Biocomputation: When Cells Compute and Slime Mold Solves Mazes

What is Biological Computation?

From Calculating Machines to Calculating Organisms

Biological computation proposes that living organisms naturally perform computations ² . The abstract ideas of information processing may be key to understanding biology itself—from molecular and cellular information processing networks to ecologies, economies, and brains ² . As one researcher notes, "life computes" across multiple levels, though we still lack complete principles to understand precisely how computation occurs in living matter ² .

Several specialized areas fall under the biocomputation umbrella:

How Does Natural Computing Work?

In biological systems, computation occurs through different mechanisms than in digital computers. Rather than electrons moving through circuits, information processing happens through molecular interactions, cellular signaling pathways, and network connections.

For example, in slime molds, computation emerges from the physically distributed foraging behavior of the organism. In neuronal networks, information processing occurs through the timing and pattern of electrical spikes. The fundamental difference lies in how biological systems integrate processing with their physical embodiment and adaptive capabilities.

Nature's Problem Solvers: Remarkable Case Studies

The Slime Mold That Solves Complex Puzzles

The slime mold Physarum polycephalum—a yellowish, single-celled organism—has demonstrated astonishing computational capabilities despite lacking a nervous system. Researchers have discovered that this humble organism can solve the Traveling Salesman Problem (TSP), a classic combinatorial test with exponentially increasing complexity, in linear time ² .

Slime mold demonstrating path-finding capabilities in a laboratory setting

The TSP involves finding the shortest possible route that visits a set of cities exactly once and returns to the origin. For conventional computers, this problem becomes dramatically more difficult as cities increase—what computer scientists call an "NP-hard" problem. Yet slime molds achieve high-quality approximate solutions efficiently through their natural growth and foraging behaviors.

In distributed systems experiments, slime molds have been used to approximate motorway graphs, effectively mapping efficient transportation networks ² . Researchers have even built logical circuits using slime molds, demonstrating their potential as living computing elements ² .

The Fungal Computer: Mycelium Networks as Processors

Beyond slime molds, other organisms show computational promise. Fungi such as basidiomycetes can form sophisticated computing networks. In a proposed "fungal computer," information is represented by spikes of electrical activity traveling through the mycelial network ² .

Computation is implemented through the complex interconnected web of fungal threads, with the fruit bodies potentially serving as an interface . This approach demonstrates how even stationary organisms can process information through their internal electrical signaling and network structures.

Inside a Groundbreaking Experiment: Computing with Slime Mold

Methodology: How to Make a Mold Solve Problems

The experimental procedure for harnessing slime mold's computational abilities involves a clever setup that transforms spatial configuration into a computational problem:

This methodology capitalizes on the slime mold's natural foraging optimization behavior, evolved to efficiently explore territory and connect nutrient sources with minimal energy expenditure.

Results and Analysis: Nature's Efficient Solutions

When presented with TSP configurations, slime molds consistently produce high-quality approximate solutions. The remarkable finding isn't just that they solve these problems, but that they do so with linear time complexity ² . This means that as problem size increases, the slime mold's solution time increases at a constant rate—significantly more efficient than algorithmic approaches on digital computers.

The solutions achieved, while not always mathematically perfect, are functionally excellent—typically within 5-10% of optimal solutions. This trade-off of absolute precision for efficiency mirrors many natural systems where "good enough" solutions obtained quickly are more valuable than perfect solutions requiring extensive computation.

The Scientist's Toolkit: Essential Resources for Biocomputation

Advancing this interdisciplinary field requires specialized tools and databases. Here are key resources enabling cutting-edge research:

These resources highlight how traditional bioinformatics tools are now being joined by specialized software for understanding and harnessing biological computation itself.

Beyond Experimentation: The Future of Computational Biology

The rise of biocomputation reflects a broader transformation in biology itself. As noted by Christos Ouzounis, biology is evolving from an observational science through experimentation toward maturity as a computational science ⁸ . This shift means that increasingly, biological research relies on computational models and predictions that guide or sometimes even replace physical experiments.

This transformation is enabled by massive datasets from projects like the Human Cell Atlas and Vertebrate Genomes Project, sophisticated computational tools, and infrastructure initiatives like ELIXIR that coordinate resources across Europe ⁹ . The field now encompasses not just biological computation but also bio-inspired computing—developing algorithms based on biological principles like evolution, neural networks, and swarm behavior.