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Why can't traditional immune research predict overall function? What new perspectives does systems biology provide?
The immune system has always been an important area of biological research. However, traditional immune research methods often cannot effectively predict overall function. This is because traditional research mostly adopts a "reductionist" approach, breaking down the immune system into single components for analysis, but ignoring the complex interactions between these components. Systems biology, as a new research perspective, provides a more comprehensive analysis of the immune system through mathematical modeling and computational methods.
Systems biology uses computational methods and mathematical models to help us understand interactions in cells and molecular networks.
Within the framework of systems biology, researchers are able to conduct in silico experiments, which allows them to mimic processes that cannot be observed in vivo. For example, in recent years, mathematical models have been used to explore the dynamic behavior of the innate and adaptive immune systems. These models allow scientists to predict the effects of vaccines and drugs on immune responses without having to conduct clinical trials.
These new models enable in-depth analysis of processes such as T cell activation, cancer-immune interactions, and migration and death of various immune cells such as T cells, B cells, and neutrophils.
Immune cell modeling technology
In immunology, model building includes both quantitative and qualitative approaches. Each of these methods has advantages and disadvantages. Quantitative models can predict certain kinetic parameters and the behavior of the system at a specific time point or concentration, but they are limited in that they can only be applied to a few reactions and require prior knowledge of certain kinetic parameters. In contrast, qualitative models are able to take into account more reactions but provide less detail on the system dynamics. Therefore, when the number of components increases dramatically, these approaches may lose simplicity and become useless.
Ordinary differential equation model
Ordinary differential equations (ODEs) are used to describe the dynamics of biological systems and are widely used at the microscopic, mesoscopic, and macroscopic scales to investigate the temporal evolution of the concentrations of proteins, transcription factors, or cell types. In particular, ordinary differential equations provide important analytical tools in modeling processes such as immune synapses, microbial recognition, and cell migration.
Partial differential equation model
The partial differential equation (PDE) model is an extension of the ODE model and can describe the temporal evolution of each variable in time and space. These models are able to handle the spatial distribution of continuous variables, especially in cell signaling, where spatial dynamics are an important aspect in understanding cell-cell interactions.
Particle-based stochastic models
Stochastic models take into account discrete variables of the components in the system rather than continuous variables. These models are used for specific immune transduction pathways and interactions between immune cells and cancer cells, and can more realistically present the randomness and uncertainty between cells.
Proxy-Based Model
Agent-based models view the components of the system as independent agents that can interact with other agents and the environment, providing an intuitive representation of the complexity of the immune system and the potential to observe events at multiple scales.
Boolean Model
Boolean network models model biochemical species by representing them as nodes with a finite number of states, an approach that is more suitable for studies that do not require detailed dynamics. Although the Boolean model simplifies the modeling process, it is difficult to perfectly handle simultaneous events because it only provides a qualitative approximation.
Computer Tools
In system modeling, different computational tools are used for model construction, calibration, validation, analysis, and visualization. Commonly used tools include GINsim, Boolnet, Cell Collective, etc. These tools help scientists conduct complex immune research more efficiently.
Conference and Future Outlook
With the rise of systems immunology, related conferences and seminars are constantly being held to promote communication and cooperation in the academic community. In the future, we hope to see further developments in this area in terms of model complexity and predictive accuracy.
With the support of systems biology, can we better understand the complexity and interactions of the immune system and ultimately predict its overall functional performance?
