INVESTIGATING THE EFFECTS OF NOISE ON A CELL-TO-CELL COMMUNICATION - - PowerPoint PPT Presentation

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INVESTIGATING THE EFFECTS OF NOISE ON A CELL-TO-CELL COMMUNICATION - - PowerPoint PPT Presentation

Mar 21, 2023 258 likes •573 views

INVESTIGATING THE EFFECTS OF NOISE ON A CELL-TO-CELL COMMUNICATION MECHANISM FOR STRUCTURE REGENERATION GIORDANO FERREIRA 1 , MATTHIAS SCHEUTZ 1 , MIKE LEVIN 2 GIORDANO.FERREIRA@TUFTS.EDU 1 HUMAN ROBOT INTERACTION LABORATORY AT TUFTS UNIVERSITY

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INVESTIGATING THE EFFECTS OF NOISE ON A CELL-TO-CELL COMMUNICATION MECHANISM FOR STRUCTURE REGENERATION

GIORDANO FERREIRA1, MATTHIAS SCHEUTZ1, MIKE LEVIN2 GIORDANO.FERREIRA@TUFTS.EDU

1HUMAN ROBOT INTERACTION LABORATORY AT TUFTS UNIVERSITY 2ALLEN DISCOVERY CENTER AT TUFTS UNIVERSITY

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INTRODUCTION

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INTRODUCTION

  • Note that the shape to which an

animal regenerates upon damage can be altered without genetic changes

  • For example, it is possible to produce

two headed planarian worms

  • Genes and proteins involved in

regeneration are known, but the exact mechanism of storing and using morphological information for

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COMPUTATIONAL MODEL OF MORPHOLOGY DISCOVERY AND REPAIR

  • We previously developed a model that could discover the

morphological information of an organism, during a discovery phase

  • Later, when the organism was lesioned the dynamic

messaging mechanism in the model was able to cause regeneration of the damaged parts

  • While the model has not been linked to biological

mechanisms yet, it has demonstrated a variety of functional properties of regeneration displayed by planaria

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FEATURES OF THE MODEL

  • Proposed in Ferreira et al. 2016 3
  • Morphological information is stored in a dynamic distributed fashion

across cells

  • The genome is hypothesized to encode the computational machinery

necessary for carrying out morphological discovery and repair

  • A key feature of the model is that it can dynamically learn and

maintain new morphologies using the same computational mechanism

3 Ferreira, G. B. S., Smiley, M., Scheutz, M., and Levin, M. (2016). Dynamic structure discovery and repair

for 3d cell assemblages. In Proceedings of the Fifteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFEXV)

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DISCOVERY

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Cells send messages to other cells containing information about the path that those messages traveled.

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REGENERATION

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Then those message packets ”backtrack” verifying if there exists a missing cell in the previous path, repairing it.

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REGENERATION

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REGENERATION

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PREVIOUS FINDINGS

  • In Ferreira et al (2016) 3 we showed that this model was

capable of maintaining the structure of the worm indefjnitely in the light of random damages happening to parts of it

  • However, communication was assumed to be perfect and

without losses, which is not realistic in any actual organism

  • Hence, the goal of this work was to investigate the extent to

which the model can handle various types of noise

10 3 Ferreira, G. B. S., Smiley, M., Scheutz, M., and Levin, M. (2016). Dynamic structure discovery and repair

for 3d cell assemblages. In Proceedings of the Fifteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFEXV)

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EXPERIMENTS WITH NOISE ON PACKETS

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NOISE ON DISTANCE

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NOISE ON DISTANCE

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NOISE ON DIRECTION

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NOISE ON DIRECTION

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NOISE ON DIRECTION

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EXPERIMENTS WITH NOISE ON PACKETS

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RESULTS OF EXPERIMENTS WITH PACKETS CONTAINING NOISE

  • The model completely regenerated the simulated worm in

63% of the parameter space with no noise

  • The model completely regenerated the simulated worm in 0%
  • f the parameter space that contained noise

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REGENERATED WORMS

Original worm: Sim = 1 Best worm: Sim = 0.828 Worst worm: Sim = 0.18119

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ACTIVATION MECHANISM - NOISE

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ACTIVATION MECHANISM - NOISE

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ACTIVATION MECHANISM - NOISE

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ACTIVATION MECHANISM

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ACTIVATION MECHANISM

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ACTIVATION MECHANISM

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ACTIVATION MECHANISM

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ACTIVATION MECHANISM

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RESULTS OF EXPERIMENTS WITH THE ACTIVATION MECHANISM

  • The model completely regenerated the simulated worm in

39% of the parameter space with no noise (63% without the activation mechanism)

  • The model completely regenerated the simulated worm (with

100% similarity) in 5.7% of the parameter space that contained noise, compared to 0% of the parameter space without the activation mechanism

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REGENERATED WORMS

Original worm: Sim = 1 Worst worm with activation mechanism: Sim = 0.664

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Best worm without the activation mechanism: Sim = 0.828

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CONCLUSION

  • We investigated our model of dynamic messaging morphology

discovery and repair under various conditions of noise and proposed simple extensions to overcome the detrimental efgects of noise

  • Large parameter sweeps of the model determined that in about 6%
  • f the parameter space the model was able to fully regenerate the
  • riginal morphology with noise on the direction of packets
  • We are currently investigating why the proposed extensions do not

suffjce for noise on packet distances

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