What are the biggest challenges in creating computational models of the immune system?

With the advent of single-cell technology, understanding the immune system has become more accessible. Just you have to choose right way to assess your genomics data.

Modeling the immune system computationally poses challenges because a comprehensive understanding of all influencing factors is lacking in current scientific knowledge. Despite its foundational connection to evolutionary principles, differences exist, notably in the limited cell count and the intricate communication system regulated by cytokines. This complex language triggers various behaviors in distant cells, such as differentiation, propagation, and migration. While acknowledging that all models are imperfect, some prove useful in therapeutic modulation. Strategies like checkpoint inhibitors are already disrupting the system's balance, contributing to ongoing learning in this field.

The cell is the basic unit of all living matter, from a multicell organism like a human being to a simple Escherichia coli for example, or a single-celled bacterium. Every cell is remarkable: not only can carry out complex tasks, for example, uptake of nutrients and conversion to energy, and the ability to replicate, but it also contains instructions to carry out these instructions. So in my humble opinion, it wouldn't be better to understand almost completely cell engineering in humans and animals from the Biology, Bio-Chemistry and Biophysics points of view, before we try to simulate stochastic procedures of very complex parts of human beings.? So simulations will have more matches between diversities and variabilities.

Actually, enormous amount of data w.r.t Human Immune System must be present, all from the medical domain perspective. But in the same form it will not be useful for applying technological aspects for processing to extract certain inferences. For making it possible, the subject matter experts (SMEs) from both the domains should have discussions with objectives of setting Working Groups and do require R & D work to define The Data Structures of representing this data. We can extract all the attributes in a format to apply processing in digital domain. With this basic work, we can get the medical data into a more manageable formats to apply computational algorithms and infer certain points very fast with more meaning too.

In my research, accessing comprehensive immune system data was challenging. Collecting data across various sources required merging datasets from different experiments, which often led to managing hundreds of datasets from diverse experiments, making it time-consuming and challenging to ensure consistency and quality.

Our current understanding of computer science, is based on Boolean Algebra and binary mathematics. They are the bases for our programming science and the computer-controlled systems that we use in the industry and research today. However, cells and complex organism systems are not Booleans or binary as such. So we will require a better mathematics model that must include perhaps quantum algorithms, to improve our capacity for better simulations. In general, I believe that we must start from the simple bacteria and viruses, simulations, to create more powerful database banks of success and errors, as the platform for more powerful computer simulations of more complex systems.

Creating a model incorporating various immune cell types, signaling pathways, and their interactions proved intricate. Building a model that encompassed the complexity of immune responses required incorporating thousands of interacting components, making it difficult to strike a balance between accuracy and computational efficiency.

All computational models, no matter what improvement they have in their algorithms, are limited, in matching the reality to the organic system. Perhaps our better approach will be the collection of s/e, (the success-errors paradigm), as a technique to improve the understanding and increase the precision of the model to the reality. Perhaps before we go into the creation of medical models, wouldn't be better to collect errors and successes of surgeries and treatment of people in any particular area, before we try to simulate.? In that way, we could establish a solid foundation for a possible computational and realistic model of the human organs or even functions. just a suggestion, perhaps due to my extensive ignorance in this field.

Validating the model against experimental data involved comparing predictions with real-world observations. Aligning the model's behavior with experimental results from diverse studies demanded cross-validation against multiple datasets, each comprising thousands of data points, to ensure its accuracy and reliability.

Integrating molecular-level details with higher-level immune responses presented a significant challenge. Incorporating data from genomics, proteomics, and cellular interactions into a single coherent model required handling vast amounts of data and reconciling discrepancies between different experimental findings.

Some of the tools you can explore: C-ImmSim COPASI ImmSim CompuVac PyBioNetFit CellSys Simmune VCell ABMImmune VirtualImmune Checkout these immune databases to explore more: ImmPort IEDB (Immune Epitope Database) IEDB-AR (IEDB Analysis Resource) ImmuneSpace VDJServer The Immunology Database and Analysis Portal (ImmPort) The Human Immunology Project Consortium (HIPC) Data Coordinating Center (DCC) The Immune Response In Silico (IRIS) Database The Cancer Immune Monitoring and Analysis Centers (CIMAC) Data Coordinating Center (DCC) The Infectious Diseases (ID) Clinical Research Consortium (CRC) Data Management and Coordination Center (DMCC)