Complex Systems
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Volume 27, Number 3 (2018)


Synthetic Biology and Artificial Intelligence: Toward Cross-Fertilization
Luisa Damiano, Yutetsu Kuruma, and Pasquale Stano

The workshop “What Can Synthetic Biology Offer to Artificial Intelligence?,” hosted by the 14th European Conference on Artificial Life (Lyon, France, 4–8 September 2017), brought together specialists from different disciplines to discuss the possibility of generating synergies between synthetic biology (SB) and artificial intelligence (AI). The specific goal was the exploration of cognition through “understanding-by-building” strategies. The workshop participants were asked to define potentially effective roles that SB could play in the development of the “embodied approach” that characterizes contemporary cognitive science and AI, with a focus on frontier research on minimal artificial life and cognition. Keywords: embodied AI; embodiment; minimal cognition; minimal life; synthetic biology; synthetic cells; synthetic method


Synthetic Biology and Artificial Intelligence: Grounding a Cross-Disciplinary Approach to the Synthetic Exploration of (Embodied) Cognition
Luisa Damiano and Pasquale Stano

Recent scientific developments—the emergence in the 1990s of a “body-centered” artificial intelligence (AI) and the birth in the 2000s of synthetic  biology (SB)—allow and require the constitution of a new cross-disciplinary synergy, that elsewhere we called “SB-AI.” In this paper, we define the motivation, possibilities, limits and methodologies of this line of research. Based on the insufficiencies of embodied AI, we draw on frontier developments in synthetic cells SB to introduce a promising research program in SB-AI, which we define as Chemical Autopoietic AI. As we emphasize, the promise of this approach is twofold: building organizationally relevant wetware models of minimal biological-like systems, and contributing to the exploration of (embodied) cognition and to the full realization of the “embodiment turn” in contemporary AI. Keywords: autopoiesis; embodied AI; lipid vesicles; minimal cognition; SB-AI; synthetic biology; synthetic cells


Attractor Landscape: A Bridge between Robotics and Synthetic Biology
Andrea Roli and Michele Braccini

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. Keywords: genetic regulatory networks; embodied robotics; dynamical systems; attractors; cell differentiation; Boolean network robotics


The Problem of Prediction in Artificial Intelligence and Synthetic Biology
Francesco Bianchini

The problem of prediction is a general problem in the philosophy of science. It is important in every discipline for which prediction concerns the behavior of an artificial or a biological system, such as artificial intelligence or synthetic biology. Synthetic biology shares with artificial intelligence some theoretical issues from the point of view of prediction. My claim is that the problems related to the prediction of system behaviors are analogous because: (a) artificial intelligence and synthetic biology aim at producing autonomous systems; and (b) their products interact with an open-ended and uncertain context. I argue my claim by providing three versions of the prediction problem in artificial intelligence and synthetic biology, to show the analogies between them within this framework and to suggest some useful consequences. Keywords: artificial intelligence; synthetic biology; prediction; autonomous systems; philosophy of science


Synthetic Modelling of Biological Communication: A Theoretical and Operational Framework for the Investigation of Minimal Life and Cognition
Leonardo Bich and Ramiro Frick

This paper analyses conceptual and experimental work in synthetic biology on different types of interactions considered as minimal examples or models of communication. It discusses their pertinence and relevance for the wider understanding of this biological and cognitive phenomenon.  It critically analyses their limits and it argues that a conceptual framework is needed. As a possible solution, it provides a theoretical account of communication based on the notion of organisation, and characterised in terms of the functional influence exerted by the sender upon the receiver. It shows that this account can be operationalised in synthetic biology, and that it can supply criteria and guidelines for the design and evaluation of synthetic models. Keywords: regulation; synthetic biology; minimal cognition; influence; organisation; biological functions; protocells

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