Can we understand a cell as an organic computational device? In that case the whole organism can be considered an ensemble of a large number of interacting computational devices?

If this is the case, biology can be studied using methods of graph theory, network theory, computation theory and complex systems theory.

  • $\begingroup$ The answer is YES, if you can do this, But you can't (at least currently) because your devices are not powerful. $\endgroup$
    – MySky
    Mar 14, 2015 at 18:17

2 Answers 2


If you abstract enough, anything cam be considered as a computational device. The issue with doing this with cells is the sheer number of variables.

For any given cell, the following internal variables exist:

  • Internal ion concentrations for dozens of ions of importance importance
  • Internal concentrations of hundreds or thousands of various simple organic molecules, including "raw" internalised molecules, various steps in dozens or even hundreds of metabolic processes, and metabolic end-products and by-products.
  • Internal concentrations of hundreds or thousands of different proteins and other complex biological "mechanical parts", as well as the states of these "parts".
  • The physical state of the cell - stretched, contracted, relaxed, hot, cold, etc.

This list is not exhaustive.

It's also worth noting that the above variables may also exist for multiple separate "compartments" within the cell; vesicles, the endoplasmic reticulum and the golgi apparatus being three which come to mind immediately.

The other issue is that cells do not exist in a vacuum; the external environment plays an important role in their functioning. The human body is at any one moment existing in direct contact and interaction with the following extracellular environments:

  • The atmosphere (Mainly, but not only, temperature exchange)
  • The air within the respiratory system, including nose and mouth
  • Stomach contents
  • Small intestine contents (which would need to be considered in several segements, since the nature of the interaction changes along the path of the small intestine.
  • Large intestine contents
  • Blood
  • Cerebrospinal fluid (Fluid found "inside" the brain)
  • Extracellular fluid, or "tissue fluid" (A separate compartment for each small swatch of tissue in the body)
  • Lymph
  • Pleural fluid (Fluid surrounding the lungs; a separate compartment for each lung)
  • Pericardial fluid (A small amount of fluid surrounding the heart)
  • Joint capsule contents (A separate compartment for each joint)

...and the list goes on. Each of these compartments requires tracking of the same variables as individual cells.

This is yet further complicated by the fact that some of these compartments can't easily be considered as one big compartment, because of the importance of spatial relations. For example, the oxygen and carbon dioxide concentrations of blood (as well as the concentrations of other substances like alcohol) change centimeter-to-centimeter. Yet another issue is the fact that cells are not static in terms of their relations to each other; red blood cells move with blood flow, and experience turbulence and other effects, and other cells (like macrophages) are capable of "deliberate" movement in the blood and in tissue.

You would also have to account for physical disturbances - things like a stab wound or even a pinprick are ludicrously complex at a cellular level.

Of course, human beings are very complex organisms, and there exist much simpler organisms. You might be interested in OpenWorm, which is an attempt to computationally simulate Caenorhabditis elegans, a species of roundworm, at a cellular level. Doing so even for an organism as simple as C. elegans is a massive undertaking, as evidenced by the fact that even with the contributions of dozens of experts in their fields the project has been ongoing for some time and is yet to reach stage one.

The short version: Is it possible? Perhaps. Is it easy? Definitely not.


As a mathematician interested in biology I am very curious about informed answers, here I add mine, with the understanding that it is in no way complete and it might be very biased or ignorant as concerns biology.

We may understand a cell as a chemical and physics based program which runs itself. The cell is a computational device in the sense that the outcomes of its activity are computable functions of its inputs (a reasonable hypothesis), but more than that: at the cell level there is no distinction between the computer, the program which runs on the computer, input and output data AND the execution of the program. All is at the same level, i.e. every abstraction is embodied in a concrete chemical or physical thing.

This concreteness part adds, I believe to the difficulty, because the usual thinking in computer science is all about structuring abstractions, while in biology everything is ultimately at only one level: real, physics and chemistry embodied.

This is a claim which needs strong supporting evidence. Being a matter of principle, it cannot be proved rigorously, but it might be given a rigorous support by constructing simple proofs of principle models.

There are many computing models which are inspired by chemistry, therefore in return they can be seen as such proof of principles.

There are Chemical Reaction Networks and Petri Nets models which are more like structuring tools than real embodied models of computation, because they don't consider the structure of molecules (they are just nodes in a graph), nor the way chemical reactions happen (they are edges in a graph). They are very useful tools though and the description given here is very much simplified.

There is the CHAM (chemical abstract machine), G. Berry and G. Boudol. The chemical abstract machine. Theoretical Computer Science, 96(1):217–248, 1992. In this model states of the machine (imagine: a cell) "are chemical solutions where floating molecules can interact according to reaction rules" (cite from the abstract). In this model "solution" means a multiset of molecules, reaction rules are between molecules and they do not apply inside molecules. This is a limitation of the model because the structure of the molecule is not as important as the number of molecules in a species.

Another very interesting model is the Algorithmic Chemistry of Fontana and Buss. The main idea is that chemistry and computation are basically the same. The reasoning goes as following. There are two pillars of the rigorous notion of computation: the Turing Machine (well known) and Church's lambda calculus. Lambda calculus is less known outside computer science, but is a formalism which may be more helpful to chemists, or biologists even, than the Turing machine. Fontana and Buss propose that lambda calculus is a kind of chemistry, in the sense that the its basic operations, namely abstraction and application, can be given chemical analogies. Molecules are like mathematical functions, abstractions are like reaction sites and applications are like chemical reactions.

The Algorithmic Chemistry is almost as closer as possible to be (proof of principle) answer to the question.

Finally I mention chemlambda, or the Chemical concrete machine, which is like Algorithmic Chemistry, but it is far more concrete. Molecules are graphs, applications and abstractions are molecules, chemical reactions are graph rewrites.

What is very interesting in all these models, in my opinion, is that they suggest that answering to the question "Can we understand a cell as an organic computational device?" is somehow relevant to the Computer Science question "How to design an asynchronous, decentralized Internet?".


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