AI invaded the Bio Technology
The year is 2020 AD, the sequencing of a complete human genome now costs less than a thousand dollars and the growth of transplantable human organs inside other animals is now gradually entering clinical trials. Advances such as this are the result of Biotechnology, which can be defined as technology based on biology. As we all know Information Technology has taken a paramount role in recent times and one major section of it which is Artificial Intelligence is rising rapidly. The combination of both these fields forms a novel topic called AI for Biotechnology.
Drug Discovery and
Development
The current process of creating a drug is a
long and time consuming one. It starts when scientists discover a biological
target after which they begin testing different chemical mixtures in different
dosages against it. Finally, they need to regulate the finished drug so that it
is compatible with a wide range of people. Machine learning can be used to
speed up lower the costs of this process. Primary stages in figuring out the drugs chemical structure and in the latter stage
investigating the effect of a drug – both in basic preclinical research and clinical trials, in
which a lot of biomedical data is produced.
Plant Biotechnology
Genetically
modified Plants have become widely popular ranging from Golden
Rice
to the Flavr Savr Tomato. Biotechnology
firms are now leveraging Artificial Intelligence and Machine Learning
techniques to develop and program autonomous robots that handle important
agricultural tasks like harvesting crops such as these at a much faster pace
than humans. Computer Vision and Deep Learning algorithms are leveraged to
process and analyze the data captured by the drones. Moreover, companies like Zymergen and Ginkgo Bioworks use
vast metagenomic databases, machine learning, and robust automated laboratories
to design microbes custom-suited to manufacture a desired good such as the
cotton in your T-shirt.
Animal Biotechnology
Much like Molly
the cloned sheep, molecular biology techniques are applied to
genetically engineer/modify animals to bring about desirable characteristics
such as higher muscle mass, and improved hardiness. Selective breeding is a
very common practice where animals with good features are bred with each other
so that their offspring will also result in the same traits. This is performed
on the molecular level too where genetic characteristics among the animals are
selected and such animals are bred. Machine Learning helps in understanding the
genomics and making informed decisions and enhancing the capabilities of
scientists in predicting the expression of those genes.
Human genomics
Each individual human has its own unique genetic footprint also known as their genome, by sequencing the genome of a human we can understand all minute details about him/her, from any allergies, to possible future medical conditions like cancer or heart disease, to even resistances and efficacies to drugs. After sequencing the size of a human genome is around 200 Gigabytes! This is only for a single person and is a lot of data to sift through manually. With advances in artificial intelligence and machine learning applications, researchers are better able to interpret and act on this genomic data.
With tools like Google’s DeepVariant, geneticists can now get
an accurate picture of the full genome while being able to detect small
mutations from random errors. This data could warn doctors of future diseases
pertaining to this individual. AI & Deep learning was instrumental in
effectively training DeepVariant.
Advantages of AI in
Biotechnology
· Time-saving - Since biotechnology is a field in
which large amounts of data are constantly gathered, keeping track, and
processing all this data would take tremendous amounts of time for humans
alone. Computers have made this task quicker, and now the advent of Artificial
Intelligence promises to shave off even more
time.
· Cost-effective – As mentioned above, crunching
through piles and piles of data will use up a considerable amount of man-hours,
If a neural network is trained to do such
tasks the need for manual labor will be nullified. Furthermore, since errors
are fewer, less money is wasted
· Scalability – An AI-powered system will be able
to adapt to new challenges better than a usual computerized system, furthermore
neural networks can be trained for a wide range of functions and then adapt
differently to specific ones.
The FUTURE.....
The future of
this marriage between these 2 titans of science is bright, from human genome
sequencing being a cheap and mandatory procedure, scientists and doctors being
able to halt a pandemic such as the recent Covid-19 in its tracks by the speedy
development of a cure, no more waiting lists for organ donations therefore no
more unnecessary deaths. And maybe humanity will finally have a chance at
tackling the big fish like world hunger and overpopulation. We must not be
afraid to venture on ahead and discover, for it is then that we truly support the
human race.