Understanding Computational Biology: What and How It Differs From Bioinformatics

Bioinformatics allows the study and scrunching of large biological datasets using statistics, which is fundamental to interpreting the scientific significance of experiments. For example, whether or not a specific gene is upregulated in breast cancer in a group of patients versus a non-affected or control group. Computational Biology focuses on finding solutions to biological problems raised through Bioinformatics. As such, it is the practical application of the obtained biological data. 

As both fields have emerged recently, they are often used interchangeably within job descriptions despite the above subtle differences. Computational Biology relies on Computer Science to shed light and model all processes (eg biochemical pathways) and structures (eg proteins). Indeed, scientists depend on computers to carry out Fourier analyses (mathematical data analysis method) needed to determine 3D structures of proteins using X-ray crystallography. X-ray crystallography is a structural method which uses the X-ray diffraction patterns of crystalized biological samples to determine the final structure. 

Computational Biology is widely used across different fields, some of which are listed below:

Artificial Intelligence: relies on developing machines with the ability to perform tasks that normally require human intelligence to function. As such, they are programmed to think like humans (learning and problem solving skills) or mimic their actions. 

Genetics: focuses on the study of genes, the fundamental building blocks of life which are inherited by both of your parents. Geneticists are interested in how genes are inherited from the parents to the offspring “inheritance patterns” and how certain genes are expressed differently amongst patients or healthy controls “genetic upregulation or downregulation”. 

Molecular Medicine: studies how different genes, proteins and cellular molecules interact with each other. It aims to understand why and how such molecules are altered in diseases including diabetes, cancer, neurodegeneration. This is important for early stage diagnosis and effective therapeutic options. 

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Neural Networks: depend on modeling the human brain using a set of algorithms. They help classify and cluster a large unlabeled dataset into smaller groups based on their similarities. The data is organized due to an original labeled dataset which allow the recognition of certain patterns. 

Oncology: based on the prevention (scans, genetic analysis), diagnosis (extracting biological samples, scans) and treatment (radiotherapy, chemotherapy, immunotherapy) of cancer. Cancer approximately touches 1 in 5 people and is characterised with uncontrolled cell growth and proliferation. It becomes dangerous when the cancerous cells migrate from their primary site to another body part (metastasis).