Converting models to English

The semantics used in TinkerCell allows conversion of a model into plain English. The Export menu has this feature. Below is an example:

Model:

English export:

transcription factor (repressor) LacI represses repressor binding site (target) lac_op

transcription factor (repressor) TetR represses repressor binding site (target) tet_op

coding (template) gfp_gene produces reporter (product) GFP

molecule (inhibitor) IPTG inhibits transcription factor (target) LacI

molecule (inhibitor) aTc inhibits transcription factor (target) TetR

coding (repressor) gene represses repressor binding site (target) op3

Automatically iterating through models

The same "diagram" can be modeled in different ways. So, what is the consequence of the type of model we pick? The new python plugin called tryAllModules.py plots the simulation of all the combinations of submodels and then clusters the results, showing a picture of how the system can behave depending the modeling methods used.



The key functions used by the python code are:

tc_listOfPossibleModels
tc_substituteModel
tc_holdPlot
tc_clusterPlots

Important note: The load model function in TinkerCell caches each model that is loaded. The caching checks if the file has changed. In file has not changed, the re-loading of the same model is very fast, since it does not need to parse the file itself. This results in an order or magnitude speed up when doing tc_substituteModel repeatedly.

Sample Python Code for Creating a Little Network

cds4 = tc_insert("cds4","Coding")
p3 = tc_insert("p3","Inducible Promoter")
cds3 = tc_insert("cds3","Coding")
t3 = tc_insert("t3","Terminator")
p2 = tc_insert("p2","Inducible Promoter")
cds2 = tc_insert("cds2","Coding")
t2 = tc_insert("t2","Terminator")

listOfParts = [cds4, p3, cds3, t3, p2, cds2, t2]
tcArray = toTC(listOfParts) #convert Python data structure to

tc_alignParts(tcArray)

#production of P4
P4 = tc_insert("P4", "Transcription Factor")
x = tc_getX(cds4)
y = tc_getY(cds4)
tc_setPos(P4, x, y - 200)

listToConnect = [cds4, P4]
tcArray = toTC(listToConnect)
R1 = tc_insertConnection(tcArray, "R1", "Protein Production")

#P4 regulates p3

listToConnect = [P4, p3]
tcArray = toTC(listToConnect)
R2 = tc_insertConnection(tcArray, "R2", "Transcription Activation")

#production of P3
P3 = tc_insert("P3", "Transcription Factor")
x = tc_getX(cds3)
y = tc_getY(cds3)
tc_setPos(P3, x, y - 200)

listToConnect = [cds3, P3]
tcArray = toTC(listToConnect)
R3 = tc_insertConnection(tcArray, "R3", "Protein Production")

#P3 regulates p2

listToConnect = [P3, p2]
tcArray = toTC(listToConnect)
R4 = tc_insertConnection(tcArray, "R4", "Transcription Activation")

#production of GFP
GFP = tc_insert("GFP", "Reporter")
x = tc_getX(cds2)
y = tc_getY(cds2)
tc_setPos(GFP, x, y - 200)

listToConnect = [cds2, GFP]
tcArray = toTC(listToConnect)
R5 = tc_insertConnection(tcArray, "R5", "Protein Production")