Biology after virus: think first, count later
When I was a student at Patha Bhavan, a school that was founded by Tagore in Santiniketan, my father presented me with a book. The title of the book was Two Cultures by C.P. Snow, later Lord Snow. I discovered that Snow had a PhD in physics and became a fellow in Cambridge University. The literary bug proved too strong even for the pulls of gravity and Snow took to writing which later included a memorable profile of the mathematician Ramanujam. In Two Cultures, Snow bemoans the chasm between science and humanities and laments the fact that Britain rewarded humanities over science. Some 40 years have gone by. The strapping youth from Santiniketan is now a biologist in Bangalore. As the pandemic sweeps across the world, I am struck by a different kind of cultural divide, this time within science.
Two aspects of this health crisis strike me as a scientist – one at a “micro”, and the other at the “macro”, level. First, the micro level issue is exemplified by the question I am asked frequently – when will we have a vaccine or effective treatment? This, in turn, centres on a detailed scientific understanding of how the virus does damage, and how to prevent it. Unfortunately, this is where the word “novel” in “novel coronavirus” looms large. To quote the infamous phrase used by Donald Rumsfeld to describe the situation in Iraq – in our war against this new virus, we are stuck somewhere between “known unknowns” and “unknown unknowns”. In fact, much of the biomedical sciences, in the initial stages of research, function in this domain. And in a massive and rapidly evolving health crisis like this, the best we can do is “learn on the job”. So, this is what clinicians and biologists are doing – collecting noisy, real world data. The hope is that with enough data we will be able to make sense of it all – how the virus invades and damages body cells and gives rise to life threatening symptoms. The trouble is even within this one subclass of viruses – the coronavirus – there are enough differences that make it difficult to predict how it will affect us or what effective treatment may look like.
This is why biology is so messy. We understand the broad principles of how these little monsters attack our bodies. But one size never fits all. So, for every challenge posed by these “unknowns” we have to first collect enough data before we can devise an effective strategy to fight it. And this is largely true for how we study biological phenomena. Hence, the need to gather good data invariably precedes the process of understanding what the data “means”.
At the other end of the spectrum, the “macro” level, we face similar challenges in trying to guess how the virus may spread across large populations – how many will get infected, and how many may die or recover. All talk of “flattening the curve” and “rate of doubling”, which have now become part of our daily vocabulary, are a reflection of how theoretical models strive to predict the rate at which the disease is likely to affect us over time. No matter how sophisticated the algorithms or computers may be, all this depends on how good the data is. So, once again – data first, make sense of it later. So, at both ends – from the actions of the virus within tiny cells of the human body to its devastating impact on millions of human beings – data reigns supreme.