MARC Record from marc_columbia

LEADER: 03556fam a2200433 a 4500
001 3985728
005 20221027013401.0
008 931020s1994 enka b 001 0 eng
010 $a 93040873
020 $a019859688X (hbk.) :$c$52.50
020 $a0198596871 (pbk.)
035 $a(OCoLC)29313178
035 $a(OCoLC)ocm29313178
035 $9AJC0941HS
035 $a(NNC)3985728
035 $a3985728
040 $aDLC$cDLC$dNNC-M
050 00 $aQP406$b.R677 1994
082 00 $a574/.01/13$220
100 1 $aRowe, Glenn$q(Glenn W.)$0http://id.loc.gov/authorities/names/n93103007
245 10 $aTheoretical models in biology :$bthe origin of life, the immune system, and the brain /$cGlenn Rowe.
260 $aOxford ;$aNew York :$bClarendon Press ;$aNew York :$bOxford University Press,$c1994.
300 $axvi, 420 pages :$billustrations ;$c24 cm
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
504 $aIncludes bibliographical references (p. [405]-412) and index.
505 2 $aI. The Origin of Life. 1. The molecular basis of life. 2. Molecular evolution and quasi-species. 3. Stochastic processes. 4. A spin glass model of the origin of life. 5. The origin of the genetic code. 6. Hypercycles. 7. Artificial life -- II. The Immune System. 8. The immune system. 9. Bell's model. 10. Adaptive walks. 11. Maturation of the immune response. 12. The symmetric immune network model. 13. A shape space network model. 14. AIDS -- III. The Brain. 15. Neurons and synapses. 16. Memory. 17. The McCulloch-Pitts neural net. 18. Perceptrons. 19. Connectionism. 20. Attractor neural networks. 21. Unsupervised learning. 22. Evolutionary learning -- A Differential equations -- B A statistics primer -- C Computer simulation.
520 $aThis book surveys theoretical models in three broad areas of biology (the origin of life, the immune system, and memory in the brain), introducing mathematical and (mainly) computational methods that have been used to construct simulations.
520 8 $aMost current books on theoretical biology fall into one of two categories: (a) books that specialize in one area of biology and treat theoretical models in considerable depth; and (b) books that concentrate on purely mathematical models, with computers used only to find numerical solutions to differential equations, for example.
520 8 $aAlthough some mathematical models are considered in this book, the main emphasis is on stochastic computer models of biological systems. Such techniques have a much greater potential for producing detailed, realistic models of individual systems, and are likely to be the preferred modelling methods of the future. By considering three different areas in biology, the book shows how several of these modelling techniques have been successfully applied in diverse areas.
520 8 $aPut simply, this book is important because it shows how the power of modern computers is allowing researchers in theoretical biology to break free of the constraints modelling that were imposed by the traditional differential equation approach.
650 0 $aMemory$xComputer simulation.
650 0 $aImmunity$xComputer simulation.
650 0 $aLife$xOrigin$xComputer simulation.
650 2 $aModels, Theoretical.$0https://id.nlm.nih.gov/mesh/D008962
650 2 $aComputer Simulation.$0https://id.nlm.nih.gov/mesh/D003198
650 2 $aMemory.$0https://id.nlm.nih.gov/mesh/D008568
650 2 $aImmunity.$0https://id.nlm.nih.gov/mesh/D007109
852 00 $boff,hsl$hQP406$i.R677 1994