Two young scientists in a lab with test tubes

BIOTECH AWAY FROM THE BENCH

Programming at the forefront of Biotechnology

Today, biology and empiricism are being transformed: We are replacing theory with data itself to advance engineering in biology.

June 7, 2021

BY KATHLEEN DUFFY

Biotech Away From the Bench

If the field of Bioinformatics/Computational Biology (BCB) has taught us one thing, it is that novel cutting-edge technologies emerge at the conjunction of once separated spheres of research. This specialized field synergistically brings together the theories of computer sciences with those of life sciences, a school of study which existed centuries before the first computer ever saw the light of day. Old school biology ran on empiricism- running physical experiments again and again until they produced results. This theory succeeded in many ways; however, this approach can also waste billions of dollars in the drug development process, especially given the immense complexity of living organisms.

Luckily for us, the benefits of computation — i.e., software, machine learning and AI are allowing new entrepreneurs to develop breakthroughs away from the lab bench, providing a powerful, frictionless, and far more cost-effective ways for research.

Applications of machine learning and AI to the core principles of life science will help us understand biology far greater than human ever did, bringing us closer to solving some of the biggest health issues, and opening doors to exploring new spaces in biology, all without ever picking up a pipette.

The New Era of Biology

Before the modern computer age, biologists performed their research in the lab, relying on living model organisms and careful conditions, as well as plenty of time and funding.

Through the 20th century, new technologies began to provide researchers more control of biology’s complex systems as science advanced. For instance, the advent of recombinant DNA, DNA combined from two or more sources, allowed biologists to harness the power of genetics to create useful products. In 1978, Genentech made history with insulin produced from bacteria via insertion of recombinant DNA. Advances like this led to the notion of programmable biology: re-engineering cells using the code of DNA, as if they were mini super-computers, to create a new breed of therapeutics.

In the following decades, innovation in biotechnology expanded to focus on this kind of genetic engineering, creating medicines from the programming of cells through their DNA, rather than the simple application of molecules alone, as was done before.

Today, biology and empiricism are being transformed: We are replacing theory with data itself to advance engineering in biology.

Software programs are helping researchers in life sciences every day, providing crucial tools that accelerate the processes of research and development ever faster. BCB milestones such as the Human Genome Project, a whole map of the human genome completed from the 1990’s to the 2000’s, have enabled great bounds forward in fields like genomics.

Thanks to data like this, we have seen new discoveries and technologies take form completely in the digital world, away from the lab benches of traditional biology.

Also, the rise of numerous, novel quantitative measurements of biology — i.e., big data sets in biology — has opened the door to incorporating other engineering approaches. Biologists and developers can now build entirely new things from scratch. Simulation software and databases of genes allow studies to begin on the fast track with unprecedented accuracy. Programing is facilitating the programming of organisms, engineering biological circuits. New companies are specializing in this art of computational biology, analyzing critical biological data, and creating programs for the research and development of new biotechnology.

The New era built on a new Mindset

The time and costs of advancing biotechnology has drastically decreased since the completion of the Human Genome Project nearly 20 years ago. New data and software for life sciences requires much less time and resources today, helping us design biology using software engineering principles of modularity, abstraction, and hierarchy.

Today, students in biotechnology and medical sciences often carry a tech-savvy mindset that sets them apart from previous generations.

Many of these university students can use computer programming languages and bioinformatics software, or at least discuss these topics in depth. Biology labs in many major universities have expanded to include rooms with rows of desktop computers. Here, students researching life sciences can utilize modern programs like the Benchling bioinformatics platform to piece together genetic sequences found in vast online databases like GenBank to build digital models of recombinant DNA vectors. These recombinant vectors could theoretically go on to treat genetic diseases or re-engineer the DNA of cells and microbes, using contemporary genetic engineering technologies like CRISPR, which make the once daunting process of editing cells’ DNA quicker, more reproducible, and less costly than ever before.

These students can easily access all of the research, literature, genetic sequences, and resources needed to complete such a task without leaving the computer. New software programs continue to make the expansion of this information easier, allowing more and more genetic data to expand in biotechnology.

This digital framework improves the reproducibility of results. Conditions can be reproduced in computer biology platforms much more readily than could ever occur in a traditional lab setting. For example, companies such as Asimov.ai, Interax AG, and Viome are all using a systems biology first approach to applying ML/AI to biotechnology. This allows them to have a more complete digital model that can be reproduced in silico with ease. Reproducibility can help ease regulatory hurdles, increase trust, and allow even greater collaboration.

The new age of applications

We have seen that artificial intelligence too can find new purposes in the life sciences, as clever innovators find ways to give these modern miracles of technology unique purposes in all fields of science. This year, a novel visual AI system known as AI-Scan, originally designed for a program to identify fresh, unwrapped pastries at bakery checkouts, became the basis of a new AI program that could efficiently identify cancer cells. The serendipitous story swept the internet, featuring on a variety of news sites, magazines, and social media platforms as the surprising tale captured all types of audiences. The value of computer-based innovations like this lies in their potential to power new ventures in a variety of fields.

Anyone with a background in life sciences could tell you of many more scenarios where an AI could make their jobs easier, and thus accelerate biotech developments. To name a couple possibilities, an AI might learn to identify types of bacteria growing on agar plates, or analyze the growth of cells in a culture. AI software could contribute to the field of BCB as well, helping expand the role of software’s in life sciences even further.

A clever AI program might sort through data and information in bioinformatics systems and databases quicker than humans or currently existing programs could, and help experts analyzing data with far better predictive power. Such activity already simplifies the daunting task of sorting through endless amounts of genetic data for researchers.

With better reproducibility, accuracy and gradual improvement over time is adding up to even more massive advancements.

How NODES is supporting this new Era

The cycles of innovation in science are accelerating, the science is moving much more quickly than it has in the past.

We should all look forward to the promise of software engineering and machine learning AI at the forefront of life sciences. Each new application of software in biotechnology is taking us to other emerging areas with unexplored territory in biology: new breakthroughs in fighting a debilitating disease, or to new ways of conducting biology that improve the productivity of anyone in the field, or perhaps even to all of these advancements at once.

New technologies serve as an important complement to traditional methods of biological research, setting apart modern biology from its predecessors. Novel computer software and AI systems help researchers break down the incredible complexity of biology like never before, enabling them to better understand the processes of living organisms and how to engineer them.

Science has reached an age in which computer intelligence will lead us onward and upward into greater technological achievements, hand-in-hand with the pioneers in research and entrepreneurs that create and perfect these programs. Business investors, scientists, academics, and all of humanity should turn their eyes towards the computer revolution with excitement.

Whether detecting cancer early or identify universal biomarkers, NODES works with innovators in the biotech space to deliver on this new hope. We celebrate the great successes of recent decades and express optimism for the years to come. NODES is helping to pave the way to a greater future by supporting entrepreneurs in the space where biology meets technology.