Computational Biology

By Vidyawini Ganapathy


The National Institutes of Health define computational biology as the development and application of data-analytical and theoretical methods, mathematical modelling and computational simulation techniques to the study of biological, behavioural, and social systems. In simple terms, computational biology is the use of computational methods to understand and solve problems in biology.

As early as the 1970s, biological researchers began to recognise the ability of computers to process large sets of biological data. This coincided with breakthroughs in artificial intelligence, which also used network techniques to understand how the human brain works. Through the years, the amount of data available to researchers also grew exponentially. By the 1990s, computational biology emerged as a branch of study that used computational methods to model biological data.

Though the term ‘computational biology’ is often used interchangeably with bioinformatics, the two differ. Bioinformatics include research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioural or health data, including those to acquire, store, organize, archive, analyse or visualise such data. Bioinformatics uses data science to store and study large datasets, called biodata. Computational biology uses computational approaches to understand and solve problems identified through bioinformatics.



Molecular dynamics simulation of trypsin


Applications:

  • Gene sequencing and editing:

The genome is the collection of all of a person's genes. Genes are a set of instructions that determine what an organism is like, its appearance, how it survives and how it behaves in its environment. Genes help determine a variety of things, ranging from eye colour to blood group to diseases a person can inherit.

Sequencing the human genome means that genetic disorders can be identified and subsequently treated through gene editing therapy. A one-year old girl who had acute lymphoblastic leukaemia underwent gene therapy to receive genetically modified T-cells. She was the first person whose life had been saved by gene editing.

The Human Genome Project, the world's largest collaborative biological project, was launched to sequence the entire human genome. The project concluded in April 2003.

Genome sequencing could also be used in:

  1. Personalised/precision medicine, in which medical treatment is tailored to the individual characteristics of each patient

  2. Biomarker testing for cancer (a part of precision oncology), which looks for certain ‘biomarkers’ in a patient’s cancer to ensure the most effective treatment

  3. Agricultural development, which involves editing of crop genomes to ensure higher yield, pest resistance and better adaptation to environmental conditions

  4. Understanding the interrelation between organisms, which in turn could help us understand evolution

The branch of computational biology that deals with gene sequencing and editing is called computational genomics.

  • Drug discovery:

Eroom’s Law states that “the number of new drugs approved per billion U.S. dollars spent on R&D has halved roughly every 9 years since 1950, falling around 80-fold in inflation-adjusted terms.” It describes the exponentially increasing cost of developing drugs. The pharmaceutical industry devoted $82.9 billion to R&D expenditures in 2019, up from about $2.3 billion in 1981.

Each successive phase of trials to develop a drug requires more funding. Pre-clinical trials are performed primarily on mice. This is the least expensive phase of trials. After the pre-clinical trial, the Food and Drug Administration (FDA) mandates a three-phase clinical trial. Each phase of this clinical trial can cost $100 million. Around 1 in 10 medicines being tested in the FDA-mandated trial get approved. A drug that passes each of the three phases applies with the FDA’s New Drug Application, which can cost between $1.4 million and $2.9 million in 2021.

Modelling and simulations can help predict the outcomes of a particular drug while lowering the expenditure needed to test it. This branch of study, called pharmacokinetic-pharmacodynamic modelling, can describe the dose-concentration-response relationship between a drug and the human body. Computational methods can reduce the cost of drug development by up to 50%.

With advances in genomics, drugs are also being manufactured for specific groups of people classified based on their requirements and predicted response. Testing drugs only on people belonging to a specific subset can help decrease costs by eliminating unnecessary trials.

  • Precision medicine:

Precision medicine aims to develop effective treatment plans that are tailored to each patient. It involves classifying people into subpopulations based on the presence/absence of a certain gene, susceptibility to a disease or response to a particular treatment. Precision medicine relies on large datasets gathered on diseases that detail their environmental, pharmacological, molecular and prognostic information. These large datasets and the outcomes of the diseases are then run through predictive models that would assign likelihood measures of individuals contracting the disease and possible treatments with better outcomes. Since such research and prediction is contingent on large numbers of data that encompass a wide range of factors to establish confidence percentages, computational methods go a long way in reducing time and increasing the precision of disease prediction and treatment pathways.

Precision oncology, used to treat cancer, is based on identifying certain gene mutations and understanding their effects. Cancers are caused when genes undergo mutations, causing cells to divide uncontrollably. Gene mutations can affect the tumour’s response to different treatments.

Through genetic testing for cancers, the gene variant can be identified. This can help decide which therapy should be used to treat the cancer—some of them would be targeted therapy, immunotherapy, chemotherapy and hormonal therapy. Identifying and understanding the gene mutation can ensure an effective treatment for the patient.

People who are at high risk for certain cancers can also take preventive measures to decrease their cancer risk. For example, women who have the hereditary BRCA1 or BRCA2 gene have a higher risk of developing breast cancer or ovarian cancer. They may choose to undergo a prophylactic mastectomy, surgery that removes breast tissue, or a prophylactic oophorectomy, surgery that removes the ovaries, to reduce their risk.

Genetic testing can be used to treat and prevent breast cancer, ovarian cancer, colorectal cancer and prostate cancer.



References:

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Image:

  1. Okimoto, N., Futatsugi, N., Fuji, H. et al. (2009, October 9). System for MD simulation of trypsin [Simulated Photograph]. Public Library of Science. Retrieved 13 August, 2021 from https://doi.org/10.1371/journal.pcbi.1000528.g006