Revision as of 12:06, 16 October 2018

Gene doping is the administration of exogenous genetic material for performance enhancement. Detection of gene doping requires identifying target sequences and detection windows. Our targeted sequencing method includes our novel dxCas9-linker-Tn5 fusion protein and requires a minimal set of guide RNAs aligning with gene doping sequences. As there are 10¹⁰⁴ possible codon variations in our proof of concept target, the EPO gene, it’s not feasible to target all possible sequences in practice. We therefore implemented a search function to identify areas with minimal variation within the sequence, which reduced the testing set to twelve gRNAs. Second, we modeled the process of infection and degradation of gene doping DNA in blood. From this model, we provided the laboratory with the time dependent concentration of the target DNA. Based on our wetlab sensitivity analyses, the model predicts that with microdosing, our detection method could effectively catch gene dopers.

1. Approach

We identified a threat in detecting gene doping that lays in the possibility of modifying the genetic sequence of a gene without changing the protein sequence produced. This allows gene dopers to creatively modify their DNA sequence in several possible combinations and complicates the design of detection methods. To combat this, the exon-exon junctions with the smallest possible variation need to be identified and gRNA sequences generated cover all possible combinations of one gene.

Once the testing set of gRNAs is generated for our fusion protein, we identified time during which the detection of the gene doping DNA in an athlete’s blood sample is possible. This was accomplished by modelling gene doping administration. We considered the entire process of gene doping to fully understand the underlying mechanisms of gene doping and its effect on the athlete. With our chosen model gene being the erythropoietin (EPO) gene, we included the EPO dependent production of red blood cells in our model.

A vector is a DNA molecule used as a vehicle to artificially carry foreign genetic material into another cell. Once there it can be replicated and/or expressed. The effect and detection of gene doping is highly dependent on the vectors that are being used. In the early stages of our project we talked to prof. Hidde Haisma from Groningen University, a gene doping expert, who told us the main vectors he would expect athletes to use now are plasmids and adenoviruses. This is because their relative safety compared to vectors that integrate the DNA into the cells genome. Integrating vectors, such as the retrovirus, present the threat of insertional mutagenesis which can lead to the development of cancer. In Table 1 we made an analysis of possible gene doping vectors and some of their properties based on the work of Ratko et al. 2003.

**Table 1:** Gene doping vectors advantages and disadvantages.
Vector	Advantages	Disadvantages
Plasmid	Relatively safe Generally low immune response Low cost and easy large quantity production Variable transgene insertion upto ±20 kb (Lodish et al. 2000) Long storage (Munier et al. 2005, Kircheis et al. 2001, Li and Huang 2000)	Very low transfection efficiency (Bergen et al. 2008) No targeting Transient expression
Adenovirus	High transduction efficiency Transduces proliferating and nonproliferating cells Transduces many cell types Easy Production Very high titers (1012 pfu/mL)	No targeting Transient expression Limited insert size: 4–5 kb Immune-related toxicity with repeated administration Potential replication competence
Adeno-associated virus	Continued expression No viral genes	No targeting Difficult production Not characterized well Potential Insertional Mutagenesis Limited insert size: 5kb
Lentivirus	Transduces proliferating and nonproliferating cells Prolonged expression Relatively high titers (106–107 pfu/mL)	Integrating virus Clinical experience limited Difficult to manufacture and store Limited insert size: 8 kb
Retrovirus	Relatively high titers (106–107 pfu/mL) Prolonged stable expression Larger insert size: 9–12 kb	Inefficient transduction Integrating virus Insertional mutagenesis Broad cell tropism No targeting Potential replication competence

Each vector has its own benefits and drawbacks. Plasmid vectors, as non-viral DNA vectors, have several advantages over viral vectors. Virus production is expensive (Templeton et al. 2002, Nagasaki and Shinkai, 2007) and safety of viral transfection remains a concern after several deaths (McCormack et al. 2004, Hacein-Bey-Abina et al. 2008). However, plasmid vectors have a very low transfection efficiency (Murakami et al. 2011), especially compared to adenoviruses that have been shown to have a 95% transfection efficiency in hepatocytes (Huard et al. 1995, Sullivan et al 1997). Transfection efficiency of plasmids can be increased through methods such as in vivo electroporation (Ataka et al. 2003). However, this method requires the insertion of electrode needles into the athlete to increase transfection efficiency. This is more invasive to the athlete than a single injection of vector particles. Therefore, adenoviruses could be seen as the most likely transfection method for gene doping at this stage and especially in the near future. Hence, we based most of our numerical values on this type of vector.

2. Model Design

2.1 gRNA Array Model

The four input variables of the model are the coding sequence of a gene, the protospacer adjacent motif (PAM) of the Cas protein, the length of the gRNA, and the Cas-dependent identity between gRNA seed and target (off target effect). A general view on the generated algorithm is shown below in Figure 3:

The steps that the algorithm uses for the determination and creation of gRNAs are listed below, step by step:

Convert coding sequence into numerical sequence (A=1, T=2, C=3, G=4).

Translate genetic sequence into amino acid sequence.

Based on the amino acid sequence, determine the number of possible different codons that will code for the same amino acid.

Determine positions of PAM sequence close to the exon-exon junctions.

Analyze the total number of gRNAs necessary for each PAM sequence to cover all possibilities of synonymous mutations (product of possible codons). Determined by the identity necessary between gRNA and target sequence.

Gather the number of all PAM positions in the gene and determine their total number of gRNAs necessary, based on the codon variations.

Chose the PAM sequence that has the minimal number of gRNAs possible and generate gRNAs.

Output the position of PAM sequence in the gene and all the gRNAs’ sequence.

2.2 Gene Doping Model

We first modeled the transit of the injected gene doping viral vectors from injection site to target cells. Both intramuscular (IM) and intravenous (IV) injection methods were considered.

Our model starts from the point of injection, which is mostly either intramuscularly (IM) or intravenously (IV). The advantage of intravenous injections is that the viral vectors immediately enter blood circulation. The means that more vectors reach the target kidney cells before they are degraded. However, intravenous injections require a qualified doctor to administer and can lead to vein damage such as phlebitis. The advantages of intramuscular injections are that they are easy to administer and do not rely on a qualified doctor for administration. On top of this, they can supply relatively large volumes of the gene doping DNA as the muscles have larger uptake capacity than the veins. Also, the gene doping vectors are not directly getting into the bloodstream, which can lead to sustained release. The big disadvantage of intramuscular injection in comparison with intravenous injections though is the poor absorption. Also, for the athlete, intramuscular injections can cause local swelling, drainage and severe pain at the site of injection. Nevertheless, we take both ways of administration into account.

Pharmacological compartment models are developed to understand the distribution of drugs administered to the human body by oral or, intramuscular, or intravenous routes (M.A. Khanday et al. 2017). They are formulated based on diffusion processes using Fick’s principle and law of mass action. The same diffusion processes affect the administered adenoviral vectors. We start with pharmacological compartment models for both injection types as displayed in Figure 10. Here it is assumed that the mixing of the vectors with blood is instantaneous, based on an article by Tarr et al. 1933). Prof. Beltman, Assistant Professor Biomedical Modelling at Leiden University, later agreed with this assumption.

There are multiple tissues producing EPO, including liver, brain and kidney tissue. The main cell population responsible for EPO production are the interstitial fibroblasts in the kidney, spanning an average population of approximately 100 million cells, which produce more than 80% of the EPO in blood (Weidemann & Johnson 2009).

From the compartment models in Figure 10, equations 1 and 2 can be derived for the intravenous administration.

$$\frac{d[c_{blood}]}{dt} = -(k_{blood}+kel_{blood})[c_{blood}]+k_{tissue}[c_{tissue}] \tag{1}$$

$$\frac{d[c_{tissue}]}{dt} = k_{blood}[c_{blood}]-(k_{bind\,uptake}+k_{tissue})[c_{tissue}] \tag{2}$$

Similarly, equations 3, 4, and 5 can be derived for the intramuscular administration. In intramuscular administration, the viral vectors must first diffuse out of the muscle and enter the bloodstream. While in the muscle, the muscle macrophages break down and eliminate the viral vectors.

$$\frac{d[c_{muscle}]}{dt} = -(k_{muscle}+kel_{muscle})[c_{muscle}]\tag{3}$$

$$\frac{d[c_{blood}]}{dt} = -(k_{blood}+kel_{blood})[c_{blood}]+k_{tissue}[c_{tissue}]+k_{muscle}[c_{muscle}] \tag{4}$$

$$\frac{d[c_{tissue}]}{dt} = k_{blood}[c_{blood}]-(k_{bind\,uptake}+k_{tissue})[c_{tissue}] \tag{5}$$

The initial values and constantsused are as specified in Table 2 and Table 3 respectively.

**Table 2:** Overview of the nitial values used in the human body model for an adenoviral vector.
Constants	Values	Meaning
$$[c_{blood} (t=0)]$$	94 billion [#/mL] for IV Single Dose 64 billion [#/mL] followed by smaller doses of 18 billion vectors every 20 days for IV Microdosing 0 [#/mL] for IM	Initial injection of vectors intravenously
$$[c_{tissue} (t=0)]$$	0 [#/mL]	Initial vector concentration in in tissue
$$[c_{muscle} (t=0)]$$	141 billion [#/mL] for IM 96 billion vectors followed by smaller doses of 27 billion vectors every 20 days for IM Microdosing 0 [#/mL] for IV	Initial injection of vectors intramuscularly

**Table 3:** Overview of the constants used in the human body model for an adenoviral vector.
Rate Constants	Values (days^-1)	Meaning	Source
$$k_{tissue}$$	1440	Vector displacement from tissue to blood	Estimate based on mean blood circulation time
$$k_{muscle}$$	1440	Vector displacement from muscle to blood in IM injections	Estimate based on mean blood circulation time
$$k_{blood}$$	1440	Vector displacement from blood to tissue	Estimate based on mean blood circulation time
$$kel_{blood}$$	720	Elimination of viral vectors from the blood	Ganesan et al. 2011
$$kel_{muscle}$$	720	Elimination of viral vectors from the muscle	Ganesan et al. 2011
$$k_{bind\,uptake}$$	8.64	Endosomal uptake	Varga et al. 2005

Upon reaching the target kidney cells, the gene doping viral vectors infect the cells. The infection process and production of gene doping EPO was modeled through a series of kinetic equation. Kidney cells have a lifespan of around 57 days. Upon their death, the infected cells release the gene doping DNA back into the bloodstream as cell free DNA (cfDNA). This increases the detection window of the gene doping DNA.

Cellular uptake

After the uptake of the vectors in the tissue, the stage of cellular uptake ensues. In this process we modelled multiple steps as indicated in Figure 11 according to a model first developed by Varga et al. 2005. The cellular uptake of gene doping vectors as depicted in Figure 11 can be dissected into multiple steps described by a set of coupled differential equations, for which the constants are given in Table 3.

First, the complex is taken up by endocytosis after which it is either degraded or taken up, as represented by equations 6 and 7.

$$\frac{d[vesicle]}{dt} = k_{bind\,uptake}[c_{tissue}]-(k_{Escape}+k_{deg\,vesicle})[vesicle] \tag{6}$$

$$\frac{d[complex\,intracell]}{dt} = k_{escape}[vesicle]-k_{unpack}[complex\,intracell]-k_{bind\,vector}[complex\,intracell]\tag{7}$$

Second, vector dissociation and either degradation or nuclear target complex binding takes place in either dissociated or complexed form, as given by equations 8, 9 and 10.

$$\frac{d[plasmid]}{dt} = k_{unpack}[complex\,intracell]-k_{bind \,plasmid}[plasmid]-k_{deg}[plasmid]\tag{8}$$

$$\frac{d[plasmid\,bound]}{dt} = k_{bind \,plasmid}[plasmid]-k_{NPC}[plasmid\,bound] \tag{9}$$

$$\frac{d[complex\,bound]}{dt} = k_{bind \,vector}[complex\,intracell]-k_{NPC}[complex\,bound] \tag{10}$$

Subsequently, transport to the inner part of the nucleus is believed to take place through first binding to a nuclear pore complex (NPC) and finally inside the nucleus dissociation of the nuclear target complex takes place. This is represented by equations 11 till 16.

$$\frac{d[complex\,boundNPC]}{dt} = k_{NPC}[complex\,bound]-k{in}[complex\,boundNPC] \tag{11}$$

$$\frac{d[complex\,bound\,nucleus]}{dt} = k{in}[complex\,boundNPC]-k_{dissociation}[complex\,bound\,nucleus] \tag{12}$$

$$\frac{d[complex\,nucleus]}{dt} = k_{dissociation}[complex\,bound\,nucleus] - k_{unpack2}[complex\,nucleus] \tag{13}$$

$$\frac{d[plasmid\,boundNPC]}{dt} = k_{NPC}[plasmid\,bound] -k_{in2}[plasmid\,boundNPC] \tag{14}$$

$$\frac{d[plasmid\,bound\,nucleus]}{dt} =k_{in2}[plasmid\,boundNPC]-k{kissociation2}[plasmid\,bound\,nucleus] \tag{15}$$

$$\frac{d[plasmid\,nucleus]}{dt} =k{kissociation2}[plasmid\,bound\,nucleus] + k_{unpack2}[complex\,nucleus]$$

$$- k_{cell\,death}[plasmid\,nucleus] \tag{16}$$

Detection of cell free doping DNA

Apart from the effect of the gene doping EPO on the production of red blood cells, the purpose of the model is to determine the dynamics of the detectable cfDNA concentration in the blood. Cell free doping DNA is released from dying infected cells and circulates in the blood where it is assumed to degrade at the same rate as natural cfDNA.

$$\frac{d[Doping\,DNA]}{dt} =k_{cell\,death}[plasmid\,nucleus]-kel_{cfDNA}[Doping\,DNA] \tag{17}$$

Equation 17 provides us with a detection window for which we assume that we can detect both, DNA left in the tissue and bloodstream after injection, and DNA released after cell death (kcelldeath). Any other degradation terms or transient expression we incorporated in the constants used. Based on the above model we obtained the concentration developments of cfDNA in time for both intramuscular and intravenous injections and the estimated detection windows where we assumed an estimated detection limit of 100 copies DNA. The concentrations of DNA over time were used in the laboratory for our sample preparation to mimic real life detection potential. The cell death rate constant is directly linked to the average lifetime of renal interstitial fibroblasts, which we estimated to be around 57 days based upon measurements in chicks by Weissmanshomer et al. 1975.

According to Haller et al. 2018, the expected concentration of doping cell free DNA may be higher for athletes in endurance and intermittent sports. Haller et al. found a 22.7 fold increase in venous cfDNA concentrations in footballers after a professional football match. Given the high amount of training top level athletes endure, this finding leads us to believe that we might be able to have even longer detection windows than our model predicts.

The Protein Effect

Lastly, the uptaken DNA can be translated into protein according to equation 18, after which it can be exported to the extracellular environment according to equation 19.

$$\frac{d[protein]}{dt} =k_{protein}[plasmid\,nucleus]-k_{deg\,protein}[protein]-k_{export}[protein] \tag{18}$$

$$\frac{d[protein\,extracellular]}{dt} =k_{export}[protein]-k_{deg\,protein\,extracellular}[protein\,extracellular]\tag{19}$$

**Table 4:** Overview of the constants used in the human body model for an adenoviral vector.
Rate Constants (Ad5)	Values (days^-1)	Meaning	Source
$$k_{bind\,uptake}$$	8.64	Endosomal uptake	Varga et al. 2005
$$k_{deg\,vesicle}$$	28.8	Degradation of complex within uptake vesicle	Varga et al. 2005
$$k_{escape}$$	23.0	Complex movement from endosome to intracellular	Varga et al. 2005
$$k_{bind\,vector}$$	144	Binding of gene delivery vector to compound targeting for the nucleus	Varga et al. 2005
$$k_{unpack}, k_{unpack2}$$	144	Plasmid detaches from vector either in cytoplasm(1) or in the nucleus(2)	Varga et al. 2005
$$k_{deg}$$	7.2	Degradation of unbound plasmid in the cytoplasm	Lechardeur et al. 1999
$$k_{bind\,plasmid}$$	2.88	Binding of plasmid to compound targeting for the nucleus	Varga et al. 2001
$$k_{NPC}$$	1.44*10⁶	Binding formed complexes to Nuclear Pore Complex	Vacik et al. 1999 Wilson et al. 1999 Chan et al. 1999 Dean et al. 1997
$$k_{in}, k_{in2}$$	2.88	Uptake nucleus through Nuclear Pore Complex	Varga et al. 2001
$$k_{dissociation}, k_{dissociation2}$$	1.44*10⁶	Dissociation from the NPC targeting compound	Moroianu et al. 1996
$$k_{protein}$$	14.4	Protein production from plasmid	Schaffer et al. 1998
$$k_{degprot}$$	1.04	Cytoplasmic degradation of the protein	Fuertinger et al. 2012
$$k_{export}$$	1.44*10⁶	Export protein to extracellular environment	Estimate
$$k_{deg\,protein\,extracellular}$$	1.04	Cytoplasmic degradation of the protein	Fuertinger et al. 2012
$$k_{cell\,death}$$	0.0167	Average death rate of renal interstitial fibroblast	Estimate based on chicks; Weissmanshomer et al. 1975
$$kel_{cfDNA}$$	100	Clearance of cfDNA from the blood	Alegre et al. 2015

The EPO from the infected cells is released into the bloodstream. The EPO reaches the bone marrow where it stimulates red blood cell production through erythropoiesis. Red blood cells begin as stem cells and go through a series of cell differentiations before maturing into red blood cells.

.

EPO promotes the proliferation of CFU-E cells. Increases in EPO levels led to faster proliferation rates of CFU-E cells. Increases in EPO levels reduces the marrow transit time of cells for marrow reticulocytes, releasing the cells into the blood in a shorter time frame. In the blood, the reticulocytes mature into red blood cells which increase the oxygen carrying capacity of blood. If the concentration of EPO in blood is low due to an excess of circulating red blood cells, the programmed death of young red blood cells occurs to reduce the red blood cell count. This is referred to as neocytolysis.

With the doping DNA degradation and doping EPO formation determined, the effect of EPO on erythropoiesis, the process which produces red blood cells, is determined. We developed a model using an anemia EPO treatment model by Fuertinger et al. 2012 as reference.

Red blood cells begin as stem cells and progress into different cell stages as they age. As progenitor and precursor cells age, they proliferate or undergo apoptosis at a rate dependent on the cell stage they are in. The resulting growth or decay rates may be constant or dependent on the concentration of EPO in the blood. Burst-Forming Unit-Erythroid (BFU-E) cells have a very small number of EPO receptors. EPO concentration has no effect on BFU-E proliferation, their proliferation is assumed to be constant. After leaving the stem cell stage, cells stay in the BFU-E stage for 7 days, after which they enter the Colony-Forming Unit-Erythroid (CFU-E) stage. In this stage, the cells divide at a faster rate than in the BFU-E cell stage. The CFU-E cells have a large number of EPO receptors and are strongly dependent on EPO for their survival. Their rate of apoptosis is inversely related to the concentration of EPO. Under normal conditions within the human body, a large number of CFU-E generated do not survive. As the concentration of EPO in the blood increases, the number of cells which survive increases.

After spending 6 days as CFU-E cells, the cells enter the erythroblasts stage. Here, the number of EPO receptors decline. During this stage, there is no evidence that additional divisions occur when production of EPO increases. For this reason we assume that the proliferation of erythroblasts is constant (Lichtman et al. 2005).

The cells stay in the erythroblasts stage for 5 days until they stop dividing, extrude their nuclei and mitochondria, and become marrow reticulocytes. Marrow reticulocytes no longer proliferate and their mortality rate is inversely dependent on iron concentration in the plasma. Since we assume that athletes have a sufficient iron supply, a constant apoptosis rate for marrow reticulocytes is assumed. The time cells stay in the reticulocytes stage is between 0.75-3 days. An increase in EPO concentration shortens the marrow transit time of reticulocytes.

Once reticulocytes are released from the bone marrow and enter the blood, they mature into erythrocytes (red blood cells) within 1-3 days. Reticulocytes have a hemoglobin content of around 27.5 ± 2.8 pg per cell and, red blood cells have a hemoglobin content of around 26.4 ± 2.4 pg per cell (Fishbane et al. 1997). Due to the similar ability of blood reticulocytes and red blood cells to carry oxygen, when red blood cells are discussed, we refer to both blood reticulocytes and mature red blood cells. The lifespan of red blood cells (RBCs) in healthy human adults is about 120 days before their components are recycled by microphages (Jandl 1987). Over the course of this time a small number of RBCs die due to random daily breakdown or, internal or external bleeding. This is taken into account with a small apoptosis rate for RBCs. Adults have a red blood cell count ranging from about 20 to 30 trillion. Women have a blood cell count range of 3.5-5.5 trillion cells per liter, while men have a range of 4.3-5.9 trillion cells per liter (Dean 2005). The average red blood cell count is estimated to be 24.98 trillion by Lichtman et al. 2005.The entire process, from stem cell to red blood cell recycling by microphages, takes 141 days.

The endogenous release of EPO is inversely related to the partial pressure of oxygen in the blood. The partial pressure of oxygen in the blood is proportionally related to the number of red blood cells circulating. An increase in the red blood cell population in blood will decrease endogenous EPO production. If the concentration of EPO in the blood falls below a certain level (9.8 mU/ml in the case of this model), neocytolysisis is triggered. Neocytolysisis the selective lysis of young red blood cells by the body to allow it to decrease its red blood count at a faster rate and reach the desired partial pressure of oxygen in the blood.

The series of partial differential equations (PDEs) that describe red blood production was modeled with a simplified linear age population method which provided similar accuracy to more computationally intensive PDEs solvers. The red blood cell production model is then combined with the compartment and infection model to determine the effects on EPO gene doping on red blood cell count.

The process of red blood cell formation, from stem cell to red blood cell can be modelled in time through a series of partial differential equations based on the age of the cells . Each of cell stage, BFU-E, CFU-E, erythroblasts, marrow reticulocytes and red blood cells, is represented by a partial differential equation.The general form is seen below.

Starting at cell age 0 days, when stem cells become BFU-E cells, the age span up until cell age 141 days, when red blood cells are recycled by macrophages, the cells can be broken up into populations of cells at a certain age. For every cell stage, the population density of cells at a given maturity and time can modelled as a population mesh. A population mesh is a numerical estimation that breaks down the process of red blood cell production into populations at a given age. A mesh point is population of cells at a specific age. When modelled as a mesh, an assumption of the relation between a change in time and a change in maturity can be made.

Assumptions

For a population of cells u of maturity μ and at time t, a change in time Δt while result in an equal change in maturity Δμ, assuming that the maturation velocity $vs(E(t))=1$.
The maturation velocity is 1 for every stage except the marrow reticulocytes, where the time cells stay in this stage is dependent on the concentration of EPO.

The commitment of stems cells to becoming red blood cells is an irreversible event (Fuertinger et al. 2012). Cells cannot regress back to a previous cell type or switch to a different differentiation pathway. For this reason, assumption 1 can be made. A change in time will lead to an equal change in the age of the cell. As a result, the finer you make the population mesh the smaller the time step is. This increases the accuracy of the model as the cells react to changes in EPO concentration in time faster. Once they reach the age at which they differentiated into another cell stage, the resulting growth and decay rates that govern them will change.

When a time step occurs, the cells in each mesh point ($u_n$) experience growth or decay based on the stage they are in. The new population of cells is then shifted to the next age mesh point. The general form of the equation that governs the change of population density at each time step is below.

$$ u_n = u_{n-1}\times e^{((\beta-\alpha(EPO(t)) \times \Delta t)} \tag{20}$$

$$ u_{n+1} = u_{n}\times e^{((\beta-\alpha(EPO(t)) \times \Delta t)} \tag{21}$$

$$ \Delta t = \mu_{n+1} - \mu_n = \Delta \mu \tag{22}$$

The dynamics of the mesh in response to a gene doping injection can be seen below in Figure 15.

To calculate the total population of each stage, the trapezoid rule was used to calculate the area under the population mesh and estimate the total population.

BFU-E Cells

10⁸ stem cells become BFU-E cells on day 0. BFU-E cells grow exponentially as they age. Their proliferation rate is not dependent on EPO. As result, the population of BFU-E cells grows at a constant exponential rate as they age.

$$ BFU{-}E_n = BFU{-}E_{n-1}\times e^{(\beta_{BFU-E} \times \Delta t)} \tag{23}$$

$$ BFU{-}E_{n+1} = BFU{-}E_{n}\times e^{(\beta_{BFU-E} \times \Delta t)} \tag{24}$$

Cells committed to become red blood cells continue to grow in this manner until they reach the age of 7 days. At this point, the BFU-E differentiate into CFU-E cells.

CFU-E Cells

CFU-E cells are strongly dependent on EPO for their survival.

$$ CFU{-}E_n = CFU{-}E_{n-1}\times e^{((\beta_{CFU{-}E}-\alpha_{CFU{-}E}(EPO(t)) \times \Delta t)} \tag{25}$$

$$ CFU{-}E_{n+1} = CFU{-}E_{n}\times e^{((\beta_{CFU{-}E}-\alpha_{CFU{-}E}(EPO(t)) \times \Delta t)} \tag{26}$$

The apoptosis rate of CFU-E cells is dependent on the concentration of EPO at a given time. As the concentration of EPO increases, the apoptosis rate of CFU-E cells decreases.

The logistic equation that governs the apoptosis rate of CFU-E is seen in equation 27 below.

$$\alpha_{CFU{-}E}(EPO(t))= \frac{(a_1 - b_1)}{1+e^{k_1 \times EPO(t) - c_1}}+b_1 \tag{27}$$

The CFU-E stage starts once cells reach the age of 7 days and continues until cells differentiate into Erythroblasts at an age of 13 days.

Erythroblasts

The proliferation of erythroblasts is not dependent on the concentration EPO. However, the population of cells entering the erythroblast stage at age 13 days is dependent on the concentration of EPO due to the CFU-E stage coming before.

$$ Erythroblasts_n = Erythroblasts_{n-1}\times e^{(\beta_{Erythroblasts} \times \Delta t)} \tag{28}$$

$$ Erythroblasts_{n+1} = Erythroblasts_{n}\times e^{(\beta_{Erythroblasts} \times \Delta t)} \tag{29}$$

Upon reaching the age of 18 days, erythroblast cells differentiate into marrow reticulocytes.

Marrow Reticulocytes

For the marrow reticulocytes, where the marrow transit time varies with EPO concentration, the same mesh method is used. Even with sufficient iron supply, a constant fraction of marrow reticulocytes is phagocytosed (Fuertinger et al. 2012). This is represented by a constant apoptosis rate independent of EPO concentration.

$$ Reticulocytes_n = Reticulocytes_{n-1}\times e^{(-\alpha_{Reticulocytes} \times \Delta t)} \tag{30}$$

$$ Reticulocytes_{n+1} = Reticulocytes_{n}\times e^{(-\alpha_{Reticulocytes} \times \Delta t)} \tag{31}$$

The amount time it takes for marrow reticulocytes to leave the bone marrow and enter the blood is dependent on the concentration of EPO. A faster marrow transit time means marrow reticulocytes spend more time as blood reticulocytes. The oldest marrow reticulocytes will become RBCs first when marrow transit time decreases. To account for the change in transit time, the stage boundary between reticulocytes and red blood cells is allowed to shift depending on the concentration of EPO.

The logistic equation that governs the marrow transit time can be seen below.

$$ \mu_{Reticulocytes,max}(EPO(t)) = a_2 - \frac{b_2}{1+e^{k_2 \times EPO(t) - c_2}} \tag{32}$$

Reticulocytes will stay in the bone marrow until age 18.75 to 21 days when they leave the bone marrow and circulate in the blood. There they become mature red blood cells.

Red Blood Cells and Blood Reticulocytes

There is a fixed rate of random daily breakdown of red blood cells due to random daily breakdown or, internal or external bleeding. This is represented by a constant apoptosis rate independent of EPO concentration.

$$ RBCs_n = Reticulocytes_{n-1}\times e^{(-\alpha_{RBCs}(EPO(t), \, \mu_{RBCs}) \times \Delta t)} \tag{33}$$

$$ Reticulocytes_{n+1} = Reticulocytes_{n}\times e^{(-\alpha_{Reticulocytes}(-\alpha_{RBCs}(EPO(t),\, \mu_{RBCs}) \times \Delta t)} \tag{34}$$

During the age range of 35 to 42 days, young red blood cells will undergo neocytolysis if the concentration of EPO in the blood drops below 9.8 mU/ml. The neocytolysis rate will increase as EPO levels drop until the level of EPO reach 3.3 mU/ml, when it reaches its maximum rate.

$$\alpha_{RBCs}(EPO(t), \mu_{RBCs}) = \alpha_{RBCs, \, r} + min\Big(\frac{c_E}{EPO(t)^{k_E}}, \, b_E \Big), \, for \, EPO(t) < \tau_E, \, 35 \, days \leq \mu_{RBCs} \leq 42 \, days \tag{35}$$

$$\alpha_{RBCs}(EPO(t), \mu_{RBCs}) = \alpha_{RBCs, \, r}, \, otherwise \tag{36}$$

Feedback via EPO

The natural release of EPO from the kidneys is dependent on the partial pressure of oxygen in the blood. If the partial pressure of oxygen in the blood decreases due to a lack of red blood cells, EPO release increases. This leads to an increase in red blood cell production, leading to a higher population of circulating red blood cells and a higher partial pressure of oxygen. The partial pressure of oxygen and the number of circulating red blood cell are therefore assumed to be directly proportional.

The amount $E^{end}_{in}(t)$ of EPO released by the kidney can be estimated by the use of the total population of red blood cells $RBCs(t)$, which consist of all circulating red blood cells:

$$ EPO^{end}_{in}(t) = \bigg(\frac{(a_3 - b_3)}{1+e^{k_3 \times \tilde{M}(t) - c_3}}+b_3\bigg)\ \times \frac{1}{TBV} \tag{37} $$

$$ \tilde{M}(t) = 10^{-8} \times \frac{RBCs(t)}{TBV} \tag{38} $$

The logistic equation that governs the natural release of EPO uses a scaled red blood cell count with $TBV$ being the total blood volume.

The dynamics of the endogenous EPO concentration Eend(t) in plasma is modeled by the following ordinary differential equation:

$$\frac{dEPO_{end}(t)}{dt} = EPO^{end}_{in}(t) - k_{deg\,protein\,extracellular}EPO_{end}(t) \tag{39}$$

In the case of gene doping administration, the concentration of gene doping EPO at a given time is given by Equation 19. It is assumed that the degradation of natural EPO and gene doping EPO occurs at the same rate. The overall concentration of EPO in plasma consists of the naturally produced erythropoietin and the administered gene doping EPO:

$$ EPO(t) = EPO_{end}(t) + EPO_{doping}(t) \tag{40}$$

The parameters used in the model are based on the parameters derived in Fuertinger et al. (2012) except fora3and b3which are derived from WE and WL Owen Roberts (2011).

**Table 4:** Table of the parameters used in the red blood cell production model, their values, their units, and their meaning.
Parameters	Values	Units	Meaning
$$\beta_{BFU-E}$$	0.2	1/day	Proliferation rate for BFU-E cells
$$\beta_{CFU-E}$$	0.57	1/day	Proliferation rate for CFU-E cells
$$\beta_{Erythroblasts}$$	1.024	1/day	Proliferation rate for erythroblasts
$$\mu_{BFU-E, \, max}$$	7	Days	Maximal maturity for BFU-E cells
$$\mu_{CFU-E, \, min}$$	7	Days	Minimal maturity for CFU-E cells
$$\mu_{CFU-E, \, max}$$	13	Days	Maximal maturity for CFU-E cells
$$\mu_{Erythroblasts, \, min}$$	13	Days	Minimal maturity for erythroblasts
$$\mu_{Erythroblasts, \, max}$$	18	Days	Maximal maturity for erythroblasts
$$\mu_{Reticulocytes, \, max}(EPO(t))$$	18.75 to 21	Days	Maximal maturity for marrow reticulocytes
$$\alpha_{Reticulocytes}$$	0.089	1/day	Rate of ineffective erythropoiesis in the marrow reticulocytes stage
$$\alpha_{RBCs}$$	0.005	1/day	Intrinsic mortality rate for erythrocytes
$$a_1, \, b_1$$	0.35, 0.07	1/day	Constants for the sigmoid apoptosis rate for CFU-E cells
$$c_1, \, k_1$$	3, 0.14	Dimensionless, ml/mU	Constants for the sigmoid apoptosis rate for CFU-E cells
$$a_2, \, b_2$$	3.225, 2.475	Days	Constants for the sigmoid maturation velocity/marrow transit time for marrow reticulocytes
$$c_2, \, k_2$$	2.3, 0.2	Dimensionless, ml/mU	Constants for the sigmoid maturation velocity/marrow transit time for marrow reticulocytes
$$a_3, \, b_3$$	9.1, 0.2	Dimensionless, ml/mU	Constants for the sigmoid function governing the release of EPO from the kidneys
$$\mu_{RBCs, \, neocytolysis \, min}$$	35	Days	Lower bound of erythrocytes which are possibly exposed to neocytolysis
$$\mu_{RBCs, \, neocytolysis \, max}$$	42	Days	Upper bound of erythrocytes which are possibly exposed to neocytolysis
$$\mu_{RBCs, \, max}$$	141	Days	Maximal life span for red blood cells
$$b_E$$	0.1	1/day	Constant in the mortality rate for red blood cells
$$c_E$$	3.5	mU³/(ml³$\times$day)	Constant in the mortality rate for red blood cells
$$k_E$$	3	Dimensionless	Exponent in the mortality rate for red blood cells
$$\tau_E$$	9.8	mU/ml	EPO threshold for neocytolysis
$$k_{deg\,protein\,extracellular}$$	1.04	1/day	Degradation rate of EPO in the blood
$$S_0$$	10⁸	1/day	Rate at which cells are committing to the erythroid lineage
$$TBV$$	5000	ml	Total blood volume

3. Results

3.1 gRNA Array Model

The functionality of our algorithm lays in creating a tool to generate an array of gRNAs necessary to screen for gene doping with our novel targeted sequencing platform. The model works with any input gene . It was tested for the EPO gene, as EPO is our main model target for gene doping. We worked with the following information:

Gene cds sequence: Human EPO cds (GenBank: BC143225.1)
Type of Cas: dxCas9
PAM sequence: NG
gRNA length: 20 bp of target gRNA
Off target possibility (seed to target): 10 bp (50 % adjacent to PAM should be identical)

As presented in Figure 20, the algorithm detected several PAM sequences close to exon-exon junctions and found a minimal number of necessary guides for each one.

Figure 20 clearly shows that in junction 3 there is the optimal PAM sequence with smallest number of gRNAs possible. This way, the algorithm generates the gRNAs and gives an output with the position of such gRNA. In this specific case, there were two PAM sequences near Junction 3 that had the same minimal number of possible gRNAs necessary (12 guides each). The algorithm doesn’t neglect one or another, but outputs both options (or more if possible). This array can be used to generate gRNAs’ libraries for targeted next generation sequencing of gene doping.

3.2 Gene Doping Model

The concentration of EPO gene doping DNA in blood increased rapidly in the first 1.5 days after injection before decreasing exponentially. Due to the rapid clearance of the adenoviral vectors from the blood and muscle, the majority of the infection events occurs right after injection. As more infected cells die, the number of infected cells decreases. This decreases the amount of doping DNA released into the blood over time.

Regression was performed to determine that the half life of EPO gene doping DNA in the blood is around 41 days. While the half-life of cfDNA in blood is around 10 minutes, the slow release of the gene doping DNA from the dying infected cells increases the detection window significantly.

Microdosing was determined to be the best doping method for doping athletes. The benefit of this method is that it avoids detection through the biological passport. Assuming that the athlete begins the treatment prior to becoming a professional athlete and continued microdosing after, their red blood cell count would appear to be constant and naturally high.

Test

The downside of this method is the requirement for constant microdosing. Repeated injections would cause noticeable damage to the veins. The athlete would then be required to disguise the injection sites or perform the IV microdosing in parts of the body normally covered. For this reason athletes would likely favor IM injection to IV injection as IM is less invasive.

Though the microdoses are smaller, the copies of DNA stay above our detection limit (40,000 to 50,000 fragments per mL) due to IM injections occuring every 20 days. While the athlete would bypass detection through the biological passport, they would be detected by our gene doping detection method.

4. References

5. Download

In case we want to include any download here.

@@ Line 201: / Line 201: @@
          <p>We first modeled the transit of the injected gene doping viral vectors from injection site to target cells. Both intramuscular (IM) and intravenous (IV) injection methods were considered. </p>
-<figure><center> <img src="https://static.igem.org/mediawiki/2018/5/54/T--TUDelft--Compartment_Model_Figure.png" width="100%" height="auto" alt="Figure 4 Model"></center><br>
+<figure><center> <img src="https://static.igem.org/mediawiki/2018/5/54/T--TUDelft--Compartment_Model_Figure.png" width="100%" height="auto" alt="Figure 10 Model"></center><br>
-<figcapture class="figjnnbl"> <b>Figure 4:</b>  Pharmacological compartment models displayed for both IV and IM injections.The arrows indicate viral vector transfer from one body compartment to another and the kinetic rate constant associated with the transfer.</figcapture></figure><br>
+<figcapture class="figjnnbl"> <b>Figure 10:</b>  Pharmacological compartment models displayed for both IV and IM injections.The arrows indicate viral vector transfer from one body compartment to another and the kinetic rate constant associated with the transfer.</figcapture></figure><br>
 <button class="collapsible cjnnbl">Would you like to know more? Detailed Compartment Model</button>
@@ Line 209: / Line 209: @@
      <p>Our model starts from the point of injection, which is mostly either intramuscularly (IM) or intravenously (IV). The advantage of intravenous injections is that the viral vectors immediately enter blood circulation. The means that more vectors reach the target kidney cells before they are degraded. However, intravenous injections require a qualified doctor to administer and can lead to vein damage such as phlebitis. The advantages of intramuscular injections are that they are easy to administer and do not rely on a qualified doctor for administration. On top of this, they can supply relatively large volumes of the gene doping DNA as the muscles have larger uptake capacity than the veins. Also, the gene doping vectors are not directly getting into the bloodstream, which can lead to sustained release. The big disadvantage of intramuscular injection in comparison with intravenous injections though is the poor absorption. Also, for the athlete, intramuscular injections can cause local swelling, drainage and severe pain at the site of injection. Nevertheless, we take both ways of administration into account.</p>
-     <p>Pharmacological compartment models are developed to understand the distribution of drugs administered to the human body by oral or, intramuscular, or intravenous routes (<a class="jnnbl" href="#References">M.A. Khanday et al. 2017</a>). They are formulated based on diffusion processes using Fick’s principle and law of mass action. The same diffusion processes affect the administered adenoviral vectors. We start with pharmacological compartment models for both injection types as displayed in Figure 4. Here it is assumed that the mixing of the vectors with blood is instantaneous, based on an article by <a class="jnnbl" href="#References">Tarr et al. 1933</a>). Prof. Beltman, Assistant Professor Biomedical Modelling at Leiden University, later agreed with this assumption.</p>
+     <p>Pharmacological compartment models are developed to understand the distribution of drugs administered to the human body by oral or, intramuscular, or intravenous routes (<a class="jnnbl" href="#References">M.A. Khanday et al. 2017</a>). They are formulated based on diffusion processes using Fick’s principle and law of mass action. The same diffusion processes affect the administered adenoviral vectors. We start with pharmacological compartment models for both injection types as displayed in Figure 10. Here it is assumed that the mixing of the vectors with blood is instantaneous, based on an article by <a class="jnnbl" href="#References">Tarr et al. 1933</a>). Prof. Beltman, Assistant Professor Biomedical Modelling at Leiden University, later agreed with this assumption.</p>
      <p>There are multiple tissues producing EPO, including liver, brain and kidney tissue. The main cell population responsible for EPO production are the interstitial fibroblasts in the kidney, spanning an average population of approximately 100 million cells, which produce more than 80% of the EPO in blood (<a class="jnnbl" href="#References">Weidemann & Johnson 2009</a>).</p>
-     <p>From the compartment models in Figure 4, equations 1 and 2 can be derived for the intravenous administration.</p>
+     <p>From the compartment models in Figure 10, equations 1 and 2 can be derived for the intravenous administration.</p>
       <p>$$\frac{d[c_{blood}]}{dt} = -(k_{blood}+kel_{blood})[c_{blood}]+k_{tissue}[c_{tissue}] \tag{1}$$</p>
@@ Line 324: / Line 324: @@
 <p>Upon reaching the target kidney cells, the gene doping viral vectors infect the cells. The infection process and production of gene doping EPO was modeled through a series of kinetic equation. Kidney cells have a lifespan of around 57 days. Upon their death, the infected cells release the gene doping DNA back into the bloodstream as cell free DNA (cfDNA). This increases the detection window of the gene doping DNA. </p>
-<figure><center><img src="https://static.igem.org/mediawiki/2018/9/97/T--TUDelft--2018_cellularuptake.png" width="100%" height="auto" alt="Figure 4 Model"></center> <br>
+<figure><center><img src="https://static.igem.org/mediawiki/2018/9/97/T--TUDelft--2018_cellularuptake.png" width="100%" height="auto" alt="Figure 11 Model"></center> <br>
-<figcapture class="figjnnbl"> <b>Figure 5:</b>  Cellular uptake of gene doping vectors. Figure inspired by <a class="jnnbl" href="#References">Varga et al. 2005</a>.</figcapture></figure><br>
+<figcapture class="figjnnbl"> <b>Figure 11:</b>  Cellular uptake of gene doping vectors. Figure inspired by <a class="jnnbl" href="#References">Varga et al. 2005</a>.</figcapture></figure><br>
 <button class="collapsible cjnnbl">Would you like to know more? Detailed Intracellular Gene Delivery Model</button>
@@ Line 331: / Line 331: @@
      <p class="jnnbl"><u><b>Cellular uptake</b></u></p>
-     <p>After the uptake of the vectors in the tissue, the stage of cellular uptake ensues. In this process we modelled multiple steps as indicated in Figure 5 according to a model first developed by <a class="jnnbl" href="#References">Varga et al. 2005</a>. The cellular uptake of gene doping vectors as depicted in Figure 5 can be dissected into multiple steps described by a set of coupled differential equations, for which the constants are given in Table 3.</p>
+     <p>After the uptake of the vectors in the tissue, the stage of cellular uptake ensues. In this process we modelled multiple steps as indicated in Figure 11 according to a model first developed by <a class="jnnbl" href="#References">Varga et al. 2005</a>. The cellular uptake of gene doping vectors as depicted in Figure 11 can be dissected into multiple steps described by a set of coupled differential equations, for which the constants are given in Table 3.</p>
      <p>First, the complex is taken up by endocytosis after which it is either degraded or taken up, as represented by equations 6 and 7.</p>
@@ Line 512: / Line 512: @@
 <p>The EPO from the infected cells is released into the bloodstream. The EPO reaches the bone marrow where it stimulates red blood cell production through erythropoiesis. Red blood cells begin as stem cells and go through a series of cell differentiations before maturing into red blood cells.</p>.
-<figure><center> <img src="https://static.igem.org/mediawiki/2018/0/02/T--TUDelft--ModelRBCsGif.gif" width="100%" height="auto" alt="Figure 6 Model"></center><br>
+<figure><center> <img src="https://static.igem.org/mediawiki/2018/0/02/T--TUDelft--ModelRBCsGif.gif" width="100%" height="auto" alt="Figure 12 Model"></center><br>
-<figcapture class="figjnnbl"> <b>Figure 6:</b>  Cell stages in red blood cell production. Stage 1 is the BFU-E cell stage. Stage 2 is the CFU-E cell stage. The proliferation rate of CFU-E cells is dependent on the concentration of EPO. 3 is the erythroblasts stage. Stage 4 is the marrow reticulocytes stage. The length of time cells stay in this stage is dependent on the concentration of EPO. Stage 5 is the circulating red blood cell (RBC)  stage. Neocytolysis of RBCs is triggered when the concentration of EPO drops below a threshold. The red blood cell count acts as a feedback loop that determines the endogenous production of EPO. </figcapture></figure><br>
+<figcapture class="figjnnbl"> <b>Figure 12:</b>  Cell stages in red blood cell production. Stage 1 is the BFU-E cell stage. Stage 2 is the CFU-E cell stage. The proliferation rate of CFU-E cells is dependent on the concentration of EPO. 3 is the erythroblasts stage. Stage 4 is the marrow reticulocytes stage. The length of time cells stay in this stage is dependent on the concentration of EPO. Stage 5 is the circulating red blood cell (RBC)  stage. Neocytolysis of RBCs is triggered when the concentration of EPO drops below a threshold. The red blood cell count acts as a feedback loop that determines the endogenous production of EPO. </figcapture></figure><br>
 <p>EPO promotes the proliferation of CFU-E cells. Increases in EPO levels led to faster proliferation rates of CFU-E cells. Increases in EPO levels reduces the marrow transit time of cells for marrow reticulocytes, releasing the cells into the blood in a shorter time frame. In the blood, the reticulocytes mature into red blood cells which increase the oxygen carrying capacity of blood. If the concentration of EPO in blood is low due to an excess of circulating red blood cells, the programmed death of young red blood cells occurs to reduce the red blood cell count. This is referred to as neocytolysis.</p>
@@ Line 537: / Line 537: @@
 <div class="content">
      <p>The process of red blood cell formation, from stem cell to red blood cell can be modelled in time through a series of partial differential equations based on the age of the cells . Each of cell stage, BFU-E, CFU-E, erythroblasts, marrow reticulocytes and red blood cells, is represented by a partial differential equation.The general form is seen below.</p>
-     <figure><center> <img src="https://static.igem.org/mediawiki/2018/8/8c/T--TUDelft--PDE_Equation.png" width="80%" height="auto" alt="Figure 6 Model"></center><br>
+     <figure><center> <img src="https://static.igem.org/mediawiki/2018/8/8c/T--TUDelft--PDE_Equation.png" width="80%" height="auto" alt="Figure 13 Model"></center><br>
-     <figcapture class="figjnnbl"> <b>Figure 7:</b>  Breakdown of the general PDE used to model the various cell stages that occur in the process of red blood cell production.</figcapture></figure><br>
+     <figcapture class="figjnnbl"> <b>Figure 13:</b>  Breakdown of the general PDE used to model the various cell stages that occur in the process of red blood cell production.</figcapture></figure><br>
      <p>Starting at cell age 0 days, when stem cells become BFU-E cells, the age span up until cell age 141 days, when red blood cells are recycled by macrophages, the cells can be broken up into populations of cells at a certain age. For every cell stage, the population density of cells at a given maturity and time can modelled as a population mesh. A population mesh is a numerical estimation that breaks down the process of red blood cell production into populations at a given age. A mesh point is population of cells at a specific age. When modelled as a mesh, an assumption of the relation between a change in time and a change in maturity can be made.</p>
@@ Line 550: / Line 550: @@
      <p>The commitment of stems cells to becoming red blood cells is an irreversible event (<a class="jnnbl" href="#References">Fuertinger et al. 2012</a>). Cells cannot regress back to a previous cell type or switch to a different differentiation pathway. For this reason, assumption 1 can be made. A change in time will lead to an equal change in the age of the cell. As a result, the finer you make the population mesh the smaller the time step is. This increases the accuracy of the model as the cells react to changes in EPO concentration in time faster. Once they reach the age at which they differentiated into another cell stage, the resulting growth and decay rates that govern them will change.</p>
-     <figure><center> <img src="https://static.igem.org/mediawiki/2018/6/6b/T--TUDelft--CellStageTransition.png" width="70%" height="auto" alt="Figure 6 Model"></center><br>
+     <figure><center> <img src="https://static.igem.org/mediawiki/2018/6/6b/T--TUDelft--CellStageTransition.png" width="70%" height="auto" alt="Figure 14 Model"></center><br>
-     <figcapture class="figjnnbl"> <b>Figure 8:</b>  Transition of cells from one stage to another through differentiation. (a) Follows one populations of reticulocyte cells as they age over time and transition to marrow reticulocytes. (b) Shows the population density of cells every 0.1 days of maturity at a steady state EPO concentration of 9.8 mU/ml.</figcapture></figure><br>
+     <figcapture class="figjnnbl"> <b>Figure 14:</b>  Transition of cells from one stage to another through differentiation. (a) Follows one populations of reticulocyte cells as they age over time and transition to marrow reticulocytes. (b) Shows the population density of cells every 0.1 days of maturity at a steady state EPO concentration of 9.8 mU/ml.</figcapture></figure><br>
      <p>When a time step occurs, the cells in each mesh point (\(u_n\)) experience growth or decay based on the stage they are in. The new population of cells is then shifted to the next age mesh point. The general form of the equation that governs the change of population density at each time step is below.</p>
@@ Line 559: / Line 559: @@
      <p>$$ \Delta t = \mu_{n+1} - \mu_n = \Delta \mu \tag{22}$$</p>
-     <p>The dynamics of the mesh in response to a gene doping injection can be seen below in Figure 9. </p>
+     <p>The dynamics of the mesh in response to a gene doping injection can be seen below in Figure 15. </p>
-     <figure><center> <img src="https://static.igem.org/mediawiki/2018/3/3e/T--TUDelft--Dynamic_Pop_Mesh.gif" width="70%" height="auto" alt="Figure 6 Model"></center><br>
+     <figure><center> <img src="https://static.igem.org/mediawiki/2018/3/3e/T--TUDelft--Dynamic_Pop_Mesh.gif" width="70%" height="auto" alt="Figure 15 Model"></center><br>
-     <figcapture class="figjnnbl"> <b>Figure 9:</b>  A gif of the dynamic red blood cell formation model. The injection 141 billion copies of the EPO cDNA in adenoviruses occurs at day 0. The infected cells produce extra EPO leading to a maximum concentration of EPO of 31.3 mU/ml (3.2 times more that the steady state EPO concentration of 9.8 mU/ml).</figcapture></figure>
+     <figcapture class="figjnnbl"> <b>Figure 15:</b>  A gif of the dynamic red blood cell formation model. The injection 141 billion copies of the EPO cDNA in adenoviruses occurs at day 0. The infected cells produce extra EPO leading to a maximum concentration of EPO of 31.3 mU/ml (3.2 times more that the steady state EPO concentration of 9.8 mU/ml).</figcapture></figure>
      <p>To calculate the total population of each stage, the trapezoid rule was used to calculate the area under the population mesh and estimate the total population.</p>
@@ Line 584: / Line 584: @@
      <p>The apoptosis rate of CFU-E cells is dependent on the concentration of EPO at a given time. As the concentration of EPO increases, the apoptosis rate of CFU-E cells decreases. </p>
-     <figure><center> <img src="https://static.igem.org/mediawiki/2018/9/96/T--TUDelft--CFU-E_Apoptosis_Rate.png" width="70%" height="auto" alt="Figure 10 Model"></center><br>
+     <figure><center> <img src="https://static.igem.org/mediawiki/2018/9/96/T--TUDelft--CFU-E_Apoptosis_Rate.png" width="70%" height="auto" alt="Figure 16 Model"></center><br>
-     <figcapture class="figjnnbl"> <b>Figure 10:</b>  The effect of EPO on the apoptosis of CFU-E cells.</figcapture></figure><br>
+     <figcapture class="figjnnbl"> <b>Figure 16:</b>  The effect of EPO on the apoptosis of CFU-E cells.</figcapture></figure><br>
      <p>The logistic equation that governs the apoptosis rate of CFU-E is seen in equation 27 below.</p>
@@ Line 610: / Line 610: @@
      <p>The amount time it takes for marrow reticulocytes to leave the bone marrow and enter the blood is dependent on the concentration of EPO. A faster marrow transit time means marrow reticulocytes spend more time as blood reticulocytes. The oldest marrow reticulocytes will become RBCs first when marrow transit time decreases. To account for the change in transit time, the stage boundary between reticulocytes and red blood cells is allowed to shift depending on the concentration of EPO.</p>
-     <figure><center> <img src="https://static.igem.org/mediawiki/2018/2/23/T--TUDelft--Marrow_Transit_Time.png" width="70%" height="auto" alt="Figure 11 Model"></center><br>
+     <figure><center> <img src="https://static.igem.org/mediawiki/2018/2/23/T--TUDelft--Marrow_Transit_Time.png" width="70%" height="auto" alt="Figure 17 Model"></center><br>
-     <figcapture class="figjnnbl"> <b>Figure 11:</b>  The effect of EPO on reticulocyte marrow transit time.</figcapture></figure><br>
+     <figcapture class="figjnnbl"> <b>Figure 17:</b>  The effect of EPO on reticulocyte marrow transit time.</figcapture></figure><br>
      <p>The logistic equation that governs the marrow transit time can be seen below.</p>
@@ Line 617: / Line 617: @@
      <p>$$ \mu_{Reticulocytes,max}(EPO(t)) = a_2 - \frac{b_2}{1+e^{k_2 \times EPO(t) - c_2}} \tag{32}$$</p>
-     <figure><center> <img src="https://static.igem.org/mediawiki/2018/8/82/T--TUDelft--Single_Injection_Mat_Vel.gif" width="70%" height="auto" alt="Figure 12 Model"></center><br>
+     <figure><center> <img src="https://static.igem.org/mediawiki/2018/8/82/T--TUDelft--Single_Injection_Mat_Vel.gif" width="70%" height="auto" alt="Figure 18 Model"></center><br>
-     <figcapture class="figjnnbl"> <b>Figure 12:</b>  The effect of EPO concentration on marrow transit time. The injection 141 billion copies of the EPO cDNA in adenoviruses occurs at day 0. The infected cells produce extra EPO leading to a maximum concentration of EPO of 31.3 mU/ml (3.2 times more that the steady state EPO concentration of 9.8 mU/ml). As EPO concentration in the blood increases, the marrow transit time will decrease, shifting the boundary between marrow reticulocytes and circulating red blood cells to the left. A decrease in EPO concentration increases the marrow transit time and shifts the boundary to the right. </figcapture></figure>
+     <figcapture class="figjnnbl"> <b>Figure 18:</b>  The effect of EPO concentration on marrow transit time. The injection 141 billion copies of the EPO cDNA in adenoviruses occurs at day 0. The infected cells produce extra EPO leading to a maximum concentration of EPO of 31.3 mU/ml (3.2 times more that the steady state EPO concentration of 9.8 mU/ml). As EPO concentration in the blood increases, the marrow transit time will decrease, shifting the boundary between marrow reticulocytes and circulating red blood cells to the left. A decrease in EPO concentration increases the marrow transit time and shifts the boundary to the right. </figcapture></figure>
      <p>Reticulocytes will stay in the bone marrow until age 18.75 to 21 days when they leave the bone marrow and circulate in the blood. There they become mature red blood cells.</p>
@@ Line 647: / Line 647: @@
      <p>The logistic equation that governs the natural release of EPO uses a scaled red blood cell count with \(TBV\) being the total blood volume.</p>
-     <figure><center> <img src="https://static.igem.org/mediawiki/2018/1/18/T--TUDelft--EPO_Release_per_ml.png" width="70%" height="auto" alt="Figure 13 Model"></center><br>
+     <figure><center> <img src="https://static.igem.org/mediawiki/2018/1/18/T--TUDelft--EPO_Release_per_ml.png" width="70%" height="auto" alt="Figure 19 Model"></center><br>
-     <figcapture class="figjnnbl"> <b>Figure 13:</b> The effect of red blood cell population on the release of EPO from the kidneys. The units of EPO released from kidneys is mU per ml per day.</figcapture></figure>
+     <figcapture class="figjnnbl"> <b>Figure 19:</b> The effect of red blood cell population on the release of EPO from the kidneys. The units of EPO released from kidneys is mU per ml per day.</figcapture></figure>
      <p>The dynamics of the endogenous EPO concentration Eend(t) in plasma is modeled by the following ordinary differential equation:</p>
@@ Line 874: / Line 874: @@
              </ul>
-<p>As presented in Figure 7, the algorithm detected several PAM sequences close to exon-exon junctions and found a minimal number of necessary guides for each one.</p>
+<p>As presented in Figure 20, the algorithm detected several PAM sequences close to exon-exon junctions and found a minimal number of necessary guides for each one.</p>
 <figure><center> <img src="https://static.igem.org/mediawiki/2018/6/63/T--TUDelft--Heat_Map_gRNAs.png" width="100%" height="auto" alt="Figure 20 Model"></center><br>
-<figcapture class="figjnnbl"> <b>Figure 8:</b> Results from the model on finding possible PAM sequences and minimal number of gRNAs needed at each position on EPO gene coding sequence.</figcapture></figure><br>
+<figcapture class="figjnnbl"> <b>Figure 20:</b> Results from the model on finding possible PAM sequences and minimal number of gRNAs needed at each position on EPO gene coding sequence.</figcapture></figure><br>
-<p>Figure 7 clearly shows that in junction 3 there is the optimal PAM sequence with smallest number of gRNAs possible. This way, the algorithm generates the gRNAs and gives an output with the position of such gRNA. In this specific case, there were two PAM sequences near Junction 3 that had the same minimal number of possible gRNAs necessary (12 guides each). The algorithm doesn’t neglect one or another, but outputs both options (or more if possible). This array can be used to generate gRNAs’ libraries for targeted next generation sequencing of gene doping.
+<p>Figure 20 clearly shows that in junction 3 there is the optimal PAM sequence with smallest number of gRNAs possible. This way, the algorithm generates the gRNAs and gives an output with the position of such gRNA. In this specific case, there were two PAM sequences near Junction 3 that had the same minimal number of possible gRNAs necessary (12 guides each). The algorithm doesn’t neglect one or another, but outputs both options (or more if possible). This array can be used to generate gRNAs’ libraries for targeted next generation sequencing of gene doping.
 </p>
          </div>
@@ Line 887: / Line 887: @@
          <p>The concentration of EPO gene doping DNA in blood increased rapidly in the first 1.5 days after injection before decreasing exponentially. Due to the rapid clearance of the adenoviral vectors from the blood and muscle, the majority of the infection events occurs right after injection. As more infected cells die, the number of infected cells decreases. This decreases the amount of doping DNA released into the blood over time. </p>
-<figure><center> <img src="https://static.igem.org/mediawiki/2018/9/99/T--TUDelft--Doping_DNA_in_Blood.png" width="50%" height="auto" alt="Figure 8 Model"></center><br>
+<figure><center> <img src="https://static.igem.org/mediawiki/2018/9/99/T--TUDelft--Doping_DNA_in_Blood.png" width="50%" height="auto" alt="Figure 21 Model"></center><br>
-<figcapture class="figjnnbl"> <b>Figure 9:</b> Concentration of doping DNA in the blood over time after a single IM injection of 141 billion viral vectors. The detection limit of 1000 copies per mL of blood is estimated based on the loss of DNA that occurs during sample preparation and targeted sequencing preparation.</figcapture></figure><br>
+<figcapture class="figjnnbl"> <b>Figure 21:</b> Concentration of doping DNA in the blood over time after a single IM injection of 141 billion viral vectors. The detection limit of 1000 copies per mL of blood is estimated based on the loss of DNA that occurs during sample preparation and targeted sequencing preparation.</figcapture></figure><br>
 <p>Regression was performed to determine that the half life of EPO gene doping DNA in the blood is around 41 days. While the half-life of cfDNA in blood is around 10 minutes, the slow release of the gene doping DNA from the dying infected cells increases the detection window significantly.</p>
@@ Line 894: / Line 894: @@
 <p>Microdosing was determined to be the best doping method for doping athletes. The benefit of this method is that it avoids detection through the biological passport. Assuming that the athlete begins the treatment prior to becoming a professional athlete and continued microdosing after, their red blood cell count would appear to be constant and naturally high.</p>
-<figure><center> <img src="https://static.igem.org/mediawiki/2018/8/88/T--TUDelft--IM_MicrodosingResults.png" width="60%" height="auto" alt="Figure 9 Model"></center><br>
+<figure><center> <img src="https://static.igem.org/mediawiki/2018/8/88/T--TUDelft--IM_MicrodosingResults.png" width="60%" height="auto" alt="Figure 22 Model"></center><br>
-<figcapture class="figjnnbl"> <b>Figure 10:</b> Effect of EPO gene doping using IM microdosing on red blood cell count. The initial dose is 96 billion vectors followed by smaller doses of 27 billion vectors every 20 days. The black horizontal lines are the range of EPO concentrations found in healthy adults. The red line indicates the red blood cell count at which the risk of stroke to due blood clot becomes significant.
+<figcapture class="figjnnbl"> <b>Figure 22:</b> Effect of EPO gene doping using IM microdosing on red blood cell count. The initial dose is 96 billion vectors followed by smaller doses of 27 billion vectors every 20 days. The black horizontal lines are the range of EPO concentrations found in healthy adults. The red line indicates the red blood cell count at which the risk of stroke to due blood clot becomes significant.
 </figcapture></figure><br>
@@ Line 905: / Line 905: @@
 <p>The downside of this method is the requirement for constant microdosing. Repeated injections would cause noticeable damage to the veins. The athlete would then be required to disguise the injection sites or perform the IV microdosing in parts of the body normally covered. For this reason athletes would likely favor IM injection to IV injection as IM is less invasive. </p>
-<figure><center> <img src="https://static.igem.org/mediawiki/2018/e/e7/T--TUDelft--Doping_DNA.png" width="60%" height="auto" alt="Figure 10 Model"></center><br>
+<figure><center> <img src="https://static.igem.org/mediawiki/2018/e/e7/T--TUDelft--Doping_DNA.png" width="60%" height="auto" alt="Figure 23 Model"></center><br>
-<figcapture class="figjnnbl"> <b>Figure 10:</b> Concentration of EPO gene doping DNA in the blood during IM microdosing with an initial dose is 96 billion vectors followed by smaller doses of 27 billion vectors every 20 days.
+<figcapture class="figjnnbl"> <b>Figure 23:</b> Concentration of EPO gene doping DNA in the blood during IM microdosing with an initial dose is 96 billion vectors followed by smaller doses of 27 billion vectors every 20 days.
 </figcapture></figure><br>

Difference between revisions of "Team:TUDelft/Model/HBK"