Commentary on "Can a biologist fix a radio?"

I was recently assigned to write a couple of pages commenting on the famous paper "Can a biologist fix a radio?" from 2002, commenting on the importance of systems biology. I think it's highly relevant to modern neuroscience, where we seem to be encountering what Lazebnik (the author of the paper - http://www.cell.com/cancer-cell/abstract/S1535-6108(02)00133-2) calls David's Paradox: every year we have more and more newer and better findings, but we don't seem to be getting much closer to a holistic understanding of how the brain works.

This is what I had to say: 

Lazebnik’s 2002 paper leads to some very important conclusions that I believe are ultimately right, but I fundamentally disagree his arguments from the radio analogy. He seems to believe that the task facing a biologist trying to understand a cell is similar to that facing an engineer trying to understand a radio. I would claim that this is simply false.

The first obvious reason is that cells indeed are much more complex than radios. Lazebnik seems to believe this is an overstatement, claiming that biologists would also find a radio to be vastly more complex than any engineer would judge it to be. I would argue that, in general, biological phenomena pose significantly more complex scientific problems than their physical counterparts. Maybe this only seems to be the case because as of yet we don’t have many universal principles or laws to explain them. But I think this is mainly due to the fact that biology really is that complicated – even the most universal principles we find tend to have dramatic exceptions (e.g. ploidy, epigenetics, extremophiles). Physics, on the other hand, tends to have an easier time postulating principles that hold across all instances of a given phenomenon (e.g. electromagnetism). There’s a reason why we’ve been able to tackle physics questions using mathematical formulae for about 2000 years, while mathematical modeling has only arisen as a tool in biology in the past couple of decades. Because life is such an active process, the number of components interacting together leads to systems that are much harder to explain than transistors and capacitors carefully wired together to transform electromagnetic waves into sound waves.

Cell and radio science differ at a much more fundamental level, however. Even if you forget the complexity argument I put forth above, there is a sense in which the problem of understanding a cell fundamentally differs from the problem of understanding a radio: since we built them, we know exactly what radios do. But we don’t actually know what cells are for! We know that they are useful for survival – otherwise they wouldn’t have evolved. But we don’t even know if they are the optimal solution to the problem of survival (since natural selection is a satisficing, rather than optimizing, process), and we can only guess as to how it is that they provide an evolutionary advantage. Certain signal transduction pathways may seem to provide immunological benefits, and others may look like they serve purely metabolic purposes. But fundamentally we can’t actually know what a biological process is for, we can only hypothesize. An engineer approaching a radio, on the other hand, knows exactly what a radio is for. More than that, she also knows what capacitors and transistors are for.

Note how crucial this information is to repairing or understanding an object of study. The engineer can approach the open radio and think about what processes are necessary to transform electromagnetic waves into sound waves. She can make some hypotheses about what components are necessary to implement these processes and then look for them in the machine. She can also interpret the consequences of removing certain components, thus aiding her understanding of what each component does. But a biologist can do none of this. When a biologist removes a component from the system, they may have no idea what has gone wrong. This simple fact enables Lazebnik’s cartoon: it is hard for a biologist to infer anything deeper than whether the machine still works, leading to such simple classifications as most important/really important/undoubtedly most important components. A more nuanced understanding of the deficiencies of the system is impossible without knowing what the system is actually supposed to do.

Before going on to describe how I would approach the radio problem, I pause to note how this is particularly true for brain science. We don’t really have any idea of what the brain’s components are actually for. Sensory neuroscientists seem to pretend to know what sensory systems are for, but we don’t actually even know that. Is the visual system designed to minimize the error between percepts and the real world? (Hoffman, 2009) Is it designed to learn a generative model of the statistics observed in the natural world? (Schwartz et al., 2007) Is it designed to constantly predict what will appear in the visual scene? (Friston, 2009) This idea in fact stands at odds with one of the central tenets of cognitive science: Marr’s three levels of analysis. David Marr contended that to understand the brain we must start by specifying what it is meant to be doing (the “computational level”). But, unlike a radio or a cash register (Marr’s own analogy; Marr, 1980), we can’t know what the brain does. Presumably, all we really know for sure is that it evolved through natural selection, so must be useful for survival. But we can’t interrogate how or why natural selection did what it did, only speculate.

It is for this reason that repairing or understanding a cell is inherently a different problem from repairing a radio. If I were to repair a radio, I would take as a starting point its purpose. Then, I would approach the problem of transforming electromagnetic waves into sound waves from first principles, trying to postulate properties that the radio must have to be able to do this. Then I would open it up and see if I can find anything that endows the radio with these properties. If, through my exploratory analyses, I were to find some other principles at play inside, I would try to see how these fit into a solution to the overarching problem of transforming radio waves to sound waves. Note that this approach relies on the fact that I know what this overarching problem is. It allows me to interpret what I find in the radio and to execute my dissection in a guided and principled manner.

Biologists don’t have this luxury. We need to uncover the overarching principles from the ground up. However, this does not mean that our dissection should comprise the core of our investigation. On the contrary, it is crucial that we maintain an active theoretical examination to allow experimental findings to build on each other so we can eventually reach the universal principles and answer the questions about functional significance (which lead to practical repairs). This is where I coincide with Lazebnik. There is no way the experimental findings can build on each other without precisely formulated theories. As he aptly articulates in the article, such theories are only possible with a universal formal language such explicit wiring diagrams, or mathematics.

REFERENCES

Friston, K. (2009). The free-energy principle: a rough guide to the brain?. Trends in cognitive sciences, 13(7), 293-301.

D. Hoffman. The interface theory of perception: Natural selection drives true perception to swift extinction. In Object categorization: Computer and human vision perspectives, S. Dickinson, M. Tarr, A. Leonardis, B. Schiele (Eds.) Cambridge, UK: Cambridge University Press, 2009, 148–165

Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information.

Schwartz, O., Hsu, A., & Dayan, P. (2007). Space and time in visual context.Nature Reviews Neuroscience, 8(7), 522-535.