Intelligence As An Emergent Property In Biological Systems

“Intelligence” is a word we generally think of in the context of the ability of a person to acquire knowledge or skills. We refer to humans as being “intelligent” because we can cooperate and collaborate to achieve things greater than ourselves. It is without a doubt one of the most important features that humans have evolved to possess, one that has skyrocketed our position to the top of the ecological food chain and has established the dominance of our species on this planet.

For a layperson, “intelligent” isn’t the first word that comes to their mind when they think of lower organisms, such as an ant or bacteria. Yet, the behavior displayed by these biological entities and biological systems is observed to be highly intelligent. A common feature of biological intelligence in these organisms is that they rely on swarm intelligence. Essentially, each individual entity possesses little cognitive capacity and hence can process and relay only a small set of signals. Yet, when combined with large populations of similar entities, the collective  intelligence begins to display high levels of decision making and problem solving capabilities. As a collective, these entities accomplish complex goals that would be unimaginable if we were to focus only on the individuals. An ant, for example, is not the smartest of creatures. It can carry out a very limited number of tasks, especially when compared to a human. Yet, as a collective, ants are known to be some of the best engineers, with the largest ant colony spanning 6000 kilometres! Ants from one end of the colony were able to recognize ants from the other end of the colony and straddle oceans as well![1] The discovery of agriculture and animal husbandry were arguably the biggest turning points in the history of mankind, and perhaps a testament to our intelligence, the product of which is our ability to recognize and exploit natural systems. However, millions of years before humans discovered agriculture, ants were farming fungi and keeping herds of smaller bugs as livestock too![2,3]

Intelligent behavior is displayed by a large spectrum of creatures living on the planet. We can see biological systems employing relatively complex solutions to adapt and survive. This behavior is not limited to humans, animals and insects, but can be observed in fungi and plants as well. Even microbes display great intelligence and complex adaptive behavior.

Bacteria are known to aggregate together to display a remarkable degree of social intelligence. Mechanisms such as quorum signaling enable these organisms to set up a decentralized communication system very efficiently. Bacteria can exchange genetic information in the form of plasmids, hence transferring and preserving valuable information that it can use to increase its odds of survival long after experiencing and adapting to a new environment. Combining several such mechanisms, bacteria display advanced adaptive behavior, such as phenotypic variability and bet hedging, making them the ultimate survivalists[4].

Another very interesting example is that of Physarum polycephalum. This organism is made up of a network of tubes through which it circulates metabolites. It does not possess a brain or any neural matter, yet it has been found to be able to solve complex optimization problems that took human engineers years to solve, such as when it replicated the Tokyo subway rail network to efficiently build a network of tubes to harvest nutrients that were arranged in a manner similar to the human population density in Tokyo[5]. It has also been shown to possess the capacity to approximate solutions to established problems such as the travelling salesmen in computer science. The travelling salesman problem is defined as follows: Given a list of cities and the distances between each pair of cities, what is the shortest possible route that he visits each city exactly once and returns to the original city? The time complexity of a naïve algorithm to solve this problem on a conventional computer would grow factorially, but a physarum-based computational device could do it such that the time complexity would grow linearly[6]. This implies that a simple amoeboid may be able to solve these complex problems with orders of magnitude fewer steps than a regular computer.

Borrowing words from systems theory, we can think of intelligence as an “emergent” property in these biological systems. “Emergence” can be thought of as a phenomenon where an entity is observed to have properties that emerge only when all of its components interact as a whole. In a way, we can say that the whole is greater than the sum of its individual parts.

The study of intelligence as an emergent property in biological systems is certainly one of the most interesting and promising fields of interdisciplinary research. This field of study is a subset of a much wider field, i.e., systems biology. Biology can teach us several ways to design intelligent systems and provide us with an entirely new perspective on what “intelligence” means. This research has the potential to contribute to the advancement of both quantitative sciences as well as biological sciences. An enhanced understanding of biological intelligence in lower organisms can lead to the development of better decentralized computing systems and algorithms. Forexample, studying the intelligent behavior of amoeboids such as physarum can lead to insights into developing low-power decentralized computing architectures that may perform certain tasks better than conventional computers.

It may also be relevant for developing therapeutics aimed at “outsmarting” biological entities that are harmful to us. It can lead us towards designing newer and more effective antibiotics in an era where antibiotic resistance threatens to cut human life expectancy by several decades. It can also lead to curing ailments that have eluded the strongest and most advanced therapeutics to this day, such as cancer. Cancer cells are known to mimic bacterial swarm intelligence. They can survive in hostile conditions by using several intelligent “tricks” to modify their microenvironment. By understanding the nature of their intelligence, we can counter them by disrupting or sabotaging the underlying processes that contribute towards emergent behavior, such as certain cell-cell communication channels, hence disarming these “intelligent” cells effectively[7]. This is akin to “hacking” a decentralized intelligent system.

We are still far away from the point where we can begin to accurately describe and model intelligence as an emergent property in biological systems, especially such that it can be used for creating substantially improved therapeutics or computing systems. Yet, it may hold the key towards the next giant leap in medical science or even computer science. With better and increased computing power and an increased application of quantitative sciences in the field of biology, we may see accelerated progress in this field of research. Additionally, with scientists planning to be in “cyber warfare” with biology, and simultaneously take inspiration from it to build better computing systems, the next few decades will certainly prove to be very interesting for systems biology, and for science as a whole.

References:

  1.  Evolution of supercolonies: The Argentine ants of southern Europe
    Tatiana Giraud, Jes S. Pedersen, Laurent Keller
    Proceedings of the National Academy of Sciences Apr 2002, 99 (9) 6075-6079;
    DOI: 10.1073/pnas.092694199
  2. Nygaard, S., Hu, H., Li, C. et al. Reciprocal genomic evolution in the ant–fungus agricultural symbiosis. Nat Commun 7, 12233 (2016). https://doi.org/10.1038/ncomms12233
  3. Imperial College London. “Herding Aphids: How ‘Farmer’ Ants Keep Control Of Their Food.” ScienceDaily. ScienceDaily, 11 October 2007. <www.sciencedaily.com/releases/2007/10/071009212548.htm>
  4. Beaumont, H., Gallie, J., Kost, C. et al. Experimental evolution of bet hedging . Nature 462, 90–93 (2009). https://doi.org/10.1038/nature08504.
  5. Atsushi Tero, et. al.. (2010). Rules for Biologically Inspired Adaptive Network Design. Science (New York, N.Y.). 327. 439-42. 10.1126/science.1177894.
  6. Liping Zhu et al. 2018. Remarkable problem-solving ability of unicellular amoeboid organism and its mechanism. R. Soc. open sci 5 (12): 180396; doi: 10.1098/rsos.180396
  7. Ben-Jacob E, Coffey DS, Levine H. Bacterial survival strategies suggest rethinking cancer cooperativity. Trends Microbiol. 2012 Sep;20(9):403-10. doi: 10.1016/j.tim.2012.06.001. Epub 2012 Jun 29. PMID: 22750098.

Written by Yash Mundewadi
B.Tech Biotechnology and Biochemical Engineering,
Indian Institute of Technology, Kharagpur

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