Systems Biology (Methods in Molecular Biology)

used when a molecule inhibits/suppresses other molecules (e.g.,
interaction fromX 3 toX 1 ;BF 1 ). In case of independent regulation
of more than one node on downstream species, we connected them
using OR gate (e.g., interactions fromX 1 andX 2 toX 3 ;BF 3 ). An
ACTIVE gate is used when one molecule activates another (e.g.,
interaction fromX 1 toX 3 ;BF 3 ). Finally, an AND gate represents
the interaction where more than one molecule together regulate
the expression level of the downstream molecule (e.g., interactions
fromX 1 andX 3 toX 2 ;BF 2 ). Boolean functions NOT and ACTIVE
are derived based on the network structure but the rules for OR and
AND gate determined by training/calibrating the model with sort
of qualitative data [56]. Numerous simulation tools are available to
simulate logic-bases models, for example, the CellCollective [57],
CellNetAnalyzer [58], CellNOpt [56], GINSim [59], BoolSim,
BoolNet [60], BooleanNet [61], SimBoolNet [62], SQUAD
[63], and ADAM [64].

Model construction: We constructed a small toy logic-based model
of signaling and transcriptional pathway in cancer shown in Fig.9.
The upper central part of the model captures the mechanism how
the extra-cellular ligands, i.e., epidermal growth factor (EGF) binds
to the epidermal growth factor receptor (EGFR) kinase to regulate
the cell cycle progression by accumulating the Cyclin dependent
kinase (CDK) via downstream activation of protein kinase like Ras,
Raf, and ERK. The Cyclins and CDKs complex plays a critical role
in cell cycle. It phosphorylates the retinoblastoma (RB) and E2F1
complex causing a transition of the cell through the check point
G1/S and enters in S-phase [65]. The top right part of the model
represents the survival signaling pathway (PI3K/AKT), which usu-
ally has oncogenic behavior in cancer [66]. The survival signaling
pathway inhibits the pro-apoptotic genes regulated by E2F1 and
leads the cancer cell to an uncontrolled proliferation [67]. The

Boolean functions

BF 1 :X 1 (t+1) = NOTX 3 (t)

BF 2 :X 2 (t+1) = X 1 (t) ANDX 3 (t)

BF 3 :X 3 (t+1) = X 1 (t) OR X 2 (t)

BF 4 :X 4 (t+1) = X 1 (t) OR X 2 (t)

BF5:Phenotype(t+1) = X 2 (t) ORX 3 (t) ORX 4 (t)

x 1

x 2 x 3

x 4

OR

AND

Phenotype

NOT

OR

(a) (b)

Fig. 8Logic-based representation of biochemical network.Left: A model of
biological network consisting of four nodes (X1–4), and regulatory interactions
among, linked to a certain phenotype.Right: Derivation of Boolean functions
(BF) based on regulatory interactions among nodes

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