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GeneNet with Tensorflow [recommended]
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GeneNetTF.py is a single python script that trains gene circuit models. The example shown is training a 3-node circuit to create a "stripe".
(See https://www.biorxiv.org/content/biorxiv/early/2017/11/03/213587.full.pdf for further details).

The core elements of GeneNetTF.py can be modified to train different kinds of gene circuits, and for different desired outputs. Specifically:
    A. To specify the gene circuit model, modify the function "simulate". 
    B. To specify the design objective of the circuit, modify the function "newBatch".

To train the model, run the function "trainModel" with the following parameters:
     iterations:     the number of steps to perform in the optimization procedure
     regularize:     if TRUE, train with L1 regularization
                     if FALSE, train without L1 regularization [default] 
     prune:          if TRUE, train with pruning
                     if FALSE, train without pruning [default] 
     pruneLimit:     threshold for pruning parameters
     print:          if TRUE, output the running cost estimate as the algorithm proceeds
                     if FALSE, do nothing [default]
                                        
To run the model, use "simulateModel".


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GeneNet with Theano:
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To use GeneNet, you must:						(example:)
(1) specify a model 							(Model.py)
(2) specify a desired function 					(Model.py)
(3) import GeneNet and theano 					(GeneNet.py)
(4) train / simulate the model using GeneNet 	(exampleScript.py)


There are two key functions that GeneNet executes: (1) train the network to perform some desired function using "trainNetwork"; and (2) simulate the network and examine its behaviour, using "simulateNetwork". These are explained below.


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GeneNet.trainNetwork()

FUNCTION: fit the parameters of a network model to perform some desired function

INPUT ARGUMENTS:
 - desiredFunction:
 		a python function that generates training data for the network, specifically:
 			1. initial conditions for network, 	y0
 			2. inputs to the network, 			x
 			3. desired network output, 			y_
 		returns [y0,x,y_]
 - network:
 		a python class that specifies the model to be trained, including:
 			1. initialization of the model 				(def __init__)
 			2. a list of parameters to be optimized		(network.parameters)
 			3. the equations governing network dynamics	(network.networkFunction)
 			4. a regularization function  				(network.regularize)
 			5. a normalization function 				(network.normalize)
 - iterations:
 		the number of steps to perform in the optimization procedure
 

OPTIONAL ARGUMENTS

 - mode:
 		if none, or 'default' is specified, then optimization is in three phases: (1) without regularization, (2) with regularization, and (3) with a pruned network

 		other modes are:
 			1. 'vanilla'  	(unregularized)
 			2. 'regularize' (regularized)
 			3. 'prune'		(pruned network)

 - L1:
 		regularization parameter (lambda in paper)
 		default = 0.1

 - pruneLimit:
 		pruning parameter (epsilon in paper) 
 		If mode is 'prune' (or in stage (3) of 'default'), then network parameters below 		this value are set to zero 
 		default = 1.0

 - plotCost:
 		if 'true', plot the evolution of cost vs. iteration number after optimization
 		default: 'false'

 OUTPUTS

 None. However, the parameters in the network are updated. 



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GeneNet.simulateNetwork()

FUNCTION: simulate a network model

INPUT ARGUMENTS:
- inputData
 		a python function that generates the necessary simulation inputs for the network,
 		specifically:
 			1. initial conditions for network, 	y0
 			2. inputs to the network, 			x
 			3. desired network output, 			y_ (optional)
 		returns [y0,x,y_] 
 		note that this can be identical, or different, to the desiredFunction input in GeneNet.trainNetwork()
 		
 - network:
 		the same model as specified in the GeneNet.trainNetwork() inputs

 - batchSize:
 		the number of inputs to simulate over