Attractor Landscape: A Bridge between Robotics and Synthetic Biology

Abstract

Genetic regulatory networks (GRNs) model the dynamics and interactions among genes. From a robotics perspective, GRNs are extremely interesting because they are capable of producing complex behaviors. Notably, cell differentiation can be modeled using GRNs, and the dynamics of this process can be studied by means of dynamical systems methods. In a nutshell, the state of a cell is represented by an attractor in the state space of a dynamical system, and the transitions between cell states correspond to transitions between attractors. This view suggests a visionary approach: apply the metaphor of landscape attractor to design specific cell dynamics that can match the attractor landscape required for attaining a target behavior in a robotic system. The constraints prescribed by the robotic application are just the correspondence between behavioral attractors in the robot and cell attractors in the cell, along with specific transitions between attractors. This perspective may lead to applications in biorobotics, and it may also help synthetic biology systems design, which may benefit from methods developed for complex dynamical systems. We believe that this level of abstraction can provide a common vocabulary and a shared set of categories between researchers in robotics and synthetic biology. In this paper, we elaborate on previous results on GRNs-controlled robots and propose some guidelines for making this approach viable, illustrating these concepts with examples and case studies in biorobotics.

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