Field of Science

Can we turn biology into engineering?

Vijay Pande of Andreessen-Horowitz/Stanford has a thought-provoking piece in Scientific American in which he lays out a game plan for how we could potentially make biology more like engineering. He takes issue with what Derek (Lowe) once called the “Andy Grove fallacy”, which in a nutshell says that you can make fields like biotechnology and drug discovery as efficient as semiconductor or automobile engineering if you borrow principles from engineering.

There are parts of the piece that I resoundingly agree with; for instance, there’s little doubt that fields like automation and AI are going to have a significant impact on making biological experiments more reproducible, many of which are still more art than science and subject to the whims and sloppiness of their creators and their lab notebooks. Vijay is also optimistic about making biology more modular, so that one can string along parts of molecules, cells and organelles to enable better biological engineering of body parts, drugs and genetic systems. He also believes that bringing more quantitative measurements encoded into key performance indicators (KPI) will make the discipline more efficient and more mindful of its successes and failures. One point which I think is very important is that these kinds of approaches would allow us to gather more negative data, a data collection problem that still hobbles AI and machine learning approaches.

So far I am with him; I don’t believe that biology can’t ever benefit from such approaches, and it’s certainly true that the applications of AI, automation and other engineering-based approaches are only going to increase with time. But the article doesn’t mention some very fundamental differences between biology and engineering which I think demarcate the two substantially from each other and which make knowledge transfer between them highly problematic.

Foremost among these are non-linearity, redundancy and emergence.

Let’s take two examples which the piece talks about that illustrate all three concepts – building bridges and the Apollo project. Comparisons with the latter always make me wince a bit. Vijay is quite right that the right approach to the Apollo program was to break the problems into parts, then further break those parts up into individual steps such as building small models. The scientists and engineers working on the program gradually built up layers of complexity until the model that they tested was the moon landing itself.

Now, the fact is that we already do this in biology. For instance, when we want to understand or treat a disease, we try to break it down to simpler levels – organelles, cells, proteins, genes – and then try to understand and modulate each of these entities. We use animal models like genetically engineered mice and dogs as simpler representatives of complex human biology. But firstly - and as we keep on finding out - these models are pale shadows of true human biology; we use them because we can't do better. And secondly, even these ‘simple’ models are much more complex than we think. The reasons are non-linearity and emergence, both of which can thwart modular approaches. The sum of proteins in a cell is not the same as the cell phenotype itself, just like the sum of neurons in a human brain is not the brain itself. So modulating a protein for instance can cause complex downstream effects that depend on both the strength and nature of the modulating signal. In addition, biological pathways are redundant, so modulating one can cause another one to take over, or for the pathway to switch between complex networks. Many parts downstream, even ones that don’t seem to be directly connected, can interact with each other through complex, non-linear feedback through far-flung networks.

This is very unlike engineering. The equivalent of these unpredictable consequences in building a bridge, for example, would be for a second bridge to sprout out of nowhere when the first one is built, or the rock on the other side of the river suddenly turning from metamorphic to sedimentary, or the sum of weights of two parallel beams on the bridge being more than what simple addition would suggest. Or imagine the Apollo rocket suddenly accelerating to ten times its speed when the booster rockets fall off. Or the shape of the reentry vehicle suddenly changing through some weird feedback mechanisms as it reaches a certain temperature when it’s hurtling through the atmosphere. 

Whatever the complexities of challenging engineering projects like building rockets or bridges, they are still highly predictable compared to the effects of engineering biology. The fact of the matter is that the laws of aerodynamics and gravity were extremely well understood before the Apollo program (literally) took off the ground, so as amazing as the achievement was, it didn't involve discovering new basic scientific laws on the fly, something that we do a lot in biology. Aircraft design is decidedly not drug design. And all this is simply a product of ignorance, ignorance of the laws of biology and evolution – a clunky, suboptimal, haphazard, opportunistic process if there ever was one – relative to the laws of (largely predictable) Newtonian physics that underlie engineering problems.

The concept of modularity in biology therefore becomes very tricky compared to engineering. There is some modularity in biology for sure, but it’s not going to take you all the way. One of the reasons is that unlike modularity in engineering, biological modularity is flexible, both spatially and temporally. This is again a consequence of different levels of emergence. For instance, a long time ago we thought that the brain was modular and that the fundamental modules were neurons. This view has now changed and we think that it’s networks of neurons that are the basic modules. But we don’t even think that these modular networks are fixed through space and time; they likely form, dissolve and change members and locations in the brain according to need, much like political groups fleetingly forming and breaking apart for convenience. The problem is that we don’t know what level of modularity is relevant to addressing a particular problem. For instance, is the right ‘module’ for thinking about Alzheimer’s disease the beta-amyloid protein, or is it the mitochondria and its redox state, or is it the gut-brain axis and the microbiome? In addition, modules in biology are again non-linear, so the effects from combining two modules are not going to simply be twice the effects of one module – they can be twice or half or even zero.

Now, having noted all these problems, I certainly don’t think that biology cannot benefit at all from the principles of engineering. For one thing, I have always thought that biologists should really take the “move fast and break things” philosophy of software engineering to heart; we simply don’t spend enough time trying to break and falsify hypotheses, and this leads to a lot of attrition and time chasing ghosts down rabbit holes. More importantly though, as a big fan of tool-driven scientific revolutions, I do believe that inventing tools like CRISPR and sequencing will allow us to study biological systems at an increasingly fine-grained level. They will allow us to gather more measurements that would allow better AL/machine learning models, and I am all for this.

But all this will work as far as we realize that the real problem is not improving the measurements, it’s knowing what measurements to make in the first place. Otherwise we find ourselves in the classic position of the drunkard trying to find his keys below the lamp, because that’s where the light is. Inventing better lamps, or a metal detector for that matter, is not going to help if we are looking in the wrong place for the keys. Or looking for keys when we should really be looking for a muffin.


  1. Great article. An example that hits a couple of the checkboxes is the estrogen inhibitor examples, where hormone signalling often has bimodal shapes, defying the simple "dose makes the poison"- an effect at low and high doses, and a middle portion with low effect at "medium" doses. (I'm missing references to the original scientist(s)- I believe the original work was done close to, or in Michigan lakes, rivers, or waterways, and published in the early '90's in the ecology literature).

    And bizarre impacts on real fish starting out male and turning female.

    Non-linear toxicology

    1. Great example. Assuming a linear dose-response relationship won't work in that case.


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