A method for analyzing metabolic state of a cell at a genome scale by measuring concentrations of one or more metabolites, the method comprising: measuring a concentration of the one or more metabolites present in the cell and cell culture; measuring a growth rate of the cell in the cell culture; creating, via one or more processors, a genome scale metabolic model for the cell using genomic information of cellular organism, wherein the GEM is a Genome Scale Metabolic Network referring to an ensemble of chemical reactions that take place inside a living organism or a cell type or an aggregation of dissimilar cell types, and the GEM is a computable model representing metabolic potential of a single organism or the cell and also representing a sub-system of enzymatic reactions from the organism or the cell, wherein the GEM is using a biomass function/reaction as an objective function; performing, via the one or more hardware processors, a first steady state simulation of the GEM while optimizing flux through the biomass function/reaction by applying only minimal constraints on reaction fluxes, wherein the minimal constraints are based on predefined input parameters, and wherein simulation results pertain to steady state flux values through all reactions in the GEM; constructing, via the one or more hardware processors, a combinatorial sink reaction for the GEM using the measured concentration of the one or more metabolites; constraining, via the one or more hardware processors, a lower bound of the biomass function by one of restricting the reaction flux to a predetermined first percentage value of the flux through the biomass function/reaction obtained using the first steady state simulation or to the predetermined first percentage value of the flux calculated based on measured growth rate of the cell; constraining, via the one or more hardware processors, an upper bound of the biomass function/reaction by one of restricting the reaction flux to a predetermined second percentage value of the flux through the biomass function/reaction obtained using the first steady state control simulation or to the predetermined second percentage value of the flux calculated based on the measured growth rate of the cell; performing, via the one or more hardware processors, a second steady state simulation to optimize the flux through the constructed combinatorial sink reaction as the objective function, wherein the combinatorial sink function derived using union of the metabolite concentration as given below: wherein m is a number of metabolite concentrations measured, m ⊆ M where M is all the produced metabolites present in the network, c i is the concentration of the metabolite measured (in mM units) and v accum is a sink function is a new objective to reconcile metabolic measurements; and predicting, via the one or more hardware processors, the fluxes through all reactions in the GEM using the second steady state simulation, wherein the predicted fluxes from the second steady state simulation is used for inferencing. measuring a concentration of the one or more metabolites present in the cell and cell culture; measuring a growth rate of the cell in the cell culture; creating, via one or more processors, a genome scale metabolic model for the cell using genomic information of cellular organism, wherein the GEM is a Genome Scale Metabolic Network referring to an ensemble of chemical reactions that take place inside a living organism or a cell type or an aggregation of dissimilar cell types, and the GEM is a computable model representing metabolic potential of a single organism or the cell and also representing a sub-system of enzymatic reactions from the organism or the cell, wherein the GEM is using a biomass function/reaction as an objective function; performing, via the one or more hardware processors, a first steady state simulation of the GEM while optimizing flux through the biomass function/reaction by applying only minimal constraints on reaction fluxes, wherein the minimal constraints are based on predefined input parameters, and wherein simulation results pertain to steady state flux values through all reactions in the GEM; constructing, via the one or more hardware processors, a combinatorial sink reaction for the GEM using the measured concentration of the one or more metabolites; constraining, via the one or more hardware processors, a lower bound of the biomass function by one of restricting the reaction flux to a predetermined first percentage value of the flux through the biomass function/reaction obtained using the first steady state simulation or to the predetermined first percentage value of the flux calculated based on measured growth rate of the cell; constraining, via the one or more hardware processors, an upper bound of the biomass function/reaction by one of restricting the reaction flux to a predetermined second percentage value of the flux through the biomass function/reaction obtained using the first steady state control simulation or to the predetermined second percentage value of the flux calculated based on the measured growth rate of the cell; performing, via the one or more hardware processors, m ⊆ M where M is all the produced metabolites present in the network, c i is the concentration of the metabolite measured (in mM units) and v accum is a sink function is a new objective to reconcile metabolic measurements; and predicting, via the one or more hardware processors, the fluxes through all reactions in the GEM using the second steady state simulation, wherein the predicted fluxes from the second steady state simulation is used for inferencing.