(332e) Systems Analysis of Signal Transduction in Insulin Mediated PI3K/AKT Pathway in Self-Renewal State of Human Embryonic Stem Cells

Motivation: Human Embryonic Stem Cells (hESCs) are an attractive raw material for
regenerative medicine due to their unique properties of self-renewal and
lineage specific differentiation. While the therapeutic potential of the
differentiated lineages is well known, production of large quantities of the desired
lineage requires abundant supply of pure starting material. During self-renewal,
hESCs proliferate to give more copies of them and this is initiated by
inhibition of differentiation signals and maintenance of signals that promote
proliferation. As a result, self-renewal provides an avenue for obtaining large
quantities of the raw material. Like any biological process, self-renewal is
also prone to variability and rational methods are necessary to control and
maintain a robust state. Currently, this is achieved by experimental means
alone. While experiments have identified the key signaling players, knowledge
of the systems level functioning of the signaling pathways in maintaining a
robust self-renewal state is still under-developed. In this work, we have
analyzed the systems level signal transfer properties of an important self-renewal
pathway using a mechanistic mathematical model adapted for hESC specific
behavior. Our primary goal is to identify robustness promoting mechanisms in
the pathway using a quantitative framework in conjunction with efficient
computational tools.

Methods: The phosphorylation dynamics of the key components of the insulin
mediated PI3K/AKT pathway was first analyzed by experimentally stimulating H1 hESCs
with 100 nM insulin after
growth factor starvation. The dynamics was then
compared to in silico simulations
using a detailed mechanistic differential equation model of insulin mediated
PI3K/AKT pathway (20 state variables, 25 parameters in the current form) by
Sedaghat et al. [1]. Global
sensitivity analysis (GSA) was used to identify the key interactions in the
pathway that affect the dynamics of self-renewal molecules like p-AKT. We
utilized a computationally efficient algorithm called Random Sampling High
Dimensional Model Representation (RS-HDMR) to capture sensitivity under
combinatorial interactions between the model parameters. We recently validated the
application of this algorithm for a signal transduction model [2]. In this
work, using GSA, we identified robustness promoting mechanisms that ensure (1)
maintenance of a first order or overshoot dynamics of self-renewal molecule,
p-AKT and (2) robust transfer of signals from oscillatory insulin stimulus to
p-AKT in the presence of noise. For the latter, we tested various oscillatory
scenarios of insulin stimulus to study the noise filtering capabilities of the
PI3K/AKT pathway. The robustness metrics propounded by Kitano were used to
quantify the input-output relationships [3].

Results and Discussion: In the
first theme, we analyzed the dynamics and steady states of four major nodes of
the PI3K/AKT pathway. These include the active insulin receptors (p-IR),
tyrosine IRS1 (p-IRS1 (Y)), serine IRS1 (p-IRS1 (S)) and p-AKT. Dynamics from
insulin stimulation experiments showed that intracellular components of
PI3K/AKT in hESCs show the typical overshoot behavior influenced by negative
feedback by p-IRS1 (S). Using phosphatase PTP as a control point to modulate
the dynamics, we found that presence of negative feedback acts as a robustness
promoting mechanism. For hESC specific parameter ranges that promote overshoot
behavior, the system is insensitive to perturbations associated with the
cascade reactions that propagate signals from the active receptors to the
downstream kinases (or the direct trunk of the pathway topology). We predict
that if the system looses its negative feedback character or shifts to a regime
where PTP levels are very low, it will become vulnerable to the otherwise
insensitive perturbations. In such a regime, the levels of self-renewal
molecules like p-AKT will be high but also highly variable. This sets up a
classical robustness tradeoff often seen in complex systems.

In the second theme, we evaluated the efficiency of
signal transfer for time dependent variation in the input signal. Our results
indicate that oscillations of small frequency (ω) and high amplitude (α)
are transduced down the pathway, but with amplitude attenuation under nominal
conditions. Our results also demonstrate that the downstream molecules follow
the main signal with very high fidelity even in the presence of noise. Any
modulation of upstream positive regulator IRS1 (p-IRS1 (Y)) can result in
amplification or attenuation of the signals. Increasing the oscillation frequency,
however, results in a regime where significant attenuation of the amplitude may
be achieved. Finally, at very high frequencies (log₁₀(ω
min^-1) > 0.5), all oscillations are cut-off. This region was not
affected by parameter perturbations. This shows that the PI3K/AKT pathway has
an intrinsic minimal response time and when the oscillations are faster than
this response time, they are not transmitted.

Conclusions: Our
detailed mechanistic model and experimental analysis identified the precise
mechanisms to modulate self-renewal molecules like p-AKT. Our results show that
faithful transfer of signal from the stimulating ligand to p-AKT occurs even in
the presence of noise, albeit with signal attenuation and high frequency
cut-off. This ensures that informative signals are transmitted down the pathway
and high frequency noise is cut-off. Negative feedback contributes to signal
attenuation, while positive regulators upstream of p-AKT contribute to signal
amplification. Quantitative measures of robustness can be used to finely
tune the signal transfer process in hESCs to ensure that the level as well as
the variability is kept within sufficient limits. Further, understanding the
parameter dependences of the signal filtering and cut-off processes can help in
the design of optimal input stimulation scenarios to modulate the pathway. Our
work presents a framework towards the design of targeted growth media to
maintain robust cellular fate of hESCs.

References:

[1] Sedaghat et al. A mathematical
model of metabolic insulin signaling pathways. American J of Physiol Endocrinol and Metabol (2002) 283: E1084-E1101

[2] Mathew et al. Bioinformatics 2014,
Article in Press, ID: bioinf-2013-2200.r1 (btu209).

[3] Kitano,
H. (2007). Towards a theory of biological robustness. Mol Syst Biol, 3,
137.