From DNA to Data: When Nature Does Math

Estimated reading time: 7 minutes

Have you ever wondered how a group of cells came together to form you? A smart, complex organism. Similarly, have you thought about how scientists created machines that can mimic decision-making and do computation? Imagine the journey from DNA to data, if you zoomed into cells. Past the bundle, focus on one of them, strip the outer layers and cytoplasm, and ultimately, you will reach the nucleus. What does this have? A microscopic system that stores information, copies itself, corrects any errors, and runs a code. Sound complex? It’s just our genetic material indeed! Without screens, it is molecules computing math.

But isn’t computation something only computers do? Churning results, calculating solutions, predicting models – yes as well as no.

It’s easy to imagine algorithms as cold, mathematical tools. However they are far more intricate than that. Still, long before AI and computers, nature was already busy solving complex problems, demonstrating a profound DNA to Data connection through strands of DNA, neural circuits, and evolutionary logic.

DNA: the OG code

One of the most famous molecules, DNA, is more than a twisted ladder. Made of 4 bases, A, T, C & G, it is capable of storing information and working on it. The sequence of the bases decides which proteins are made, when and how. Sounds like programming indeed, isn’t it?

In fact, DNA behaves a lot like computer program. DNA stores data (genetic information), which can be copied (in replication), gets read and translated (during protein synthesis), and even has built-in error checking (to fix mutations).

The only difference? Nature figured it out 3.5 billion years before humans invented computers.

Problem-solving is important in math, computing, and also in life. Wonder how in life? Try answering the following:

• How does a bird migrate with no GPS?
• How do plants and animals survive in deserts? In the tundra?
• How can a microorganism thrive in highly acidic areas? Or scorching hot ones? Or in nuclear waste zones?

Such problems are solved by optimisation by nature, otherwise known as Aka: Evolution

DNA, Data, Natural Algorithms, and Puzzles

In the 1960s, John Holland pioneered genetic algorithms certainly.
But what is it? A search and optimization technique inspired by natural evolution. It works by iteratively improving a population of potential solutions to a problem, using principles like reproduction, crossover, and mutation, similar to how biological organisms evolve over generations. It is a type of program that evolves with better answers over time, just as life evolves with time.

A Miracle in the Lab

In 1994, DNA chiefly solved a complex mathematical problem. Leonard Adleman used actual DNA, encoded the puzzle into the strands, and finally mixed it in a test tube to let nature run its course. It came later than it might have in some computers, but he gave proof to an idea: molecules can compute.

Out of fiction, into the real – DNA to Data computing was born.

The idea that nature does not look like computation, but actually does computation, was shown. From logistics to encryption, researchers are evidently exploring DNA to solve data and other problems.

Brains and Networks

The human brain is touted to be the most powerful computer in existence. Processing information through billions of neurons, and learning better the more a connection gets used. It is awe-inspiring.

Perhaps you’ve heard about neural networks? They are in fact based on the brain, and how it works. Consequently, these techniques are used from AI bots, to NLP, to medical diagnosis and financial modelling.

Every time you use either voice typing or face unlock, you’re using an algorithm inspired by the math your brain does naturally.

And guess what? The idea of learning from nature continues to grow.

Here’s a wild thought: every movie ever made, every book ever written, and every photo ever taken could fit into a single teaspoon of DNA.

Yes, really.

Because DNA is so dense and stable, scientists have started storing digital data inside it. In 2012, researchers successfully encoded an entire book in DNA. Since then, people have stored videos, songs, and even malware in tiny strands of synthetic DNA.

We’re not at the point of replacing your USB drive yet. But one day, we might back up our entire lives into biological molecules.

Why is it important for me?

Are you a student who loves biology?
Or a person who loves math?
Are you a student who loves coding?
Or someone who simply loves technology?

If your answer to the above is yes, it is more relevant than you might think at first glance. No longer shall you have to choose between science and technology or biology and coding.

Conclusion

So, the next time you see a leaf, snowflake, or your hair, remember that it is not just the randomness of life but also mathematics at play. From the swirl of a galaxy to the shell of a snail, nature adheres to the same rules. Nature has always acted as a silent mathematician; computing, optimising, and evolving long before we built machines to perform similar tasks. From the elegance of DNA sequences to the electrical impulses in our brains, and from the problem-solving capabilities of evolution to the algorithms inspired by it, biology and math are deeply interconnected.

This isn’t just an interesting fact; it’s a new way of perceiving the world. Whether you are passionate about science, math, or coding, you don’t have to choose just one. The future belongs to those who can intertwine disciplines. By understanding how nature employs mathematics, you don’t merely study life – you decode it. The future belongs to those who can connect disciplines. Because when you understand how nature does math, you don’t just study life – you decode it.

Additionally, to stay updated with the latest developments in STEM research, visit ENTECH Online. Basically, this is our digital magazine for science, technology, engineering, and mathematics. Furthermore, at ENTECH Online, you’ll find a wealth of information.

F.A.Q.

1. Is DNA really similar to a computer code?

Answer: Yes. DNA is made of four nitrogenous bases (A, T, G, C) arranged in specific sequences, like a long biological program. These sequences inform the cells how to build proteins, just like computer code tells a machine what to do.

2. How is evolution similar to an algorithm?

Answer: An algorithm is a step-by-step way to solve a problem. Evolution works by trial and error — organisms change slightly, and the best changes survive. Over time, nature “selects” what works best, like improving results in a computer program.

3. If I don’t like biology, can I still explore this field?

Answer: Absolutely yes. You can approach it through math, coding, data science, or logic. Such fields are at intersections of various domains like biology, computer science, statistics, etc. and you can join from any side. Examples of these fields include computational biology, bioinformatics, and neuroscience.

4. What does bioinformatics mean?

Answer: Bioinformatics is the science of using math, stats, and coding to understand biological data – especially DNA. It’s a fast-growing field that helps in drug development, personalized medicine, and more. It is similar to, but not the same as, computational biology.

5. What can I do now if I’m interested in this topic?

Answer: You can start with small steps, like learning programming, exploring various domains of biology, solving logic problems and systems puzzles. Resources include free online courses (ex. SWAYAM, edX & Coursera), YouTube and STEM clubs.

References:

1. Watson, C. (2022, January 2). What if math is a fundamental part of nature, not something humans came up with?. ScienceAlert. https://www.sciencealert.com/the-exquisite-beauty-of-nature-reveals-a-world-of-math
2. Lambora, A., Gupta, K., & Chopra, K. (2019). Genetic Algorithm- a literature review. 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), 380–384. https://doi.org/10.1109/comitcon.2019.8862255
3. Adleman, L. M. (2002). Molecular computation of solutions to combinatorial problems. Current Biology, 12(16), R605–R607. https://doi.org/10.1016/S0960-9822(02)00471-2