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Download Cells and Robots: Modeling and Control of Large-Size Agent by Rod Mollise PDF

By Rod Mollise

Cells and Robots is an final result of the multidisciplinary learn extending over Biology, Robotics and Hybrid platforms conception. it truly is encouraged by means of modeling reactive habit of the immune procedure mobile inhabitants, the place each one mobilephone is taken into account as an self sustaining agent. In our modeling strategy, there isn't any distinction if the cells are clearly or artificially created brokers, akin to robots. This appears to be like much more glaring once we introduce a case learn bearing on a large-size robot inhabitants state of affairs. below this situation, we additionally formulate the optimum keep an eye on of maximizing the chance of robot presence in a given sector and speak about the appliance of the minimal precept for partial differential equations to this challenge. Simultaneous attention of phone and robot populations is of mutual gain for Biology and Robotics, in addition to for the final knowing of multi-agent method dynamics.

The textual content of this monograph relies at the PhD thesis of the 1st writer. The paintings was once a runner-up for the 5th version of the Georges Giralt Award for the simplest ecu PhD thesis in Robotics, each year provided by way of the eu Robotics examine community (EURON).

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

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This is allowed because our hypothesis test takes into account only the shape of the model predicted TCR PDF evolution, which does not depend on the parameter k2 . In the following analysis, we will estimate this parameter comparing ρ(x, t) and ρexp (x, tj ), where t and tj are measured under the same time frame. 35) where ρ(xi , t|k2 ) is the value of ρ(x, t) calculated for the parameter k2 at x = xi , and ρexp (xi , tj ) is the value of ρexp (x, tj ) at x = xi . This function is a sum of KLbased distances between the predicted PDF at time tj , for the given parameter k2 and the experimentally observed ρexp (xi , tj ).

Stochastic Micro-Agent Model of the T-Cell Receptor Dynamics Fig. 5. Solution of the PDE system for the T-cell CT M CμA model Case III: (a) ρ1 (x, t), (b) ρ2 (x, t), (c) ρ3 (x, t), (d) η(x, t); x - TCR quantity, normalized values the discrete state q = 2). This larger parameter k3 makes ρ2 (x, t) flatter than in Case II. This is because, comparing to Case II, and due to the higher rate of increase k3 , more T-cells in the population have a larger quantity of TCRs. 5. 2) is composed of three functions ρi (x, t), i = 1, 2, 3, representing the PDF state of T-cell CT M CμA model.

6) where x is the quantity of expressed TCRs and k2 is the reaction rate constant. 7) x˙ e (t) = −k2 xe (t) Let us assume now that not only the initial average quantity of the expressed TCRs is known, but also the overall TCR distribution over the T-cell population. If the TCR distribution is normalized, then we get the TCR PDF. To simplify, we assume that the initial TCR PDF is Gaussian with the variance σ 2 (0). 6) is linear, the distribution of the TCRs over the T-cell population will be Gaussian at each time instant.

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