Casual Physics Enjoyer

I’ve done a read of How Life Works: A User’s Guide to the New Biology by Philip Ball. Here is a collection of quotes and thoughts on my first reading. I am new to this so please send corrections if you think I’ve misrepresented anything.

In the book, Ball argues that current ways of thinking about biology are too simplistic. The argument is introduced mostly in the first chapter, where he argues that we take metaphors too seriously in biology.

One of the fundamental messages of this book is that we cannot properly understand how life works through analogies or metaphorical comparison with any technology that humans have ever invented (so far) …

[current and historical ideas on biology place] a strong reliance on metaphor. To some extent that is true of all science—indeed, of all language, even all thought. But biology perhaps has greater need of it than other sciences precisely because the principles seem so hard to grasp and to articulate.

I’ve found most of this core message of the book to be in this first chapter. In particular, Ball argues against the metaphor of thinking about cells in terms of machines as we know them today, referencing Bruce Albert’s influential 1998 article “The Cell as a Collection of Protein Machines.”.

The rest of the chapters have a host of cool examples to substantiate the idea of ‘not thinking about biology in terms of our current machines’. He warns against thinking about biology in terms of networks, code, modularity, and other ideas borrowed from technology. For a beginner like me, I’ve found the examples pretty helpful in getting the magnitude of how faulty some analogies I’ve learnt are. Here are some of my picks below.

Ball argues against the idea that genes are code that dictates most of form in biology, or that it is the key to what gives living things their identity:

results of gene knockouts were often utterly bewildering. Studies might have persuaded everyone that gene X was absolutely central to the proper functioning of the organism, but then when it was knocked out, there was no apparent effect whatsoever, and the organism seemed to remain perfectly healthy. Or maybe gene Y was known to be involved in eye development, but knocking it out had no effect on eye growth; instead it resulted in a faulty immune system.

Ball uses the redundancy of our genome to illustrate how counter-intuitive things can be.

The HGP confirmed that just 2 percent or so of our genome consists of protein-coding genes, and most of the rest of the DNA was therefore considered “junk” accumulated across millennia of evolution, with no useful function

He also criticises the network picture in systems biology:

As if this isn’t already bad enough, there’s another issue with the simple network picture of systems biology. As we saw in the previous chapter, the interactions between biomolecules in the cell aren’t always beautifully selective, ensuring precise, reliable, and predictable channels down which signals and information pass. In particular, some proteins lack the well-defined shapes necessary for such exquisite molecular recognition to happen—they might bind to a variety of other molecules, with varying degrees of precision and stickiness. What’s more, that kind of promiscuity is particularly common among the molecules at the heart of the interactome

He makes sure to stress the role of disorder in biochemical processes:

The importance of disorder in our proteins was barely recognized before the mid-1990s. Biochemist Sarah Bondos and her colleagues said in 2021 that even now, because there is still little recognition in basic biochemistry and cell biology teaching of how widespread and significant disorder is in the shapes of proteins, many molecular and cell biologists are probably unaware of it. Yet, they added, it is simply not possible to fully understand how signals get passed around the cell without taking this disorder into account.

Fun fact, I co-edited a book on order and disorder with Denis Noble and Benedict Rattigan, where we talked about some of these examples in physics and biology.

To the best of my knowledge, I think most people have enough nuance to view the the book’s central claims as uncontroversial. But it is a good reminder to always try to step out of thinking automatically in metaphors, in any situation! I like however, Levin’s framing of turning things around and viewing biology as what machines could be.

I try to look for gappy areas of science, and the book is arguing that the biology right in front of us is way more gappy that people are lead to believe. I’m new to biology but slightly less new to physics, and the book is encouraging since it’s pretty bullish on multidisciplinary approaches to rethink biology (information theory, high dimensional dynamical systems, stochastic theory, Friston’s free energy principle). It is definitely energy-giving, it makes me feel welcome in trying to get into the field.

One of my favourite examples is how wacky symmetry breaking occurs in embryology

the fundamental puzzles of embryo and tissue growth is what physicists call symmetry-breaking: how a system that is initially symmetrical becomes one in which there is less symmetry. The human embryo begins as a highly symmetrical ball of seemingly identical cells, and ends up as an elaborately shaped

Or the idea that organs are considered as ‘attractors’

This view of the shape of tissues and organisms as attractors in morphospace is supported by experiments Levin has conducted on frogs. To become a frog, a tadpole has to rearrange its face. The frog genome was thought to hard-wire a set of cell movements for every facial feature. “I had doubts about this story,” Levin says, “so we made what we call Picasso tadpoles. By manipulating the electrical signals, we made tadpoles where everything was in the wrong place. It was totally messed up, like Mr. Potato Head.” And yet from this abstract rearrangement of tadpole features, normal frogs emerged.

Physics has traditionally been a huge source of inspiration for mathematics, but reading stuff like this makes me think biology could be next.

Right now I’m thinking hard about what I can actually demonstrate from this book in my home lab. Stay tuned! And thanks to David Jordan for the book recommendation!