Language
Country/Area
No result found
The secrets behind the data: How digital simulations can revolutionize vaccine and drug development?
With epidemics raging frequently today, the rapid development of vaccines and drugs is more urgent than ever. With the advancement of science and technology, digital simulation technology has gradually become an important tool for researchers to develop new treatment methods. Not only will these simulations reduce experimental costs, they could also speed up research and change how we understand the immune system and how it works.
Systems immunology uses mathematical methods and computational techniques to study the interactions among the cellular and molecular networks of the immune system.
Scientists have traditionally taken a "reductionist" approach, studying the individual components of the immune system and their functions, but this approach cannot predict the overall functioning of the immune system because it strongly relies on the interactions between these components. .
Different from previous experimental methods, systems immunology focuses on simulations on computers, so-called in silico experiments. In recent years, driven by research in the fields of experimental and clinical immunology, a number of mathematical models have emerged that discuss the dynamic characteristics of the innate and acquired immune systems. These models allow us to understand T cell activation, cancer-immunity interactions, migration and death of various immune cell types (such as T cells, B cells, and neutrophils), and how the immune system responds to specific vaccines or drugs. response without the need for clinical trials.
Technology for immune cell modeling
In immunological modeling, the techniques used can be divided into two aspects: quantitative and qualitative. Quantitative models predict certain kinetic parameters and the behavior of the system at a specific time point or concentration point, but are often limited to a small number of reactions and require certain kinetic parameters to be known in advance. In contrast, qualitative models can take into account more reactions but provide less information on kinetic details. As the number of system components proliferates, both approaches lose simplicity and, as a result, their utility.
Ordinary Differential Equation Model
Ordinary differential equations (ODEs) are used to describe the dynamic behavior of biological systems and can analyze continuous variables at the microscopic, mesoscopic, and macroscopic scales. These equations represent the temporal evolution of observed variables such as protein concentrations, transcription factor quantities, etc. and are commonly used to model immune synapses, microbial recognition, and cell migration.
Over the past decade, these models have been used to study TCR sensitivity to agonist ligands, as well as the role of CD4 and CD8 coreceptors.
These models can show how each interacting molecule in the network behaves at both concentration and steady state. ODE models are usually defined by linear and nonlinear equations, the latter of which are widely used because they are easier to simulate on computers.
Partial differential equation model
The partial differential equation (PDE) model is an extension of the ODE model and describes the evolution of each variable in time and space. PDEs are usually applied at the microscopic level to mimic the sensing and recognition pathways of pathogens. It can describe how proteins interact with each other and direct their movement in the immune synapse.
Compared to the ODE model that considers spatial distribution, the PDE model is more computationally intensive.
The spatial dynamics of cellular signaling is key to study, especially during T cell activation, when the formation of the immune synapse is an important process that needs to be considered.
Particle-based stochastic model
Particle-based stochastic models are based on the dynamics of ODE models, which treat the components in the system as discrete variables rather than continuous ones. On this basis, the model analyzes immune-specific transduction pathways and immune cell-cancer interactions at the microscopic and mesoscopic levels.
The model dynamics are determined by a Markov process, which in this case expresses the probability of each possible state of the system over time, and is usually solved through computer simulation. Stochastic simulations are relatively expensive, so the size and scope of the models are limited.
Proxy-Based Model
Agent-based models (ABMs) treat the observed system components as discrete agents and are able to simulate the interactions between them. This model can be observed not only at the microscopic level but also at multi-scale levels, and is increasingly favored by other disciplines.
Boolean Model
Logical models are used to simulate phenomena such as cell life cycle, immune synapse, and pathogen recognition. Unlike ODE models, logistic models generally do not require fine kinetic or concentration details.
Each biochemical species is represented as a node in the network and may have a finite number of discrete states, typically: on/off.
This approach is widely used to explore specific pathways in the immune system, such as affinity maturation and hypermutation. With the development of technology, various computing tools have gradually emerged.
Calculation Tools
To simulate systems and use differential equations, computational tools must perform a variety of tasks including model building, calibration, validation, analysis, simulation, and visualization. No single software tool meets all needs, and scientists often need to use multiple tools.
Future Thinking
As digital simulation technology matures step by step, how to combine it with clinical practice and power to explore new ways of drug manufacturing and vaccine development, can it change the face of future medical care?
