title: A connectionist model of development.
abstract: We present a phenomenological modeling framework for development. Our purpose is to provide a systematic method for discovering and expressing correlations in experimental data on gene expression and other developmental processes. The modeling framework is based on a connectionist or "neural net" dynamics for biochemical regulators, coupled to "grammatical rules" which describe certain features of the birth, growth, and death of cells, synapses and other biological entities. We outline how spatial geometry can be included, although this part of the model is not complete. As an example of the application of our results to a specific biological system, we show in detail how to derive a rigorously testable model of the network of segmentation genes operating in the blastoderm of Drosophila. To further illustrate our methods, we sketch how they could be applied to two other important developmental processes: cell cycle control and cell-cell induction. We also present a simple biochemical model leading to our assumed connectionist dynamics which shows that the dynamics used is at least compatible with known chemical mechanisms.
authors.affiliation: Department of Computer Science, Yale University, New Haven, CT 06520-2158.
authors.forename: E
authors.initials: E
authors.lastname: Mjolsness
authors.forename: D H
authors.initials: DH
authors.lastname: Sharp
authors.forename: J
authors.initials: J
authors.lastname: Reinitz
mesh_terms: D000818:Animals; D001756:Blastoderm / Q000502:physiology; D002453:Cell Cycle / Q000502:physiology; D002455:Cell Division / Q000502:physiology; D004331:Drosophila melanogaster / Q000196:embryology; D004627:Embryonic Induction / Q000502:physiology; D015870:Gene Expression / Q000502:physiology; D005796:Genes / Q000502:physiology; D006128:Growth; D008433:Mathematics; D008957:Models, Genetic; D016571:Neural Networks, Computer