micro metastatic) and macroscopic (distant metastases) level may occur in the future? 1. After the program is started the user would be prompted by the start-up screen from a menu at the user interface to select the module to be used. The Metastatic Module is selected. 2. Next the three selections of metastatic module subroutmes would appear, statistical and clinical outcome extrapolation, molecular biological and cancer origin run forward. For this example the user would select all three. 3. Next the user interface would ask how many models were desired to be run by the engine in each subroutine. The physician is interested in looking at the potential outcomes for a prediction of an excellent to a moderate response to a single course of treatment so she selects two models for each. 4. Next the user interface would ask which model is to be run first and if continuous reporting was desired. If continual reporting was desired, the interface would ask which subroutine was to be run first. The user interface will allow one subroutine to operate continuously on display with a selection of desired parameters of information continually reporting the selected information. The user could select all the information that the subroutine is capable of producing or be limited to the selections the user makes. The subroutine would allow for this. For simplicity we will say that the user wishes to run the clinical and statistical outcome, molecular biological and cancer ongin/run forward in that order and two models for each to allow for vaπation m the treatment regimens and assumptions for the models We will assume that the user m this example requests that final reports be produced, not continuaUy displayed, although at the end of this example we will demonstrate the continual display option as a replay of one of the stored programs 5. At this pomt, the user mterface will display a menu of inputs needed for the statistical and clinical outcome extrapolation. The user will be requesting information about the patient's name, height, weight, age, treatment steps such as chemotherapy, surgery and radiation, when treatments are planned, when treatment's took place, patient response to treatment or estimates thereof, initial tumor size and volume, tumor geometπc shape, tumor location, health conditions of relevance to patient such as HIV status, immune system strength through secondary biomarkers such as white blood cell and T-cell count, family history of relevance to cancer epidemiology, other complicating factors or diseases, local and distant metastases location, biomarkers of human cancer such as estrogen receptivity m breast cancer and others, mitosis rate, cell diploidy, etc Additionally it is envisioned that some of the mput information may change and improve as the engme is developed, or may be tadored for certain kinds of cancer to provide better predictive and prognostic information to the user For instance, for the breast cancer patient example, m addition to the mformation above a prompt may appear to request staging of patient 1-4, specific questions related to the staging such as numbers of lymph nodes mvolved if radical or modified radical mastectomy has occuπed, the presence of the BRCA1 gene, p53 tumor suppressor gene activity in the tumor, HER 2 new expression, breast micro calcifications from X-ray, date of first estrus, menstruation cycle and other factors that could be useful for subroutine algonthms. The user enters this mformation and if unavailable a prompt for default will be provided with any additional instructions The user is then prompted to go to the next step, the selection and customization of the report. For this example we will say that the patient is a pre-menopausal 40 year old African Ameπcan woman with a discovered 2.5 cm pear shaped mvasive ductal carcinoma tumor with micro calcifications m her πght breast. The tumor is removed and biopsied. The tumor was showed mild vasculaπty and the margins around the biopsy sample were not clean indicating spread beyond the tumor. The pathology report mdicated HER 2 new expression, p53 loss, estrogen receptivity, a mitosis rate of 1 due to a low S phase fraction, tumor cell DNA intact (high diploidy) and good cellular differentiation, by some factors a slow to moderate growing tumor but mvasive. Due to micro calcifications and of the other tumor factors that mdicate the tumor may be active, breast conservation surgery is abandoned and the breast is removed and lymph nodes sampled, no lymph node activity is seen and the patient is classified as a stage 2 patient. The physician has entered all the above mformation mto prompted questions from the program. She is considermg adjuvant chemotherapy and the type and time to admimster it. The adjuvant therapy she is considermg will be the standard Cytoxan, Methotrexate and 5-Fluorouracιl (CMF) The woman is healthy m all other respects. The physician will enter two scenaπos mto the metastatic program, one m which the patient response to chemotherapy will be considered supenor, the other where it is moderate, a reflection of the aggressive genetic tumor factors versus its srmilanty to normal cells by good cell differentiation and only a low mitosis grade, making it more difficult to eradicate with chemotherapy As stated earlier, information wdl be generated by the engme to help answer two questions, is there an optimal time to admimster the adjuvant therapy 9 What kind of reoccurrence scenanos on the microscopic (micro metastatic) and macroscopic (distant metastases) level may occur m the future 9 The Metastatic Module will assist answering these two questions. 6. The physician now sees a menu of other report selections from the metastatic clinical and statistical outcome subroutine on the screen before her. The possible reports available to her mclude a) metastatic occuπence, target organ and percentage possibility, b) percentage disease free survival, c) micro metastatic and metastases volume, d) projected cancer cellular mitosis phase table, e) projected blood biomarker concentration over time. By keyboard selection the physician chooses all five. 7. After the physician enters the basic patient mformation and selects the reports to be issued, she will see a menu screen with a vanety of options to provide the mformation back m the form of reports The first selection is the final time domam of the engme's simulation. This is the pomt m the future from the present that the subroutine programs selected should end their calculations and generate results. This could range from short penods of time of days to approximately 20 years. The upper limit may change and wiU be bounded by the scientific information avadable to provide mformation useful to the subroutine calculation. The lower range will be determined by the information the engme could produce that would be of value beyond observation. Each simulation would be bounded by fame but for example, the user could chose five metastatic simulation each with longer and longer time frames, or stop the simulation at vanous points and request a report or begm new simulations with a desired new time frame sequentially on the previous one. For this example we will say the physician user requests a 5 year projection for the two simulations and display the mformation for the first six months by day for subroutine d) the projected cellular mitosis phase table. This enables information to be provided about follow up m reasonable time increments to be estimated from the engme's programs, given the young woman's age, medical aspects of the case and the fact that follow-up adjuvant chemotherapy is bemg considered. 8. Now the mterface prompts the physician to begm and she hits start The mvention does the rest of the work until the reports appear at the user mterface. That sequence of events is descnbed next 9 The computer now engages the connection module, a computer software program which takes the information supplied by the physician about the patient and extracts mformation from the epidemiological database and other databases as needed and automatically transfers retneved information to the five numeπcal sub-module selected. 10. The numeπcal sub-modules automaticaUy conduct their calculations and descnptions usmg their algoπthms and other aspects of theu programs, produce their reports and send them to the user mterface. 11. After the engme has completed its work as descnbed m steps 9 and 10, after starting the program in step 8, what the physician would receive, m this example first, is five reports for two simulations from the clinical and statistical outcome subroutines. Each is descnbed here m example of what the inventions subroutines could produce with a visualization of what could appear m the content and its possible decision making value: Metastatic occuπence, target organ and percentage possibility This report would contam a table of the target organs of reoccuπence, as an estimate this would be the left breast, chest wall, lungs, skeletal system and bram among others, with a percentage range m the 5 year time frame selected that reoccuπence would appear m vanous organs for the two scenaπos selected. The optimal response would likely contam a lower percentage but given other factors in this case and the evaluation of information from the epidemiological database the percentage differences may be great or small and provide useful information for follow up. The report would be structured in a table of predictions at 6-month intervals in this example. Percentage disease free survival. The report would provide an estimate of no reoccuπence for the optimal and moderate breast cancer patient response scenarios. It is envisioned that confidence intervals could be applied to this value based on the information in the database to allow better statistical value. Mortality and morbidity statistics would be reported in 6-month intervals over the 5-year time frame, in this example the optimal and moderate response scenarios could be compared and contrasted. Again the examination of the database information may show large or small differences, and possible improvement in the lessening of reoccurrence in certain organs over time. This could be reported positively to the patient and helps with decision making about follow-up activity. Micro metastatic and metastases volume. This report would estimate the size, shape and potential volume of a recuπent tumor in the various target organs in six month increments over five years in a tabular format. The report would also indicate an estimate of the total micro metastatic volume, or an estimate of the total cancer localized in tumors and non-localized within the whole body and in organs at given times in a tabular format. The value of this report would be an indication of when cancer mass may be large enough to be detected by various imaging techniques or other means in distant organs. In this example the physician would have an indication, say in the left breast, when micro metastasis would reach a point where micro calcifications may appear in the breast before small tumors in a mammogram appear in a moderately responsive patient. This could assist the physician in optimal timing patient follow-up for maximum probability of detection of any cancer spreading. If follow up mammograms are performed and micro calcification are not seen at the times predicted, this could be used as an indication of more optimal response to therapy, assisting the physician by focusing parameters for the subroutine assumptions in this invention if future simulations are conducted in the future. I. Projected cancer cellular mitosis phase table. This report would contain an estimate of populations of cancer cellular growth, cancer cell death, and micro metastatic growth in terms of numbers of cells in various phases of cell division in the whole body and various target organs. What the physician would see, in this example since a daily report over the next 6 month time frame was selected would be a daily table indicating the number of potential cancer cells and what phase of growth or division they were in for the two optimal and moderate response to chemotherapy. In this example chemotherapy has not been administered yet, but the physician is estimating potential responses. Also we know that the cell grade, mitosis rate and general DNA structure are fairly intact. Therefore, for example, we would expect to see a table representing shallowly rising curves indicative of a slow doubling rate. Given the more regular, rather than eπatic cell reproduction rate, an estimation may appear in the daily tables indicative of high points when the remaining micro metastatic cancer left after surgery would be in certain phases of reproduction or cell cycles. The physician could use this information in the near term to plan to admimster chemotherapeutic drugs so that the maximum concentration of the standard CMF regimen would be available in the target organs or whole body to interfere or destroy the cancer the best. In short this information may help physicians estimate how to get the medicine where and when it is needed most. She may even plan smaller dosages of chemotherapeutic agents m the regimen at later times and specifically to target organs to coπespond to estimated cancer cellular peaks to destroy potential remaining cells, not caught on the first go around with a particular agent or to improve the effectiveness of response m a sub-optimally respondmg patient. This could be especially useful m the case of breast cancer patients m the use of Adnamycm, which has cardiotoxic side effects at high dosages but is one of the most effective anti-cancer agents. Adnamycm and possibly other chemotherapy agents could be timed and effectively used at large, as well as smaller dosages, for maximum positive effect while diminishing negative side effects. Projected blood biomarker concentration over time. The report would estimate the concentration m nanograms per milhliter of a vanety of cancer biomarkers m the bloodstream at six-month interval over the 5-year time peπod selected m this example This area is new and npe for discovery and will be a more valuable piece of report mformation m the future than today. For this example possibly the presence of HCG (human choπomc gonadotrophm) would be listed smce it has been implicated with many cancers, CEA (carcinoembryionic antigen) has been shown to πse m severely metastatic breast cancer patients but current test methods may not be sensitive or the CEA may not expressed sufficiently to be useful for a stage 2 breast cancer patient at this time Others may provide useful indications of microscopic molecular activity indicative of micro metastasis What the physician would see m the reports would be two estimates based on an optimal and moderate response of concentrations of chemicals that could be present m the bloodstream pertinent to the cancer selected and a report of other metabolic factors deemed useful for predictive and prognostic applications. At the conclusion of readmg the reports, the physician decides she would like to view the projected cancer cellular mitosis phase table m a continual fashion She would only need to request that of the user mterface when the program is brought back to start m step 4 The process for the physician m this example steps 1 through 11 and the process of the operation of the mvention m carrying out its functions is the same for the other two subroutine modules, the molecular biological and the cancer ongin/run forward with the same title reports issued back to the user mterface Because the underlying assumptions and the mathematical and descnptive calculation are different m these numencal modules, as descnbed earlier m the numencal module section for the metastatic module in total, the reports may reach similar or very different conclusions. For example, it is fully expected that because DNA and molecular mathematical and descnptive algonthms are used to generate the information m the molecular biology and the cancer run forward metastatic subroutines that predictions m the cellular mitosis, micro metastatic volume and biomarker reports will be more precise and capable of generating meaningful predictions down to smgle cellular cycles and will vastly improve over time. Initially the statistical and clinical outcome extrapolation will provide the most useful predictions for disease free survival and distant metastases, target organ and probability predictions because it is based on real world information and subject to less uncertainty. All three approaches provide useful information for companson against real world patient behavior, decision making and for research and educational purposes. For ease of usage, the mput to run all three metastatic sub-modules and their five numeπcal solution subroutines is envisioned to be the same. So the user need only enter the mformation one time, or can modify parameters and mput selectively and generate 15 reports for human cancer metastatic behavior, or 5 reports each to provide predictive and prognostic mformation from 3 modelmg approaches. The Metastatic Module and all its subroutines and the mathematical and descπptive algonthms mclude a plurality of components to provide information that simulates the functions of living systems, m this case the behavior of cancer m the human body The preparation of these modules is not a static occurrence and the numeπcal solution modules and the invention's databases, will be subject to modification and improvement with advancing scientific knowledge. Information supplied by these modules can be used to dπve animations or other types of visual display beyond tables and reports to provide informative visualization and expression of the predictive and prognostic mformation that is deπved from them DATA FLOW TO MODULES Fig. 11 is a block diagram dlustrating the types of data and information sent to each of the modules from a user mterface (GUI) and from a patient mformation database For each patient, the user mputs mto the tumor ongm module data and results of genetic tests, family history information, and information pertaining to life style (smoking, drug use, etc.). The tumor ongm module also receives from the database mformation on genetic relationships and possible mutations, mcludmg data on protem reactions relating to the synthesis of genetic mateπal, and genetic information related to the mteraction between cells m a normal envuonment, such as cell adhesion, intracellular structure, etc. The tumor ongm module further receives statistical data from the statistical database regarding which genetic markers should exist and which genes, if any, are mutated. The cellular module receives from the databases data pertaining to cell life cycle control, such as which genes are responsible for cell cycle control, for example Cyclm Dependent Kinesis (CDK), statistical data on cell cycle control and physical properties of cells, and data pertaining to the compounds and associated concentrations required for proper cell function. The colony module receives from the databases data related to the mteraction between cells, such as the identification of genes responsible for cell mteraction (e.g for production of proteins used m cell adhesion, etc.), the identification of genes responsible for physical properties of ceUs (e.g. cell shape and size), and statistical data concemmg cell mteraction (e.g. cell bond strength, nutntion distnbution, etc ), and genetic information responsible for cell structure (e.g. cell cycle, bond strength, membrane strength) The tissue module receives from the databases data related to the protems and other biochemical compounds and elements mvolved m the mteraction between cells and m tissue structure, both m a normal environment and m a malignant environment, and other genetic and protein/biochemical information not specific to metastatic spread. The tumor module receives from the databases data related to genetic, biochemical and statistical mformation concemmg cell cycle, bond strength, membrane strength, tissue structure, mteraction between cells, etc m a malignant environment. The metastatic module receives from the databases data relatmg to the mechamsms of metastatic spread, and statistical data relating to cellular activity, both specific to metastatic spread and generally. DATA FLOW BETWEEN MODULES Fig. 12 is a block diagram illustrating the types of data and information passed between the various modules and between the user interface and database. The subroutines of the tumor origin module develop from the inputted data, data relating to cell cycle rate, genetic changes in cellular DNA, and protein expression. This information is inputted to the cellular module for use in the cellular module subroutines. The tumor origin module also develops diagnosis data from the inputted patient information and relevant data from the databases, concerning possible genetic changes and expression of unusual proteins as indicating possible staging of disease. The cellular module subroutines utilize the information developed by the tumor origin module to calculate ceU cycle rates, genetic changes and protein expression on a cellular level, and to determine cell size, shape, growth rate, and protein expression related to cell structure. This information is passed to the colony module, where the subroutines of the colony module use this information in conjunction with the data received from the databases to calculate cell structure, size, shape, etc. of a tissue matrix, which information is inputted to the tissue module. The tissue module in turn utilizes this data to calculate the size, shape, structure, bond strength, etc. of tissue, which information is passed on to the tumor module. The tumor module in turn utilizes this information to predict tumor growth, shape and size, etc. into the future. The metastatic module takes the tumor-related information and utilizes it to predict metastatic spread of carcinogenic cells to other systems of the body into the future. DATABASE FORMATION, UPDATING, AND MODEL DEVELOPMENT Fig. 13 is a block diagram illustrating the building and updating of the various databases used in the HCVS system. As shown, data from various external sources, such as research and development institutions, medical and scientific journals, textbooks, university databases, public and private databases, public research institutions, research laboratories and insurance companies, is inputted to an eπor screening module for filtering of the data to eliminate eπoneous, iπelevant or incomplete data. The filtered data is then inputted to data type distribution module which separates the data and groups it by type, such as input data, relationship data, or output data, and formats the data into a matrix. The data matrix is then inputted into a modeling data spreadsheet module for preparation of data sets. The data sets are then inputted to a learning system module, for development and updating of the various models used in the subroutines. The models are stored in a model staging storage memory, from which they are inputted to the apphcation environment of the system, for use with the specific patient information to calculate diagnosis and predictive results which are then displayed to the user on a graphical user interface. CELL CYCLE ALGORITHMS Fig. 15 illustrates a general algorithm for determining particular protein expressions in cell life cycle process. At step 150, the maximum possible amount of protein capable of being produced by the ceU under study. At step 151, the percentage of this maximum possible amount being produced is calculated. At step 152, it is determined whether any other processes are involved upon which protein expression is dependent. If not (step 153), then the deteπnined protein concentration is outputted at step 154. If it is determined that protein production is dependent on a following process (step 155), at step 156 the calculation is paused to await the information from the following process, and at step 157 the needed information is imported from the following process algorithm, and the process advances to step 160. If it is determined (step 158) that the following process itself is dependent on the protein at issue, then at step 164 the calculated protein concentration is outputted. If it is deteπnined (step 159) that the protein at issue is dependent on a previous process, then at step 160 the dependence ratio or relationship is determined. The value of the dependence is then determined at step 161 and the operation needed to simulate the dependence is selected at step 162. At step 163 the percentage of protein production controlled by the dependence is calculated, and the deteπnined protein concentration is then outputted at step 164. Fig. 14 provides an example of a cell cycle algorithm for calculating cell life cycle from the Gl (start) cycle phase through to the M phase. In each phase, the production of various free proteins such as cyclin A, cyclin B, cyclin D, cyclin E, Cdk 1, Cdk 2, Cdk4, and RB are calculated, according to the algorithm of Fig. 15. The results of each calculation are then used to calculate the next protein/ protein complex in the cell cycle phase, leading to the production of growth factors. If complex failure is detected at any point during the calculation run, a cell cycle halt is triggered. The calculated growth factors are then transported as expressed growth factors to the next phase of the cell cycle. Specific Operation Example of the Tissue Level Module and the Metastatic Module In this example we will assume that a physician has a patient with a large colon tumor, 7 cm in diameter and the patient is 75 year old male in frail health. Given the health of the patient possibly radiation treatment may be a better option than surgery. The physician would like to use the tissue level and then the metastatic level simulation engine to examine the effects of radiation on the tumor and decide if this is a viable option for treatment in lieu of surgery. 1. First the program would be loaded as in the previous example and the physician would choose the tissue module and would see the pre-neoplastic interface, additionally the tumor module and the neoplastic interface and the metastatic module and the metastatic interface would be activated by the physician in this example. 2. The physician would then see the menu of options that would allow patient information to be entered. 3. The physician, similar to the response information in the previous breast cancer example, would have the option to estimate the response that radiation would have on the tumor and the suπounding tissues. For this example we will assume two scenarios, a simulation where no radiation treatment is given and a simulation where a high dose of radiation is given within a week of the simulation. In the operational version of the invention it is envisioned that a wide variety of parameters would be available to the physician based upon the latest indices of cancer radiation treatment to easily enable entry of the strength, type and dose of radiation and times of treatment to assist estimation of the number of tissue and tumor cells destroyed in the process. 4. The physician would then need to follow instructions and enter the types of reports he would like to see and the frequency of information from the simulation. For the tissue module example we will look at daily reports rate of mitosis, survival rates of cells and a dady readout of the projected cell mitosis phase report from the metastatic module for the next sixty days or two months. 5. The order of the reports that the physician requests are the no radiation treatment first, and then the high dose radiation treatment reports. 6. Once the patient tumor mformation is entered mto the tissue module mterface, the physician would hit the start button. 7. The engme now goes through the same process of taking the mput to the connection module and then retnevmg information from the inventions database necessary to run the algoπthms m the tissue and metastatic level numeπcal solution modules, when the tissue level and metastatic numencal solution modules have completed their work, they produce the reports and send them back to the user mterface 8. What the physician sees at the user mterface is six reports waiting to be reviewed Four related to the mcrease m tissue growth (rate of mitosis and survival rate of ceUs) from the tissue module m a no intervention versus a high intervention scenaπo. The last two would be an analysis of cell population, death and cell growth phases, with numbers of cells and times from the metastatic module's projected ceU phase mitosis report for a no intervention versus high dose radiation intervention scenaπo. The physician could, if desired, have activated other subroutines through the pre neo-plastic mterface accessmg the tissue module to generate reports such as physical properties of cells, nutntional consumption, cell bonding and intracellular structure. The physician could, if desired, have activated other subroutines through the neo-plastic mterface for the tumor module to generate reports such as tumor mass, tumor growth rate, genetic mutations present and vascular construction. In this example the metastatic reports requested mclude some of these areas but not all The physician now has some compansons of what the effectiveness of a high dose radiation treatment may be at the tissue level for a tumor of certain size and molecular biological properties charactenstic of the mdividual patient. Several pieces of mformation can be gleaned from reports of this kmd For the sake of example we will explore them. 1. First m the no treatment example, the tissue level reports of projected cell mitosis phases and metastases volume can be very useful m estimating the behavior of the tumor m the next two months for treatment options. Is it slow growing or fast? Based on patient mput what kmd of mitosis pattern is expected m the next sixty days 9 Is the mitosis pattern m the tissue of the tumor penodic or eπatic? The report we expect will have statistical information associated with the engmes predictions based partly on the quality and quantity of the mformation mputted and also on the information available from the database to assist m its predictions and will help answer these questions at the tissue level. How does this compare with predictions m the metastatic module from its report 9 2. The physician will have a readout of exactly one week from the present day on a kill off of the cancer cells m the tumor and then a projection of growth for the next 53 days after that. How much would the tissue metastases shrink? 3. Does the projected cell mitosis report mdicate a pattern that would help m the optimal timmg of a high dose radiation treatment? Radiation is most effective m disrupting and destroying mammalian cells when apphed during the mitotic phase during actual cell division. The mitosis phase can be very brief and the shortest phase in the cell hfe cycle. Possibly the physician would see from the HCVS reports a period of statistically high mitotic activity within the tumor in 2 weeks or sixty day period and would delay treatment until that period. The invention could and most likely would be rerun to estimate the effects of radiation treatment on a colon cancer under these potentially better conditions to see what the effect would be. 4. Following the pattern of inquiry further, could lower doses at different times in the next sixty days be as effective as one high dose? Would there be a patient benefit? Again comparing the result of the two scenarios and the reports associated with each may indicate directly that this could be the case or point to the need for further simulations with the invention to fully explore radiation treatment effect on colon cancer tumor tissue in the near term. In the beginning of this example we mentioned the metastatic module in conjunction with the tissue module. As Fig 3 shows the linear progression of interfaces and modules, the next logical step to take would be to do a more distant metastatic projection on the patient. The physician would have two new pieces of information to achieve this, a scenario in the next sixty days that could be plugged into the metastatic module indicating no treatment and projections could be made from the present line, second information on tumor shrinkage based on a high dose radiation treatment. The process would be the same as in the previous example for the metastatic module and once the metastatic module would be activated the interface would ask various question of value in making a longer-term prediction. At this point, the morbidity and mortality statistics from the clinical outcome report would be of interest In considering this example, it is worth thinking that a logical process of usage of the information from this simulation would be for the physician to: 1. Examine the options and efficacy for radiation treatment using the tissue module simulation 2. Decide the best treatment at the tissue level and admimster it 3. Compare the tissue level modules results against the actual patient response 4. Enter the patient actual response information into the metastatic module at a later time and validate or change assumption parameter for the metastatic module interface based on real world results to improve predictions and maximize the possibilities for a favorable outcome. System Configuration There are two general configurations for the HCVS system according to one prefeπed embodiment of the invention. The first of the two is medical. This is by far the most complex and interactive. The purpose and usefulness of the diagnostic, treatment, and research configuration is to deal with real life patients. This configuration will use the information entered into the HCVS to artificially generate in the computer a rephcation of the actual situation at hand. This configuration will run with preprogrammed cases of cancer. The purpose and usefulness of this configuration is to train and prepare present and future healthcare professionals. The second of the configurations is the educational configuration. While particular specifics of the present invention have been disclosed, it is to be understood that various different modifications are possible and are contemplated within the true spirit and scope of the claims. All such modifications and variations of the invention herein described as would be apparent to those skilled in the art are intended to be encompassed within the following claims.

Claims: What is claimed is: 1 A computer-implemented system for simulating the occuπence and metastases of cancer m the human body, compπsmg: a database containing information relating to genetics, molecular biology, statistics, and metastatics as apphed to occuπences and metastases of human cancer; an operator mterface for inputting mto said system information and instructions coπesponding to patient data; a plurality of program modules, each mcludmg at least one subroutine, for processmg information and data mputted through said operator mterface m conjunction with information obtamed from said database, and outputting said information to said operator mterface, wherem each of said program modules carnes out descπptive and mathematical processes coπespondmg to different levels of human cancer biological processes, and information generated by modules performing lower level processes also is outputted to modules performing higher level processes, whereby predictive future cancer metastases as well as past ongm of cancers are provided; and an output device for communicating results of subroutine processmg to a user. 2. The system of claim 1, wherem said system mcludes a medical applications configuration which allows diagnostic, treatment and research human cancer simulations to be performed by accepting user mputted mformation, and an educational configuration usmg pre-programmed situations which allows mteraction for medical student educational purposes 3. The system of claim 1, wherem said plurality of program modules compπses six modules for simulating the biological process of a cell's transformation from a normal ceU to a cancerous cell and then metastatic activity, the modules compnsmg tumor ongm, cellular, colony, tissue, tumor and metastatic modules 4. The system of claim 3, further compnsmg withm each module subroutines that have responsibihty over smaUer descπptive and mathematical processes needed to simulate human cancer biology, each of the subroutines producmg results in forms needed by the user to descnbe the biological process over which the subroutine has responsibihty, said subroutines mcludmg: a genetic mutation subroutine and diagnostic subroutine of the tumor ongm module, a cell cycle subroutine and a physical properties subroutine of the cellular module, an mteraction between cells subroutine and a structure subroutine of the colony module, an mteraction between cells subroutine and a tissue structure subroutine of the tissue module, an mteraction between cells subroutine, a tissue structure subroutine, and a physical properties of the tumor subroutine of the tumor module, and a statistical and clinical outcome subroutine, a molecular biological subroutine, and a cancer origin/run forward subroutine of the metastatic module. 5. A computer-implemented method of simulating the occuπence and metastases of cancer in the human body, comprising the steps of: coUecting and providing information relating to genetics, molecular biology, statistics, and metastatics as applied to occuπences and metastases of human cancer; providing information and instructions coπesponding to patient data; processing information and data related to a patient in conjunction with said information relating to occuπences and metastases of human cancer and outputting said processed information, wherein said processing comprises the steps of carrying out descriptive and mathematical processes coπesponding to different levels of human cancer biological processes, with information generated by performing lower level processes being outputted to higher level processes, whereby predictive future cancer metastases as well as past origin of cancers are provided; and communicating the results of processing to a user.

Inventors: MEAGHER JOHN F, THOMAS AUSTIN W, THOMAS JOEL, THOMAS RICHARD D, THOMAS STERLING, MEAGHER, JOHN, F., THOMAS, AUSTIN, W., THOMAS, JOEL, THOMAS, RICHARD, D., THOMAS, STERLING
Applicants: INTERCET LTD, MEAGHER JOHN F, THOMAS AUSTIN W, THOMAS JOEL, THOMAS RICHARD D, THOMAS STERLING, INTERCET, LTD., MEAGHER, JOHN, F., THOMAS, AUSTIN, W., THOMAS, JOEL, THOMAS, RICHARD, D., THOMAS, STERLING
Application year: 2000
Main classification: A61B5
IPC codes: A61B10, G06F19, G06Q50