What makes matter alive

Most people do not consider crystals to be alive, for example, yet they are highly organized and they grow. Fire, too, consumes energy and gets bigger. In contrast, bacteria, tardigrades and even some crustaceans can enter long periods of dormancy during which they are not growing, metabolizing or changing at all, yet are not technically dead.

How do we categorize a single leaf that has fallen from a tree? Most people would agree that, when attached to a tree, a leaf is alive: its many cells work tirelessly to turn sunlight, carbon dioxide and water into food, among other duties.

When a leaf detaches from a tree, its cells do not instantly cease their activities. Does it die on the way to the ground; or when it hits the ground; or when all its individual cells finally expire?

If you pluck a leaf from a plant and keep its cells nourished and happy inside a lab, is that life? Such dilemmas plague just about every proposed feature of life. Responding to the environment is not a talent limited to living organisms—we have designed countless machines that do just that. Even reproduction does not define a living thing.

Many an individual animal cannot reproduce on its own. So are two cats alive because they can create new cats together, but a single cat is not alive because it cannot propagate its genes by itself? Consider, also, the unusual case of turritopsis nutricula , the immortal jellyfish, which can indefinitely alternate between its adult form and its juvenile stage. A jelly vacillating in this way is not producing offspring, cloning itself or even aging in the typical fashion—yet most people would concede it remains alive.

But what about evolution? During discussions about how best to find life on other worlds, Joyce and his fellow panelists came up with a widely cited working definition of life : a self-sustaining system capable of Darwinian evolution. But does it work? Let's examine how this definition handles viruses, which have complicated the quest to define life more than any other entity. Viruses are essentially strands of DNA or RNA packaged inside a protein shell; they do not have cells or a metabolism, but they do have genes and they can evolve.

Because of this constraint, he argues that viruses do not satisfy the working definition. After all, a virus must invade and hijack a cell in order to make copies of itself. When you really think about it, though, NASA's working definition of life is not able to accommodate the ambiguity of viruses better than any other proposed definition.

Likewise, a virus has all the genetic information required to replicate itself, but does not have all the requisite cellular machinery. Both the worm and virus reproduce and evolve only "in the context" of their hosts. In fact, the virus is a much more efficient reproducer than the worm.

So if we use NASA's working definition to banish viruses from the realm of life, we must further exclude all manner of much larger parasites including worms, fungi and plants. Defining life as a self-sustaining system capable of Darwinian evolution also forces us to admit that certain computer programs are alive. Genetic algorithms, for instance, imitate natural selection to arrive at the optimal solution to a problem: they are bit arrays that code traits, evolve, compete with one another to reproduce and even exchange information.

Similarly, software platforms like Avida create " digital organisms " that "are made up of digital bits that can mutate in much the same way DNA mutates. These things replicate, they mutate, they are competing with one another. The very process of natural selection is happening there. He and many other scientists favor an origin of life story known as the RNA world hypothesis. In modern living organisms, DNA stores the information necessary to build the proteins and molecular machines that together form a bustling cell.

At first, scientists thought only proteins known as enzymes could catalyze the chemical reactions necessary to construct this cellular machinery. In the s, however, Thomas Cech and Sidney Altman discovered that, in collaboration with various protein enzymes, many different kinds of RNA enzymes—or ribozymes—read the information coded in DNA and build the different parts of a cell piece by piece.

The RNA world hypothesis posits that the earliest organisms on the planet relied solely on RNA to perform all these tasks—to both store and use genetic information—without the help of DNA or an entourage of protein enzymes.

A geothermal pool in Wyoming. Nearly four billion years ago, what we call life may have first evolved in similar "warm little ponds," as Darwin put it.

Credit: Caleb Dorfman, via Flickr. Simple self-assembling membranes enveloped these early ribozymes, forming the first cells. And proteins took on many catalytic roles because they were so versatile and diverse. But the cells of modern organisms still contain what are likely remnants of the original RNA world.

The ribosome, for example—a bundle of RNA and proteins that builds proteins one amino acid at a time—is a ribozyme.

There's also a group of viruses that use RNA as their primary genetic material. In the mids, Joyce and Tracey Lincoln constructed trillions of random free-floating RNA sequences in the lab, similar to the early RNAs that may have competed with one another billions of years ago, and isolated sequences that, by chance, were capable of bonding two other pieces of RNA.

By pitting these sequences against one another, the pair eventually produced two ribozymes that could replicate one another ad infinitum as long as they were supplied with sufficient nucleotides. Not only can these naked RNA molecules reproduce, they can also mutate and evolve. For the final phase of its mission in this behaviour-based robot was allowed to operate autonomously, and successfully navigated the surface of Mars.

The behaviour-based approach to AI has merged somewhat with the Alife endeavour, and a community of researchers has formed that is separate from the traditional AI community. The former is interested in understanding how living systems work and in building computational and physical models of them.

The latter is interested in building systems with maximal performance, and is usually wary of biological inspiration as taking away from mathematically optimized engineering solutions. Although they are much more lifelike than the pure engineering artefacts of traditional AI, in some sense the systems built under the behaviour-based and Alife approaches do not seem as alive as we might hope.

We build models to understand the biological systems better, but the models never work as well as biology. We have become very good at modelling fluids, materials, planetary dynamics, nuclear explosions and all manner of physical systems. Put some parameters into a program, let it crank, and out come accurate predictions of the physical character of the modelled system.

But we are not good at modelling living systems, at small or large scales. Something is wrong. What is going wrong? There are a number of possibilities: 1 we might just be getting a few parameters wrong; 2 we might be building models that are below some complexity threshold; 3 perhaps it is still a lack of computing power; and 4 we might be missing something fundamental and currently unimagined in our models of biology.

Getting just a few parameters wrong would mean that we have essentially modelled everything correctly, but are just unlucky or ignorant in some minor way. With a bit more work on our part, things will start working better. It could be that our current neural-network models will work quantitatively better if we have five layers of artificial neurons, rather than today's standard of three.

Or that artificial evolution works much better with populations of , or more, rather than the typical thousand or less. But this seems unlikely. One would expect that someone would have stumbled by now across a combination of parameters that worked qualitatively better than anything else around. That success would have led to theoretical analysis and we would have already seen rapid progress. Building models that are below some complexity threshold also would mean that there is nothing in principle that we do not understand about intelligent or living systems.

We have all the ideas and components lying around, we just have not yet put enough of them together in one place, or one model. When, and if, we do, then everything will start working a lot better. As for the first possibility, while this may be true, it does seem unlikely that is true across so many different aspects of biology. We have recently seen an example of this. Deep Blue was no different in essence from the earlier versions he had been playing in the late s.

Deep Blue still had no strategic planning phase, as other chess programs designed to model human playing had. It still had only a tactical search, albeit a very deep, fast tactical search. This appeared to Kasparov to be about game plans, not because there was anything new, but because more computer power made the approach feel qualitatively different. The same might happen to our models of intelligence and life, if we could only get enough computer power. If any of the above is the case then we should expect great progress in AI and Alife as soon as someone stumbles across the things that need to be fixed.

The details will not particularly surprise anyone, although the new developments will have great practical impact. They will lead to new insights in all the sciences that study living organisms, as they will give us new sorts of computer models with which we can test rafts of new hypotheses about how living systems operate.

But what if we are missing something fundamental and currently unimagined in our models? We would then need to find new ways of thinking about living systems to make any progress, and this will be much more disruptive to all biology.

As an analogy, suppose we were building physical simulations of elastic objects falling and colliding. If we did not quite understand physics, we might leave out mass as a specifiable attribute of the objects. Their falling behaviour would at first seem correct, but as soon as we started to look at collisions we would notice that the physical world was not being modelled correctly.

So what might be the nature of this unimagined feature of life? One possibility is that some aspect of living systems is invisible to us right now. The current scientific view of living things is that they are machines whose components are biomolecules. It is not completely impossible that we might discover some new properties of biomolecules or some new ingredient. One might imagine something on a par with the discovery of X-rays a century ago, which eventually led to our still-evolving understanding of quantum mechanics.

Relativity was the other such discovery of the twentieth century, and had a similarly disruptive impact on the basic understanding of physics. Some similar discovery might rock our understanding of the basis of living systems. Let us call this the 'new stuff' hypothesis — the hypothesis that there may be some extra sort of 'stuff' in living systems outside our current scientific understanding.

Roger Penrose 8 , for one, has already hypothesized a weak form of 'new stuff' as an explanation for consciousness. He suggests that quantum effects in the microtubules of nerve cells might be the locus of consciousness at the level of the individual cell, which combines in bigger wave functions at the organism level. Penrose has not worked out a real theory of how this might work. Rather, he has suggested that this may be a critical element that will need to be incorporated in a final understanding.

This is a weak form of new stuff because it does not rely on anything outside the realm of current physics. For some it may have a certain appeal in that it unifies a great discovery in physics with a great question in biology — the nature of consciousness.

David Chalmers 9 has hypothesized a stronger form of new stuff as an alternative explanation for consciousness. He suggests that a fundamentally new type, of the order of importance of spin or charm in particle physics, say, may be necessary to explain consciousness.

It would be a new sort of physical property of things in the Universe, subject to physical laws that we just do not yet understand. Other philosophers, both natural and religious, might hypothesize some more ineffable entity such as a soul or elan vital — the 'vital force'.

Another way that the unimaginable discovery might come about is through 'new mathematics'. This would not require any new physics to be present in living systems. We may simply not be seeing some fundamental mathematical description of what is going on in living systems and so be leaving it out of our AI and Alife models.

What might this 'new mathematics' be? Candidates have included catastrophe theory, chaos theory, dynamical systems and wavelets. When each of these new mathematical techniques hit the market, researchers noticed ways in which they could be used to describe what is going on in living systems, and then tried to incorporate the same thing into their computational models. It is not clear whether the mathematical techniques in question are best used as descriptive tools or as generative components within the computational models.

The latter approach seems at times misguided. However, none of these wonder techniques has really made the hoped-for improvements in our models. Looking at the physical nature of living systems, there seem to be certain mathematical properties that are not handled at all by any of these new techniques, or by any current model.

One property is that the matter that makes up living systems obeys the laws of physics in ways that are expensive to simulate computationally. For instance, the membranes of cells have a shape determined by the continuous minimization of forces between molecules within the membrane and on either side of it.

Another property is that matter does not simply appear and disappear in the physical world, but great care must be taken in a computational simulation to enforce this. An analogy to the sort of thing that might be missing is computation — not as the undiscovered feature itself but as an analogy for the type of thing we might be looking for.

For most of the twentieth century we have poked electrodes into living nervous systems and looked for correlations between the signals measured and events that occur elsewhere in the creature. These data are used to test hypotheses about how the living system 'computes' in the broadest sense of the word. In a liquid , the particles are more loosely packed than in a solid and are able to flow around each other, giving the liquid an indefinite shape.

Therefore, the liquid will conform to the shape of its container. Much like solids, liquids most of which have a lower density than solids are incredibly difficult to compress. In a gas , the particles have a great deal of space between them and have high kinetic energy. A gas has no definite shape or volume. If unconfined, the particles of a gas will spread out indefinitely; if confined, the gas will expand to fill its container. When a gas is put under pressure by reducing the volume of the container, the space between particles is reduced and the gas is compressed.

Plasma is not a common state of matter here on Earth, but it may be the most common state of matter in the universe, according to the Jefferson Laboratory. Stars are essentially superheated balls of plasma. Plasma consists of highly charged particles with extremely high kinetic energy. The noble gases helium, neon, argon, krypton, xenon and radon are often used to make glowing signs by using electricity to ionize them to the plasma state.

At this extremely low temperature, molecular motion comes very close to stopping. Since there is almost no kinetic energy being transferred from one atom to another, the atoms begin to clump together.

There are no longer thousands of separate atoms, just one "super atom. A BEC is used to study quantum mechanics on a macroscopic level.

A BEC also has many of the properties of a superfluid , or a fluid that flows without friction. BECs are also used to simulate conditions that might exist in black holes. Adding or removing energy from matter causes a physical change as matter moves from one state to another.

For example, adding thermal energy heat to liquid water causes it to become steam or vapor a gas. And removing energy from liquid water causes it to become ice a solid.