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							<h1> 3 Signaling module </h1>


	<h2> 3.1 Introduction </h2>



In our laboratory work, we performed CPP tests to explore the impact of downregulating 

MOR protein on mouse behavior after morphine administration, which is the ultimate goal 

of our project. In this module, computational and systems biology approaches were 

applied to examine the root of behavior changes quantitatively at the molecular level. 

The most important brain reward circuit involves dopamine-containing neurons in the VTA 

of the midbrain. Morphine can cause indirect excitation of VTA dopamine neurons by 

reducing inhibitory synaptic transmission mediated by GABAergic neurons [1,2]. 

<br><br>



<B>

We modeled the signaling network to investigate the emergent properties of the reward 

pathway. By comparing the activation degree of the reward pathway before and after 

downregulating MOR protein levels, we could have a better mechanistic understanding of 

drug effects. Although we did not perform any experiment to support this modeling 

module, the methods and parameters we chose are grounded in literature reports.

</B>

<br><br>

<!--插入第八张图--> <img src="https://static.igem.org/mediawiki/2015/e/ec/NJU-China-

Model_Figure8.jpg" style="width:600px" > <br><br>

Figure 8. Reward pathway of acute morphine administration. We focused on activation of 

MOR, inhibition of AC and release of GABA vesicles in this module. The reference 

pathway and figure are adapted from Kyoto Encyclopedia of Genes and Genomes database 

(KEGG).<br><br>




	<h2> 3.2 Model methods </h2>




<B> We used both deterministic and stochastic models to describe the activation of GPCR 

and release of GABA. </B>

In biological systems, signal transmission occurs primarily through two mechanisms: (i) 

mass-action laws governing protein synthesis, degradation and interactions; and (ii) 

standard Michaelis-Menten formulation for reactions catalyzed by enzymes [3]. 

<br><br>



Broadly, mathematical models of biochemical reactions can be divided into two 

categories: deterministic systems and stochastic systems [3]. In deterministic models, 

the change in time of the components’ concentrations is completely determined by 

specifying the initial and boundary conditions; by contrast, the changes in 

concentrations of components with respect to time cannot be fully predicted in 

stochastic models [3]. In the previous two modules, we modeled the delivery device and 

RNA interference using deterministic models. 

<br><br>
	

	<h3> 3.2.1 Modeling the activation of MOR </h3> 



MOR belongs to the class A (Rhodopsin) family of heterotrimeric Gi/o protein-coupled 

receptors [4]. The binding of opioids to MOR activates the G protein, upon which both 

G-protein α and βγ subunits interact with multiple cellular effector systems. As the 

first step of signal transmission, the degree of activation of MOR in response to 

opioid has a direct and far-reaching influence on the behavior of mice. 
<br><br>



Deterministic models were applied to describe the biochemical reactions occurring in 

the diagram below. We used the Matlab Simbiology package to draw the diagram and to 

design the equation, the details of which are accessible on the uploaded files. This 

model was created on the basis of work by Bhalla and Iyengar on the activation of 

glutamate receptor [5]. 

<br><br>


<!-- 插入第九张图>   <img src="https://static.igem.org/mediawiki/2015/e/e3/NJU-China-

Model_Figure9.jpg" style="width:600px" > <br><br>


Figure 9. Reaction schemes for the activation of MOR in the simulation. Reversible 

reactions are represented as bidirectional arrows; irreversible reactions, as 

unidirectional arrows. This figure is adapted from the literature [5]. <br><br>


	<h3> 3.2.2 Modeling adenylate cyclase inhibtion </h3>


The concentration of second messenger is a significant indicator of excitability of 

GABAergic neurons. Thus, we chose to simulate cAMP levels and adenylate cyclase (AC) 

activity to determine the effect of downregulating MOR protein levels on morphine 

reward signaling networks. 

<br><br>



AC1/8 is a type of adenylate cyclases involved in the signaling of the acute morphine 

reward pathway [6]. When MOR is activated, the disassociated Gα subunit reacts with 

AC1/8 and subsequently inhibits its activity, leading to a decrease in cellular cAMP 

levels. The parameters of this model were primarily derived from the literature [5] 

with slight modifications to fit to the data presented in the literature [7]. 

<br><br>


<!-- 插入第十张图 --> <img src="https://static.igem.org/mediawiki/2015/7/78/NJU-China-

Model_Figure10.jpg" style="width:600px" > <br><br>




Figure 10. Reaction schemes for inhibition of AC in simulation. Reversible reactions 

are represented as bidirectional arrows, and enzyme reactions are drawn as an arrow 

with two bends. AC: adenylate cyclase; PDE: phosphodiesterase. 
<br><br>

	<h3> 3.2.3 Modeling GABA vesicle releases </h3>


A stochastic model was applied to describe the random behavior of neurotransmitter 

vesicles release [8]. GABA is an important inhibitory neurotransmitter, the level of 

which directly determines the firing rate of dopamine neurons and other physiological 

and behavioral statuses. The GABA synaptic vesicle cycle consists of three discrete 

processes: synthesis of GABA vesicles, docking of GABA vesicles at the inner membrane 

of presynapses and release of GABA vesicles reacting to a certain signal. The release 

of GABA vesicles is strictly regulated by cellular signaling networks. When Gi/o is 

activated and the cellular cAMP level drops, the release of GABA is inhibited. Many 

complicated mechanisms are involved in the inhibition of GABA release due to activation 

of Gi/o. Here, we simply studied the action potential-independent pathway of GABA 

release, through which the release of GABA is directly inhibited by activated Gβγ 

subunits [9]. 

<br><br>


<!-- 插入第十一张图 --> <img src="https://static.igem.org/mediawiki/2015/1/16/NJU-China-

Model_Figure11.jpg" style="width:600px" > <br><br>


Figure 11. Schematic representation of GABA release in which four steps are modeled 

using mass action law and the stochastic method. <br><br>

	<h3> 3.2.4 Gillespie’s algorithm </h3>



When spatially restricted reactions, such as the release of neurotransmitter vesicles, 

are studied, the traditional deterministic model is no longer effective for ignoring 

the discrete nature of the problem [3]. Stochastic models convert reaction rates to 

probability, which allows users to explore the noise and randomness of signaling 

networks. A standard algorithm dealing with stochastic model is Gillespie’s algorithm. 

This algorithm starts with the initial condition for each molecule type in the reaction 

network. Then, Monte Carlo simulation is applied to generate some random variables and 

to calculate the smallest time interval in which the reaction will occur [3,10]. 

Finally, the number of molecules in the reaction network is updated, and the process is 

repeated. 

<br><br>

	<h2> 3.3 Results </h3>


The simulation results revealed the kinetics of MOR activation in case and control 

studies. In the CPP test, the Western blot result demonstrated that the relative level 

of MOR protein after MOR-siRNA injection was 0.5. 

<B> Thus, the concentration of MOR protein was set at half of the level in the case 

study. </B>

<br><br>

<B> The results indicated that almost all the MOR protein is activated in response to 

morphine. The quantity and action of Gα and βγ subunits highly correlates with the 

quantity of MOR protein. By downregulating the MOR protein to half of its initial 

level, we also inhibit approximately half of activated Gα and βγ subunits. </B>

<br><br>

<!-- 插入第十二张图 --> <img src="https://static.igem.org/mediawiki/2015/b/b3/NJU-China-

Model_Figure12.jpg"  > <br><br>


Figure 12. Concentration-time curves for the activation of MOR in response to morphine. 

A: Control study with the concentration of MOR set at 1 mM. B: Case study with the 

concentration of MOR set at 0.5 mM due to downregulation by MOR-siRNA. Ga_GTP and Gbg 

represents activated Gα and βγ subunit, respectively.

<br><br>



The primary effector of activated Gα subunit is AC. The activation degree of AC 

influences its product cAMP—an important second messenger that indicates the 

excitability of GABAergic neurons. We now theoretically predicted and compared the 

inhibition of Gα subunit on AC and the subsequent decrease in cellular cAMP levels in 

control (wild type) and case (MOR-siRNA injected) studies. 

<br><br>


<!-- 插入第十三张图 --> <img src="https://static.igem.org/mediawiki/2015/d/d4/NJU-China-

Model_Figure13.jpg" > <br><br>


Figure 13. Effect of downregulating MOR protein on AC activity (A) and cellular cAMP 

levels (B) in response to morphine. The input level of MOR protein is based on the 

result shown in Figure 10. 

<br><br>



<B> Activation of wild type MOR protein inhibited over 25% of AC, and relative cellular 

cAMP levels dropped below 70%, which is consistent with findings in the literature[7].
</B>

The injection of MOR-siRNA reduces the activation quantity of MOR and significantly 

attenuates the inhibition of AC and decrease in cAMP levels. Maintaining the cellular 

cAMP level induced by the drug plays a crucial role in blocking reward pathways.

<br><br>



Finally, we explored the relationship between MOR activation and GABA release. The wild 

type study revealed significant inhibition of GABA vesicles due to activated G βγ 

subunits. MOR-siRNA counteracted this trend by downregulating MOR protein and activated 

G βγ subunit levels as depicted in the case study. Maintaining GABA release reduces 

the excitability and firing rate of dopamine neurons, which is consistent with the 

expected drug effect on blockage of the reward pathway and explain the behavioral 

changes observed in the CPP tests.

<br><br>

<!-- 插入第十四张图 --> <img src="https://static.igem.org/mediawiki/2015/5/51/NJU-China-

Model_Figure14.jpg"  > <br><br>


Figure 14. Stochastic modeling of GABA release. A: Control study with the establishment 

of mass balance between synthesized, docked and released GABA vesicles. B: Case study 

with MOR-siRNA injected to attenuate the inhibition of GABA release. C: Wild type study 

with a normal level of MOR protein activation resulting in inhibition of GABA release. 

D: Summary of numbers of released and inhibited GABA vesicles in different treatments. 

The results are presented as the mean±S.D. 

<br><br>

	<h2> 3.4 Conclusion and remarks </h4>
<B>



In this module, we used deterministic and stochastic methods to model the cell 

signaling network and to predict the blockage of the reward pathway by injecting MOR-

siRNA. The simulation results could somewhat explain the behavioral changes observed in 

the CPP tests (function tests) mechanistically.

<br><br>

</B>

	<h2> 3.5 Model equations, variables and parameters </h5>


The modeling details of the activation of MOR protein and inhibition of AC are 

truncated here because we use software to help us design the model and there are too 

many parameters and equations. We have uploaded relevant source code and files for 

those individuals interested in exploring the models. However, we want to emphasize 

that our parameters are all derived from the literature. The modeling of GABA release 

is inspired by the literature [8] and the parameters were estimated from literature 

[2].

These parameters, as well as initial conditions, can be accessed in our uploaded files 

and we selectively list part of them below.

<br><br>


These parameters, as well as initial conditions, can be accessed in our uploaded files 

and we selectively list part of them below.

<br><br>


	<h3> 3.5.1 Activation of MOR </h3>

Model Parameters 

<br><br>

<img src="https://static.igem.org/mediawiki/2015/d/de/NJU-China-model-sig-1.jpg" >

<br><br>

???: Although activation of MOR has not been modeled yet, we use activation of 

Glutamate receptor, which has been modeled in the literature as an approximation.

<br><br>

Model Equations

<!-- 插入第一张公式 --> <img src="https://static.igem.org/mediawiki/2015/b/ba/NJU-China-

Equation_Sig_1.jpg" style="width:600px"> <br><br>

<!-- 插入第二张公式 --> <img src="https://static.igem.org/mediawiki/2015/0/0b/NJU-China-

Equation_Sig_2.jpg" style="width:600px" > <br><br>


	<h3> 3.5.2 Activation of MOR </h3>

Model Parameters 

<br><br>

<img src = "https://static.igem.org/mediawiki/2015/f/f2/NJU-China-model-sig-2.jpg" >

<br><br>

???: No literature has directly reported binding and disassociation constant of Gi to 

AC. Therefore, we use the binding and disassociation constant of Gs to AC as an 

approximation derived from literature[5]. 

<br><br>


Model Equations

<!-- 插入第三张公式 --> <img src="https://static.igem.org/mediawiki/2015/a/a7/NJU-China-

Equation_Sig_3.jpg"> <br><br>


	<h3> 3.5.3 GABA release </h3>

Model Parameters 

<br><br>

<img src = "https://static.igem.org/mediawiki/2015/1/19/NJU-China-model-sig-3.jpg" >

<br><br>

References: <br>
1.Fields, H.L. and Margolis, E.B. (2015) Understanding opioid reward. Trends in 

neurosciences, 38, 217-225. <br>
2.Sotomayor, R., Forray, M.I. and Gysling, K. (2005) Acute morphine administration 

increases extracellular DA levels in the rat lateral septum by decreasing the GABAergic 

inhibitory tone in the ventral tegmental area. Journal of neuroscience research, 81, 

132-139. <br>
3.Eungdamrong, N.J. and Iyengar, R. (2004) Computational approaches for modeling 

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