352,276 research outputs found
Neural Networks, Cell Genome and Interactome Nonlinear Dynamic Models
Operational logic and bioinformatics models of nonlinear dynamics in complex functional systems such as neural networks, genomes and cell interactomes are proposed. Łukasiewicz Algebraic Logic models of genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable 'next-state functions' is extended to a Łukasiewicz Topos with an n-valued Łukasiewicz Algebraic Logic subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in developmental processes and carcinogenesis
Notes on stochastic (bio)-logic gates: the role of allosteric cooperativity
Recent experimental breakthroughs have finally allowed to implement in-vitro
reaction kinetics (the so called {\em enzyme based logic}) which code for
two-inputs logic gates and mimic the stochastic AND (and NAND) as well as the
stochastic OR (and NOR). This accomplishment, together with the already-known
single-input gates (performing as YES and NOT), provides a logic base and paves
the way to the development of powerful biotechnological devices. The
investigation of this field would enormously benefit from a self-consistent,
predictive, theoretical framework. Here we formulate a complete statistical
mechanical description of the Monod-Wyman-Changeaux allosteric model for both
single and double ligand systems, with the purpose of exploring their practical
capabilities to express logical operators and/or perform logical operations.
Mixing statistical mechanics with logics, and quantitatively our findings with
the available biochemical data, we successfully revise the concept of
cooperativity (and anti-cooperativity) for allosteric systems, with particular
emphasis on its computational capabilities, the related ranges and scaling of
the involved parameters and its differences with classical cooperativity (and
anti-cooperativity)
Hybrid automata dicretising agents for formal modelling of robots
Some of the fundamental capabilities required by autonomous vehicles and systems for their intelligent decision making are: modelling of the environment and forming data abstractions for symbolic, logic based reasoning. The paper formulates a discrete agent framework that abstracts and controls a hybrid system that is a composition of hybrid automata modelled continuous individual processes. Theoretical foundations are laid down for a class of general model composition agents (MCAs) with an advanced subclass of rational physical agents (RPAs). We define MCAs as the most basic structures for the description of complex autonomous robotic systems. The RPAās have logic based decision making that is obtained by an extension of the hybrid systems concepts using a set of abstractions. The theory presented helps the creation of robots with reliable performance and safe operation in their environment. The paper emphasizes the abstraction aspects of the overall hybrid system that emerges from parallel composition of sets of RPAs and MCAs
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