Internet, ants and cancer.

Internet, Ants and cancer

I love the weirdness of internet, especially losing myself in it. 

Sometimes it is smooth, and sometimes it feels like being walking in a thick fog. Chaotic is probably the first word coming to my mind to describe it, should it be used though ? 

In everyday use, chaos means disorder and confusion [1]. It fits quite well with the impression internet gives me: humongous quantities of information from different platforms are available, all with very different levels of quality and a very broad range of topics and reasons for being watched or interacted with. This overbearing quantity can overload our decision-making and increase the sensation of pressure [2].

However, there seems to be structure in how internet comes to us. Everyone is aware, for example, of the algorithms creating our feeds on social media. But then, what could that structure be?

The same as developers trying to optimise software, how are ants organising themselves? Thousands of ants moving in every direction can look completely uncoordinated. Yet underneath this apparent mess there are signals, interactions and organisation. Research on social insects has shown how interactions between relatively simple individuals can generate organised collective patterns and decisions at the colony level [4].

Interestingly, ant foraging has also been modelled as a transition from chaotic to more ordered dynamics. Individual movement can evolve toward collective behaviours such as homing and path-building [5]. In other words, apparent disorder at one scale can participate in creating order at another.

This is where I see the bridge between linguistic and mathematical chaos.

In mathematics, a chaotic system can actually be deterministic. It follows rules, but small differences in initial conditions, combined with interactions and feedback, can eventually produce extremely different outcomes.

Scientists have investigated parts of the internet through this lens. TCP congestion control, one mechanism regulating information flow through networks, can under certain conditions display properties associated with deterministic chaos, including unpredictability and sensitivity to initial conditions [3].

So something can follow rules and still be very difficult to predict at a single scale.

But what about systems where coordination is much harder to see, or where anomalies appear inside the coordination itself?

Through evolution, complex life required cells to specialise and cooperate, allowing tissues and organs to form. This cooperation depends on controls regulating when cells divide, die, communicate and use resources. Cancer can partly be seen as a breakdown of this multicellular cooperation, where some cells escape those controls and grow aggressively [6][7].

Cancer is obviously neither an Internet network nor a crowd and has its own particularities. But mathematically, it gives a much more complex example of the same problem.

Tumours interact with healthy cells, immune cells, their environment and treatments. Nonlinear models of those interactions can produce stable, periodic and even chaotic dynamics [8]. Mathematical models can also investigate how treatment itself may move tumour–immune systems between different dynamical regimes [9].

Those constructed models can help us understand how tumours may behave over time. Adaptive therapy pushes this idea further: instead of assuming that a tumour stays identical throughout treatment, therapy can be adjusted according to its evolution. Models of competition between treatment-sensitive and treatment-resistant cells have already reached clinical research, particularly in prostate cancer [10][11].

The objective is not to predict every single cancer cell. It is to understand enough of the system to act inside its uncertainty.

And then I look again at my internet maze.

With ants, apparent disorder hides organisation. With cancer, mostly invisible interactions can still be understood enough to improve treatment.

Internet is obviously different, but its structure is huge while one person’s perception of it is tiny. No wonder we — or at least I — sometimes feel lost.

There will probably be better ways of understanding and predicting what happens inside it, helping us build better architectures, use resources more intelligently and maybe stop using AI for whatever nonsense we want.

But structure and unpredictability can exist at the same time.

Maybe that is what I was looking for from the beginning.

In both the mathematical and common meanings, embracing chaos may simply be part of living.

Because feeling lost is human, but so is the need to try to understand.

Sources :

[1] Larousse. Chaos. Dictionnaire de français Larousse.

[2] Shahrzadi, L., Mansouri, A., Alavi, M., & Shabani, A. (2024). “Causes, consequences, and strategies to deal with information overload: A scoping review.” International Journal of Information Management Data Insights, 4(2), 100261. https://doi.org/10.1016/j.jjimei.2024.100261

[3] Veres, A., & Boda, M. (2000). “The chaotic nature of TCP congestion control.” In Proceedings IEEE INFOCOM 2000, Vol. 3, pp. 1715–1723. IEEE. https://doi.org/10.1109/INFCOM.2000.832571

[4] Deneubourg, J.-L., & Goss, S. (1989). “Collective patterns and decision-making.” Ethology Ecology & Evolution, 1(4), 295–311. https://doi.org/10.1080/08927014.1989.9525500

[5] Li, L., Peng, H., Kurths, J., Yang, Y., & Schellnhuber, H. J. (2014). “Chaos–order transition in foraging behavior of ants.” Proceedings of the National Academy of Sciences, 111(23), 8392–8397. https://doi.org/10.1073/pnas.1407083111

[6] Aktipis, C. A., Boddy, A. M., Jansen, G., Hibner, U., Hochberg, M. E., Maley, C. C., & Wilkinson, G. S. (2015). “Cancer across the tree of life: Cooperation and cheating in multicellularity.” Philosophical Transactions of the Royal Society B, 370(1673), 20140219. https://doi.org/10.1098/rstb.2014.0219

[7] Trigos, A. S., Pearson, R. B., Papenfuss, A. T., & Goode, D. L. (2018). “How the evolution of multicellularity set the stage for cancer.” British Journal of Cancer, 118(2), 145–152. https://doi.org/10.1038/bjc.2017.398

[8] Moghtadaei, M., Hashemi Golpayegani, M. R., & Malekzadeh, R. (2013). “Periodic and chaotic dynamics in a map-based model of tumor–immune interaction.” Journal of Theoretical Biology, 334, 130–140. https://doi.org/10.1016/j.jtbi.2013.05.031

[9] Bashkirtseva, I., Ryashko, L., Seoane, J. M., & Sanjuán, M. A. F. (2024). “Chaotic transitions in a tumor-immune model under chemotherapy treatment.” Communications in Nonlinear Science and Numerical Simulation. https://doi.org/10.1016/j.cnsns.2024.107946

[10] Zhang, J., Cunningham, J. J., Brown, J. S., & Gatenby, R. A. (2017). “Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer.” Nature Communications, 8, 1816. https://doi.org/10.1038/s41467-017-01968-5

[11] West, J., Adler, F., Gallaher, J., et al. (2023). “A survey of open questions in adaptive therapy: Bridging mathematics and clinical translation.” eLife, 12, e84263. https://doi.org/10.7554/eLife.84263