The Cold War has been over for decades, but in the dark, frigid waters deep under the oceans, nuclear armed submarines still ply their grim patrol.

These instruments of war are pinnacles of advanced engineering. They are propelled by nuclear reactors; they slide through the depths in near silence; they can remain submerged for weeks, making drinking water from the sea and creating their own breathable atmosphere. While patrolling in total darkness, these vessels are fully aware of their environment, maintaining a map of the natural world around them and any potential adversaries.

Although the systems integrated into submarines represent the most advanced fruits of human ingenuity in science and engineering, many of them have origins that are millions of years old. Subs exploit ideas generated not in the brains of men and women, but by the directionless forces of biological evolution itself.

From its overall shape, inspired by the efficient contours of fish and dolphins, to its active sonar, originating in the study of bats, the answers to problems of natural selection can be found throughout the submarine. Since ancient times, humans have sought to solve problems with technology, and they've often been inspired to do so by observing how animals and plants found elegant solutions to similar problems. In looking deeper into these solutions recently, scientists have been repeatedly astonished at the sophistication and optimal performance of evolutionary adaptations. They have discovered light sensors that can detect single photons, skin that can magically repel water, acoustic lenses that focus sound beams, and bugs that can solve calculus problems.

To put it plainly: we humans are increasingly realizing that nature offers a lot of great designs to steal.

Optimality in biology

Before understanding how we steal from nature, it's important to know why we would want to. Under some circumstances, evolution through natural selection can lead to optimal solutions to particular engineering problems faced by organisms. What this means is that, given a well-defined problem under a stable set of constraints, a series of minor adjustments acted upon by selective pressure can, over the course of millions of generations, produce something very close to the best possible solution. There is simply no way, given the materials at hand and the constraints of biology and physics, to produce a significantly better performing apparatus for the task. This is what we term “optimality.”

It might be hard to believe that the blind process of evolution can find the solutions to physics or engineering problems that it took humankind centuries of scientific progress before we could even realize they were problems, but there are many examples in biology where this has occurred. Our science only recently become sophisticated enough to even be aware that such optimal solutions are all around us in nature. Let’s look at a few examples of optimal solutions produced by evolution.

Harking back to our submarine, notice how its shape resembles that of a dolphin. It has long been thought that the

dolphin’s profile minimizes the animal’s drag as it swims at its characteristically speedy pace through the seas. But it's difficult to settle this question definitively, because dolphin locomotion is so complex. One is not pulling or pushing a rigid body through the water, as in the case of a submarine or its torpedoes, but trying to analyze an object that is constantly flexing and undulating. There is speculation that the dolphin manipulates its boundary layer (the area of flow very close to the surface of an object that has a profound effect on the forces developed between the object and the surrounding fluid), which massively complicates the analysis. Nevertheless, some models suggest that the dolphin profile is nearly optimal, so submarine designers consciously mimicked its shape.

Some of the most interesting examples of optimality in biology take the form of exquisitely sensitive and discriminating sensors. Our own eyes provide a surprising instance of this. We are all aware that our vision is not the best to be found in the animal kingdom. We can’t see in the dark like many of our pets, and we have nothing close to the acuity of a bird of prey. But inside our eyes, on our retinas, are photoreceptors that can detect individual photons. The quantum nature of light means that, for light in the visible (to us) spectrum, it is physically impossible for our photoreceptors to be any better. Why?

As Einstein showed in 1905, the interaction between light and matter (such as a retinal neuron) is not a smooth, continuously variable process. Light energy is absorbed (and emitted) in indivisible units that we now call photons. As a source of light of a particular color becomes weaker, it's not because the photons themselves are weaker—instead, the rate at which the photons arrive becomes slower. Once a sensor can respond to individual photons, it’s not missing anything and can’t be made more sensitive.

The circulatory system in our bodies and the bodies of most other animals consists of an intricate system of tubes that form a branching, tree-like structure. Consider the arterial system: near the heart are large arteries, which branch off into smaller and smaller vessels until we arrive at the capillaries. The heart must push the blood through this network of tubes by applying pressure at the beginning of the network; the resulting flow through the branching structure is governed by the complex and incompletely understood laws of fluid dynamics.

The pressure that the heart needs to apply is governed by the total resistance, or drag, of the network as a whole. This depends on several factors, and one key factor is the angle at which smaller arteries branch off of larger ones. Several investigations in mammals have shown that the particular angles at which our arteries branch is extremely close to the theoretically optimal solution. This means that any change to these angles would create a network with larger drag and require either a larger heart or place more stringent limits on the animal’s activities. The solution to this complex fluid engineering problem was solved by natural selection influencing the evolution of mammalian circulatory systems over millennia.

For our final example of optimality in biology, we’ll look at behavior rather than structure. There are examples of animals, or, in this case cooperating communities of animals, that have evolved optimal behaviors for accomplishing a particular task.

Ant colony behavior has been a subject of fascination for biologists and mathematicians for decades. It was recently discovered that ant colonies are able to solve brachistochrone problems. These are problems where one needs to find the fastest path between two points. If everything is uniform between you and your destination, this path is, obviously, a straight line. But if the terrain varies somewhere along the way, such that your speed of travel is different in different areas, then you need something called the “calculus of variations” to find the best path.

A classic example is the lifeguard problem: the guard is standing on the beach and needs to reach a drowning swimmer as fast as possible. He can run on sand faster than he can swim. Proceeding in a straight line drawn between his starting position and the swimmer, although the shortest distance, is not the fastest path. The solution involves following a longer path on the beach and changing direction when entering the water. The path is “refracted” at the water’s edge (the refraction of light happens because light rays also follow the fastest path when traveling through media where the speed of light varies).

Ants don’t know the calculus of variations. They don’t “know” anything. Yet the colony’s collective behavior routinely finds the correct solution to the brachistochrone problem when traveling between home and a food source across varied terrain. And since a solution to a brachistochrone problem is, by definition, an optimal path, this is an example of optimality in biology.