Minimal model of regeneration
Conference Year
January 2020
Abstract
This research aims to develop a minimal computational model of simulated organisms in a cellular automaton that are capable of growth and regeneration to a specified target shape. The set of local rules that update the state of the organism at each time step are executed by each cell individually in the organism and are embodied as a simple feed forward neural network. The inputs to the network include signaling information and presence or absence of neighboring cells. Most computational models which aim to reproduce biological phenomena are complex and/or require a lot of computational effort. This minimal model can be used as a platform to reverse-engineer regenerative processes by gradually incorporating biological insights into the model in hopes of determining some of the necessary elements which lead to regeneration.
Primary Faculty Mentor Name
Josh Bongard
Faculty/Staff Collaborators
Sam Kriegman
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Computer Science
Primary Research Category
Engineering & Physical Sciences
Minimal model of regeneration
This research aims to develop a minimal computational model of simulated organisms in a cellular automaton that are capable of growth and regeneration to a specified target shape. The set of local rules that update the state of the organism at each time step are executed by each cell individually in the organism and are embodied as a simple feed forward neural network. The inputs to the network include signaling information and presence or absence of neighboring cells. Most computational models which aim to reproduce biological phenomena are complex and/or require a lot of computational effort. This minimal model can be used as a platform to reverse-engineer regenerative processes by gradually incorporating biological insights into the model in hopes of determining some of the necessary elements which lead to regeneration.