Latest revision as of 03:27, 3 October 2015

model

3 Signaling module

3.1 Introduction

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].

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.

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).

3.2 Model methods

We used both deterministic and stochastic models to describe the activation of GPCR and release of GABA. 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].

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.

3.2.1 Modeling the activation of MOR

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.

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].

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].

3.2.2 Modeling adenylate cyclase inhibtion

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.

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].

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.

3.2.3 Modeling GABA vesicle releases

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].

Figure 11. Schematic representation of GABA release in which four steps are modeled using mass action law and the stochastic method.

3.2.4 Gillespie’s algorithm

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.

3.3 Results

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. Thus, the concentration of MOR protein was set at half of the level in the case study.

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.

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.

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.

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.

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]. 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.

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.

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.

3.4 Conclusion and remarks

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.

3.5 Model equations, variables and parameters

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.

These parameters, as well as initial conditions, can be accessed in our uploaded files and we selectively list part of them below.

3.5.1 Activation of MOR

Model Parameters:

???: 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.

Model Equations:

3.5.2 Activation of MOR

Model Parameters:

???: 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].

Model Equations:

3.5.3 GABA release

Model Parameters

References:
1.Fields, H.L. and Margolis, E.B. (2015) Understanding opioid reward. Trends in neurosciences, 38, 217-225.
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.
3.Eungdamrong, N.J. and Iyengar, R. (2004) Computational approaches for modeling regulatory cellular networks. Trends in cell biology, 14, 661-669.
4.Waldhoer, M., Bartlett, S.E. and Whistler, J.L. (2004) Opioid receptors. Annual Review of Biochemistry, 73, 953-990.
5.Bhalla, U.S. and Iyengar, R. (1999) Emergent properties of networks of biological signaling pathways. Science, 283, 381-387.
6.Nestler, E.J. and Aghajanian, G.K. (1997) Molecular and cellular basis of addiction. Science, 278, 58-63.
7.Charalampous, K.D. and Askew, W.E. (1977) Cerebellar cAMP levels following acute and chronic morphine administration. Can J Physiol Pharmacol, 55, 117-120.
8.Ribrault, C., Sekimoto, K. and Triller, A. (2011) From the stochasticity of molecular processes to the variability of synaptic transmission. Nature reviews. Neuroscience, 12, 375-387.
9.Stephens, G.J. (2009) G-protein-coupled-receptor-mediated presynaptic inhibition in the cerebellum. Trends Pharmacol Sci, 30, 421-430.
10.Gillespie, D.T. (1977) Exact stochastic simulation of coupled chemical reactions. The Journal of Physical Chemistry, 81, 2340-2361.

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-					<h1> 1 Delivery module </h1> <br>
+							<h1> 3 Signaling module </h1>
-	<h2> 1.1 Introduction </h2> <br><br>
-Pharmacokinetics is the quantitative study of drug absorption, distribution and
+	<h2> 3.1 Introduction </h2>
-metabolism in the body. Pharmacokinetic data are indispensable for phase I clinical
-trials to evaluate the tissue distribution and safety of drugs. To construct a strategy
-for developing efficient and safe in vivo RNAi therapy systems, pharmacokinetics at
+In our laboratory work, we performed CPP tests to explore the impact of downregulating
-whole body, organ, cellular and sub-cellular levels need to be considered [1]. <
+MOR protein on mouse behavior after morphine administration, which is the ultimate goal
-br><br>
+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.
-In our laboratory study (GFP experiment), we obtained a qualitative description of in
+The most important brain reward circuit involves dopamine-containing neurons in the VTA
-vivo drug distribution after systematic administration. A computational and
+of the midbrain. Morphine can cause indirect excitation of VTA dopamine neurons by
-compartmental model was built to provide mechanistic insights into a quantitative
+reducing inhibitory synaptic transmission mediated by GABAergic neurons [1,2].
-explanation of the experimental results.
 <br><br>
-<B> Three primary aspects were counted in this pharmacokinetic model: <br>
-i) theoretically predicting the effect of RVG modification of the targeting ability of
-exosomes,<br>
-ii) approximating time-series exosome (siRNA) concentration data for use in modeling
-RNAi kinetics in target tissue and subsequently calculating the effective dose, and
+<B>
-<br>
+We modeled the signaling network to investigate the emergent properties of the reward
-iii) determining what portion of the delivery system could be improved based on
-simulation data.
+pathway. By comparing the activation degree of the reward pathway before and after
-</B>
+downregulating MOR protein levels, we could have a better mechanistic understanding of
-<br><br>
+drug effects. Although we did not perform any experiment to support this modeling
-	<h2> 1.2 Model methods </h2> <br><br>
+module, the methods and parameters we chose are grounded in literature reports.
-The process of drug delivery in humans and mice is quite complex. Physiologically
+</B>
-speaking, drug delivery after administration can be simplified into two separate
+<br><br>
-phases:
+<!--插入第八张图--> <img src="https://static.igem.org/mediawiki/2015/e/ec/NJU-China-
-<br><br>
+Model_Figure8.jpg" style="width:600px" > <br><br>
-<B>
+Figure 8. Reward pathway of acute morphine administration. We focused on activation of
-i) circulation from a central compartment (blood) to a peripheral compartment (body
+MOR, inhibition of AC and release of GABA vesicles in this module. The reference
-tissues), and
+pathway and figure are adapted from Kyoto Encyclopedia of Genes and Genomes database
-</B>
+(KEGG).<br><br>
-<br>
-<B>
-ii) uptake and trafficking at cellular and sub-cellular levels in target tissues.
-</B> <br>
-Although physiologically based pharmacokinetic (PBPK) models have been widely used in
-clinical trials, few described the cellular uptake behavior because most of the
+	<h2> 3.2 Model methods </h2>
-available drugs, at present, are chemically synthesized and have different biological
-properties compared with exosomes. Exosomes differ from conventional chemical drugs
-because of their distinct biological characteristics as microvesicles [2]. <B>Thus, we
-would like to modify the current PBPK model and add details regarding cellular uptake
+<B> We used both deterministic and stochastic models to describe the activation of GPCR
-behavior based on the biological nature of exosomes.
+and release of GABA. </B>
-</B>
+In biological systems, signal transmission occurs primarily through two mechanisms: (i)
-<br><br>
+mass-action laws governing protein synthesis, degradation and interactions; and (ii)
-	<h3> 1.2.1 Modeling multi-compartmental transport</h3> <br>
+standard Michaelis-Menten formulation for reactions catalyzed by enzymes [3].
+<br><br>
-In our laboratory work, we measured the relative level of GFP in the brain, liver, lung
-and spleen after injecting anti-GFP siRNA into mouse.
- <B> Thus, we
-examined separate compartments for the brain, liver, lung and spleen.</B>
-Other tissues were merged into one compartment. Each peripheral compartment had blood
-exchange with the central blood circulation, during which a certain percentage of
+Broadly, mathematical models of biochemical reactions can be divided into two
-exosomes were captured into the extracellular matrix of endothelial cells in different
+categories: deterministic systems and stochastic systems [3]. In deterministic models,
-tissues.
+the change in time of the components’ concentrations is completely determined by
-<br><br>
+specifying the initial and boundary conditions; by contrast, the changes in
-     <img src="https://static.igem.org/mediawiki/2015/2/22/NJU-China-Model_Figure1.jpg"> <!--
+concentrations of components with respect to time cannot be fully predicted in
-插入第一幅图--> <br><br>
+stochastic models [3]. In the previous two modules, we modeled the delivery device and
+RNA interference using deterministic models.
-Figure 1. Schematic diagram of the arrangement of different tissues in the
+<br><br>
-pharmacokinetic model. The blood, along with exosomes, circulates from the central
+	<h3> 3.2.1 Modeling the activation of MOR </h3>
-compartment to five peripheral compartments.<br><br>
-As membrane vesicles, exosomes may rapidly shift from associating with other complexes
+MOR belongs to the class A (Rhodopsin) family of heterotrimeric Gi/o protein-coupled
-and disassociating into a free format during blood circulation. Moreover, the ultimate
+receptors [4]. The binding of opioids to MOR activates the G protein, upon which both
-fate of exosomes, similar to other microvesicles, is degradation by lysosomes after
+G-protein α and βγ subunits interact with multiple cellular effector systems. As the
-internalization via a common process discussed later. Research has shown that
+first step of signal transmission, the degree of activation of MOR in response to
-microvesicles, containing miRNAs or siRNAs, are stable in serum and play significant
+opioid has a direct and far-reaching influence on the behavior of mice.
+<br><br>
-biological roles in cell communication [3]. Furthermore, the elimination of exosomes
-occurs primarily in specific tissues rather than in blood circulation, albeit that the
-half-life of exosomes in blood circulation is much shorter [4].
+Deterministic models were applied to describe the biochemical reactions occurring in
-<B> These two findings suggest that the elimination rate of exosomes in blood
+the diagram below. We used the Matlab Simbiology package to draw the diagram and to
-circulation is negligible compared with that in target tissues and does not need to be
+design the equation, the details of which are accessible on the uploaded files. This
-considered in this portion of the pharmacokinetic model.
+model was created on the basis of work by Bhalla and Iyengar on the activation of
-</B>
+glutamate receptor [5].
 <br><br>
-Using standard mass action kinetics, the equations below describe the change in the
+<!-- 插入第九张图 -->   <img src="https://static.igem.org/mediawiki/2015/e/e3/NJU-China-
-concentration (mass) of free exosomes over time in blood and target tissues. Here,
+Model_Figure9.jpg" style="width:600px" > <br><br>
-<I>kblooddis</I> and <I>kbloodbind</I> represents the association and disassociation,
-respectively, of exosomes to other complexes in the blood circulation.
+Figure 9. Reaction schemes for the activation of MOR in the simulation. Reversible
-<br><br>
+reactions are represented as bidirectional arrows; irreversible reactions, as
- <img src="https://static.igem.org/mediawiki/2015/6/62/NJU-China-Equation_delivery_1.jpg">
+unidirectional arrows. This figure is adapted from the literature [5]. <br><br>
-<!-- delivery公式1 -->
+	<h3> 3.2.2 Modeling adenylate cyclase inhibtion </h3>
-<br><br>
+The concentration of second messenger is a significant indicator of excitability of
-&nbsp;&nbsp;&nbsp;Notably, not all exosomes are effective or completely absorbed by
+GABAergic neurons. Thus, we chose to simulate cAMP levels and adenylate cyclase (AC)
-tissues. Therefore, <I>partitiontissue</I> is included to describe the effective
+activity to determine the effect of downregulating MOR protein levels on morphine
-fraction of the dose. Additionally, <I>Et</I> represents the quantity of exosomes
+reward signaling networks.
-captured by the extracellular matrix of cells in tissues, but does not represent the
+<br><br>
-final quantity of exosomes in tissues, which will be discussed in the next portion of
-the model. <I>Qtissue</I> and <I>Qc</I> represents the velocity of blood flowing in
-peripheral and central compartments, respectively.
+AC1/8 is a type of adenylate cyclases involved in the signaling of the acute morphine
-<br><br>
+reward pathway [6]. When MOR is activated, the disassociated Gα subunit reacts with
-<img src="https://static.igem.org/mediawiki/2015/7/7c/NJU-China-Equation-
+AC1/8 and subsequently inhibits its activity, leading to a decrease in cellular cAMP
-Equation_delivery_2.jpg">
+levels. The parameters of this model were primarily derived from the literature [5]
-<!-- 这里要插第三张图，是第二个出现的一条公式 -->
+with slight modifications to fit to the data presented in the literature [7].
 <br><br>
-This work is supported by model of IGEM Slovenia 2012, IGEM NJU-China 2013 and other
+<!-- 插入第十张图 --> <img src="https://static.igem.org/mediawiki/2015/7/78/NJU-China-
-literatures [5,6].
+Model_Figure10.jpg" style="width:400px" > <br><br>
-<br><br>
-<h3>1.2.2 Modeling cellular uptake and intracellular trafficking</h3> <br><br>
+Figure 10. Reaction schemes for inhibition of AC in simulation. Reversible reactions
-Extracellular vesicles can be internalized by cells via a variety of pathways, namely,
+are represented as bidirectional arrows, and enzyme reactions are drawn as an arrow
-phagocytosis, clathrin- and caveolin-mediated endocytosis and macropinocytosis [7]. We
+with two bends. AC: adenylate cyclase; PDE: phosphodiesterase.
+<br><br>
-assume that receptor-mediated endocytosis is the major pathway of primary exosome
+	<h3> 3.2.3 Modeling GABA vesicle releases </h3>
-internalization.
-<br> <br>
+A stochastic model was applied to describe the random behavior of neurotransmitter
-The cellular uptake pathway is summarized in Figure_2. Exosomes bind to the membranes
+vesicles release [8]. GABA is an important inhibitory neurotransmitter, the level of
-of target cells after being captured by the extracellular matrix and then internalized
+which directly determines the firing rate of dopamine neurons and other physiological
-through endocytosis. The receptor-ligand interaction may facilitate this process. After
+and behavioral statuses. The GABA synaptic vesicle cycle consists of three discrete
-internalization, the RISC complex may escape from endosomes, and endosomes may be
+processes: synthesis of GABA vesicles, docking of GABA vesicles at the inner membrane
-ultimately eliminated by lysosomes. Although other pathways such as transcytosis and
+of presynapses and release of GABA vesicles reacting to a certain signal. The release
-exocytosis following endocytosis may occur, we did not take them into account for
+of GABA vesicles is strictly regulated by cellular signaling networks. When Gi/o is
-simplification.
+activated and the cellular cAMP level drops, the release of GABA is inhibited. Many
-<br><br>
+complicated mechanisms are involved in the inhibition of GABA release due to activation
-<img src="https://static.igem.org/mediawiki/2015/6/65/NJU-China-Model_Figure2.jpg"> <!--插入
-第二幅图--> <br><br>
+of Gi/o. Here, we simply studied the action potential-independent pathway of GABA
-Figure 2. Pathways that participate in exosomes uptake by target cells. Exosomes are
+release, through which the release of GABA is directly inhibited by activated Gβγ
-transported from the extracellular matrix to the cell surface and undergo intracellular
+subunits [9].
-trafficking after internalization. The RISC complex in exosomes is released, and
+<br><br>
-exosomes are ultimately degraded.
-<br><br>
+<!-- 插入第十一张图 --> <img src="https://static.igem.org/mediawiki/2015/1/16/NJU-China-
-&nbsp;&nbsp;&nbsp;We used several equations to describe the above pathway. RVG
+Model_Figure11.jpg" style="width:600px" > <br><br>
-modification helps exosomes bind acetylcholine receptors specifically expressed in
-neuronal cells. <B>Exosomes internalization is much easier provided that more exosomes
+Figure 11. Schematic representation of GABA release in which four steps are modeled
-bind target cells.</B> The binding process is modeled using mass action kinetics.
+using mass action law and the stochastic method. <br><br>
-<I>AR</I> denotes the number of acetylcholine receptors on target cells, and <I>km</I>
+	<h3> 3.2.4 Gillespie’s algorithm </h3>
-represents the specific binding constant. Non-receptor-ligand interaction--mediated
-binding is summarized using <I>kbindtissue</I>.
-<br><br>
+When spatially restricted reactions, such as the release of neurotransmitter vesicles,
-<img src="https://static.igem.org/mediawiki/2015/9/93/NJU-China-Equation-
+are studied, the traditional deterministic model is no longer effective for ignoring
-Equation_delivery_3.jpg">
+the discrete nature of the problem [3]. Stochastic models convert reaction rates to
-     <!-- 这里要插第三个公式 -->
-     <br><br>
-The internalization and elimination of exosomes are formulated below using the
+probability, which allows users to explore the noise and randomness of signaling
-parameters <I>kinttissue</I> and <I>kelimttissue</I>, respectively. Note that different
+networks. A standard algorithm dealing with stochastic model is Gillespie’s algorithm.
-tissues have different internalization and elimination rates.
+This algorithm starts with the initial condition for each molecule type in the reaction
-<br> <br>
+network. Then, Monte Carlo simulation is applied to generate some random variables and
-img src="https://static.igem.org/mediawiki/2015/2/27/NJU-China-Equation-
+to calculate the smallest time interval in which the reaction will occur [3,10].
-Equation_delivery_4.jpg">
+Finally, the number of molecules in the reaction network is updated, and the process is
-     <!-- 这里要插第四个公式 -->
-     <br><br>
+repeated.
+<br><br>
-The quantity of the endosomal RISC complex and escape behavior is modeled using the
+	<h2> 3.3 Results </h3>
-following equation. The concentration of siRNA in exosomes is determined by real-time
-RT-PCR in the literature [8] and represented by <I>kc</I>. <I>kescendvec</I>
+The simulation results revealed the kinetics of MOR activation in case and control
-represents the escape rate of the RISC complex from exosomes (endosomes) to the
+studies. In the CPP test, the Western blot result demonstrated that the relative level
-cytosol.
+of MOR protein after MOR-siRNA injection was 0.5.
-<br> <br>
+<B> Thus, the concentration of MOR protein was set at half of the level in the case
+study. </B>
-<img src="https://static.igem.org/mediawiki/2015/6/6d/NJU-China-Equation-
+<br><br>
-Equation_delivery_5.jpg">
+<B> The results indicated that almost all the MOR protein is activated in response to
-     <!-- 这里要插第五个公式 -->
-     <br><br>
-This part of work is based on literature [5].<br> <br>
+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
-	<h2> 1.3 Parameter finding and adjustment </h2> <br><br>
+level, we also inhibit approximately half of activated Gα and βγ subunits. </B>
-The most challenging part of modeling is finding and adjusting parameters. After
-reviewing the literature, we unfortunately found that few of the parameters have been
+<br><br>
-measured or reported directly. The original paper written by Bartlett and Davis uses
+<!-- 插入第十二张图 --> <img src="https://static.igem.org/mediawiki/2015/b/b3/NJU-China-
-synthetic polyplexes as carriers to deliver siRNA [5]. The stability and targeting
+Model_Figure12.jpg"  > <br><br>
-ability of synthetic polyplexes diverge considerably from exosomes due to their
-different biochemical nature. Using all the parameters in the original paper without
+Figure 12. Concentration-time curves for the activation of MOR in response to morphine.
-adjustment would not be appropriate because of different biochemical natures and
+A: Control study with the concentration of MOR set at 1 mM. B: Case study with the
-consequences of these delivery systems.
+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>
-Parameter adjustment is not unusual in modeling biological processes. This endeavor is
+The primary effector of activated Gα subunit is AC. The activation degree of AC
-a somewhat uncertain endeavor and lacks specific procedures. In an iterative process,
+influences its product cAMP—an important second messenger that indicates the
-each set of parameters must be run through the model and modified to bring the output
+excitability of GABAergic neurons. We now theoretically predicted and compared the
-of the model into better and better agreement with observed experiment and literature
+inhibition of Gα subunit on AC and the subsequent decrease in cellular cAMP levels in
-results [9]. <B>Following this doctrine, we ran our simulation and attempted to fit the
+control (wild type) and case (MOR-siRNA injected) studies.
-results to the experimental and literature data.
+<br><br>
-</B><br><br>
+<!-- 插入第十三张图 --> <img src="https://static.igem.org/mediawiki/2015/d/d4/NJU-China-
-<B>
+Model_Figure13.jpg" > <br><br>
-You can access the description of model variables and parameters here.</B> The
-determination of the parameters is also described in the list. When one parameter was
-reported in the literature, we cited the literature directly; when the parameter was
+Figure 13. Effect of downregulating MOR protein on AC activity (A) and cellular cAMP
-not accessible but could be estimated and fitted to the literature or experimental
+levels (B) in response to morphine. The input level of MOR protein is based on the
-results, we used the terms “estimated from literature and experimental results”.
+result shown in Figure 10.
 <br><br>
-	<h2>1.4 Results</h2> <br><br>
-We simulated the pharmacokinetic model and obtained initial results. Unfortunately, the
-results showed that the model was not accurate. <B>Distinguishing the effects of RVG
-modification on the tissue distribution of exosomes was difficult, as shown in the
+<B> Activation of wild type MOR protein inhibited over 25% of AC, and relative cellular
-figure below.
+cAMP levels dropped below 70%, which is consistent with findings in the literature[7].
+</B>
-</B><br><br>
+The injection of MOR-siRNA reduces the activation quantity of MOR and significantly
-<img src="https://static.igem.org/mediawiki/2015/d/d1/NJU-China-Model_Figure3.jpg"> <!--插入
+attenuates the inhibition of AC and decrease in cAMP levels. Maintaining the cellular
-第三幅图--> <br><br>
+cAMP level induced by the drug plays a crucial role in blocking reward pathways.
-Figure 3. Effect of RVG modification on the tissue distribution of exosomes. A: Without
+<br><br>
-RVG modification; B: With RVG modification. The initial results are simulated with
-partitionbrain set at 1×10-1.<br><br>
-Why did we obtain unrealistic simulation results? The answer simply lies in the
+Finally, we explored the relationship between MOR activation and GABA release. The wild
-parameter set we chose. <B>After performing parameter sensitivity analysis, we were
+type study revealed significant inhibition of GABA vesicles due to activated G βγ
-surprised to find that exosome bindings to the neuronal cell surface does not determine
+subunits. MOR-siRNA counteracted this trend by downregulating MOR protein and activated
-the internalization rate.</B> In contrast, <I>paritionbrain</I> is more sensitive,
+G βγ subunit levels as depicted in the case study. Maintaining GABA release reduces
-indicating that the rate limiting step for exosome internalization is its effective
+the excitability and firing rate of dopamine neurons, which is consistent with the
-dose fraction to targeted cells.
+expected drug effect on blockage of the reward pathway and explain the behavioral
-<br> <br>
+changes observed in the CPP tests.
-We next carefully investigated the presence of BBB and the effect of RGV modification
+<br><br>
-on paritionbrain. The blood brain barrier is formed by endothelial cells at the level
+<!-- 插入第十四张图 --> <img src="https://static.igem.org/mediawiki/2015/5/51/NJU-China-
-of cerebral capillaries [10]. The cerebral endothelial cells may form complex tight
+Model_Figure14.jpg" style="width:600px" > <br><br>
-junctions that interfere with permeability. The binding of RVG to acetylcholine
-receptors, which are present in high density at the neuromuscular junction, would
+Figure 14. Stochastic modeling of GABA release. A: Control study with the establishment
-provide a mechanism whereby exosomes could be locally concentrated at sites in
+of mass balance between synthesized, docked and released GABA vesicles. B: Case study
-proximity to peripheral nerves, facilitating subsequent uptake and transfer to the
+with MOR-siRNA injected to attenuate the inhibition of GABA release. C: Wild type study
-central nervous system [11]. <B>The local concentrating of exosomes at proximal sites
+with a normal level of MOR protein activation resulting in inhibition of GABA release.
-may significantly increase the effective dose fraction available to targeted cells,
+D: Summary of numbers of released and inhibited GABA vesicles in different treatments.
-resulting in a greater number of exosomes passing through the BBB and captured by the
+The results are presented as the mean±S.D.
-extracellular matrix of target cells.</B> To our knowledge, this mechanism is why
+<br><br>
-exosomes may pass through the BBB much more easily after RVG modification. <B>Thus, we
+	<h2> 3.4 Conclusion and remarks </h2>
+<B>
-hypothesized that <I>partitionbrain</I> may also be influenced by RVG modification.
-</B><br><br>
-With <I>partitionbrain</I> increased by 6-fold, we finally obtained optimized
+In this module, we used deterministic and stochastic methods to model the cell
-simulation results. The biological meaning of this parameter adjustment is that RVG
+signaling network and to predict the blockage of the reward pathway by injecting MOR-
-modification helps exosomes bind acetyl-choline receptors, not only facilitating
+siRNA. The simulation results could somewhat explain the behavioral changes observed in
-internalization into target cells but also increasing the ability of exosomes to pass
+the CPP tests (function tests) mechanistically.
-though the BBB by at least 6-fold.
 <br><br>
+</B>
-<img src="https://static.igem.org/mediawiki/2015/e/e8/NJU-China-Model-delivery_Figure4.PNG">
+	<h2> 3.5 Model equations, variables and parameters </h2>
-<!--插入
-第四幅图--> <br><br>
+The modeling details of the activation of MOR protein and inhibition of AC are
-Figure 4. Effect of RVG modification on the tissue distribution of exosomes. The
+truncated here because we use software to help us design the model and there are too
-results are simulated with <I>partitionbrain</I> increased by 6-fold. A-B: Control
+many parameters and equations. We have uploaded relevant source code and files for
-study of the time course of the tissue-distribution of exosomes without RVG
+those individuals interested in exploring the models. However, we want to emphasize
-modification. C-D: Case study of the time course of the tissue-distribution of exosomes
+that our parameters are all derived from the literature. The modeling of GABA release
-with RVG modification and MOR-siRNA as cargo. E: In situ simulation of the tissue-
+is inspired by the literature [8] and the parameters were estimated from literature
-distribution of exosomes.
+[2].
-<br><br>
+These parameters, as well as initial conditions, can be accessed in our uploaded files
-We now better understand our delivery device using computational simulation data. The
+and we selectively list part of them below.
-half-life of exosomes in blood is short, which is consistent with findings with the
+<br><br>
-literature [12]. The tissue distribution pattern of exosomes with or without RVG
-modifications is also consistent with findings in the literature [13] and our GFP
+These parameters, as well as initial conditions, can be accessed in our uploaded files
-experiment.
+and we selectively list part of them below.
 <br><br>
-Furthermore, the simulation data shows that a small portion of exosomes may also pass
-into non-targeted tissues due to circulation. We could improve the targeting precision
+	<h3> 3.5.1 Activation of MOR </h3>
-by further modifying the exosomes.
+Model Parameters:
 <br><br>
+<img src="https://static.igem.org/mediawiki/2015/d/de/NJU-China-model-sig-1.jpg" >
-	<h2>1.5 Conclusion and Remarks</h2> <br><br>
+<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.
-<B>In this module, we created a pharmacokinetic model to simulate the time-dependent
+<br><br>
-tissue distribution of exosomes at whole organ and cellular levels. We theoretically
+Model Equations:
-tested the effect of RVG modification on the capability of exosomes to pass through the
+<table>
+<tr>
+<td>
-BBB. The simulation results are consistent with experimental measurements, and provide
+<!-- 插入第一张公式 --> <img src="https://static.igem.org/mediawiki/2015/b/ba/NJU-China-
-clues regarding improvements to the delivery device.</B>
+Equation_Sig_1.jpg" style="width:500px"> </td> <br><br>
-<br><br>
+</tr>
+<tr>
+<td>
+<!-- 插入第二张公式 --> <img src="https://static.igem.org/mediawiki/2015/0/0b/NJU-China-
-	<h2>1.6  Model Variables</h2> <br><br>
+Equation_Sig_2.jpg" style="width:500px" > </td> <br><br>
-————表格还有表格底下的注释你自己弄哦————<br><br>
+</tr>
+</table>
-	<h2>1.7	 Model Parameters</h2> <br><br>
+	<h3> 3.5.2 Activation of MOR </h3>
-————表格还有表格底下的注释你自己弄哦——<br><br>
+Model Parameters:
 <br><br>
-References:<br>
+<img src = "https://static.igem.org/mediawiki/2015/f/f2/NJU-China-model-sig-2.jpg" >
-.&nbsp;&nbsp;&nbsp;Takakura, Y., Nishikawa, M., Yamashita, F. and Hashida, M. (2001)
-Development of gene drug delivery systems based on pharmacokinetic studies. European
+<br><br>
-journal of pharmaceutical sciences : official journal of the European Federation for
+???: No literature has directly reported binding and disassociation constant of Gi to
-Pharmaceutical Sciences, 13, 71-76.<br>
+AC. Therefore, we use the binding and disassociation constant of Gs to AC as an
-.&nbsp;&nbsp;&nbsp;El Andaloussi, S., Lakhal, S., Mager, I. and Wood, M.J. (2013)
-Exosomes for targeted siRNA delivery across biological barriers. Adv Drug Deliv Rev,
+approximation derived from literature[5].
-, 391-397.<br>
+<br><br>
-.&nbsp;&nbsp;&nbsp;Zhang, Y., Liu, D., Chen, X., Li, J., Li, L., Bian, Z., Sun, F.,
-Lu, J., Yin, Y., Cai, X. et al. (2010) Secreted monocytic miR-150 enhances targeted
-endothelial cell migration. Molecular cell, 39, 133-144.<br>
+Model Equations:
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-S., Imai, T. and Takakura, Y. (2013) Visualization and in vivo tracking of the exosomes
+<table>
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+ <img src="https://static.igem.org/mediawiki/2015/a/a7/NJU-China-
-Biotechnology, 165, 77-84.<br>
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-siRNA-mediated gene silencing from live-cell and live-animal bioluminescent imaging.
+</table>
-Nucleic Acids Res, 34, 322-333.<br>
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-.&nbsp;&nbsp;&nbsp;Levitt, D.G. and Schoemaker, R.C. (2006) Human physiologically
-based pharmacokinetic model for ACE inhibitors: ramipril and ramiprilat. BMC clinical
+Model Parameters
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+<img src = "https://static.igem.org/mediawiki/2015/1/19/NJU-China-model-sig-3.jpg" >
+<br><br>
-mechanisms of extracellular vesicle uptake. J Extracell Vesicles, 3.<br>
+References: <br>
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Difference between revisions of "Team:NJU-China/signaling"

Latest revision as of 03:27, 3 October 2015

3 Signaling module

3.1 Introduction

3.2 Model methods

3.2.1 Modeling the activation of MOR

3.2.2 Modeling adenylate cyclase inhibtion

3.2.3 Modeling GABA vesicle releases

3.2.4 Gillespie’s algorithm

3.3 Results

3.4 Conclusion and remarks

3.5 Model equations, variables and parameters

3.5.1 Activation of MOR

3.5.2 Activation of MOR

3.5.3 GABA release