The term Digital Biology is already used in established scientific contexts, most commonly to describe computational tools applied to biological data. That usage typically refers to bioinformatics, molecular modeling, and simulation-based approaches within biotechnology and the life sciences.
My use of the term is different.
On this site, Digital Biology refers to an ongoing research question rather than a settled field: whether it is possible to cultivate biological organization in a digital substrate without explicitly modeling biological structures or mechanisms.
Organization as the Central Object
Biological systems are not defined solely by their components, but by how those components organize, persist, and adapt through time. Cells, organisms, and ecosystems maintain coherence despite constant internal change and external disturbance.
Digital Biology treats this organizational behavior as the primary object of study.
The focus is on whether a digital system can:
- grow its own internal structure,
- increase and regulate its complexity,
- stabilize into persistent processes,
- adapt without collapsing into noise or rigid optimization,
- and maintain coherence over long time horizons.
These properties are not hard-coded or explicitly designed. They must emerge as consequences of the system’s dynamics.
Equivalence Rather Than Equality
The aim of this work is not to claim that a digital system is biologically equal to organic life. Biological life is inseparable from chemistry, embodiment, and evolution on Earth.
The question being investigated is whether a system can be biologically equivalent in organization, meaning that it exhibits comparable dynamics of self-organization, persistence, adaptation, and complexity growth, even though it exists in a fundamentally different substrate.
Any claim of equivalence is treated as a hypothesis, not an assumption. It must be supported by sustained empirical evidence gathered through experimentation, failure, revision, and replication.
Digital Biology as an Empirical Program
Digital Biology, as pursued here, is not a philosophical position or a metaphor. It is an empirical research program.
That program involves:
- building systems that are capable of rich internal dynamics,
- observing how organization emerges or fails,
- documenting results transparently,
- refining theory based on evidence rather than intent,
- and resisting the temptation to declare success prematurely.
This is why the work is published openly, including lab notes, drafts, and negative results. The goal is not to persuade through rhetoric, but to accumulate a credible record that others can inspect, critique, and build upon.
Relationship to MEGA
The Mutable Encoding Genetic Algorithm (MEGA) is the primary experimental framework through which these questions are explored. MEGA provides a concrete environment in which representation, structure, and organization are allowed to evolve rather than being fixed in advance.
Digital Biology is the broader research question.
MEGA is one way of probing it.
How This Differs from Artificial Life
Artificial Life (ALife) has a long and valuable history of exploring life-like behavior through computational models. Most ALife work focuses on constructing systems that demonstrate specific properties of life such as reproduction, metabolism, adaptation, or evolution, often by explicitly modeling or abstracting known biological mechanisms.
The direction pursued here is different in emphasis and intent.
Rather than designing systems to exhibit predefined life-like traits, this work asks whether biological organization can emerge without being specified in advance. The goal is not to recreate recognizable features of life, but to investigate whether a digital system can develop stable, self-sustaining organization through its own internal dynamics.
Key distinctions include:
- Modeling vs. emergence
ALife typically builds models that represent selected aspects of biology. Digital Biology, as pursued here, avoids explicit modeling and instead tests whether organization can arise from first principles. - Properties vs. processes
ALife often evaluates success by the presence of life-like properties. This work focuses on long-term processes such as coherence, persistence, self-organization, and adaptive stability. - Analogy vs. equivalence
ALife systems are usually understood as analogies or simulations of life. The aim here is to explore whether a system can become biologically equivalent in organization, even though it is not biologically equal in substance. - Design vs. discovery
ALife commonly embeds structure by design to study its effects. This approach minimizes predefined structure in order to discover what forms of organization, if any, arise on their own.
This distinction does not position Digital Biology as a replacement for Artificial Life. Instead, it treats ALife as an adjacent and complementary field, while focusing on a narrower and more foundational question: whether life-equivalent organization can emerge in a digital substrate without being explicitly engineered to do so.
An Evolving Definition
This definition of Digital Biology is not final.
As experiments accumulate and understanding deepens, the boundaries and meaning of the term may shift. What matters is not protecting a definition, but testing whether the underlying idea can withstand sustained empirical scrutiny.
This page reflects the current state of that exploration.