The Use of Next-Generation Computing Technology

Technology has always been at the heart of biotech. The emergence of advanced computing technology such as machine learning and artificial intelligence enables companies to expand the scope and scale of their research and improve efficiency in the manufacturing process - both of which reduce the time it takes for biotech firms to bring new products to market.

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In medicine, for example, the ability to analyze large data sets helps drug makers identify treatments based on the cause of a disease. In addition, the evolution of cloud computing technology has removed a barrier for many innovations in biotech. The ability to run applications through the cloud allows companies to store and analyze data without buying expensive computer hardware.

This benefits early-stage startups, which try to limit operating expenses as much as possible, but it also helps larger and more established companies, as it makes it easier and cheaper to allocate resources for new projects.

Computational Biomodeling

Computational biomodeling is a field concerned with building computer models of biological systems. Computational biomodeling aims to develop and use visual simulations in order to assess the complexity of biological systems. This is accomplished through the use of specialized algorithms, and visualization software. These models allow for prediction of how systems will react under different environments. This is useful for determining if a system is robust.

A robust biological system is one that “maintain their state and functions against external and internal perturbations”, which is essential for a biological system to survive. Computational biomodeling generates a large archive of such data, allowing for analysis from multiple users.

While current techniques focus on small biological systems, researchers are working on approaches that will allow for larger networks to be analyzed and modeled. A majority of researchers believe that this will be essential in developing modern medical approaches to creating new drugs and gene therapy. A useful modelling approach is to use Petri nets via tools such as esyN.

Computational genomics

Computational genomics is a field within genomics which studies the genomes of cells and organisms. It is sometimes referred to as Computational and Statistical Genetics and encompasses much of Bioinformatics.

The Human Genome Project is one example of computational genomics. This project looks to sequence the entire human genome into a set of data. Once fully implemented, this could allow for doctors to analyze the genome of an individual patient. This opens the possibility of personalized medicine, prescribing treatments based on an individual's pre-existing genetic patterns. This project has created many similar programs. Researchers are looking to sequence the genomes of animals, plants, bacteria, and all other types of life.

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