A system of ordinary differential equations is considered, which arises in the modeling of genetic networks and artificial neural networks. Any point in phase space corresponds to a state of a network. Trajectories, which start at some initial point, represent future states. Any trajectory tends to an attractor, which can be a stable equilibrium, limit cycle or something else. It is of practical importance to answer the question of whether a trajectory exists which connects two points, or two regions of phase space. Some classical results in the theory of boundary value problems can provide an answer. Some problems cannot be answered and require the elaboration of new approaches. We consider both the classical approach and specific tasks which are related to the features of the system and the modeling object.
Citation: Inna Samuilik, Felix Sadyrbaev. On trajectories of a system modeling evolution of genetic networks[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 2232-2242. doi: 10.3934/mbe.2023104
A system of ordinary differential equations is considered, which arises in the modeling of genetic networks and artificial neural networks. Any point in phase space corresponds to a state of a network. Trajectories, which start at some initial point, represent future states. Any trajectory tends to an attractor, which can be a stable equilibrium, limit cycle or something else. It is of practical importance to answer the question of whether a trajectory exists which connects two points, or two regions of phase space. Some classical results in the theory of boundary value problems can provide an answer. Some problems cannot be answered and require the elaboration of new approaches. We consider both the classical approach and specific tasks which are related to the features of the system and the modeling object.
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Inna Samuilik, Felix Sadyrbaev. On trajectories of a system modeling evolution of genetic networks[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 2232-2242. doi: 10.3934/mbe.2023104
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