Genome space and structure genome invariants Export

@inproceedings{citeulike:4597260, abstract = {The traditional approach examines and collects data on a single gene, a single protein or a single reaction at a time. Given the state space of the cell, any gene can be represented by a point (vector) in this space and its components are the protein expressions of the same gene for any state. When the same process is repeated for all the genes, we obtain a set of points (vectors) which is a new space or genome space. Inside the genome space we can define invariants that give us the structure of the genome space. With the genome space and state space we can give a model for any cell and in particular for the neurons in the brain.}, author = {Resconi, G.}, booktitle = {Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on}, citeulike-article-id = {4597260}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/IJCNN.2005.1555871}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1555871}, doi = {10.1109/IJCNN.2005.1555871}, journal = {Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on}, keywords = {algebraic-geometry, algebraic-topology, all-possible-biological-structures, all-possible-cells, all-possible-genomes, all-possible-model-organisms, all-possible-organisms, biological-structures, cantor-space, cell-types, cells, constraints, extremal-biology, formal-biology, formal-development, genes, genetic-programming, genome-spaces, genomes, genomic-spaces, hyperplanes, invariances, lattice-of-invariants, mathematical-biology, mathematical-structures, model-organisms, order-structures, organisms, sampling, state-spaces, synthetic-biology, synthetic-dna, systems-biology, taxonomies, transformability, transformations, transforming}, pages = {440--445 vol. 1}, posted-at = {2009-05-22 15:08:59}, priority = {2}, title = {Genome space and structure genome invariants}, url = {http://dx.doi.org/10.1109/IJCNN.2005.1555871}, volume = {1}, year = {2005} }

TY - CONF ID - citeulike:4597260 L3 - citeulike-article-id:4597260 N2 - The traditional approach examines and collects data on a single gene, a single protein or a single reaction at a time. Given the state space of the cell, any gene can be represented by a point (vector) in this space and its components are the protein expressions of the same gene for any state. When the same process is repeated for all the genes, we obtain a set of points (vectors) which is a new space or genome space. Inside the genome space we can define invariants that give us the structure of the genome space. With the genome space and state space we can give a model for any cell and in particular for the neurons in the brain. JF - Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on EP - 445 vol. 1 TI - Genome space and structure genome invariants VL - 1 T2 - Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on SP - 440 KW - algebraic-geometry KW - algebraic-topology KW - all-possible-biological-structures KW - all-possible-cells KW - all-possible-genomes KW - all-possible-model-organisms KW - all-possible-organisms KW - biological-structures KW - cantor-space KW - cell-types KW - cells KW - constraints KW - extremal-biology KW - formal-biology KW - formal-development KW - genes KW - genetic-programming KW - genome-spaces KW - genomes KW - genomic-spaces KW - hyperplanes KW - invariances KW - lattice-of-invariants KW - mathematical-biology KW - mathematical-structures KW - model-organisms KW - order-structures KW - organisms KW - sampling KW - state-spaces KW - synthetic-biology KW - synthetic-dna KW - systems-biology KW - taxonomies KW - transformability KW - transformations KW - transforming AU - Resconi, G PY - 2005/// UR - http://dx.doi.org/10.1109/IJCNN.2005.1555871 ER -