Methods of determining a therapy for inhibiting growth or pathogenesis of one or more cell colonies based on modeling intracellular and/or intercellular communication mechanisms utilized by the cell type(s) in question, selective pressures on the one or more cell colonies within a cell population, and therapies or therapeutic agents available to a user. A dynamic molecular-level model of a cell population representing one or more cell colonies models differing aspects of one or more resistance forming mechanisms and the effects that the available treatment agents or therapies may have on the differing aspects. The dynamic molecular-model, in combination with a selective pressure model, formulates an optimization problem that is solved to determine amounts of the differing treatment agents or therapies.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of generating a therapy recommendation for inhibiting pathogenesis or growth of one or more cell colonies, wherein the therapy includes a plurality of differing therapeutic agents, the method performed by a computing system and comprising: receiving an indication of one or more types of cells in the one or more cell colonies; solving an optimization problem to generate at least a portion of a therapy recommendation specifying relative amounts of the plurality of differing therapeutic agents to administer in combination with one another, wherein the optimization problem includes: accounting for a dynamic molecular-level model that models 1) a cell population representing the one or more cell colonies, 2) differing components of one or more resistance-forming mechanisms of the one or more types of cells, and 3) effects that the differing therapeutic agents have on the differing components; and accounting for a selective-pressure model configured to assess probabilities of inducing selective pressure on the cell population; and providing or applying the therapy recommendation to a user, wherein the therapy recommendation is based on the relative amounts of the plurality of differing therapeutic agents. 2. A method according to claim 1, wherein said accounting for the dynamic molecular-level model includes accounting for a model of a quorum sensing system for each of the one or more cell colonies. 3. A method according to claim 2, wherein said accounting for a model of a quorum sensing system for each of the one or more cell colonies includes accounting for a model of quorum sensing for a bacterial colony. 4. A method according to claim 3, wherein said accounting for the model of quorum sensing for the bacterial colony includes accounting for two types of bacterial strains belonging to the same species. 5. A method according to claim 4, wherein said accounting for the two types of bacterial strains belonging to the same species includes accounting for wild-types and signal-blind mutants. 6. A method according to claim 1, wherein said accounting for the dynamic molecular-level model includes deriving a single substrate growth model. 7. A method according to claim 6, wherein said deriving a single substrate growth model includes accounting for when the cells of the cell population are metabolically active but not growing or dividing. 8. A method according to claim 1, wherein said accounting for the dynamic molecular-level model includes accounting for production of an extracellular polymeric substance (EPS) produced by the one or more cell colonies. 9. A method according to claim 8, wherein said accounting for production of the EPS produced by the one or more cell colonies includes accounting for production of an EPS produced by one or more cancer cell colonies. 10. A method according to claim 1, wherein said accounting for the dynamic molecular-level model includes modeling communication between cells of at least one of the cell colonies of the one or more cell colonies. 11. A method according to claim 10, wherein said modeling communication (i.e., network) between cells of the at least one cell colony includes modeling signal molecules produced by cells within the at least one cell colony. 12. A method according to claim 11, wherein said modeling signal molecules produced by cells within the at least one cell colony includes accounting for properties of the signal molecules and for a extracellular physical diffusion limit. 13. A method according to claim 11, wherein said modeling signal molecules produced by cells within the at least one cell colony includes modeling signal molecules that do not have specific destinations encoded. 14. A method according to claim 1, wherein said accounting for the dynamic molecular-level model includes accounting for a diffusion-limited signal influence range. 15. A method according to claim 1, wherein said accounting for the dynamic molecular-level model includes accounting for a varied autoinducer signal influence range for particular cells within the one or more cell colonies. 16. A method according to claim 1, wherein said accounting for the dynamic molecular-level model includes accounting for a concentration of signal receptors for a cell within the one or more cell colonies. 17. A method according to claim 1, wherein said accounting for the dynamic molecular-level model includes modeling effects of a probiotic agent on the one or more cell colonies. 18. A method according to claim 1, wherein said accounting for a selective-pressure model includes accounting at least for selective pressures sourced from social behavior of the cell colony and individual regulation of a cell within the cell colony. 19. A method according to claim 18, wherein said accounting at least for selective pressures sourced from social behavior of the cell colony and individual regulation of a cell within the cell colony includes accounting for a growth advantage of a mutant cell within the cell colony. 20. A method according to claim 1, wherein said accounting for the dynamic molecular-level model includes accounting for virulence concentration. 21. A method according to claim 1, wherein said accounting for the dynamic molecular-level model includes accounting for the effect of individual quorum sensing inhibitors. 22. A machine-readable storage medium containing machine-executable instructions for performing a method of generating a therapy recommendation for inhibiting pathogenesis or growth of one or more cell colonies, wherein the therapy includes a plurality of differing therapeutic agents, the method performed by a computing system, said machine-executable instructions comprising: a first set of machine-executable instructions for receiving an indication of one or more types of cells in the one or more cell colonies; a second set of machine-executable instructions for solving an optimization problem to generate at least a portion of a therapy recommendation specifying relative amounts of the plurality of differing therapeutic agents to administer in combination with one another, wherein the optimization problem includes: accounting for a dynamic molecular-level model that models 1) a cell population representing the one or more cell colonies, 2) differing components of one or more resistance-forming mechanisms of the one or more types of cells, and 3) effects that the differing therapeutic agents have on the differing components; and accounting for a selective-pressure model configured to assess probabilities of inducing selective pressure on the cell population; and a third set of machine-executable instructions for providing or applying the therapy recommendation to a user, wherein the therapy recommendation is based on the relative amounts of the plurality of differing therapeutic agents. 23. A machine-readable storage medium according to claim 22, wherein said accounting for the dynamic molecular-level model includes accounting for a model of a quorum sensing system for each of the one or more cell colonies. 24. A machine-readable storage medium according to claim 23, wherein said accounting for a model of a quorum sensing system for each of the one or more cell colonies includes accounting for a model of quorum sensing for a bacterial colony. 25. A machine-readable storage medium according to claim 24, wherein said accounting for the model of quorum sensing for the bacterial colony includes accounting for two types of bacterial strains belonging to the same species. 26. A machine-readable storage medium according to claim 25, wherein said accounting for the two types of bacterial strains belonging to the same species includes accounting for wild-types and signal-blind mutants. 27. A machine-readable storage medium according to claim 22, wherein said accounting for the dynamic molecular-level model includes deriving a single substrate growth model. 28. A machine-readable storage medium according to claim 27, wherein said deriving a single substrate growth model includes accounting for when the cells of the cell population are metabolically active but not growing or dividing. 29. A machine-readable storage medium according to claim 22, wherein said accounting for the dynamic molecular-level model includes accounting for production of an extracellular polymeric substance (EPS) produced by the one or more cell colonies. 30. A machine-readable storage medium according to claim 29, wherein said accounting for production of the EPS produced by the one or more cell colonies includes accounting for production of an EPS produced by one or more cancer cell colonies. 31. A machine-readable storage medium according to claim 22, wherein said accounting for the dynamic molecular-level model includes modeling communication between cells of at least one of the cell colonies of the one or more cell colonies. 32. A machine-readable storage medium according to claim 31, wherein said modeling communication (i.e., network) between cells of the at least one cell colony includes modeling signal molecules produced by cells within the at least one cell colony. 33. A machine-readable storage medium according to claim 32, wherein said modeling signal molecules produced by cells within the at least one cell colony includes accounting for properties of the signal molecules and for a extracellular physical diffusion limit. 34. A machine-readable storage medium according to claim 32, wherein said modeling signal molecules produced by cells within the at least one cell colony includes modeling signal molecules that do not have specific destinations encoded. 35. A machine-readable storage medium according to claim 22, wherein said accounting for the dynamic molecular-level model includes accounting for a diffusion-limited signal influence range. 36. A machine-readable storage medium according to claim 22, wherein said accounting for the dynamic molecular-level model includes accounting for a varied autoinducer signal influence range for particular cells within the one or more cell colonies. 37. A machine-readable storage medium according to claim 22, wherein said accounting for the dynamic molecular-level model includes accounting for a concentration of signal receptors for a cell within the one or more cell colonies. 38. A machine-readable storage medium according to claim 22, wherein said accounting for the dynamic molecular-level model includes modeling effects of a probiotic agent on the one or more cell colonies. 39. A machine-readable storage medium according to claim 22, wherein said accounting for a selective-pressure model includes accounting at least for selective pressures sourced from social behavior of the cell colony and individual regulation of a cell within the cell colony. 40. A machine-readable storage medium according to claim 39, wherein said accounting at least for selective pressures sourced from social behavior of the cell colony and individual regulation of a cell within the cell colony includes accounting for a growth advantage of a mutant cell within the cell colony. 41. A machine-readable storage medium according to claim 22, wherein said accounting for the dynamic molecular-level model includes accounting for virulence concentration. 42. A machine-readable storage medium according to claim 22, wherein said accounting for the dynamic molecular-level model includes accounting for the effect of individual quorum sensing inhibitors.