Abstract: An optical based method determines the most clinically effective antimicrobial agent treatment for a subject afflicted with a microbial infection, including those subjects that have developed resistance to said microbial agents. The provided method is based on the ability to discriminate between live and dead microbes growing in a culture medium.

Abstract: Provided herein are methods and computer-implemented systems for using computer simulations to predict likelihood of a cell population associated with a pathophysiological condition acquiring resistance to a therapeutic agent, to screen for therapeutic agents effective to suppress acquisition of resistance within a cell population and to treat the pathophysiological conditions associated therewith. The computer simulation comprises at least an input/out system and a mathematical model, including operably linked equations, parameter values and constant values, of growth response over a period of time of a cell population in contact with an therapeutic agent. Also provide is a method for determining a best-fit mathematical model of adaptation of a microbial population to a therapeutic agent over time and using the model to simulate microbial population behavior to a therapeutic agent.

Abstract: Provided herein are methods and computer-implemented systems for using computer simulations to predict likelihood of a cell population associated with a pathophysiological condition acquiring resistance to a therapeutic agent, to screen for therapeutic agents effective to suppress acquisition of resistance within a cell population and to treat the pathophysiological conditions associated therewith. The computer simulation comprises at least an input/out system and a mathematical model, including operably linked equations, parameter values and constant values, of growth response over a period of time of a cell population in contact with an therapeutic agent. Also provide is a method for determining a best-fit mathematical model of adaptation of a microbial population to a therapeutic agent over time and using the model to simulate microbial population behavior to a therapeutic agent.

Abstract: Provided herein are methods and computer-implemented systems for using computer simulations to predict likelihood of a cell population associated with a pathophysiological condition acquiring resistance to a therapeutic agent, to screen for therapeutic agents effective to suppress acquisition of resistance within a cell population and to treat the pathophysiological conditions associated therewith. The computer simulation comprises at least an input/out system and a mathematical model, including operably linked equations, parameter values and constant values, of growth response over a period of tune of a cell population in contact with an therapeutic agent.