A biological information processing method comprising the steps of: measuring an expression level of each of a plurality of blood molecules in an organism over a specific time interval to obtain time-series data y i (t), with t being the time and different i representing different blood molecules, the blood molecules of the plurality of blood molecules in an organism selected out of the group that comprises: leptin, adinoleptin, visfatin, omentin produced from a fat tissue, ACTH produced from a pituitary, insulin, glucagon, amylin, GLP produced from a pancreas, lecithin and RBP4 produced from a fat tissue, TNF-α, IL-6, MCP1 produced from an immunocyte, MCP1 produced from a fat tissue, CRP, PAI-1, NEFA, VLDL, and LDL-ox produced from a liver; decomposing the time-series data y i (t) for each of the plurality of blood molecules into a) a periodic component s(t) having a circadian period, a monthly period, and/ or a yearly period, b) an environmental stimulus response x(t) to the environmental stimulus v(t), wherein the environmental stimulus response x(t)is formulated as a multilinear model with x t = F t times t − 1 plus v t, wherein F(t) indicates a conversion function of an output with respect to the environmental stimulus v(t), c) baseline data b(t) and d) an observational error e(t), with y t = s t + x t + b t + e t; identifying constant regions of each of the time-series data y i (t) for the plurality of blood molecules from variations in the baseline data (b(t)) or from the amplitude or periodic variations of the periodic component (s(t)), and identifying causal relation between the expressed levels of blood molecules represented by the identified constant regions; and inferring information on changes in a state of the organism from the identified causal relation between the constant regions, the state of the organism being defined by a system model (x) linked to the measured time-series data y(t) as representing an observation model, wherein the system model represents a model that indicates a generating mechanism of a signal, and the observation model represents a state in which the signal is converted and noise is added when the signal is actually observed. measuring an expression level of each of a plurality of blood molecules in an organism over a specific time interval to obtain time-series data y i (t), with t being the time and different i representing different blood molecules, the blood molecules of the plurality of blood molecules in an organism selected out of the group that comprises: leptin, adinoleptin, visfatin, omentin produced from a fat tissue, ACTH produced from a pituitary, insulin, glucagon, amylin, GLP produced from a pancreas, lecithin and RBP4 produced from a fat tissue, TNF-α, IL-6, MCP1 produced from an immunocyte, MCP1 produced from a fat tissue, CRP, PAI-1, NEFA, VLDL, and LDL-ox produced from a liver; decomposing the time-series data y i (t) for each of the plurality of blood molecules into a) a periodic component s(t) having a circadian period, a monthly period, and/ or a yearly period, b) an environmental stimulus response x(t) to the environmental stimulus v(t), wherein the environmental stimulus response x(t)is formulated as a multilinear model with x t = F t times t − 1 plus v t, wherein F(t) indicates a conversion function of an output with respect to the environmental stimulus v(t), c) baseline data b(t) and d) an observational error e(t), with y t = s t + x t + b t + e t; a) a periodic component s(t) having a circadian period, a monthly period, and/ or a yearly period, b) an environmental stimulus response x(t) to the environmental stimulus v(t), wherein the environmental stimulus response x(t)is formulated as a multilinear model with x t = F t times t − 1 plus v t, wherein F(t) indicates a conversion function of an output with respect to the environmental stimulus v(t), c) baseline data b(t) and d) an observational error e(t), with y t = s t + x t + b t + e t; identifying constant regions of each of the time-series data y i (t) for the plurality of blood molecules from variations in the baseline data (b(t)) or from the amplitude or periodic variations of the periodic component (s(t)), and identifying causal relation between the expressed levels of blood molecules represented by the identified constant regions; and inferring information on changes in a state of the organism from the identified causal relation between the constant regions, the state of the organism being defined by a system model (x) linked to the measured time-series data y(t) as representing an observation model, wherein the system model represents a model that indicates a generating mechanism of a signal, and the observation model represents a state in which the signal is converted and noise is added when the signal is actually observed.