Download PDF Cells and Robots Modeling and Control of Large-Size Agent Populations by Dejan Lj. Milutinovi´c

Sinopsis

Understanding development and functions of living organisms continuously occupies the attention of science. Consequently, mathematical modeling of biological systems is a recurrent topic in research. Recently, this field has become even more attractive due to technological improvements on data acquisition that provide researchers a further insight into such systems. Technology to read DNA sequences, or to observe protein structures along with a variety of microscopy methods, has enabled collecting large amounts of data around and inside the cell, classically considered as the smallest chunk of life.
 

In this book, we are particularly concerned with cells that have an active role protecting living organisms from infections caused by foreign bodies, constituting the immune system. The role of the immune system is to continuously monitor the organism, to recognize an invader, to generate a response that will clear the invader and to help healing the damaged tissues. The major components of this chain of action are motile cells. Cells motility1 is a property intrinsic to their function, i.e., to fight against infections in the right place at the right time during an immune response. While we are quite certain about the places where cells are produced and where they reside during their life cycle, the question of how they modulate their motion and bio-chemical activity against external stimuli still presents an active field of research.
 

Apparently, the immune system cells are autonomous agents. On the other hand, an autonomous mobile robot can be seen as the most natural mechanic analogy of the cell. Hence, we find studies about the cell bio-chemical signal processing, intercellular communication and cell reactive behavior in close relation to signal processing, communication and control for mobile robots. Investigation of the cell-robot analogy has the potential to influence future biological research, but also to provide the guidelines for the development of a systembased approach to describe and analyze complex multi-agent/robot systems.

Content

1.  Introduction
2. Immune System and T-Cell Receptor Dynamics of a T-Cell Population
3. Micro-Agent and Stochastic Micro-Agent Models
4. Micro-Agent Population Dynamics
5. Stochastic Micro-Agent Model of the T-Cell Receptor Dynamics
6. Stochastic Micro-Agent Model Uncertainties
7. Stochastic Modeling and Control of a Large-Size Robotic Population
8. Conclusions and Future Work
9. Stochastic Model and Data Processing of Flow Cytometry Measurements
10. Estimated T-Cell Receptor Probability Density Function
11. Steady State T-Cell Receptor Probability Density Function and Average Amount
12. Optimal Control of Partial Differential Equations

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