1,359 research outputs found
Nonlinear Models of Neural and Genetic Network Dynamics:\ud \ud Natural Transformations of Ćukasiewicz Logic LM-Algebras in a Ćukasiewicz-Topos as Representations of Neural Network Development and Neoplastic Transformations \ud
A categorical and Ćukasiewicz-Topos framework for Algebraic Logic models of nonlinear dynamics in complex functional systems such as Neural Networks, Cell Genome and Interactome Networks is introduced. Ćukasiewicz Algebraic Logic models of both neural and 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/transfer functions is extended to a Ćukasiewicz Topos with an N-valued Ćukasiewicz Algebraic Logic subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in developmental processes and carcinogenesis.\u
Ćukasiewicz-Topos Models of Neural Networks, Cell Genome and Interactome Nonlinear Dynamic Models
A categorical and Ćukasiewicz-Topos framework for Algebraic Logic models of nonlinear dynamics in complex functional systems such as Neural Networks, Cell Genome and Interactome Networks is introduced. Ćukasiewicz Algebraic Logic models of both neural and 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 Ćukasiewicz Topos with an n-valued Ćukasiewicz Algebraic Logic subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in developmental processes and carcinogenesis
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
Bibliometric Mapping of the Computational Intelligence Field
In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996ĂąâŹâ2000 and 2001ĂąâŹâ2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.neural networks;bibliometric mapping;fuzzy systems;bibliometrics;computational intelligence;evolutionary computation
Construction of an in vitro bistable circuit from synthetic transcriptional switches
Information processing using biochemical circuits is essential for survival and reproduction of
natural organisms. As stripped-down analogs of genetic regulatory networks in cells, we engineered
artificial transcriptional networks consisting of synthetic DNA switches, regulated by RNA signals
acting as transcription repressors, and two enzymes, bacteriophage T7 RNA polymerase and
Escherichia coli ribonuclease H. The synthetic switch design is modular with programmable
connectivity and allows dynamic control of RNA signals through enzyme-mediated production
and degradation. The switches support sharp and adjustable thresholds using a competitive
hybridization mechanism, allowing arbitrary analog or digital circuits to be created in principle.
As an example, we constructed an in vitro bistable memory by wiring together two synthetic
switches and performed a systematic quantitative characterization. Good agreement between
experimental data and a simple mathematical model was obtained for switch input/output
functions, phase plane trajectories, and the bifurcation diagram for bistability. Construction of
larger synthetic circuits provides a unique opportunity for evaluating model inference, prediction,
and design of complex biochemical systems and could be used to control nanoscale devices and
artificial cells
Network motifs emerge from interconnections that favour stability
The microscopic principles organizing dynamic units in complex networksâfrom proteins to power generatorsâcan be understood in terms of network âmotifsâ: small interconnection patterns that appear much more frequently in real networks than expected in random networks. When considered as small subgraphs isolated from a large network, these motifs are more robust to parameter variations, easier to synchronize than other possible subgraphs, and can provide specific functionalities. But one can isolate these subgraphs only by assuming, for example, a significant separation of timescales, and the origin of network motifs and their functionalities when embedded in larger networks remain unclear. Here we show that most motifs emerge from interconnection patterns that best exploit the intrinsic stability characteristics at different scales of interconnection, from simple nodes to whole modules. This functionality suggests an efficient mechanism to stably build complex systems by recursively interconnecting nodes and modules as motifs. We present direct evidence of this mechanism in several biological networks
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