A computer-implemented method for treatment selection using genetic evolutionary dynamics and single cell heterogeneity characterizing a time evolution based on transition rates between different cell types defined by their resistance to therapeutic agents as well as on their individual growth and death rates, the method comprising: accepting data characterizing populations of a plurality of heterogenous cell states at least in part based on measurement of a subject's cancer, the plurality of states being defined according to their response to different combinations of therapeutic agents, transition rate from one cell type to another cell type, the plurality of cell states having an overall population, and the cell states including a first cell state having the largest population of the cell states; accepting a specification of each of a plurality of treatment strategies, each treatment strategy representing a selection of a sequence and timing of therapeutic agents to be introduced to the subject, wherein at least some of the treatment strategies are targeted to a cell state other than the first cell state without targeting the first cell state or the overall population of the plurality of cell states; determining using a computer-implemented procedure an effectiveness of each of the plurality of treatment strategies based on data characterizing expected growth of and transition between the plurality of states with introduction of different of the therapeutic agents, including computing data characterizing evolution of the populations of the plurality of cell states, wherein the effectiveness of a treatment strategy is determined from the evolution of the populations of the cell states, by determining the net growth for a cell state, wherein the net growth for the cell of type i is represented by: g 0 x i + ∑ j ≠ i T i j g 0 x j − ∑ k S a i k d k x i assuming that cell death rates are zero in the absence of therapy, that all cells have the same growth rate go, and that the therapeutic agents work by increasing cell death, and wherein x i represents the cell population of the cell type i, T(i, j) represents the transition rate (per cell per generation) from cell type j to i; S a (i,k) represents the sensitivity of cell type i to drug k, and d k represents the normalised dosage of drug k; and selecting a treatment strategy according to the determined effectiveness. accepting data characterizing populations of a plurality of heterogenous cell states at least in part based on measurement of a subject's cancer, the plurality of states being defined according to their response to different combinations of therapeutic agents, transition rate from one cell type to another cell type, the plurality of cell states having an overall population, and the cell states including a first cell state having the largest population of the cell states; accepting a specification of each of a plurality of treatment strategies, each treatment strategy representing a selection of a sequence and timing of therapeutic agents to be introduced to the subject, wherein at least some of the treatment strategies are targeted to a cell state other than the first cell state without targeting the first cell state or the overall population of the plurality of cell states; determining using a computer-implemented procedure an effectiveness of each of the plurality of treatment strategies based on data characterizing expected growth of and transition between the plurality of states with introduction of different of the therapeutic agents, including computing data characterizing evolution of the populations of the plurality of cell states, wherein the effectiveness of a treatment strategy is determined from the evolution of the populations of the cell states, by determining the net growth for a cell state, wherein the net growth for the cell of type i is represented by: g 0 x i + ∑ j ≠ i T i j g 0 x j − ∑ k S a i k d k x i assuming that cell death rates are zero in the absence of therapy, that all cells have the same growth rate go, and that the therapeutic agents work by increasing cell death, and wherein x i represents the cell population of the cell type i, T(i, j) represents the transition rate (per cell per generation) from cell type j to i; S a (i,k) represents the sensitivity of cell type i to drug k, and d k represents the normalised dosage of drug k; and selecting a treatment strategy according to the determined effectiveness.