Biotechnology is a curious connection between two apparently different worlds. On the one hand living organisms, wild and unpredictable heavenly creations that can probably never be understood or appreciated enough, on the other technology, a cold and artificial entity that exists to bring convenience, structure and mathematical security into human lives. However, contrast works well in combination with biotechnology, which is essential in both healthcare and medicine. —Exploring the deep sea, protein synthesis, regulating food quality and preventing environmental degradation. AI’s growing engagement in biotechnology is one of the main reasons for its growing scope.
So how exactly is AI affecting biotechnology? For starters, AI fits perfectly with the dichotomous nature of biotechnology. After all, technology contains its own duality: the efficiency of a machine combined with the eerily animalistic unpredictability of its operation. Generally speaking, companies and experts involved in biotechnology are using AI to improve the quality of research and improve compliance with regulatory standards.
More specifically, AI improves data acquisition, analysis, and pattern recognition in the following biotechnological applications:
Discovering and Monitoring New Marine Organisms
It is estimated that there are 15 million different types of living organisms on the planet, of which only 2 million have yet to be discovered. In addition, around 80% of the world’s rivers and oceans have not been mapped or explored to date. Basically, this means that freshwater and marine ecosystems can contain the greatest number of unknown plant and animal species known to man. for industrial and ecological development, growth and maintenance.These discoveries are at the heart of marine biotechnology research. The newly discovered organisms are used in industry to create new compounds and raw materials, which are then made into vital ingredients in finished products such as new medicines, foods and beauty products. Fishing companies will also benefit from the discovery of new species of fish as they can sell them to increase their profit. From an ecological point of view, the monitoring will help to understand the biodiversity in the oceans and to take measures to protect rare species. Marine research contributes to the preservation of the biodiversity of the oceans.
At the same time, exploring the deep sea is anything but easy, even for large and ingenious companies: researchers have to spend thousands of hours underwater without much maintenance support, even when they are clad in protective clothing with oxygen masks. After spending so much time in an underwater environment with extreme pressure, they may cramp to aid their breathing. In addition, the oceans and seas contain highly saline and corrosive water that can damage your protective gear, mining machinery, and research equipment.
The inclusion of artificial intelligence in biotechnology enables deep-sea researchers to meet most of these challenges. Researchers can use scout robots and virtual reality to remotely conduct scouting operations. The combination of computer vision, robotics, virtual reality and artificial intelligence in biotechnology research enables deep sea researchers to find new microscopic elements. Species of living organisms in areas that human explorers could not reach.
There are already several projects that have developed and trained AI algorithms designed and trained for marine research. Microscopic organisms are useful to assess marine properties such as temperature, acidity, salinity, and nutrient concentration of specific regions. In such projects, the use of AI and RV enables researchers to accurately reconstruct ocean circulation and heat flow patterns in order to understand underwater weather patterns and responses.
Creating New Proteins
At some point you may have heard the saying: “Proteins are the building blocks of life”. While it’s a cliché, that line is true. As you probably know, proteins allow you to maintain constant fluid balance and pH, support the structural framework of your body, and build and repair your cells and tissues. Additionally, there are several proteins that they haven’t yet created or discovered. The production of proteins is a necessary task for health, pharmacy and research, as well as the development of consumer products.
One of the biggest challenges in making artificial proteins is the complexity involved. Proteins are rarely, if ever, simple, small-numbered compounds. A simple organic cell contains around 42 million protein molecules. The creation of proteins involves the analysis of data and patterns. Detecting Large Amounts of Information to Extract Information Again, the AI ​​is doing the process because it can scan, ingest, clean, analyze, and deliver data by scanning a large number of data sets that are used to train the algorithms used in these operations.
In addition to the complexity, there is the precise analysis of the chemical 3D structure of the proteins, which determines their function. Traditionally, scientists have used existing protein shapes and patterns to create new ones. In the data-driven process, they change the amino acids that make up a protein. Machine learning can use computer recordings to find physicochemical simulations to generate 3D models of a new protein made based on the amino acid sequence and the new protein.
An example of artificial intelligence used to create proteins is the development of drugs to alleviate diseases such as cancer. Researchers at Chalmers University of Technology in Sweden have developed an algorithm-based tool capable of generating new proteins. Researchers believed that the current model of randomly introducing mutations into protein sequences continued to increase the cost of synthesis. Their tool, called ProteinGAN, uses generative deep learning to perform the task effectively.
In the method, the researchers feed the AI ​​algorithm with millions of images and other types of data on well-studied proteins. Over time, machine learning learns the common patterns in each protein. With this knowledge, new proteins can be made. During creation, the tool is also trained to determine whether synthetic proteins are real or fake, by making repeated comparisons with natural proteins to the point where you can no longer differentiate, so that laboratory-made proteins retain their properties to keep their naturalistic counterparts.
The complex nature of proteins and their manufacture add to the cost of their formulation. ProteinGAN and other similar models can save protein designers a huge amount of money by eliminating the labor in the process and dramatically reducing time.
The next challenge in the synthesis of artificial proteins is the use of AI in biotechnology to improve the properties of the proteins produced, for example reducing the volatility, which will be useful for a stable research operation with these proteins .
Creating Genetically Modified Foods
As you may know, meat consumption is a major contributor to environmental degradation through the generation of greenhouse gases. Therefore, sustainability through the consumption of genetically modified foods instead of meat products is a growing concept today. In the United States, a survey found that about 40 percent of Americans have tried plant-based meat foods that were genetically created in the lab. A similar application to the creation of artificial proteins is the production of such genetically modified foods. Like protein, genetically modified foods, also known as genetically modified foods, are complicated to develop and produce on a large scale using traditional methods.
The use of AI in biotechnology also plays a prominent role in the production of transgenic food. An American manufacturer of genetically modified foods called Yield10 uses its technology platform called GRAIN, an acronym for Gene Ranking Artificial Intelligence Network, to identify genomic targets to improve crop yields. The system uses a plant’s metabolic cycle to study how it can be changed. GRRAIN sifts through billions of publicly and privately available data sets before obtaining conclusive information on specifically identified genes. Essentially, the identified genes in certain plants can be modified to improve them. These GM plants and foods can be considered as alternatives to meat products, which indirectly reduces environmental degradation.
Perhaps the greatest benefit of AI in biotechnology is its impact on the planet. Almost all new technological developments have played a minimal role in preserving the environment over the years, but have easily contributed to increasing degradation. On the other hand, AI can be used in biotechnology. Operations and projects to monitor environmental changes and reverse damage, or at least mitigate its effects, in order to keep the habitability of our planet intact in the long term.
