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Biological Computing or Computational Biology: What’s the Difference?

Computation is playing a pivotal role in biology by expanding our current understanding of biological functions.

There are a lot of unsolved problems in biology, and when scrolling down their Wikipedia page, one might either feel daunted or motivated by the immense unknown. You could be feeling a mix of both in contemplation of all the biological mysteries there are to unravel.

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Spanning healthcare, informatics, archeology, computer science, engineering, social sciences, and astronomy (to name a few!), the branches of biology are ever-evolving.

The fields at the juncture of computation and biology help scientists better understand natural phenomena, manipulate life, and treat diseases. Of these many disciplines, two notable ones enter the picture: computational biology and biological computing. Beyond their names, computational biology and biological computing aren’t very similar. But what do these names mean and what branches do they encompass?

If you’re already familiar with the principles of biology, you can skip the refresher below.


Biology is important because it helps us understand how living organisms work and interact.

There are four unifying principles that matter to all life and form the basis of modern biology:

  1. Cell theory
  • All living things are made up of one or more cells
  • Cells come from other cells that already exist

2. Gene theory

  • An organism’s traits are encoded in its deoxynucleic acid (DNA)
  • DNA is a twenty micron long substance consisting of two polymer chains with four types of nucleotides or bases (A, C, G and T)
  • DNA makes up the genes of an organism
  • By way of these genes, traits are passed on from one generation to the next

3. Homeostasis

  • Organism can control its bodily functions to uphold a stable internal environment in spite of changes in external environment
  • All living things perform homeostasis
  • eg. Cells maintain a stable internal acidity (pH); warm-blooded animals maintain a constant body temperature; human body maintains proper glucose levels

4. Evolution

  • A population’s inherited traits change over time
  • All known organisms have a common origin


Computational biology is concerned with solving biological problems with computational and mathematical methods and tools. The goal is to model biology and address critical questions.

By applying the principles of simulation programs, computational biologists can explore various biological questions, such as protein-protein interactions, protein folding, and drug binding sites.

Computational biology aims to solve the issues that have been raised by studies in bioinformatics with computational tools.


  1. Computational anatomy
  • Investigating and modelling anatomical shapes quantitatively
  • Imaging anatomical structures

2. Computational biomodeling

  • Building computer models of biological systems
  • Assessing the complexity of biological systems with visual simulations
  • Predicting how biological systems will react in different environments

3. Computational genomics

  • Studying the genomes of cells and organisms
  • eg. The Human Genome Project was an international, collaborative research program that hoped to determine the base pairs of human DNA, and map and sequence all the genes of the human genome. The endeavour fell under computational genomics.
  • Scientists working in computational genomics are addressing the questions of non-coding ribonucleic acid (RNA) genes that account for 97% of the human genome, meaning that it controls gene activity but doesn’t encode genetic information.

4. Computational neuroscience

  • Analyzing brain data to create practical applications
  • Examining aspects of the neurological system and information processing properties of their structures

5. Computational pharmacology

  • Using genomic data to find links between specific genotypes and diseases
  • Analyzing large data sets required for drug design and discovery

6. Computational evolutionary biology

  • Assisting evolutionary biology to reconstruct ancestry, infer demographic or selective history and predict evolution

7. Cancer computational biology

  • Aiming to identify the future mutations of cancer
  • Using robotics and sensing detectives to gather data points from DNA, RNA and other biological structures

8. Computational neuropsychiatry

  • Modeling of brain mechanisms
  • Contributing to the understanding of neuronal circuits for mental functions and dysfunctions



Computational biology develops and applies computational methods to analyze large collections of biological data (eg. genetic sequences, cell populations, protein samples) in order to make predictions and discover new biology.

Because there is an increase of data in molecular biology and genomics, and computational biology relies on large and accurate datasets, better analytical methods can thus be developed to interpret this information and quantify the huge sets of data.


Imagine a computer with the ability to store immense quantities of data, work with incredible precision, repair itself, create new patterns and ensure its own survival. Now, picture this computer within a cell. There you go! That’s a biocomputer, albeit a very basic one.

Computing and biology are crossing paths in new ways for the discipline of biological computing, which is concerned with the creation of biocomputers.

Biocomputing is all about harnessing the potential of DNA for the benefit of humanity by engineering it to perform specific tasks. Using biological materials, biocomputers could mimic organisms and help us study them.


To perform functions and calculations, all biocomputers use biologically derived materials that are engineered to behave in a certain manner. Their behaviour depends on the input (conditions of system). The output is a pathway of resulting reactions, which hinges on the intended design of the biocomputer.

  1. Biochemical computers
  • A variety of feedback loops (biological mechanisms whereby homeostasis is maintained) of biological chemical reactions are used to achieve computational functionality
  • Many factors provide a positive (increase in chemical output) or negative (decrease in chemical output) feedback to a biochemical process
  • Chemical pathways can be engineered to form a particular product under specific conditions. This product then serves as a signal, which is interpreted as a computational output.

2. Biomechanical computers

  • The mechanical three-dimensional shape of specific molecules is the output, which relies on their nature to adopt physical configurations.

3. Bioelectronic computers

  • This is essentially the use of biocomputers for electric computing.
  • The output is the nature of electrical conductivity of the bioelectronic computer.


The chemical nature of a protein is dictated by the sequence of its amino acids. And that sequence depends on the pattern of DNA bases, which contain all the information needed for an organism’s development. By manipulating these DNA bases, the protein can perform the desired computations and calculations. Scientists and engineers would have to join forces to synthetically design molecules to create biocomputers.


Biological computers could surpass traditional computers with their unparalleled speed: the fastest supercomputer can perform around 10¹⁵ operations per second and experts believe that biocomputers could reach a level of 10¹⁷ operations per second or more!

Moreover, our current devices and gadgets are loosely inspired by patterns already patented and perfected by nature. There are behavioural similarities between computers, softwares and algorithms, and biological systems. Look at neural networks, which are a series of algorithms that identify underlying relationships in a set of data. A neural network mimics the way a human brain operates and is built on the architecture of our existing neurons.

Even if scientists don’t yet completely understand biological functions, the creation and development of biocomputers is an opportunity for further discoveries. Moreover, studying at the interface of biology and information technology has the potential to lead to important new information systems (algorithms, software) and computer devices (hardware).

That being said, there are many questions left to answer before we can expect to see biocomputers on store shelves. How do we accurately control DNA? How can the programmed DNA go awry? How will these biocomputers impact our health?

Will computers someday tackle real viruses instead of electronic ones?


Could computational biology be used to develop and analyze biological computers? To identify the necessary components to build a biocomputer?

I think so. Using computational biomodeling to visually simulate the reactions within biocomputers could allow for the assessment of their complexity and the prediction of their output under specific input conditions. Computational techniques and algorithms can be used to discover how biocomputers will behave and interact in certain environments. Moreover, genomic variants can be identified to optimize the performance of biocomputers thanks to computational biology!

I’m hopeful that computational biology and biological computing will cross paths in momentous ways.


  • Computational biology is the science of using biological data to develop algorithms and models.
  • Biological computing is the use of systems of biologically derived molecules (DNA, proteins) to calculate, store, retrieve and process information.

Here’s a short video about the prospects of computational biology:

Computation is playing a pivotal role in biology by expanding our current understanding of biological functions.

Computational biology and biological computing are two very distinct, yet promising fields, bound to blur the lines between biology and technology. And it’s about time. Their interdisciplinary nature guarantees advancements and along the way, we might unravel the greatest biological mysteries and answer some of the most challenging questions.

Thanks for reading Biological computing or computational biology: What’s the difference?! If you enjoyed my article or would like to connect, you can find me on LinkedIn.

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