Revision as of 18:39, 17 September 2015

model

Modeling overview

We created mathematical models of three aspects of our project: a pharmacokinetic model, RNA inference model and signaling network model. The source code of the modeling work is available here to help others reproduce and improve our work.

The delivery module used experimental results to simulate the kinetics of tissue-distribution of exosomes. The RNAi module modeled the kinetics of downregualtion of MOR protein in response to anti-MOR siRNA. The signaling module theoretically predicted the impacts of downregulating MOR protein on the reward pathway and explained the behavioral change of mice observed in our laratory work. We had a better understanding of our device by integrating three modules.

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-	<h1> 1 Delivery module </h1> <br>
+		<h1> Modeling overview </h1>
-	<h2> 1.1 Introduction </h2> <br><br>
+		We created mathematical models of three aspects of our project: a
-&nbsp;&nbsp;&nbsp;Pharmacokinetics is the quantitative study of drug absorption,
-distribution and metabolism in the body. Pharmacokinetic data are indispensable for
+		pharmacokinetic model, RNA inference model and signaling network model.
-phase I clinical trials to evaluate the tissue distribution and safety of drugs. To
+		The source code of the modeling work is available here to help others
-construct a strategy for developing efficient and safe in vivo RNAi therapy systems,
+		reproduce and improve our work. <br><br>
-pharmacokinetics at whole body, organ, cellular and sub-cellular levels need to be
-considered [1]. <br><br>
+		The delivery module used experimental results to simulate the kinetics
-&nbsp;&nbsp;&nbsp;In our laboratory study (GFP experiment), we obtained a qualitative
+		of tissue-distribution of exosomes. The RNAi module modeled the
-description of in vivo drug distribution after systematic administration. A
+		kinetics of downregualtion of MOR protein in response to anti-MOR
-computational and compartmental model was built to provide mechanistic insights into a
+		siRNA. The signaling module theoretically predicted the impacts of
-quantitative explanation of the experimental results. <br><br>
+		downregulating MOR protein on the reward pathway and explained the
-<B> Three primary aspects were counted in this pharmacokinetic model: <br>
+		behavioral change of mice observed in our laratory work. We had a
-i) theoretically predicting the effect of RVG modification of the targeting ability of
-exosomes,<br>
+		better understanding of our device by integrating three modules.
-ii) approximating time-series exosome (siRNA) concentration data for use in modeling
+		<br><br>
-RNAi kinetics in target tissue and subsequently calculating the effective dose, and
+       <img src="https://static.igem.org/mediawiki/2015/5/59/NJU-China-Model_Figure0.jpg">
-<br>
+<!--插入第一幅图--> <br><br>
-iii) determining what portion of the delivery system could be improved based on
-simulation data. </B> <br><br>
-	<h2> 1.2 Model methods </h2> <br><br>
-	&nbsp;&nbsp;&nbsp;The process of drug delivery in humans and mice is quite
-complex. Physiologically speaking, drug delivery after administration can be simplified
-into two separate phases: <br>
-	<B>&nbsp;&nbsp;&nbsp; i) circulation from a central compartment (blood) to a
-peripheral compartment (body tissues), and</B> <br>
-	<B>&nbsp;&nbsp;&nbsp; ii) uptake and trafficking at cellular and sub-cellular
-levels in target tissues.</B> <br>
-	&nbsp;&nbsp;&nbsp;Although physiologically based pharmacokinetic (PBPK) models
-have been widely used in clinical trials, few described the cellular uptake behavior
-because most of the 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 behavior based on the biological nature of
-exosomes.</B><br><br>
-<h3> 1.2.1 Modeling multi-compartmental transport</h3> <br>
-	&nbsp;&nbsp;&nbsp;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
-exosomes were captured into the extracellular matrix of endothelial cells in different
-tissues.<br><br>
-      <img src="https://2015.igem.org/File:NJU-China-Model_Figure1.jpg"> <!--插入第一幅图--> <br><br>
-	Figure 1. Schematic diagram of the arrangement of different tissues in the
-pharmacokinetic model. The blood, along with exosomes, circulates from the central
-compartment to five peripheral compartments.<br><br>
-	&nbsp;&nbsp;&nbsp;As membrane vesicles, exosomes may rapidly shift from
-associating with other complexes and disassociating into a free format during blood
-circulation. Moreover, the ultimate fate of exosomes, similar to other microvesicles,
-is degradation by lysosomes after internalization via a common process discussed later.
-Research has shown that microvesicles, containing miRNAs or siRNAs, are stable in serum
-and play significant 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].<B> These two findings suggest that the elimination rate of exosomes in blood
-circulation is negligible compared with that in target tissues and does not need to be
-considered in this portion of the pharmacokinetic model.</B><br><br>
-	&nbsp;&nbsp;&nbsp;Using standard mass action kinetics, the equations below
-describe the change in the concentration (mass) of free exosomes over time in blood and
-target tissues. Here, <I>kblooddis</I> and <I>kbloodbind</I> represents the association
-and disassociation, respectively, of exosomes to other complexes in the blood
-circulation.<br><br>
- <img src="https://static.igem.org/mediawiki/2015/6/62/NJU-China-Equation_delivery_1.jpg">
-<!-- delivery公式1 -->
-<br><br>
-	&nbsp;&nbsp;&nbsp;Notably, not all exosomes are effective or completely
-absorbed by tissues. Therefore, <I>partitiontissue</I> is included to describe the
-effective fraction of the dose. Additionally, <I>Et</I> represents the quantity of
-exosomes captured by the extracellular matrix of cells in tissues, but does not
-represent the 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.<br><br>
-<img src="https://static.igem.org/mediawiki/2015/7/7c/NJU-China-Equation-
-Equation_delivery_2.jpg">
-<!-- 这里要插第三张图，是第二个出现的一条公式 -->
-<br><br>
-	&nbsp;&nbsp;&nbsp;This work is supported by model of IGEM Slovenia 2012, IGEM
-NJU-China 2013 and other literatures [5,6].<br><br>
-<h3>1.2.2 Modeling cellular uptake and intracellular trafficking</h3> <br><br>
-	&nbsp;&nbsp;&nbsp;Extracellular vesicles can be internalized by cells via a
-variety of pathways, namely, phagocytosis, clathrin- and caveolin-mediated endocytosis
-and macropinocytosis [7]. We assume that receptor-mediated endocytosis is the major
-pathway of primary exosome internalization.<br> <br>
-	&nbsp;&nbsp;&nbsp;The cellular uptake pathway is summarized in Figure_2.
-Exosomes bind to the membranes of target cells after being captured by the
-extracellular matrix and then internalized through endocytosis. The receptor-ligand
-interaction may facilitate this process. After internalization, the RISC complex may
-escape from endosomes, and endosomes may be ultimately eliminated by lysosomes.
-Although other pathways such as transcytosis and exocytosis following endocytosis may
-occur, we did not take them into account for simplification.<br><br>
-——————这里放Figure.2，就是红色的点点exosome那个图——————<br><br>
-Figure 2. Pathways that participate in exosomes uptake by target cells. Exosomes are
-transported from the extracellular matrix to the cell surface and undergo intracellular
-trafficking after internalization. The RISC complex in exosomes is released, and
-exosomes are ultimately degraded.<br><br>
-	&nbsp;&nbsp;&nbsp;We used several equations to describe the above pathway. RVG
-modification helps exosomes bind acetylcholine receptors specifically expressed in
-neuronal cells. <B>Exosomes internalization is much easier provided that more exosomes
-bind target cells.</B> The binding process is modeled using mass action kinetics.
-<I>AR</I> denotes the number of acetylcholine receptors on target cells, and <I>km</I>
-represents the specific binding constant. Non-receptor-ligand interaction--mediated
-binding is summarized using <I>kbindtissue</I>.<br><br>
-<img src="https://static.igem.org/mediawiki/2015/9/93/NJU-China-Equation-
-Equation_delivery_3.jpg">
-     <!-- 这里要插第三个公式 -->
-     <br><br>
-	&nbsp;&nbsp;&nbsp;The internalization and elimination of exosomes are
-formulated below using the parameters <I>kinttissue</I> and <I>kelimttissue</I>,
-respectively. Note that different tissues have different internalization and
-elimination rates.<br> <br>
-<img src="https://static.igem.org/mediawiki/2015/9/93/NJU-China-Equation-
-Equation_delivery_3.jpg">
-     <!-- 这里要插第四个公式 -->
-     <br><br>
-	&nbsp;&nbsp;&nbsp;The quantity of the endosomal RISC complex and escape
-behavior is modeled using the 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>  represents the escape rate of the RISC complex from
-exosomes (endosomes) to the cytosol.<br> <br>
-——————这里放第五个出现的公式的图，该图在word里面的figure.2下面的下面的下面———
-—<br>
-&nbsp;&nbsp;&nbsp;This part of work is based on literature [5].<br> <br>
-<h2> 1.3 Parameter finding and adjustment </h2> <br><br>
-	&nbsp;&nbsp;&nbsp;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 measured or reported directly. The original paper written
-by Bartlett and Davis uses synthetic polyplexes as carriers to deliver siRNA [5]. The
-stability and targeting ability of synthetic polyplexes diverge considerably from
-exosomes due to their different biochemical nature. Using all the parameters in the
-original paper without adjustment would not be appropriate because of different
-biochemical natures and consequences of these delivery systems. <br><br>
-	&nbsp;&nbsp;&nbsp;Parameter adjustment is not unusual in modeling biological
-processes. This endeavor is a somewhat uncertain endeavor and lacks specific
-procedures. In an iterative process, each set of parameters must be run through the
-model and modified to bring the output of the model into better and better agreement
-with observed experiment and literature results [9]. <B>Following this doctrine, we ran
-our simulation and attempted to fit the results to the experimental and literature
-data. </B><br><br>
-	&nbsp;&nbsp;&nbsp;<B>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 not accessible but could be estimated and fitted to the
-literature or experimental results, we used the terms “estimated from literature and
-experimental results”.<br><br>
-<h2>1.4 Results</h2> <br><br>
-	&nbsp;&nbsp;&nbsp;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 figure below.</B><br><br>
-————这里放Figure.3的图，就是那个Control-Without RVG modification——<br><br>
-Figure 3. Effect of RVG modification on the tissue distribution of exosomes. A: Without
-RVG modification; B: With RVG modification. The initial results are simulated with
-partitionbrain set at 1×10-1.<br><br>
-	&nbsp;&nbsp;&nbsp;Why did we obtain unrealistic simulation results? The answer
-simply lies in the parameter set we chose. <B>After performing parameter sensitivity
-analysis, we were surprised to find that exosome bindings to the neuronal cell surface
-does not determine the internalization rate.</B> In contrast, <I>paritionbrain</I> is
-more sensitive, indicating that the rate limiting step for exosome internalization is
-its effective dose fraction to targeted cells.<br> <br>
-	&nbsp;&nbsp;&nbsp;We next carefully investigated the presence of BBB and the
-effect of RGV modification on paritionbrain. The blood brain barrier is formed by
-endothelial cells at the level of cerebral capillaries [10]. The cerebral endothelial
-cells may form complex tight junctions that interfere with permeability. The binding of
-RVG to acetylcholine receptors, which are present in high density at the neuromuscular
-junction, would provide a mechanism whereby exosomes could be locally concentrated at
-sites in proximity to peripheral nerves, facilitating subsequent uptake and transfer to
-the central nervous system [11]. <B>The local concentrating of exosomes at proximal
-sites may significantly increase the effective dose fraction available to targeted
-cells, resulting in a greater number of exosomes passing through the BBB and captured
-by the extracellular matrix of target cells.</B> To our knowledge, this mechanism is
-why exosomes may pass through the BBB much more easily after RVG modification. <B>Thus,
-we hypothesized that <I>partitionbrain</I> may also be influenced by RVG
-modification.</B><br><br>
-	&nbsp;&nbsp;&nbsp;With <I>partitionbrain</I> increased by 6-fold, we finally
-obtained optimized simulation results. The biological meaning of this parameter
-adjustment is that RVG modification helps exosomes bind acetyl-choline receptors, not
-only facilitating internalization into target cells but also increasing the ability of
-exosomes to pass though the BBB by at least 6-fold.<br><br>
-——这里放Figure.4的那三张连着的图——————<br><br>
-	&nbsp;&nbsp;&nbsp;Figure 4. Effect of RVG modification on the tissue
-distribution of exosomes. The results are simulated with <I>partitionbrain</I>
-increased by 6-fold. A-B: Control study of the time course of the tissue-distribution
-of exosomes without RVG modification. C-D: Case study of the time course of the
-tissue-distribution of exosomes with RVG modification and MOR-siRNA as cargo. E: In
-situ simulation of the tissue-distribution of exosomes.<br><br>
-	&nbsp;&nbsp;&nbsp;We now better understand our delivery device using
-computational simulation data. The half-life of exosomes in blood is short, which is
-consistent with findings with the 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 experiment. <br><br>
-	&nbsp;&nbsp;&nbsp;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 by further modifying the exosomes.<br><br>
-<h2>1.5 Conclusion and Remarks</h2> <br><br>
-	&nbsp;&nbsp;&nbsp;<B>In this module, we created a pharmacokinetic model to
-simulate the time-dependent tissue distribution of exosomes at whole organ and cellular
-levels. We theoretically tested the effect of RVG modification on the capability of
-exosomes to pass through the BBB. The simulation results are consistent with
-experimental measurements, and provide clues regarding improvements to the delivery
-device.</B><br><br>
-<h2>1.6  Model Variables</h2> <br><br>
-————表格还有表格底下的注释你自己弄哦————<br><br>
-<h2>1.7	 Model Parameters</h2> <br><br>
-————表格还有表格底下的注释你自己弄哦——<br><br>
-<br><br>
-References:<br>
-.&nbsp;&nbsp;&nbsp;Takakura, Y., Nishikawa, M., Yamashita, F. and Hashida, M. (2001)
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Difference between revisions of "Team:NJU-China/Modeling"

Revision as of 18:39, 17 September 2015

Modeling overview