Fig. 1
From: Adapting machine-learning algorithms to design gene circuits
Overview of GeneNet. a The optimization algorithm consists of three parts: defining a cost function (left), updating parameters to minimize the cost via gradient descent (middle), and analyzing the learned networks (right). b Regularization selects networks with varying degrees of complexity. c Final design of an ultrasensitive switch. Upper: the final values of each of the three genes as a function of different levels of input. Lower: time traces illustrating network dynamics for three representative values for the input level