When Dolly was unveiled to the world, she did more than just baa. She screamed a profound question into the 20th century: If life can be copied, what does that make us?

For nearly three decades, that question has haunted our labs, our ethics boards, and our sci-fi movies. We’ve gone from cloning a mammal to editing the very code of life itself. We're not just copying the "hardware" anymore; we're learning to rewrite the software.

So, where are we now, and what does the future smell like? It smells less like sheep's wool and more like pure, cold data.

Part 1: Dolly - The Disruption

Let’s rewind. Before Dolly, the scientific consensus was that a cell from an adult was set in stone. A skin cell was a skin cell, and it could never be anything else. But Dolly’s creators at the Roslin Institute did the impossible. They took a mammary cell from a six-year-old ewe, fused it with an emptied-out egg cell, and zapped it into life.

Dolly proved that the "software" (the DNA) in any cell still contained the master plan for the entire organism. It was a moment of profound discovery that felt like science fiction. It sparked immediate panic about human cloning, but the real revolution was quieter, deeper. It was about proving we could control the very engine of biology.

Part 2: The Revolution Has Been Synthesized

Cloning was just the opening act. The main event is Synthetic Biology. If cloning is like using a photocopier to duplicate a book, synthetic biology is learning to write your own novel from scratch, using the alphabet of DNA.

We aren't just copying sheep anymore. We are:

• Programming Yeast to Make Medicine: The insulin that diabetics use is now made by genetically engineered bacteria and yeast. We've turned tiny microbes into living drug factories.

• Designing "Biological Bricks": Scientists are creating standardized genetic parts that can be snapped together like Lego to build organisms with new functions like bacteria that can eat plastic or detect landmines.

• The Promise of Xenotransplantation: We're editing pig genomes to make their organs compatible with the human body, potentially ending the transplant waiting list.

It’s the ultimate form of coding. But instead of Python or JavaScript, the language is A, T, C, and G.

Part 3: Coding the Code of Life

To really grasp this, let’s get our hands dirty. The process of searching and editing DNA is becoming as easy as using a word processor. Here’s a tiny peek at how a scientist might "code" with biology using a programming language called Python.

Imagine we have a string of DNA. We want to find a specific "typo" or mutation the kind that might cause a disease and then "cut" it out, ready for a fix. This is the conceptual magic behind tools like CRISPR.

python

# Let's simulate a small piece of human DNA
# Our DNA sequence is a string of letters: A, T, C, G
dna_sequence = "ATCGGCTTAACGTACGTAGCTAGCTAGCATGATCG"

print(f"Original DNA: {dna_sequence}")

# Let's say this piece of DNA has a disease-causing mutation: "TTAACG"
# It should actually be "TTAACC" for a healthy gene.
mutation = "TTAACG"
healthy_sequence = "TTAACC"

print(f"\nSearching for the mutation '{mutation}'...")

# 1. FIND THE MUTATION (like a scientist using a guide RNA in CRISPR)
mutation_start_index = dna_sequence.find(mutation)

if mutation_start_index != -1:
    mutation_end_index = mutation_start_index + len(mutation)
    print(f"Mutation found at index {mutation_start_index} to {mutation_end_index}")

    # 2. SIMULATE THE 'CUT' (like the Cas9 enzyme)
    # We split the DNA into three parts: before, the mutation, and after.
    before_mutation = dna_sequence[:mutation_start_index]
    after_mutation = dna_sequence[mutation_end_index:]

    print(f"\n--- SIMULATING CRISPR-CAS9 CUT ---")
    print(f"Section before the cut:  {before_mutation}")
    print(f"Mutation to remove:      {mutation}")
    print(f"Section after the cut:   {after_mutation}")

    # 3. SIMULATE THE 'REPAIR' (inserting the healthy sequence)
    corrected_dna = before_mutation + healthy_sequence + after_mutation
    print(f"\n--- REPAIRING WITH HEALTHY SEQUENCE ---")
    print(f"Corrected DNA: {corrected_dna}")

else:
    print("Mutation not found. No editing needed.")

# Output:
# Original DNA: ATCGGCTTAACGTACGTAGCTAGCTAGCATGATCG
# Searching for the mutation 'TTAACG'...
# Mutation found at index 5 to 11.
# --- SIMULATING CRISPR-CAS9 CUT ---
# Section before the cut:  ATCGG
# Mutation to remove:      TTAACG
# Section after the cut:   TACGTAGCTAGCTAGCATGATCG
# --- REPAIRING WITH HEALTHY SEQUENCE ---
# Corrected DNA: ATCGGTTAACCTACGTAGCTAGCTAGCATGATCG

See? It’s a string manipulation problem with world-changing consequences. The complexity is immense, but the fundamental logic is surprisingly accessible. We are learning to find, cut, and paste in the text of life.

Part 4: The Future is a Mirror

So, what’s next? If cloning was the first draft and synthetic biology is the rewrite, the future is the digital twin.

Imagine this: At birth, your entire genome is sequenced. It’s uploaded to the cloud. For the rest of your life, this digital "you" is tested. Before you take a new drug, we run a simulation on your digital twin to see if you’ll have a bad reaction. Before a cancer treatment, we test a thousand different therapies on your digital cells to find the one that will wipe out the tumor without harming you.

Your digital twin gets sick so you don’t have to. Your synthetic biology avatar experiments so your body doesn’t have to suffer the side effects.

This is the horizon. It’s a world where the line between biology and information dissolves completely. Dolly the Sheep was the first glimmer of this reality. She wasn't just a copy; she was a message from the future, telling us that life is information, and information can be read, copied, and most powerfully of all rewritten.

The question Dolly posed is no longer "Can we do it?" It’s "What story will we write next?"

🧬 Question of the Week

If you could upload a "digital twin" of yourself to a computer to test new medicines or predict future diseases, would you do it? Why or why not?

Drop your thoughts in the comments below. Is this the ultimate power of modern medicine, or does it cross a line we shouldn't touch?

Look around you. The medicine in your cabinet, the food on your plate, even the clean air you breathe chances are, biotechnology played a role. Unlike the flashy gadgets that grab headlines, biotechnology works quietly behind the scenes, solving some of humanity's oldest problems. And here's something you might not expect: the same logical thinking that powers biotech breakthroughs is exactly what you use when writing Python code. Let's explore how this invisible revolution touches every part of our lives.

Medicine: Programming Living Cells

Insulin: The First Biotech Blockbuster

Before the 1980s, insulin for diabetics came from pigs and cows. It worked, but not perfectly. Then scientists did something remarkable: they inserted the human insulin gene into bacteria. These microscopic factories started producing human insulin, identical to what our bodies make.

Think of it like this: DNA is nature's source code, and scientists learned to edit it. In Python terms, they essentially did:

#simplified analogy of genetic engineering 
bacterial_dna = "original_bacterial_code"

human_insulin_gene = "gene_for_insulin_production"

#Insert human gene into bacterial DNA
engineered_dna = bacterial_dna.insert(gene_position, human_insulin_gene)

Biotechnology in Agriculture

GMO Crops: Feeding a Growing Population Genetically Modified Organisms (GMOs) are crops whose DNA has been slightly altered to improve traits such as:

. Higher yield

. Pest resistance

. Drought tolerance

Examples include Bt corn and Golden Rice.

Advantages:

Increased food production

Reduced pesticide use

Improved nutritional value

Pest Resistance: Protecting Crops Naturally

Some GMO crops produce natural proteins that repel pests. This means:

. Farmers spray fewer chemicals

. The environment is safer

. Crops are healthier

This is especially important in regions affected by climate change and food insecurity.

Simple example: Analyzing crop yields

import pandas as pd

#Sample data for GMO vs non-GMO corn

data = { 'crop_type': ['GMO', 'Non-GMO', 'GMO', 'Non-GMO'], 'yield_kg': [1200, 800, 1250, 750], 'pesticide_used_ml': [50, 200, 45, 220] }

#Calculate averages
average_yield = df.groupby('crop_type')['yield_kg'].mean() average_pesticide = df.groupby('crop_type')['pesticide_used_ml'].mean()
print(f"Average yield:\n{average_yield}") print(f"\nAverage pesticide used:\n{average_pesticide}")

#Beginner challenge: Try changing the numbers and see what happens!

This simple analysis helps scientists make evidence-based decisions about which crops to develop.

Environmental Biotechnology

Environmental biotechnology is the use of living organisms like bacteria, plants, or fungi to clean, protect, and restore the environment. Instead of using harmful chemicals, scientists use nature itself to solve environmental problems. Easy Examples

1️⃣ Cleaning Oil Spills:

Some bacteria can eat oil and break it down into harmless substances. Scientists release these bacteria into polluted water or soil to clean oil spills. This process is called bioremediation.

2️⃣ Wastewater Treatment Bacteria are used to: Remove waste Break down harmful chemicals Clean dirty water This helps make water safe for reuse or release into rivers.

3️⃣ Pollution Control Certain plants and microbes can absorb: Heavy metals Toxic chemicals This helps clean polluted soil and air.

Why This All Matters to You (Yes, You)

Look, I'm not here to sell you on biotechnology. Some of it makes me nervous too. We should absolutely ask questions and be careful.

But here's what I've realized: whether we're talking about medicine, food, or cleaning up the environment, biotechnology is just another tool. And like any tool, it depends on how we use it.

The insulin story? That's pretty clearly good.
The GMO crops? More complicated, but the data shows benefits when done responsibly.
The oil-eating bacteria? Honestly, that's just cool.

Your Turn to Explore

Want to dip your toes in? Try this, it's simpler than making scrambled eggs:

#Let's play with DNA sequences
#(Don't worry, I'll walk you through it)

#DNA uses four letters: A, T, C, G
#They're like nature's code
my_dna = "ATGCCGTAATGCTAG"

#Let's see what we've got
print("Here's my DNA sequence:")
print(my_dna)

#Count the G's and C's
g_count = my_dna.count("G") 
c_count = my_dna.count("C") 
total = len(my_dna)

#Calculate the percentage
gc_percent = (g_count + c_count) / total * 100


print(f"\nG's and C's make up {gc_percent:.1f}% of this sequence") 
print("(Scientists look at this because it tells them about stability)")

#Now change the sequence and run it again!
#Try: "AAAAATTTTT" or make up your own

See? You're already analyzing DNA sequences. No lab coat required.

The best technology doesn't shout. It just works. It lets people live, and eat, and breathe a little easier.

And if a regular person like me can start to understand it, maybe even write a few lines of code to explore it then honestly, what can't we learn?

P.S. If you actually tried that DNA code, you're officially a biotech explorer. High five! 🖐️

Gene editing is the process of making precise changes to DNA. Scientists can add, remove, or modify specific parts of a gene to better understand how it works or to correct genetic problems. Think of DNA as an instruction manual for life, gene editing allows scientists to edit spelling mistakes in that manual.

Early Gene Editing Tools: Restriction Enzymes & PCR

Before modern tools like CRISPR, scientists relied on simpler methods:

🔹 Restriction Enzymes These are natural enzymes found in bacteria. They act like molecular scissors, cutting DNA at specific sequences. Scientists use them to cut and study genes or insert new DNA pieces.

🔹 PCR (Polymerase Chain Reaction) PCR is not exactly a gene-editing tool, but it is essential in genetics. It allows scientists to make millions of copies of a DNA segment, making it easier to analyze, detect diseases, or prepare DNA for editing.

CRISPR: The Molecular Scissors Revolution ✂️

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) is the most powerful gene-editing tool today.

Imagine CRISPR as:

. GPS → guides the tool to the exact gene

. Scissors (Cas9 enzyme) → cuts the DNA at that spot

Once the DNA is cut, scientists can:

. Remove faulty genes

. Replace them with healthy ones

. Turn genes on or off

CRISPR is fast, accurate, and cheaper than older methods, which is why it has transformed genetic research.

Applications of Gene Editing

Medicine: Treating genetic diseases like sickle cell anemia, Cancer research and immune therapy Potential cures for inherited disorders.

Agriculture: Crops that resist pests and diseases Improved nutritional value, Better drought-tolerant plants.

Research: Understanding how genes work, Developing new drugs, Studying rare genetic conditions.

Ethical Questions Around Gene Editing

While gene editing has huge benefits, it raises important ethical concerns:

. Should we edit human embryos?

. Could gene editing be misused?

. Who decides what traits are “acceptable”?

Scientists and policymakers worldwide are working to ensure gene editing is used responsibly and safely.

How Python Fits into Gene Editing 🧬

Gene editing tools like CRISPR work in the lab, but Python works behind the scenes. Before any gene is edited, scientists must first analyze DNA data using computers, and Python is one of the most important tools for this.

1. DNA Is Data and Python Understands

   Data DNA is made up of letters: A, T, C, and G. Python helps scientists:

   . Read DNA sequences

   . Compare normal vs mutated genes

   . Find exact locations to cut using CRISPR

   In simple terms, Python helps scientists “read” DNA before they edit it.

2. Designing CRISPR Targets with Python

   CRISPR must cut DNA at a very specific spot. Python is used to:

   . Scan long DNA sequences

   . Identify safe and accurate CRISPR target sites

   . Reduce off-target errors (cutting the wrong gene)

   This makes gene editing safer and more precise.

3. Popular Python Tools in Gene Editing

   Some Python libraries commonly used in genetics include:

   . Biopython → for working with DNA and protein sequences

   . NumPy & Pandas → for analyzing large genetic datasets

   . scikit-learn & PyTorch → for predicting gene behavior using AI

   These tools help scientists make data-driven decisions before experiments.

4. Python in Medical & Agricultural Genetics Medicine

   Python helps analyze patient genetic data to:

   . Identify disease-causing mutations

   . Track gene-editing success

   . Support personalized medicine

   🌾 Agriculture

   Python helps researchers:

   . Analyze plant genomes Improve crop resistance

   . Predict yield improvements after gene editing

5. Why Python Matters for Future Genetic Scientists

   Python connects biology and technology. Today’s genetic scientists don’t just work in labs, they also:

   .Write code

   .Analyze genomic data

   .Use AI to improve gene-editing accuracy

   This is why Python is becoming a must-have skill in modern biology.

   Final Thoughts

   CRISPR edits genes in the lab, but Python prepares the roadmap. Together, they are shaping the future of medicine, agriculture, and life science research.

   #Biotech #Bioinformatics #PythonProgramming #Biotechnology #TechInBiology #LearningInPublic #WomenInSTEM #STEMCareer

What Is DNA?

DNA stands for Deoxyribonucleic Acid. It is a long molecule found in almost every cell of the body, stored mainly in the nucleus. DNA carries the instructions that make you you—your eye color, height, blood type, and even how some diseases may affect you.

Think of DNA as an instruction manual that cells read to know what to do.

What Is DNA Made Of? (A, T, G, C)

DNA is made up of smaller units called nucleotides. Each nucleotide contains:

. A sugar molecule

. A phosphate group

. A nitrogenous base

There are four bases in DNA:

A – Adenine

T – Thymine

G – Guanine

C – Cytosine

These bases always pair in a specific way:

A pairs with T

G pairs with C

This pairing forms the famous double helix structure—often described as a twisted ladder.

How Does DNA Store Genetic Information?

DNA stores information in the order (sequence) of its bases. For example: ATG-CGA-TTC

Just like letters form words and sentences, these base sequences form genes. Each gene carries instructions for making a specific protein.

So, it’s not just the bases themselves that matter, it’s how they are arranged.

From DNA to Protein: The Central Dogma (Made Simple)

In biology, there is a basic flow of information known as the central dogma:

DNA → RNA → Protein

Here’s what that means in simple terms:

1. DNA stores the instructions.

2. RNA copies those instructions (transcription)

3. Proteins are built using the RNA instructions (translation).

Proteins do most of the work in the cell—they build structures, speed up reactions, and keep the body functioning.

So, when people say DNA controls life, they really mean DNA controls protein production.

Fun Fact: Palindromic DNA Sequences 🧬

Some DNA sequences are palindromic, meaning they read the same forward and backward.

Example:

GAATTC

These sequences are fundamental in biotechnology because restriction enzymes recognize them, tools scientists use to cut DNA at specific points.

This is one of the foundations of genetic engineering.

Why DNA Is the Starting Point of Biotechnology

Biotechnology is all about using biological systems to solve problems from medicine to agriculture.

Because DNA holds all genetic instructions, it is the starting point for:

. Genetic engineering

. Gene therapy

. DNA sequencing

. CRISPR and genome editing

. Vaccine and drug development

Understanding DNA means understanding how life works at its most basic level.

Final Thoughts

DNA may be tiny, but its impact is massive. By learning how DNA stores and passes on information, you’re taking your first real step into the world of biotechnology.

In the next week, we’ll dive deeper into how genes are regulated and how small changes in DNA can lead to big differences in life.

Biotechnology begins with DNA, and so does the future. 🎆

Connecting DNA to Python

Modern biotechnology relies heavily on programming, and Python is one of the most popular languages used in bioinformatics. With Python, we can analyze DNA sequences just like reading text.

1. Representing DNA in Python

    Python dna = "ATGCGTACGTTAG" print(dna)
   

2. Counting Nucleotides (A, T, C, G)

dna = "ATGCGTACGTTAG"

print("A:", dna.count("A")) print("T:", dna.count("T")) print("G:", dna.count("G")) print("C:", dna.count("C"))

This is useful for analyzing GC content, mutations, and sequence composition.

3. Transcription: DNA → RNA

During transcription, Thymine (T) is replaced with Uracil (U) to form RNA.

Python dna = "ATGCGTACGTTAG" rna = dna.replace("T", "U") print(rna)

This simple line of code models a real biological process.

4. Finding Palindromic DNA Sequences

A palindromic DNA sequence reads the same forward and backward.

Python def is_palindrome(sequence): return sequence == sequence[::-1]

sequence = "GAATTC" print(is_palindrome(sequence))

Restriction enzymes recognize palindromic DNA. This concept is key in genetic engineering.

5. Why This Matters

By combining biology + Python, scientists can:

. Analyze thousands of DNA sequences quickly

. Detect mutations and genetic diseases

. Study evolution and population genetics

. Build tools for drug and vaccine discovery

This is where biotechnology meets data science.

Final Connection DNA provides the information. Python provides the tools to read, analyze, and understand it at scale.

Learning Python alongside biotechnology gives you a powerful advantage in modern science.

#Biotechnology #Bioinformatic #PythonProgramming #WomenInSTEM

In this article, I’ll explain the basics of biotechnology, show everyday examples, and share why I’m studying biotech and Python at the same time.

Why Biotechnology Matters Today

The world is changing fast. New diseases appear, climate issues affect food production, and more people need better healthcare. Biotechnology is one of the most important fields helping us:

Design modern vaccines

Grow stronger, safer crops

Produce clean energy

Understand and edit DNA

Develop personalized medicine

But behind many of these breakthroughs is data. Large amounts of DNA sequences, protein structures, lab results, and biological information. And that’s exactly where Python enters the story.

What Biotechnology Really Means

Biotechnology simply means using living things (cells, enzymes, DNA) to create products or solve problems. Biology + Technology = Biotechnology

Life + Tools = Solutions

Scientists use bacteria, plants, animals, and human cells to design new solutions through processes like:

Genetic engineering

Fermentation

Molecular biology

Cell culture

Bioinformatics

And one of the key tools modern scientists use to analyze all this information is programming — especially Python.

Everyday Examples of Biotechnology :

🍞 Bread & Yogurt: Traditional biotech uses yeast and bacteria to make bread, yogurt, and cheese through fermentation.

💉 Vaccines: Modern vaccines are engineered using advanced biotechnology and tested using huge datasets that Python helps analyze.

💊 Insulin: Production Genetically engineered bacteria produce human insulin — and Python is used in quality control, DNA sequence checks, and production monitoring.

🌽 GM Crops: Scientists analyze plant genomes and environmental data using Python libraries like NumPy and Pandas.

🧬 DNA Analysis: Whether it’s ancestry tests or forensic science, Python tools (like Biopython) help read and analyze DNA sequences.

How Python Connects to Biotechnology Biotechnology isn’t just lab coats and microscopes anymore. It’s data — massive amounts of biological data. Python helps in:

🔍 DNA Sequence Analysis: Using Biopython, you can read DNA Sequences:

dna = Seq("ATGCGTACGTTAG") print("Complement:", dna.complement()) print("Reverse Complement:", dna.reverse_complement()’’’

What this code does

Seq creates a DNA sequence object. complement() replaces each base with its complementary base: A ↔ T, C ↔ G reverse_complement() flips the sequence backward and then complements it.

This is commonly used in bioinformatics because DNA strands run in opposite directions

Output

Here is the result:

Complement: TACGCATGCAATC Reverse Complement: CTAACGTACGCAT

📊 Analyzing Biological Data Scientists use Python libraries (Pandas, NumPy, Matplotlib) to analyze experiment results:

import pandas as pd

data = pd.DataFrame({ "Sample": ["A", "B", "C"], "Protein_levels": [12.4, 18.9, 15.2] })

print(data)

What this code does:

Imports pandas, the main Python library for working with data. Creates a small table (DataFrame) with: Sample names (A, B, C) Their corresponding protein levels Prints the table for analysis. This shows how scientists use Python to store and interpret experimental data.

Output

Here is the result:

Sample Protein_levels 0 A 12.4 1 B 18.9 2 C 15.2

🧫 Simulating Experiments Python can model cell growth, enzyme reactions, or even genetic circuits.

🤖 Machine Learning for Biology

Python helps train models that:

Predict diseases

Classify cells

Detect gene mutations

Design new drugs

This combination of biology + code is called bioinformatics, and it’s one of the fastest-growing areas in biotech.

Why I Decided to study Biotechnology and Python Together

I realized something powerful: if biology is the language of life, Python is the language that helps us understand it.

Learning both fields gives me the ability to: Break down biological concepts

Analyze real scientific datasets

Build small bioinformatics tools

Explore genetics in a deeper way

Everyday, I’m learning more — writing simple Python programs, studying biotech topics, and discovering how both connect.

Join Me on This Learning Journey

This is just the beginning. I’ll be sharing what I learn in both biotechnology and Python, breaking them down into beginner-friendly explanations. If you’re curious about science, coding, DNA, or how technology is changing biology, follow along. Let’s explore biotech — one concept and one Python script at a time.

As a biochemistry student, I’ve always been fascinated by how life works at the most fundamental level, the dance of cells, molecules, and enzymes that keep us alive.

But one day, I stumbled on something that changed how I saw biology forever. I learned that code could help us understand and even re-engineer life itself. That’s how my journey into biotech and Python began.

Today, I’m learning biotechnology and Python online, while also working as a data annotator, and I’ve discovered that combining science and technology feels like unlocking the future, one line of code at a time. Learning about Python in biotech became one of the best things that happened to me this year.

🧫 What Biotech and Python Mean to Me

Biotechnology is simply using living organisms to solve problems, from producing vaccines to cleaning up oil spills.

But modern biotech goes even deeper: it’s powered by data. Thousands of DNA sequences, protein structures, and experimental results are being generated every second. And that’s where Python comes in.

Python helps scientists:

• 📊 Analyze massive biological datasets

• 🧬 Simulate genetic experiments

• 💊 Speed up drug discovery

• 🧠 Build AI models that understand biology

  It’s like giving scientists a new set of eyes, one that can see patterns in life itself.

🧠 My Learning Journey

I’m still at the beginning of my coding journey, learning print statements, loops, and small programs. But even at this early stage, I’m amazed at how powerful Python can be.

It wasn’t easy at first; everything felt confusing. But little by little, things started to click. Now, I challenge myself with two Python problems a day, take biotech lessons online, and explore how both fields connect in real life.

Balancing school, work, and self-learning is tough, but the thrill of progress keeps pushing me. Every successful line of code and every new concept mastered reminds me that I’m building something bigger than myself.

🧩 What I’ve Learned So Far

• Consistency beats speed — learning a little daily compounds over time.

• It’s okay to not understand everything at once — patience is part of science.

• Relating code to biology makes learning fun.

  For example, strings in Python remind me of DNA sequences, where every letter matters.

Now, when I see Python code, I don’t just see syntax. I see the language of life taking shape in a new way.

🧬 My First Python Step Toward Biotech: DNA Sequence Analyzer.
I wanted to start small and connect Python to biology in a simple but meaningful way.

This was one of my first beginner-friendly biotech-inspired codes:

```python
# Simple DNA sequence analyzer
dna_sequence = "ATGCGTACGTTAG"

# Count nucleotides
a_count = dna_sequence.count("A")
t_count = dna_sequence.count("T")
c_count = dna_sequence.count("C")
g_count = dna_sequence.count("G")

print("DNA Sequence:", dna_sequence)
print("A:", a_count)
print("T:", t_count)
print("C:", c_count)
print("G:", g_count)

This code is looking at a piece of DNA, kind of like a secret code made of four letters: A, T, C, and G.

All the program does is:

1. Takes the DNA string

2. Counts how many A’s, T’s, C’s, and G’s are inside

3. Shows you the results

And here is the result:

DNA Sequence: ATGCGTACGTTAG
A: 3
T: 4
C: 2
G: 4

Learning Python as a beginner while studying biotech has taught me that even small skills can open big opportunities.

if you’re also exploring the bridge between biology and technology, let’s connect and learn together. The future of science is computational and it’s a journey worth taking.

#Biotech #Bioinformatics #PythonProgramming #Biotechnology #TechInBiology #LearningInPublic #WomenInSTEM #STEMCareer