New heuristic for setting retained_set

nfvsmotifs/SuccessionDiagram.py

@@ -29,7 +29,10 @@ def __init__(self, network: BooleanNetwork):
         # A Petri net representation of the original Boolean network.
         self.petri_net = network_to_petrinet(network)
         # Negative feedback vertex set.
-        self.nfvs = feedback_vertex_set(network, parity="negative")#find_minimum_NFVS(network)
+        self.nfvs = feedback_vertex_set(network, parity="negative")
+        #self.nfvs = find_minimum_NFVS(network)
+        print(f"|U-| = {len(self.nfvs)}")
+        #print(self.nfvs)
         # A directed acyclic graph representing the succession diagram.
         self.G = nx.DiGraph()
         # A dictionary used for uniqueness checks on the nodes of the succession diagram.
@@ -307,29 +310,65 @@ def expand_attractors(self, node_id: int) -> list[dict[str, int]]:
         # the space is a trap and this will remove the corresponding unnecessary
         # Petri net transitions.
         retained_set = node_space.copy()
-        for x in self.nfvs:
-            if x not in retained_set:
-                """Set all nodes to 0"""
-                #retained_set[x] = 0
-
-                """Set nodes to values based on the majority of satisfying values of functions"""
-                fx = self.network.get_update_function(x)
-                fx = aeon_to_pyeda(fx)
-                n_input = len(list(fx.support))
-                n_poss_sat = pow(2, n_input - 1)
-
-                n_sat = fx.satisfy_count()
-
-                if n_sat > n_poss_sat:
-                    retained_set[x] = 1
-                elif n_sat < n_poss_sat:
-                    retained_set[x] = 0
-                else:
-                    retained_set[x] = random.randint(0, 1)
-
 
         child_spaces = [self.node_space(child) for child in self.G.successors(node_id)]
 
+        # find the child space least common with NFVS
+        if (len(child_spaces) > 0):
+            least_common_child_space = child_spaces[0]
+            least_common_nodes = len(set(least_common_child_space) & set(self.nfvs))
+            for child_space in child_spaces:
+                common_nodes = len(set(child_space) & set(self.nfvs))
+                if (common_nodes < least_common_nodes):
+                    least_common_nodes = common_nodes
+                    least_common_child_space = child_space
+
+            for x in self.nfvs:
+                if x not in retained_set:
+                    if x in least_common_child_space:
+                        retained_set[x] = least_common_child_space[x]
+                    else:
+                        """Set nodes to values based on the majority of satisfying values of functions"""
+                        fx = self.network.get_update_function(x)
+                        fx = aeon_to_pyeda(fx)
+                        n_input = len(list(fx.support))
+                        n_poss_sat = pow(2, n_input - 1)
+
+                        n_sat = fx.satisfy_count()
+
+                        if n_sat > n_poss_sat:
+                            retained_set[x] = 1
+                        elif n_sat < n_poss_sat:
+                            retained_set[x] = 0
+                        else:
+                            #retained_set[x] = random.randint(0, 1)
+                            retained_set[x] = 0
+                            #retained_set[x] = 1
+        else:
+            for x in self.nfvs:
+                if x not in retained_set:
+                    """Set all nodes to 0"""
+                    #retained_set[x] = 0
+
+                    """Set nodes to values based on the majority of satisfying values of functions"""
+                    fx = self.network.get_update_function(x)
+                    fx = aeon_to_pyeda(fx)
+                    n_input = len(list(fx.support))
+                    n_poss_sat = pow(2, n_input - 1)
+
+                    n_sat = fx.satisfy_count()
+
+                    if n_sat > n_poss_sat:
+                        retained_set[x] = 1
+                    elif n_sat < n_poss_sat:
+                        retained_set[x] = 0
+                    else:
+                        #retained_set[x] = random.randint(0, 1)
+                        retained_set[x] = 0
+                        #retained_set[x] = 1
+
+
+
         if len(retained_set) == self.network.num_vars() and len(child_spaces) == 0:
             # There is only a single attractor remaining here, 
             # and its "seed" is the retain set.            

nfvsmotifs/motif_avoidant.py

@@ -97,14 +97,14 @@ def _preprocess_candidates(
         function_bdd = expr2bdd(aeon_to_pyeda(function_expression))
         update_functions[var_name] = function_bdd
 
-    print("Finish loading functions")
+    #print("Finish loading functions")
     if is_in_an_mts == False:
         #symbolic_candidates = state_list_to_bdd(candidates)
         symbolic_candidates = deepcopy(candidates)
         filtered_candidates = []
         i = 1
         for state in candidates:
-            print(f"Run outside mts - {i}")
+            #print(f"Run outside mts - {i}")
             i += 1
             #state_bdd = state_to_bdd(state)
 
@@ -150,7 +150,7 @@ def _preprocess_candidates(
     else:
         filtered_candidates = []
         for i in range(max_iterations):
-            print(f"Run inside mts - {i + 1}")
+            #print(f"Run inside mts - {i + 1}")
             random.shuffle(variables)
             #symbolic_candidates = state_list_to_bdd(candidates)
             symbolic_candidates = deepcopy(candidates)

test.bnet

@@ -0,0 +1,25 @@
+targets, fact|s
+A, (!A & !B) | C
+B, (!A & !B) | C
+C, A & B
+
+#A, !E | (A & B & C)
+#B, !F | (A & B & C)
+#C, !D | (A & B & C)
+#D, A
+#E, B
+#F, C
+
+#A, !E | (G & B & C)
+#B, !F | (A & H & C)
+#C, !D | (A & B & I)
+#D, A
+#E, B
+#F, C
+#G, A
+#H, B
+#I, C
+
+#A, !A | !B
+#B, !A | !B
+#C, A & B

test/A_model.bnet

@@ -0,0 +1,75 @@
+targets, factors
+a_secretase, (CREB|s_NMDAR)&(!ROCK|!Cholesterol)
+Ab, ((APP&BACE1)&(Sortilin|PICALM|Cholesterol))&!Autophagy
+ABCA7, SREBP2
+AKT, (s_NMDAR&PI3K)&(!PTEN|!PP2A)&!CASP3
+APOE4, APOE4
+Apoer2, Reelin&!APOE4
+Apoptosis, CASP3
+APP, ERK&(!(a_secretase&(Sortilin|LRP6))|!SORL1|!(Dab1&Apoer2)|!(Dab1&Fyn))
+ATM, ROS
+Autophagy, (ULK1&LC3&p62&LAMP2&beclin1)&!(Cholesterol&ROS)
+BACE1, (STAT3|Cholesterol)&!PGC1a
+BAD, !AKT&PP2B
+BAX, ((Bim&PUMA)&p53)&!AKT&!Bcl2
+Bcl2, !BAD&(!p53|!JNK|!Bim)
+BDNF, (CREB|PGC1a)&Sortilin
+beclin1, (ROCK|JNK)&(!Bcl2|!CASP3)
+Bim, EGR1|FOXO
+Ca_ion, e_NMDAR
+CASP2, p53
+CASP3, BAX&Bim
+Cdk5, Ca_ion|EGR1
+Cholesterol, !CYP46A1&(SREBP2&HMGCR)|(!LPL|!ABCA7)
+CIP2A, Ab
+CLU, p53|EGR1
+CREB, (ERK&Jacob&s_NMDAR)|!(Jacob&e_NMDAR)
+CYP46A1, !APP&Ca_ion
+Dab1, Apoer2&!Fyn
+Dkk1, Ab&(CLU|PICALM)
+DLK, APOE4
+e_NMDAR, (Ab&Fyn)|PTEN
+EGR1, JNK
+ERK, !(pTau&PP2A)&((s_NMDAR&!STEP)|MKK7)
+FOXO, JNK&!AKT
+Fyn, ((Apoer2&Dab1)|mGluR)&!STEP
+GSK3beta, (Ca_ion|PP2A)&!(AKT|LRP6)
+Jacob, (s_NMDAR&ERK)|e_NMDAR
+JNK, MKK7|Vangl2
+LAMP2, PIP2|TFEB
+LC3, PIP2|TFEB|!PICALM
+LPL, SREBP2&!SORL1
+LRP6, !APOE4|!Dkk1
+MAPT, e_NMDAR&!CREB
+MDM2, p53&(!ATM&!CASP2|!GSK3beta)
+mGluR, Ab
+MKK7, DLK|e_NMDAR
+mTOR, (AKT|ERK|Cholesterol)&!SIRT1
+p38, !STEP
+p53, (p38|ATM)&!MDM2
+p62, PIP2|TFEB|!PICALM
+PGC1a, (CREB|SIRT1)&p38
+PI3K, Dab1|s_NMDAR|BDNF
+PICALM, !Ca_ion&!CASP3
+PIP2, !synj1
+PP2A, (!mGluR|!GSK3beta)&!APOE4&!CIP2A
+PP2B, mGluR|Ca_ion
+PTEN, ROCK
+PUMA, !s_NMDAR&(FOXO|p53)
+Reelin, !Apoptosis
+RhoA, CASP2|Vangl2
+ROCK, RhoA|!SIRT1
+ROS, (Ca_ion|Ab)&!PGC1a
+s_NMDAR, (Fyn|BDNF)&!STEP&!pTau
+SORL1, !ROCK|BDNF
+Sortilin, ROCK
+SREBP2, (!Cholesterol|!APP|!ABCA7)|AKT
+STAT3, Cdk5
+STEP, (PP2B)&(!s_NMDAR|!e_NMDAR)
+synj1, APOE4|(!Cdk5&PP2B)
+pTau, (MAPT&(GSK3beta|Cdk5|ERK))&!PP2A&!Autophagy
+TFEB, (!APOE4|!mTOR)&PP2B
+ULK1, !mTOR
+Vangl2, Dkk1
+SIRT1, (!APOE4|!ROS)
+HMGCR, BDNF|SREBP2

test/CASCADE1.bnet

@@ -0,0 +1,66 @@
+targets, factors
+AKT1S1, !AKT_f
+AKT_f, !mTORC2_c&PDPK1|mTORC2_c
+AXIN1, !LRP_f
+BTRC, !GSK3_f&!AXIN1&CK1_f|!GSK3_f&AXIN1|GSK3_f
+CHUK, AKT_f
+CK1_f, !LRP_f
+CTNNB1, !BTRC&CHUK
+DKK_f, DKK_g
+DKK_g, !MYC&TCF7_f
+DUSP1, !MAPK14&MSK_f|MAPK14
+DUSP6, !ERK_f&mTORC1_c|ERK_f
+DVL_f, FZD_f
+EGR1, !TCF7_f
+ERK_f, MEK_f&!DUSP6
+FOXO_f, !AKT_f&!NLK
+FZD_f, !SFRP1
+GAB_f, GRB2&!ERK_f
+GRAP2, !MAPK14
+GRB2, SHC1
+GSK3_f, !DVL_f&!MAPK14&!AKT_f&!ERK_f&!RSK_f&!S6K_f&!LRP_f
+IKBKB, MAP3K7&!TP53
+IRS1, !ERK_f&!S6K_f&!IKBKB
+JNK_f, !MAP2K4&MAP2K7&!DUSP1|MAP2K4&!DUSP1
+KRAS, !SOS1&PTPN11|SOS1
+LEF, CTNNB1
+LRP_f, !JNK_f&!MAPK14&!ERK_f&FZD_f&!DKK_f|!JNK_f&!MAPK14&ERK_f&!DKK_f|!JNK_f&MAPK14&!DKK_f|JNK_f&!DKK_f
+MAP2K3, !MAP3K7&MAP3K5|MAP3K7
+MAP2K4, !MAP3K4&!MAP3K11&!MAP3K7&GRAP2|!MAP3K4&!MAP3K11&MAP3K7|!MAP3K4&MAP3K11|MAP3K4
+MAP2K7, !MAP3K7&GRAP2|MAP3K7
+MAP3K11, RAC_f
+MAP3K4, RAC_f
+MAP3K5, !AKT_f
+MAP3K7, TAB_f
+MAP3K8, IKBKB
+MAPK14, !MAP2K4&!DUSP1&MAP2K3|MAP2K4&!DUSP1
+MDM2, !AKT_f&!S6K_f&MDM2_g|AKT_f&!S6K_f
+MDM2_g, !TP53&NFKB_f|TP53
+MEK_f, !ERK_f&!RAF_f&MAP3K8|!ERK_f&RAF_f
+MMP_f, LEF
+MSK_f, !MAPK14&ERK_f|MAPK14
+MYC, TCF7_f
+NFKB_f, !MSK_f&!IKBKB&CHUK|!MSK_f&IKBKB|MSK_f
+NLK, MAP3K7
+PDPK1, !PTEN&PIK3CA
+PIK3CA, !KRAS&!GAB_f&IRS1|!KRAS&GAB_f|KRAS
+PTEN, PTEN_g&!GSK3_f
+PTEN_g, EGR1
+PTPN11, GAB_f
+RAC_f, !mTORC2_c&DVL_f|mTORC2_c
+RAF_f, !AKT_f&!ERK_f&KRAS&!RHEB
+RHEB, !TSC_f
+RSK_f, !ERK_f&PDPK1|ERK_f
+RTPK_f, !MAPK14&!RTPK_g&!MEK_f&MMP_f|!MAPK14&RTPK_g&!MEK_f
+RTPK_g, FOXO_f
+S6K_f, !mTORC1_c&PDPK1|mTORC1_c
+SFRP1, SFRP1_g
+SFRP1_g, !MYC
+SHC1, RTPK_f&!PTEN
+SOS1, GRB2&!ERK_f
+TAB_f, !MAPK14
+TCF7_f, !NLK&CTNNB1
+TP53, MAPK14&!MDM2
+TSC_f, !AKT_f&!ERK_f&!RSK_f&!IKBKB&GSK3_f
+mTORC1_c, !RHEB&RSK_f&!AKT1S1|RHEB&!AKT1S1
+mTORC2_c, !S6K_f&TSC_f