Computational Molecular Bioscience, 2013, 3, 39-52 Published Online September 2013 (
Copyright © 2013 SciRes. CMB
Modeling and Simulation of Molecular Mechanism of
Action of Dietary Polyphenols on the Inhibition of
Anti-Apoptotic PI3K/AKT Pathway
Pedro Pablo González-Pérez1, Maura Cárdenas-García2
1Departamento de Matemáticas Aplicadas y Sistemas, Universidad Autónoma Metropolitana, México, D.F., México
2Facultad de Medicina, Benemérita Universidad Autónoma de Puebla, Puebla, México
Received May 9, 2013; revised June 9, 2013; accepted June 17, 2013
Copyright © 2013 Pedro Pablo González-Pérez, Maura Cárdenas-García. This is an open access article distributed under the Creative
Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original
work is properly cited.
In recent years, the role of dietary phenolic compounds in the regulation of cellular metabolism in normal and patho-
logical conditions has become increasingly important in cancer research. In most cases, the molecular mechanism of
action related to the anticarcinogenic effect of phenolic compounds has been studied in vitro and in animal models , but
these studies are still not co mplete. It is precisely here where in silico approaches can be an invaluable too l for co mple-
menting in vitro and in vivo research. In this paper, we adopt a tuple space-based modeling and simulation approach,
and show how it can be applied to the simulation of complex interaction patterns of intracellular signaling pathways.
Specifically, we are working to explore and to understand the molecular mechanism of action of dietary phenolic com-
pounds on the inhibition of the PI3K/AKT anti-apoptotic pathway. As a first approximation, using the tuple spaces-
based in silico approach, we model and simulate the anti -apoptotic PI3K/AKT pathway in the absence and presence of
phenolic compounds, in order to determine the effectiveness of our platform, to employ it in future prediction of expe-
rimentally non visualized interactions between the pathway components and phe n olic compounds.
Keywords: Anti-Apoptotic Intracellular Signaling Pathway; Computer Simulatio n; Self-Organizing Coordination;
Tuple-Based Model
1. Introduction
The phenolic compounds present in plants have attracted
much attention. In general, the main action of these
compounds is the chemical relationships of plants with
their environment. They are found in virtually all plants
and therefore are integrated in the human diet; the role of
phenolic compounds in the regulation of cellular metabo-
lism in normal and pathological conditions has become
increasingly important interest area [1]. Once incorpo-
rated into the human diet, phenolic compounds may have
varied effects. As antioxidants, they have many benefits
for health, can protect cell structures from attack by reac-
tive oxygen species and thus limit the risk of diseases
associated with oxidative stress. Numerous studies have
attributed to the dietary phenolic compounds an impor-
tant role in preventing cardiovascular diseases, cancer,
osteoporosis, diabetes mellitus and neurodegenerative
diseases [2].
During recent years, various papers have been publish-
ed that suggest a variety of other mechanisms, through
which polyphenols could exert part of their beneficial
actions on biological systems. They include polyphenols
ability to modulate the activity of enzymes such as telo-
merase [3], cyclooxygenase [4,5] and lipoxygenase [6,7],
or their ability to interact with signal transduction and
cellular receptors [8-10]. The effects in the treatment of
HIV infection were described [11], as well as their an-
ti-inflammatory [12,13], anti atherogenic, anti-diabetic
[14], and anti-obesity [15] properties.
The cell has a self-destruction system that starts and
operates in a regulated manner. It is called apoptosis and
includes the decision to start self-destruction as well as
the proper execution of the apoptotic program. As such it
requires the coordinated activation and execution of mul-
tiple subprograms. On the other hand, cancer cells initi-
ate anti-apoptotic programs since th eir goal is to survive.
Tumorigenesis and tumour progression are the result of
Copyright © 2013 SciRes. CMB
imbalance among cell proliferation, differentiation and
apoptosis. Thus, in tumour cells, activation of the anti-
apoptotic signaling pathway associated with p hosphatidyl
inositol-3-kinase (PI3K), mitogen-activated protein kina-
ses (MAPKs) and protein kinase C (PKC) occurs. The
tumour cells survive after exposure to stress and these
proteins are only expressed at high levels in transformed
cells [16]. Dietary polyphenols may interfere at different
developmental stages of carcinogenesis through several
mechanisms, and its effects will depend on the tissue or
cell type, as well as on the dose and duration of treat-
In most cases, the molecular mechanism of action re-
lated to the anticarcinogenic effect of polyphenols has
been studied in vitro and in animal models, but these
studies are still not complete. It is precisely here that in
silico approaches can be an invaluable tool for comple-
menting in vitro and in vivo research.
In this paper, we propose an in silico approach based
on biochemical tuple spaces [17], and show how it can be
applied to the simulation of complex interaction patterns
of intracellular signaling pathways, particularly in the
si mul ation of molecular mechanism of action of dietary
polyphenols on the inhibition of anti-apoptotic intracel-
lular pathways.
2. BTSSOC-Based in Silico Approach
The simulation approach presented here is based on the
notion of Biochemical Tuple Space for Self-Organizing
Coordination (BTSSOC), as introduced in [17] for per-
vasive services ecosystems. In BTSSOC each tuple space
works as a compartment where biochemical reactions
take place, chemical reactants are represented as tuples,
and biochemical laws are represented as coordination
laws by the coordination abstraction. Technically, bio-
chemical tuple spaces are built as ReSpecT tuple centres
[18], running upon a TuCSoN c oordination infra structure
[19]. Tuples are logic-based tuples, while biochemical
laws are implemented as ReSpecT specification tuples—
so they can be inserted, modified and removed from the
compartment (the type centre) via simple Linda-based
coordina tion primitives [20].
As can be seen from the Figure 1, the high level ar-
chitecture of the BTSSOC-based in silico approach is
defined in terms of the following two main modules:
BTSSOC-based model.
GUI application.
2.1. The BTSSOC-Based Model
This module represents our general model for intracellu-
lar signaling pathways based on the notion of Biochemi-
cal Tuple Spaces for Self-Organizing Coordination (BT-
SSOC). A detailed explanation of this model can be
found at [21]. As shown in Figure 1, four main compo-
nents define the structure of the BTSSOC-based model:
Tuple centres.
Chemical reaction sets.
Reactants (R).
Coordination laws.
The cellular compartments involved in signaling path-
ways are represented as biochemical tuple spaces—that
is, tuple centres suitably programmed with the ReSpecT
logic language [18]. In particular, each biochemical tuple
space is built around a ReSpecT chemical engine, whose
core is an action selection mechanism based on Gillespie
algorithm [22]an algorithm typically used to simulate
systems of chemical/biochemical reactions efficiently
and accurately—to execute chemical reactions with the
proper rate.
The components representing intracellular signaling
elements must focus only on the transformation of input
signals into output signals, according to the behavior of
the corresponding signaling element. So, if the main ta sk
of the signaling components (i.e., those modeling mem-
brane receptors, proteins, enzymes and genes) is to per-
form chemical reactions for signal transduction, the most
appropriate solution in the BTSSOC-bas ed approach is to
model each signaling component as the set of the chemi-
cal reactions that defines its behavior.
The elements recorded as tuples in a tuple centre (i.e.,
reactants and concentration) represent the information
about the two main sorts of intracellular signal in the
model: signaling molecules and activation/deactivation
signals. Such elements are the inputs and outputs for the
chemical reactions that belong to each tuple centre, and
set their activation, duration, or deactivation either di-
rectly or indirectly. As a result, in the same way as in
Figure 1. A high-level architecture for the BTSSOC-based
bioinformatics platform.
Copyright © 2013 SciRes. CMB
biological systems, evolution depends on the concentra-
tion and state of the reactants.
Biochemical laws are represented as coordination laws
by the coordination abstraction, in the model the tuple
concentration evolves over time according to a rate in the
same way as chemical substances in a solution. Also,
BTSSOC laws allow for tuple diffu sion, making it possi-
ble for products to cross compartment boundaries as a
result of bi ochemical reactions.
2.2. The GUI Application
The module GUI application provides the tools for users
so that they can perform the following activities:
Creation, modification and visualization of the simu-
lation componentse.g., cellular compartments, sig-
naling components, signaling molecules and cellular
processes—either during the implementation of the
simulation or during the execution of it.
Feedback on the results provided by the simulation
run—e.g., 1) the behavior of the simulated system
over time, as a result of the interactions that take
place between its components, and 2) the current state
of each component (i.e., signaling elements) of the
As shown in Figure 1, the module GUI application is
composed by three main types of components:
User agent.
Monitor agents.
Here we refer only to the component View, which
corresponds to the graphical user interface of BTS-SOC-
based in silico approach (BTS-SOC GUI). This compo-
nent contains four major elements: 1) a main menu, 2) a
drawing canvas, 3) the buttons to start and stop the simu-
lation, and 4) the Tucson command line. The functional-
ity distributed among these elements is precisely what
allows BTSSOC infrastructure appearing to the users as a
true virtual laboratory, where the experiments can be
initiated, developed, stored, recovered, modified, visual-
ized, restarted, and stopped. Figures 2 and 3 show two
BTSSOC GUI snapshots.
3. The Inhibition of Anti-Apoptotic
PI3K/AKT Path way
In this paper we simulate the potential role of polyphe-
nols induced apoptosis in human cells of breast cancer
[23-27]. According to reports in the literature, polyphe-
nols inhibit the prolif eration of MCF-7 cells in a concen-
tration and time dependent manner [23,24].
As mentioned, activation of anti-apoptotic PI3K path-
way is frequently observed in many human cancer cells
[28]. Survivin, a member of the family of inhibitors of
apoptosis, is expressed in most human cancers but is un-
detectable in normal differentiated tissues. Survivin is
elevated in cancer cells and it is induced by some growth
factors through the activation of PI3K. Forced expression
Figure 2. The main GUI of BTSSOC-based computer simulation showing its four main components: 1) the main menu, lo-
cated on the bottom of the scr een, 2) the drawing canvas, represented by the large central area of the screen, 3) the buttons to
start and stop the simulation, placed on the far right of the screen, and 4) the Tucson command line, located on the bottom of
the screen.
Copyright © 2013 SciRes. CMB
Figure 3. The drawing canvas of the BTSSOC GUI. Different charts showing the state of the simulation components and its
interac ti on s can be supported by the dr aw ing canvas.
of PTEN leads to decreased levels of survivin mRNA.
PI3K regulates the expression of survivin through the
activation of Akt. P70S6K1 over expression alone is suf-
ficient to induce survivin expression. PI3K/Akt/p70S6K1
pathway is essential for the regulation of mRNA expres-
sion of survivin.
4. The Incremental Modeling and Simulation
As can be seen in Figure 4, modeling an intracellular
signaling network with the BTSSOC-based a pproach can
be handled as an incremental process of definition and
refinement of the signaling pathways and components
Copyright © 2013 SciRes. CMB
Figure 4. Incremental modeling process of anti-apoptotic signaling pathway PI3K/AKT, the cube represents the characteris-
tics of our current work and moves it according to our needs.
in the network.
The modeling process begins by considering a single
signaling pathway from the complex network of the anti-
apoptotic signaling pathways. The PI3K/AKT pathway
has been selected for this purpose. At the beginning of
the modeling process it is appropriate to consider only
the main features of the components involved in the anti-
apoptotic signal transduction, i.e. those needed for
achieving a comprehensible and functional simulation.
The incremental development of the modeling process
proceeds by incorporating other signaling components,
defining the interactions among them (set of chemical
reactions), and including other features of the biochemi-
cal elements that are required for the accurate modeling
of the signaling system. Clearly, the more the modeling
process preserves the essential features of signal trans-
duction, the more the intracellular signaling model be-
comes significant.
5. Results and Discussion
As can be seen in Figure 5, the simulation presented here
represents the PI3K/AKT signaling pathway in the pres-
ence of phenolic compounds in a breast cancer cell. The
pathway begins with the grow th or survival signals, these
signals can lead to disruption of apoptosis, but in the
presence o f ph e nolic compounds apoptosis may continue.
5.1. Modeling PI3K/AKT Anti-Apoptotic
Signaling Pathway
The PI3K/AKT pathways influence either directly or
indirectly whether a cell will undergo apoptosis. These
events are probably essential for the survival of both
cancer and normal cell. In this paper, we start with a mi-
nimalist model (see Figure 4) where each signaling
component belonging to PI3K/AKT pathway is described
by the following attributes:
Concentration in each cellular compartment.
Free concentration.
Bound concentration.
Cellular compartment to which it belongs.
Chemical reactions involving the component and the
order in which they occur according to the affinity of
the components.
Reaction temporality situation.
Such a coarse-grained modeling strategy leads to an
initial BTSSOC-based model of the PI3K/AKT signaling
pathways, whose main features are summarized in
Tables 1 and 2.
As far as this initial PI3K/AKT anti-apoptotic intra-
cellular signaling model is concerned, only two further
remarks are required here. For each chemical reaction, 1)
the interaction between reactants is set to 1:1 in terms of
Copyright © 2013 SciRes. CMB
Table 1. Modeling the signal components belonging to anti-
apoptotic signaling pathway. The symbol @ on the right
side of an equation indicates the cellular compartment in
which the resultant reactant must be registered.
compartment Reactions
space and
membrane RTK + SF RTK*@cytosol
RTK* + PI3K PI3K* + RTK*
RTK* + Ras Ras* + RTK *
PI3K* + PIP3 PIP3* + PI3K*
PI3K* + AKT AKT* + PI3K*
PIP3* + PDK1 PDK1*
PIP3* + PDK2 PDK2*
AKT* + XIAP XIAP*@nucleus + AKT*
AKT* + Raf Raf1*@nucleus + AKT*
AKT* + Mdm2 Mdm2* + AKT
Ras* + PI3K PI3K* + Ras
AKT* AKT*@nucleus
Mdm2 + p53* Mdm2 + p53
4p53* 2p53*@nucleus
GSK3* + BAD/1433* BAD/1433 + GSK3
BAD/1433* + BclXL* BclXL@nucleus + BAD1433*
BAD/1433* + Bcl2* Bcl2@nucleus + BAD1433*
4GSK3* 2GSK3*@cytoso l + 2GSK3*@nucleus
4BclXL* 2BclXL*@cyt osol + 2BclXL*
Raf1* + p70S6K p70S6K* + Raf1
GSK3* + p21Cip1* p21Cip1 + GSK3
GSK3* + cyclinD1* cyclinD1 + GSK3
mTOR* + p70S6K p70S6K* + mTOR
Table 2. Initial concentrations of the reactants belonging to
anti-apoptotic PI3K/AKT signaling pathway.
Identity Concentration
SF Survival factor 0.1 nM [29]
RTK Receptor tyrosine kinase 0.25 µM [29]
PI3K Phosphatidylinositide 3-kinases 0.1 µM [30]
Ras Small GTPase 18.9 µM [31]
Ras Small GTPase 18.9 µM [31]
SHIP2 Phosphatase that regulates the
PI3K/AKT pathway 0.1 µM [31]
(SHIP1) Phosphatase that regulates the
PI3K/AKT pathway 0.27 µM [31]
Raf Serine/threonine-protein kinase 0.07 µM [31]
PDK1 3-phosphoinositide dependent
protein kinase-1 1.0 µM [32]
PDK2 Pyruvate dehydrogenase 1.0 µM [32]
PIP3 Phosphatidylinositol
(3,4,5)-triphosphate 7.0 µM [32]
SF Survival factor 0.1 nM [29]
IKK IkappaB kinase 0.1 µM [32]
AKT (PKB) Serine/Threonine specific
protein kinase 0.2 µM [33,35]
XIAP X-linked in hibitor of apoptosis protein 0.1 µM [34]
P70S6K Serine/threonine kinase 0.17 µM [35]
mTOR Serine/threonine kinase 0.6 µM [31,35]
TSC1 Tumor suppressor 1 1.0 µM [36]
TSC2 Tumor suppressor 2 0.1 µM [36]
GSK3 Serine/threonine kinase 0.1 µM [37]
Mdm2 Negative regulator of the p53
tumor suppressor 0.1 µM [38]
p53 Tumor suppressor 0.1 µM [23]
Cyclin D1 G1/S specific cyclin 0.1 µM [39]
p21 Cyclin-dependent kinase inhibitor 1 0.1 µM [40]
Bcl2 Regulator protein 0.1 µM [41]
1433 Regulator protein 0.1 µM [32]
Bad Bcl-2 associated death promoter 0.1 µM [40]
BclXL Transmembran e molecule 0.1 µM [24]
PhC Phenolic Compound 34 µ M [27]
number of units—that is, 1 unit of the reactant A is re-
quired for 1 unit of the reactant B; 2) the initial rate is set
to a very small value (i.e., rate = 0.001), allowing the
Gillespie algorithm to show the evolution of chemical
Copyright © 2013 SciRes. CMB
Figure 5. The anti-apoptotic PI3K/AKT pathway.
reactions in a straightforward way.
5.2. Setting the BTSSOC-Based Simulation
A tuple centre (BTS) is required for each cellular com-
partment involved in the signaling pathway to be simu-
lated. In our study, the anti-apoptotic PI3K/AKT signal-
ing pathway begins in the extracellular space, continues
in the membrane, it goes through the cytosol, some sig-
naling components in and out of the mitochondria, finally
ending in the nucleus. In our first simulation the ex- tra-
cellular space and membrane are represented in a sin- gle
compartment. Therefore, four tuple centres (mem- brane,
cytosol, mitochondria and nucleus) are required to model
four intracellular compartment s—see F igure 6.
Next step is aimed at making every BTS to know its
neighbors—i. e., the cellular compartments that follow it
in the signaling pathway.
Our minimal case scenario is about a downstream sig-
naling pathway: therefore, every BTS has only one
neighbor here, whereas the infrastructure supports mod-
eling multiple neighbors. Once every BTS knows its own
neighbor, reactions producing reactants meant to cross
the compartment boundaries can be published in the BTS,
and made available in the linked compartment.
In order to set up the simulation system, reactants
should be introduced in the BTS. First of all, each reac-
tant belongs to a specific cellular compartment—so, it
has to be put in the appropriate BTS. Initially, only the
pre-existing reactants—i.e., those reactants already in the
compartments before the signaling pathway is acti-
vated—have to be put in the BTS.
As an example, we consider a reaction belonging to
the cytosol BTS, and described in Equation (2). Among
the reactants, only PI3K is initially present in cytosol
BTS, while RTK* is the product of another reaction
Equation (1)in the membrane BTS, and needs not to be
in the compartment at the beginning of the simulation.
In order to place a reactant “A” in a BTS, a tuple of
the kind reactant (A, concentration) has to be published
in the corresponding tuple centre—there, concentration is
an integer number that represents the amount of that
element in the cellular compartment expressed in terms
of the number of units of such a reactant.
Copyright © 2013 SciRes. CMB
Figure 6. Publication and inspection of chemical reactions in the cytosol BTS.
The last step in setting up the simulation is the intro-
duction of the reactions modeling the behavior of signal-
ing pathway. As for the reactants, it is necessary before
proceeding to clearly allocate the whole set of reactions
in the different compartments, in order to publish them in
the proper BTS. As can be seen from Figure 6, the BTS-
SOC GUI allows the user to publish the chemical reac-
tions in “everyday language”, using the notation com-
monly employed when writing these equations. In our
model, based on the Gillespie algorithm, every chemical
reaction has a rate that expresses (along with the concen-
tration of the input elements) the probability of the
5.3. Running the Simulation of the
Anti-Apoptotic PI3K/AKT Pathway
After entering all required information and setting the
initial parameters, the system is now ready to run the
PI3K/AKT pathway simulation. Biologically, apoptosis
is initiated once normal cells turn into abnormal cells,
carrying out a self-destruction mechanism. However, the
cancer cell survives by producing survival factors that
initiate anti-apoptotic pathway. PI3K/AKT signaling
pathway is initiated when a survival factor (SF), binds to
its receptor (RTK). This event triggers the first reaction
of PI3K/AKT signaling pathway, leading the signal to
the final activation of transcription factors in the nucleus
and, consequently, the activation of transcription of pro-
teins that allow the cell to survive and proliferate—see
Figure 5. The existence of the reactant SF in the mem-
brane BTS is the main triggering event for our simulation.
Once the reactant SF is available in the membrane BTS,
the biochemical engine chooses the only eligible reaction
for execution—i.e., Equation (1).
Once active RTK—i.e., RTK*and after a short,
non-deterministic period of time (defined by Gillespie
algorithm and ruled by probability), the chemical engine
chooses and performs one of two eligible chemical reac-
tions of the cytosol BTSi.e., Equations (2) and (3).
Copyright © 2013 SciRes. CMB
* **
RTKRasRasRTK .+→ + (3)
Which one among the two reactions is picked for exe-
cution by the biochemical enginegiven both the iden-
tical initial rates and the interaction 1:1 between reactants
depends on the free concentration for each reagent
involved in the reaction.
The production of PI3K* and, consequently, the in-
cremental availability of th is one in the cytoso l BTS lead
PIP3 a ctivation and thus the amplification of the original
signal, as shown in Equation (4) to Equation (10).
* **
PI3KPIP3PIP3PI3K .+→ + (4 )
* **
PI3KAKTAKTPI3K .+→ + (5)
PIP3AKTAKT .+→ (6)
PIP3PDK2PDK2.+→ (8)
(9 )
Figure 7 shows the phase of the simulation where the
activation of AKT (AKT*) have taken place. AKT acti-
vation allows the sequential activation of a series of re-
agents and hence cell survival and proliferation. AKT
target proteins can be classified into three distinct groups:
anti-apoptotic proteins, anti-p53 and proteins promoting
cell proliferation. In the PI3K/AKT simulation the phos-
phorylation of these target proteins is represented in Eq-
uation (11) to Equation (17).
AKT IKKIKK AKT.+→ + (11)
** *
AKTXIAPXIAP@nucleusAKT .+→+ (12)
** *
AKTRaf1Raf1@nucleusAKT .+→+ (13)
AKTMdm2Mdm2 AKT.
+→ +
AKTTSC1TSC1 AKT.+→ + (16)
Figures 8 and 9 show the variation in levels of reac-
tants and products during signal transduction to achieve
the activation of many downstream effector proteins—
e.g., IKK, XIAP, Raf1 and Mdm2. Thus, the signal
transduction goes ahead until reaching the activation of
transcription factors in the nucleus BTS.
As mentioned above, in each execution cycle the ac-
tion selection mechanism—through the Gillespie algo-
rithm—determines: 1) how long to wait before becomes
active again, and 2) which reaction execute among all
eligible chemical reactions.
Figure 7. Execution of the reactions involved in the first segment of the simulation: from SF binding to AKT activation. At
the bottom of the graph can be seen all the reactants implicated in these reactions. The symbol “*” refers to active/phos- pho-
rylated state of the reactant.
Copyright © 2013 SciRes. CMB
Figure 8. Execution of the reactions involved in the second segment of the simulation: from activation of AKT to activation of
many downstream effector proteins. At the bottom of the graph can be seen all the reactants implicated in these reactions.
The symbol “*” refers to active/phosphorylated state of the reactant.
Figure 9. Variations of reactant concentrations in time. Second segment of the simulation: from activation of AKT to activa-
tion of many downstream effector proteins.
Copyright © 2013 SciRes. CMB
Upon simulating this anti-apoptotic pathway, we ob-
served that a cancer cell escapes death, but if components
PTEN and SHIP2 are present in the signaling system—in
Equations (18 ) and (19)—then de cell stops growing and
dies—see Figure 10.
SHIP2PI3KPI3K SHIP2.+ →+ (19)
However, if simultaneously with the activation of
PI3K, Ras is active, then this latter also activates PI3K,
although PTEN and SHIP2 are present, according to Eq-
uation (20).
Ras PI3KPI3KRas.+→ +
(2 0)
Figure 11 shows how the mere presence of active Ras
allows the cell to survive even in the presence of regula-
tors of the PI3 K/ A KT pat hway.
As shown in Figures 7 to 11, the proposed tool suc-
cessfully simulated the effect of dietary polyphenols on
the inhibition of the anti-apoptotic PI3K/AKT pathway,
which is a great first step in predicting the molecular
mechanism explaining how dietary polyphenols can in-
hibits cancer cell growth.
6. Conclusions
Phenolic compounds are ubiquitous in plant foods, and
therefore, significant quantities are consumed in our dai-
ly diet, and play an important role in both the prevention
and the pathogenesis of many chronic diseases.
One of our existing research areas focuses on molecu-
lar mechanism for po lyphenols activity, and we are using
in vivo, in vitro and in silico models. First, we perform in
silico modeling for caspase pathway. In this work, we
model the anti-apoptotic PI3K/AKT pathway in the ab-
sence or presence of phenolic compounds, using BTS-
SOC-based model. This platform will be helpful in plan-
ning future experiments in vitro when we integrate it
with different antiapoptotic pathways.
Figure 10. The presence of PTEN and SHIP 2, as well as the activation of p53, cause the cell to stop growing and die.
Copyright © 2013 SciRes. CMB
Figure 11. Active Ras allows the cell to survive even in the presence of PTEN and SHIP2.
7. Acknowledgements
The authors would like to thank Andrea Boccacci for
making a valuable contribution to this project.
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