[Paper Review] There's Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-scale Machines

Joshua Bongard, Michael Levin|arXiv (Cornell University)|Dec 20, 2022

Modular Robots and Swarm Intelligence4 citations

TL;DR

This paper proposes viewing biological systems as evolved, multi-scale machines that perform polycomputing—simultaneously executing multiple computations on the same physical substrate. By adopting an observer-centered, pragmatic framework that dissolves artificial boundaries between computation and biology, the authors argue this perspective enables better prediction and control of complex biological functions, with transformative implications for regenerative medicine, robotics, and bioengineering.

ABSTRACT

The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., tendency to oversimplify) and prior technological limitations in favor of a more continuous, gradualist view necessitated by the study of evolution, developmental biology, and intelligent machines. Efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as "polycomputing" - the ability of the same substrate to simultaneously compute different things. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of computational materials as reported in the rapidly-growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of meso-scale events, as it has already done at quantum and relativistic scales. Here, we review examples of biological and technological polycomputing, and develop the idea that overloading of different functions on the same hardware is an important design principle that helps understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering.

Motivation & Objective

• To challenge the artificial dichotomy between biological systems and computational machines by dissolving rigid category boundaries.
• To address the difficulty of predicting and controlling multi-scale biological functions due to polycomputing—simultaneous computation across multiple functions in the same substrate.
• To develop an observer-centered framework that treats biological systems as computational materials, akin to physical computing systems.
• To demonstrate how polycomputing is a core design principle in both evolved and engineered systems, enabling new approaches in bioengineering.
• To enable advances in regenerative medicine, robotics, and computer engineering by learning to harness and redesign polycomputing substrates.

Proposed method

• Adopting an observer-dependent, pragmatic framework to analyze computations in biological systems, avoiding rigid categorizations.
• Drawing analogies between biological systems and physical computing materials, emphasizing substrate-level multi-functionality.
• Analyzing examples of polycomputing in developmental biology and tissue-level computation (e.g., morphogenesis, bioelectric signaling).
• Using multi-scale modeling to bridge molecular, cellular, and tissue-level dynamics in biological computation.
• Applying principles from multiagent systems and artificial intelligence to model emergent computational behaviors in biological substrates.
• Proposing a shift from linear, deterministic computation models to a broader, continuous view of computation in evolved systems.

Experimental results

Research questions

• RQ1 How can biological systems be meaningfully understood as computational machines despite their non-linear, multi-functional nature?
• RQ2 What mechanisms allow the same biological substrate to perform multiple, simultaneous computations (polycomputing) across different scales?
• RQ3 How does an observer-centered framework improve the modeling and control of meso-scale biological phenomena?
• RQ4 In what ways does polycomputing serve as a design principle in both evolved and engineered systems?
• RQ5 What are the implications of treating biological tissues as computational materials for regenerative medicine and robotics?

Key findings

• Biological systems perform polycomputing—simultaneously executing multiple computations on the same physical substrate, such as in morphogenesis and bioelectric signaling.
• The same substrate can encode and process information for development, homeostasis, and environmental response in parallel, demonstrating multi-scale computational capacity.
• An observer-centered framework dissolves artificial boundaries between computation and biology, enabling more accurate modeling of complex, multi-functional systems.
• Polycomputing is not a flaw but a core design principle in evolved systems, allowing robustness and adaptability across scales.
• Harnessing and redesigning polycomputing substrates offers transformative potential for regenerative medicine and bio-inspired robotics.
• The integration of computational principles from physical computing into developmental biology enables new predictive and control capabilities in living systems.