Examing Gene Expression Through Computational Biology

Gene expression was a topic I heavily struggled with in my Advanced Placement biology class, which seems to be a pretty common obstacle for most people to overcome in many biology courses. Gene expression doesn't fit well within most biology curricula; it's placed awkwardly since it's sandwiched between topics about cells and cell processes.

However, in the realm of bioengineering (especially computational biology and computational genomics - two very useful sub-fields), gene expression couldn't be talked about at a better time. In this article, I'll first be talking about what the aformentioned terms are, and then be sharing how relevant and intertwined the two are to each other.

To get an idea of what computational biology is, check out my intro to computational biology post: https://www.lavinthelab.com/post/computational-biology-an-introduction.

As for gene expression? Continue reading!

Gene expression is the fundamental process by which the information encoded in a gene is, well, expressed, and iused to synthesize products that our body can understand: proteins. Gene expression involves two main stages: transcription and translation (a shaky, traumatic flashback appears to my freshman year AP Biology course. LOL).

In the first step, transcription, the DNA sequence of a gene is copied into mRNA (another topic I'll discuss in a blog post!) in the cell nucleus. Enyzmes called RNA polymerase bind to the DNA and unwind, or unzip it, to create an RNA strand. Once mRNA is synthesized, a 5' cap and poly-A tail is added. The unnecessary, extra sequences, that are called introns, are removed. The processed mRNA then exits the nucleus and enters the cytoplasm, where translation occurs.

In translation, ribosomes will read the mRNA sequence in sets of three - known as codons. Each codon (or again, three mRNAs) correspond to a specific amino acid, and amino acids together create proteins. Transfer RNA (tRNA) molecules bring the corresponding amino acid to the ribosome, and they are linked into a long polypeptide chain, which eventually folds and twists and becomes a protein.

So essentially, gene expression is the process of creating DNA to protein. Think of it this way: you're the DNA and you want your coworkers (body) to do something, but they won't listen to you. Who will you hire? The boss! (Proteins). The boss will instruct all the body parts what to do and essential biological functions can now be executed.

Now that we know what gene expression and computational biology are, hopefully, we can get a feel for how they're similar. Gene expression and computational biology share some fundamental similarities that allow the two to be intertwined so seamlessly into such a broad field of study. First off, both fields rely heavily on data. The essence of computational biology (especially ML models) is taking advantage of computers and their ability to process biological data and further biological research. Similarly, gene expression "generalizes" many biological phenomena and trends, and requires large datasets (ex: RNA-Seq data) that must be analyzed. Both fields also employ statistical methods to interpret data. In gene expression analysis, statistical techniques are used to identify differentially expressed genes. Computational biology uses statistics for analysis in population genetics and systems biology. Additionally, many trees, diagrams, and visual connections surrounding gene expression can easily be modeled and analyzed through neural networks (blog post on neural networks coming up!) Gene expression also fits very well in computational biology since gene expression can be integrated with omics data (proteomics, betabolomics), and analysis of gene expression is best accomplished through the principles that make computational biology possible (calculus, statistics, and visual modeling).

I hope this blog post got you excited for some computational genomics modeling. In my next blog post, I'll talk about the different types of RNAs, and how we can model them computationally!