iGEM 2023 — Team uflorida

{% extends "layout.html" %}
{% block title %}Notebook{% endblock %}
{% block lead %}Document the dates you worked on your project. This should be a detailed account of the work done each day for your project.{% endblock %}
{% block page\_content %}

## What should this page contain?

---

* Chronological notes of what your team is doing.
* Brief descriptions of daily important events.
* Pictures of your progress.
* Mention who participated in what task.

## Inspirations

---

* [2018 Munich](http://2018.igem.org/Team%3AMunich/Notebook)
* [2019 Georgia State](https://2019.igem.org/Team%3AGeorgia_State/Notebook)
* [2019 Newcastle](https://2019.igem.org/Team%3ANewcastle/Notebook)
* [2020 IISER Pune India](https://2020.igem.org/Team%3AIISER-Pune-India/Notebook)
* [2020 Lund](https://2020.igem.org/Team%3ALund/Notebook)
* [2020 NOVA LxPortugal](https://2020.igem.org/Team%3ANOVA_LxPortugal/Notebook)
* [2020 RDFZ China](https://2020.igem.org/Team%3ARDFZ-China/NoteBook)

{% endblock %}

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# ENGINEERING

## Engineering Design Process Introduction

At the UFlorida this year, we decided to gather insight into the nature of sepsis and it's affects on the immune system by building an in vitro organoid system, and constructing a system of ordinary differential equations to go with it. These ODE were built using the Engineering Design Process Cycle. On this page we will describe exactly how we went through iterations of designing, building, testing, and learning in order to create a set of ODE that describes the dynamical process of both the physiological and pathophysiological states of the immune system. By treating the immune system as a set of nodes that each have a positive or negative “force” on the other nodes, we were able to build a mechanistic model based on literature that allowed us to simplify the complex immune system into something that could more readily be described using differential equations. The reason our model is different is because it takes the number of HSPC’s into account along with pro-inflammatory leukocytes and anti-inflammatory leukocytes. Having the HSPC’s in our model has given us insight into the nature of how the level of stem cells in our immune system can affect the overall immune response.

Our engineering design process was split into two sections. One iteration of the process was used to build our first set of equations. The reason we required two separate design procceses was because once we could generate our first set of graphs from our equations, it was much easier to analyze how the ODE were behaving. From here we could begin our next engineering design process, using the output graphs in our testing phase.

![](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/screen-shot-2023-10-12-at-11-17-24-am.png)

## Design 1

The first step of our our design process was creating a mechanistic model of the immune system that we would base our ODE on. This mechanistic model was created using previous literature with the goal of describing the immune system in a simple enough way that we could create simple enough equations for it, while still being able to detect realistic trends in the data. After creating this mechanistic model, we then had a plan to start building our ODE. After we were done discussing our discrepencies in the ODE and the literature, we would decide how these could potentially be fixed before going back into the build phase.

![](https://static.igem.wiki/teams/4975/wiki/screen-shot-2023-10-10-at-10-01-07-pm.png)

## Build 1

In this step, we would each dig through previous literature and put together our ODE using what we found. We would guide our search using what we decided during the design step.

## Test 1

After each iteration of this first engineering design cycle, we would meet during the test phase after we had all independently worked during the build phase. At these meetings we would compare notes that we had taken, and discuss discrepancies between certain papers, our mechanistic model, and our equations.

## Learn 1

After putting together what we had found, our discussions allowed us to find what needed to be changed, and begin the design process again.

## Engineering Design Process Two

This iteration was dedicated to tweaking our ODE to reflect what we believed should be found in a clinical setting and was fueled more by the graphs we were outputting. We iterated through this process 5 times over 5 weeks.

### Iteration 1

### Test, Learn, Design 1

In the first iteration, we focused on a few things. First of all, we noticed that the graphs did not make sense. We appreciated that the pathogen curve presented like we hoped for a death scenario, but the HSPC graph and the cytokine graphs did not line up. Since the HSPC graph fell to zero, we believed the others should also go to zero. We focused in on a couple potential problems with the model. First, we were unsure if the code implementation of the ODE was entirely correct, this needed to be checked again. Second, we needed to check the decay terms for the cytokines. Our plan was to fix these things and attempt to get a steady-state graph (with no pathogen insult).

![Photo 1](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/hspc-s-ittr2-1.png)

![Photo 2](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/antiinflammatory-ittr2-1.png)

![Photo 3](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/instagators-ittr2-1.png)

![Photo 4](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/proinflammatory-ittr2-1.png)

### Build 1

Over the course of the next week, we would build the changes discussed previously on our own.

### Test, Learn, Design 2

In the second iteration, we came closer to what we thought were proper outcomes. We ran into the problem that our anti-inflammatory cytokines were not decreasing, even when it's decay term was increasing, but we believed this run had promise as it was the first time we "killed" a pathogen insult and our HSPC level was perturbed but returned back to homeostasis. We needed to fix the cytokines not returning to homeostasis though. Also, while not shown, our Leukocyte graph showed an increase in leukocytes after the infection was killed and the HSPC level returned to normal, which did not look corect. We had also fixed the problem from before of the inflammatory cytokines not decreasing after the HSPC population had died off. This can be seen in the second set of graphs.

Our first time seeing a return to homeostasis in the HSPC population

![Photo 1](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/hspc-s-2.png)

![Photo 2](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/anti-inflammatory-cytokines-2.png)

![Photo 3](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/pathogens-2.png)

![Photo 4](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/pro-inflammatory-cytokines-2.png)

First time seeing extinction of all immune system components upon a major pathogen insult

![Photo 1](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/hspc-s-22.png)

![Photo 2](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/anti-22.png)

![Photo 3](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/pathogens-22.png)

![Photo 4](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/pro-22.png)

### Build 2

Over the course of the next week, we would build the changes discussed previously on our own.

### Test, Learn, Design 3

In the third iteration we believed was the first time we achieved many accurate cases. It was our first time achieving a steady-state without pathogen insult. We saw our predicted trend of oscillating levels of certain cytokines and leukocytes. We had a more accurate depiction of a moderate pathogen insult, as the inflammatory levels returned to normal faster, and also showed our predicted oscillating behavior. We also had our first evidence of sepsis chronic death, and sepsis chronic (no death or recovery in the time scale used). These were important devlopments, as our model was starting to achieve steady-states for multiple cases that we predicted it should. From here, we planned to try to achieve more of our predicted cases by tuning initial values, now that we had built confidence that our model had achieved many of our other predicted cases.

We were able to produce three predicted outcomes in the third round of graphs. One for a healthy person with no pathogen insult, one for someone with chronic sepsis, and one for someone with chronic sepsis with a second pathogen insult. These are presented below respectively.

Healthy person with no pathogen insult:

![Photo 1](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/untitled-4.png)

![Photo 2](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/untitled-10.png)

![Photo 3](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/untitled-3.png)

![Photo 4](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/untitled-9.png)

Chronic Sepsis:

![Photo 1](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/hspc-s-3-chronic-1.png)

![Photo 2](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/anti-inflammatory-3-chronic-1.png)

![Photo 3](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/pathogen-3-chronic-1.png)

![Photo 4](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/proinflammatory-3-chronic-1.png)

Chronic Sepsis with second infection:

![Photo 1](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/hspc-chronic-2.png)

![Photo 2](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/anit-2.png)

![Photo 3](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/pathogen-chronic-2.png)

![Photo 4](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/pro-2.png)

© 2023 - Content on this site is licensed under a [Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/).

The repository used to create this website is available at [gitlab.igem.org/2023/uflorida](https://gitlab.igem.org/2023/uflorida).

[![](https://static.igem.wiki/teams/4975/wiki/igem-updated-logo-simple.png)](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27home%27%29%20%7D%7D)

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# EXPERIMENTS

## In Vitro Bone Marrow Organoid Model

Our team worked in conjunction with our faculty advisor Dr. Jing Pan to devise a step-by-step protocol for Wet Lab Members to create our bone-marrow organoids.

Aim 1

Aim 1 includes the culturing steps of iPSCs, with a focus on sterile lab practices and documentation. Aim 1 includes matrigel coating of 6-well plates, thawing of iPSCs, iPSC maintenance, passaging of iPSCs, as well as cryopreservation. Propagation of human iPSCs is a difficult process, as cells must be grown in specific temperature conditions and can grow on top of one another. Our goal was to generate a continuous propagation of iPSCs, and once a passaged plate showed viable, undifferentiated cell colonies at 60% confluence, Aim 2 could be initiated.

Aim 2

Aim 2 describes the conversion of undifferentiated iPSC cell colonies to a vascularized, organoid system with structural and chemical similarities to human bone marrow. Through the careful addition of growth factors and collagen, we can induce vascular sprouting that replicates the hematopoietic niche of bone marrow. Our goals are to observe a highly vascularized space with specialized sinusoidal endothelium, hematopoiesis, transmigration through sinusoids, and specialized stroma. Aim 2 is a 12-day cycle, with 3 Phases of medium containing growth factors that encourage cells to commit to vascular and hematopoietic lineages.

© 2023 - Content on this site is licensed under a [Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/).

The repository used to create this website is available at [gitlab.igem.org/2023/uflorida](https://gitlab.igem.org/2023/uflorida).

[![](https://static.igem.wiki/teams/4975/wiki/igem-updated-logo-simple.png)](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27home%27%29%20%7D%7D)

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# RESULTS

## In Vitro Results

Our Wet Lab team successfully developed a procedure for efficient culturing of iPSCs from single cells to large, self-sufficient colonies (pictured). Through trial and error, we grew colonies upwards of 1,000 um without differentiation along the edges. These colonies can be used for the stepwise differentiation of iPSCs down the myeloid lineage in order to recapitulate bone marrow properties in-vitro. We have created mesodermal aggregates ready to be differentiated via growth factors such as APEL2, Bone Morphogenic Protein-4, Fibroblast Growth Factor-2, and Vascular Endothelial Growth Factor-A

![Photo 1](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/ispc-colony-1.png)

![Photo 2](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/ispc-colony-2.png)

## In Silico Results

Below are the cases we thought were most representative of our in silico model's capability. Since we only care about the trends and the fold change, we set out graphs to be density instead of concentration. Due to the arbitrary nature of our numbers, this can be fitted to actual concentration values found in clinical settings.

## Healthy person with no pathogen insult

This case is quite significant because it demonstrates that our model can accurately mimic what happens when a healthy person isn't affected by any diseases. We've simulated this scenario, and it's clear from a few key features in our graphs.

Firstly, we've chosen initial values that make sense, with no pathogens entering the system. We start with a basic hematopoietic stem and progenitor cells (HSPC) population and a steady level of leukocytes. As we run the simulation, you'll notice that none of the levels change, except for the pro and anti-inflammatory cytokines. This happens because the system isn't entirely accurate at the beginning; it needs some time to "build" its immune system. But once it's done, the system reaches homeostasis.

Another interesting thing you'll see is that none of the stable leukocytes change into pro or anti-inflammatory types. This is because there's no infection to make them act differently. This is important because it shows that our system can reach a stable state in the basic case where there are no pathogens involved. This case is representative of our models effectiveness as it has a baseline homeostasis that any other recovery inputs should be able to return to.

| Node | Initial Value |
| --- | --- |
| HSPC | 1000 |
| Pathogen | 0 |
| Pro-Inflammatory Cytokines | 0 |
| Anti-Inflammatory Cytokines | 0 |
| Stable Leuckocytes | 7000 |
| Active Luekoctyes | 0 |
| Immunosuppresive Luekocytes | 0 |
| Total Leukocytes | 7000 |

![Photo 1](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/hspc-graph-v3-1.png)

![Photo 2](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/cytokine-graph-1.png)

![Photo 3](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/leukocyte-graph-1.png)

![Photo 4](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/pathogen-graph-2.png)

## Healthy person with moderate pathogen insult

We undertook the modeling of this specific case to create a scenario where an individual transitions from a state of health to sickness and eventually returns to the homeostatic equilibrium characteristic of a pathogen-free healthy person. It is instrumental that our model achieves this.

To commence, the initial parameter selection is of biological relevance to the scenario under consideration. We prescribe specific quantities of HSPCs, pro-inflammatory and anti-inflammatory cytokines, and stable leukocytes. In the initial phase, a obvious decrease is observed in the stable leukocyte population as the immune system is invoked, prompting differentiation into either activated or immunosuppressive leukocyte subsets. The HSPC population experiences an immediate decline as these cells undergo differentiation into distinct leukocyte populations. However, this decrease is transient and is followed by a resurgence upon successful pathogen removal. Moreover, all other nodes in the model gradually revert to homeostasis once the infection has been killed.

The pathogen graph serves as a tangible indicator of infection resolution, demonstrating an initial surge as the pathogen proliferates, followed by a decline after the immune system's activation. These graphical representations collectively underscore our model's robustness in perturbation response to pathogen insult, facilitating a return to homeostasis, akin to the recuperation process witnessed when a healthy individual temporarily becomes sick and subsequently becomes healthy again. This case is an important representation of how our model works as it shows that it has the fundamental capability of returning to homeostasis, a major part of how the immune system operates.

| Node | Initial Value |
| --- | --- |
| HSPC | 1000 |
| Pathogen | 3750 |
| Pro-Inflammatory Cytokines | 1000 |
| Anti-Inflammatory Cytokines | 1500 |
| Stable Leuckocytes | 7000 |
| Active Luekoctyes | 0 |
| Immunosuppresive Luekocytes | 0 |
| Total Leukocytes | 7000 |

![Photo 1](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/hspc-graph-v3-2.png)

![Photo 2](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/cytokine-graph-2.png)

![Photo 3](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/leukocyte-graph-2.png)

![Photo 4](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/pathogen-graph-1.png)

## Person who undergoes sepsis early-death

In the context of sepsis, this affliction manifests in two primary forms: "sepsis early-death" and "chronic sepsis." For this investigation, our focus is on the former, which seeks to replicate a scenario in which an individual encounters a pathogenic insult that results in their immune system being unable to restore homeostasis. We commence our exploration with a selection of initial values that align with the biological dynamics of this scenario.

Specifically, we establish the HSPC and stable leukocyte levels at values akin to those observed in our homeostasis model. However, a significant difference occurs after the initial activation of our immune model, where we introduce a substantial pathogenic insult. It's noteworthy that this insult leads to the apparent defeat of the immune system, as evident in the graph exhibiting a standard logistic growth curve reaching its carrying capacity. While this progression may not precisely mirror a clinical case, it serves as a demonstrative construct to illustrate the point at which the pathogen overpowers the host.

The trajectory of the HSPC graph holds particular significance, as it initially declines, only to persistently diminish until it reaches zero. This occurrence is pivotal since HSPCs are indispensable for the derivation of other immune cell types within the system. Consequently, their extinction precipitates a cascade of immune cell depletion. Concurrently, all other graphical representations depict a declining trend over time.

This particular case emulation is vital as it underscores our model's capacity to not only reproduce stable states but also simulate the intricacies of sepsis, offering a comprehensive platform for analysis. It is representative of the effectiveness of our model since it emulates the immunopathological phenomenon that we have chosen to study.

| Node | Initial Value |
| --- | --- |
| HSPC | 1000 |
| Pathogen | 0 |
| Pro-Inflammatory Cytokines | 0 |
| Anti-Inflammatory Cytokines | 0 |
| Stable Leuckocytes | 7000 |
| Active Luekoctyes | 0 |
| Immunosuppresive Luekocytes | 0 |
| Total Leukocytes | 7000 |

![Photo 1](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/hspc-graph-v3-3.png)

![Photo 2](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/cytokine-graph-3.png)

![Photo 3](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/leukocyte-graph-3.png)

![Photo 4](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/pathogen-graph-3.png)

## Person who undergoes aseptic death

This case has been studied due to its capacity to mirror a situation in which an individual manages to kill an infection, yet succumbs to the immune dysregulation triggered by the very infection they overcame. Our initial parameters were selected to cause the healthy person case, but then subsequently compromised by an increase in the pathogen level. In this unique scenario, the dynamics of pro and anti-inflammatory cytokines play a pivotal role, rendering the immune system unable to effectively combat the infection and facilitate recovery.
The graphical representations accompanying this case give evidence to this claim. The pathogen graph demonstrates that the pathogen has indeed been killed, yet the other graphs show the immune system's failure. This depiction holds significant biological relevance to sepsis, contributing to a comprehensive representation of the multifaceted dynamics that our model has captured, enhancing our understanding of this condition and its diverse manifestations. This is representative of the capability of our model as it shows that it is not simply the pathogen input that leads to a death case in our model, but even the subsequent immune dysregulation can cause the whole system to fail, which is difficult to predict in a clinical setting.

| Node | Initial Value |
| --- | --- |
| HSPC | 1000 |
| Pathogen | 2000 |
| Pro-Inflammatory Cytokines | 1000 |
| Anti-Inflammatory Cytokines | 1500 |
| Stable Leuckocytes | 7000 |
| Active Luekoctyes | 0 |
| Immunosuppresive Luekocytes | 0 |
| Total Leukocytes | 7000 |

![Photo 1](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/hspc-graph-v3-5.png)

![Photo 2](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/cytokine-graph-5.png)

![Photo 3](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/leukocyte-graph-5.png)

![Photo 4](https://static.igem.wiki/teams/4975/wiki/model-graphs/model-graphs/model-graphs/pathogen-graph-5.png)

© 2023 - Content on this site is licensed under a [Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/).

The repository used to create this website is available at [gitlab.igem.org/2023/uflorida](https://gitlab.igem.org/2023/uflorida).