Cells and Robots: Modeling and Control of Large-Size Agent Populations

Dejan Lj. Milutinovic, Pedro U. Lima3540719814, 9783540719816

Cells and Robots is an outcome of the multidisciplinary research extending over Biology, Robotics and Hybrid Systems Theory. It is inspired by modeling reactive behavior of the immune system cell population, where each cell is considered as an independent agent. In our modeling approach, there is no difference if the cells are naturally or artificially created agents, such as robots. This appears even more evident when we introduce a case study concerning a large-size robotic population scenario. Under this scenario, we also formulate the optimal control of maximizing the probability of robotic presence in a given region and discuss the application of the Minimum Principle for partial differential equations to this problem. Simultaneous consideration of cell and robotic populations is of mutual benefit for Biology and Robotics, as well as for the general understanding of multi-agent system dynamics.

The text of this monograph is based on the PhD thesis of the first author. The work was a runner-up for the fifth edition of the Georges Giralt Award for the best European PhD thesis in Robotics, annually awarded by the European Robotics Research Network (EURON).

---

Table of contents :
front-matter.pdf……Page 1
Introduction……Page 13
Analogy Between an Individual Robot and a Cell……Page 14
Robot Teams and Cell Populations……Page 16
Related Work……Page 17
Book Outline……Page 19
Immune System and T-Cell Receptor Dynamics of a T-Cell Population……Page 21
Surface T-Cell Receptor Dynamics in a Mixture of Interacting Cells……Page 22
T-Cell Receptor Triggering Experimental Setup……Page 24
Summary……Page 26
Problem Formulation……Page 27
T-Cell Hybrid Automaton Model……Page 28
T-Cell Population Hybrid System Model……Page 30
Micro-Agent Individual Model……Page 32
Stochastic Micro-Agent……Page 33
Summary……Page 35
Statistical Physics Background……Page 36
Micro-Agent Population Dynamic Equations……Page 38
Summary……Page 45
T-Cell Receptor Dynamics: A Numerical Example……Page 46
Micro-Agent vs. Ordinary Differential Equation Model……Page 51
T-Cell Receptor Expression Dynamics Model Test……Page 53
T-Cell Receptor Dynamics in $Conjugated$ State……Page 57
Model Hypothesis Test……Page 59
Parameter Identification……Page 61
Summary……Page 62
Stochastic Micro-Agent Model Uncertainties……Page 64
Discrete Parameter Uncertainty Case……Page 65
Continuous Parameter Uncertainty Case……Page 71
Numerical Example……Page 74
Summary……Page 77
Stochastic Modeling and Control of a Large-Size Robotic Population……Page 78
Robotic Population Mission Scenario……Page 79
Robotic Population Position Prediction……Page 82
Robotic Population Optimal Control Problem……Page 84
Example of Using the PDE Minimum Principle for Robotic Population Control……Page 88
Complexity of Numerical Optimal Control……Page 93
Numerical Optimal Control……Page 95
Summary……Page 100
Conclusions and Future Work……Page 101
Stochastic Model and Data Processing of Flow Cytometry Measurements……Page 106
Probability Density Estimation Algorithm……Page 108
Richardson-Lucy Deconvolution Algorithm……Page 112
Estimated T-Cell Receptor Probability Density Function……Page 115
Steady State T-Cell Receptor Probability Density Function and Average Amount……Page 119
Optimal Control of Partial Differential Equations……Page 121
References……Page 125
Backmatter……Page 132
Index……Page 130