Life may be the most complex natural phenomenon that scientists have ever studied. Billions of years of evolution have resulted in intricate and deeply connected processes that we’ve only just begun to understand.

Despite our desire to learn, the traditional method of studying individual genes or proteins in isolation may be hindering greater discovery. Ivo Sbalzarini, a professor on the Faculty of Computer Science at TU Dresden, research group leader at the Max Planck Institute of Molecular Cell Biology and Genetics, and director of the Center for Systems Biology Dresden, thinks we need to broaden our horizons. For him, using supercomputing to dive into the science of systems biology is the next big step in the study of life.

“Biology over the last few decades has been the study of individual molecules, proteins, and genes,” says Sbalzarini. “Systems biology says that's not the way we should look at life’s processes. Rather, they are composed of millions of constituents, and it's their interplay and their joint actions that matter. Systems biology studies the emergence of behavior from the interactions of the parts, rather than only studying the parts themselves.”

Turning to Turing

Computational historians and computer geeks alike may be unsurprised to learn that Alan Turing was likely the first person to discuss systems biology. In his 1952 paper, The Chemical Basis of Morphogenesis, Turing attempted to explain the physical form of animals by considering them as products of reaction-diffusion systems. While this theory holds for some primitive organisms like bacteria or algae, Sbalzarini states that it is mostly insufficient in higher organisms, including humans.

"For higher organisms, a Turing system is usually not able to explain morphogenesis or any sort of more complicated behavior,” says Sbalzarini. 

In 2011, biophysicists Jonathon Howard, Stephan Grill, and Justin Bois advanced Turing’s idea by suggesting that the interplay between chemistry and mechanics can better explain biological systems.

Today, systems biologists like Sbalzarini take this one step further, by viewing cells as information processing elements. Sbalzarini compares them to microprocessors that exchange information with one another and make algorithmic decisions based on that data.

This holistic view of biology raises some very interesting questions.

For example, consider the very early development of an embryo. “One of the first things that happens after fertilization is that the cell polarizes to set up the head-tail axis of the embryo,” says Sbalzarini.

“Cells then divide and go on to different fates depending on this axis. Some form the head and others become legs and feet. But how is that very early fate decision made? In higher organisms, a combination of the location of sperm entry and a mechano-chemical signaling network inside the cell explains the decision.”

Investigating critical turning points like these could lead researchers to significant advances in health and medicine. Advances that right now still sound like science fiction. Sbalzarini points to the regenerative properties of certain lizards that are able to regrow tails or even legs.

“There is a functionality that is on in the embryo, for example to grow a finger, which then gets turned off at some later point,” he says. “But what if we could turn it back on again in an adult human? What if we had a treatment that would reprogram the cells at the edge of a wound to think that they are again in an embryo and just grow a new finger?”

And that’s just the beginning. If we could develop a computer with a billion cores, we’d be able to model systems like a developing embryo. Each core would simulate a single cell within the embryo, and we could have the cores communicate to each other as cells do.

Even a virtual brain may be possible.

“If every core simulates one neuron, then would this computer simulation be able to think?” asks Sbalzarini. “Would it display intelligence?”— a question famously asked by the Blue Brain project or the Human Brain Project.

Overcoming scientific silos

Because scientific discovery demands deep knowledge in a very specific area, researchers often become experts in one topic at the cost of learning less about other fields. This is why collaboration between disciplines is essential.

“The biggest challenge for systems biology is that computer scientists are not biologists and biologists are not computer scientists.” says Sbalzarini. “We are facing a knowledge gap that may be partially overcome through new technology. For example, we need to provide programming languages and software frameworks that make it possible for systems biologists to optimally use high-performance computing.”

That said, Sbalzarini believes these hurdles represent the exact reasons computers were developed in the first place.

“Sometimes we need a reminder that computers were invented for the purpose of scientific computation—and not around Facebook,” says Sbalzarini. “Systems biology problems have the right level of difficulty and the right level of challenge to inspire new developments in computer science. It's not just an application of computer science. It's actually in itself an inspiration for computer science.”

Read more: Inside the black box