. . "RIV/60076658:12520/11:43884048!RIV13-MSM-12520___" . . . "Decomposition of cellular systems via causal relations" . . "[3D4D4E62AC29]" . "7"^^ . "18" . "P(ED2.1.00/01.0024), S, Z(MSM6007665808)" . . "\u0160tys, Dalibor" . "Neuvedeno" . "12520" . "Decomposition of cellular systems via causal relations"@en . "Decomposition of cellular systems via causal relations" . . . "Urban, Jan" . "193063" . "Levitner, T." . . "Decomposition of cellular systems via causal relations"@en . . . . "RIV/60076658:12520/11:43884048" . . . "CZ - \u010Cesk\u00E1 republika" . "1211-5894" . . . "Materials Structure" . "2"^^ . . . "Monolayer of living cells time development is the closest approximation to organ development and function. It inspired the computational approach of cellular automata and agent-based modeling. Yet, for living cell description this approach is seldom utilized. In this paper we address reasons why biochemical /molecular biology approach is so much more popular. We present the formal structure of stochastic systems theory for formal description of the cell culture experiment. We define phenomenological attributes of cell monolayer system as cells states assigned by the operator. As system variables we consider levels of metabolic fluxes in cell compartments and between them and states of intracellular signals. For the behavior of system variables we consider that of stable orbits in the state space which arise from movement in the confined intracellular space combined with chemical reactions. Recent theoretical studies indicate also that formation and maintenance of cell shapes may arise by similar mechanism. Bio-inspired computing has been a holy grail of computational theory since its eve. Recent developments in biological systems description open a question what is really meant by this term, how much the neural networks are related to neuron and cellular automata to cells. We address in this paper also the issue of reality of bio-inspired computing in production of adequate models and/or integration of living cell elements in the computational process." . . . . . "3"^^ . "relations; causal; via; systems; cellular; Decomposition"@en . "Monolayer of living cells time development is the closest approximation to organ development and function. It inspired the computational approach of cellular automata and agent-based modeling. Yet, for living cell description this approach is seldom utilized. In this paper we address reasons why biochemical /molecular biology approach is so much more popular. We present the formal structure of stochastic systems theory for formal description of the cell culture experiment. We define phenomenological attributes of cell monolayer system as cells states assigned by the operator. As system variables we consider levels of metabolic fluxes in cell compartments and between them and states of intracellular signals. For the behavior of system variables we consider that of stable orbits in the state space which arise from movement in the confined intracellular space combined with chemical reactions. Recent theoretical studies indicate also that formation and maintenance of cell shapes may arise by similar mechanism. Bio-inspired computing has been a holy grail of computational theory since its eve. Recent developments in biological systems description open a question what is really meant by this term, how much the neural networks are related to neuron and cellular automata to cells. We address in this paper also the issue of reality of bio-inspired computing in production of adequate models and/or integration of living cell elements in the computational process."@en . .