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
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 |
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 |
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 |
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 |
