Biological systems
Biological systems offer a rich source of applications for Complexity. Below we give a quick overview of some of the topics we have looked at in the Centre for Complexity Science, often with experts from Medical Science or Ecology, along with some references at the bottom of the page.
Atrial fibrillation (AF) is the most common heart rhythm disturbance and the single biggest cause of stroke. AF is increasingly prevalent with age and affects about 35M people worldwide. This complex abnormal heart rhythm therefore represents a major global clinical challenge. According to medics, treatment of atrial fibrillation (AF) by ablation (destroying heart cells by applying heat) is more an art than a science, hence the saying “Learning while burning”. Our interdisciplinary research shows that in the context of a model displaying spontaneous AF. We develop a model that shows a direct cause-and-effect relationship with the spontaneous emergence of atrial fibrillation (AF) when the communication between heart muscle cells decreases (eg., due to fibrosis) beyond a critical value. The multidisciplinary research provides a simple conceptual framework that explains a variety of clinically observed features of AF. The work could inform surgical treatments in the future by suggesting exactly where the problem lies in a given patient’s heart.We analyse the movement of ants. Our interdisciplinary research approach shows that their movement is universal in the sense that there exists a blue-print (scaling function) that contains the information above movement on all time scales. We hypothesised that such behaviour is generic for animal behaviour (including humans) which has since been proven. Beyond its theoretical relevance, this result may be important for developing objective markers of pathological behaviour.
Antlions employ spiral digging to make deadly traps in the form of pits lined with fine sand grains. When digging in a mixture of large and small sand grains, many more large grains are ejected than seems possible – it is almost like a conjuring trick. How does the antlion do it? We formulated a computational model, mimicking an antlion digging in granular mixtures, to gain insight into the processes and the reasons why they employ spiral digging. This technique of spiral digging is a superbly efficient time-saving method that literally enables antlions to plough through a large volume of sand such that the small avalanches they create cause large sand grains differentially to cascade to the bottom of the construction trench where they can be preferentially ejected during pit construction. Antlion pits are superb examples of extended phenotypes, an extension of their builder's body optimised by natural selection. What this research has shown is that extended phenotypes produced purely from found materials can be not only efficiently constructed but extremely efficient in operation. Any prey item that ventures into the pit will ride an avalanche down to the deadly antlion at the bottom of the pit. Such pits are an intriguing example of the ever-present force of natural selection that shapes biology.
The brain is found to exhibit statistical properties reminiscent of a critical state and self-similarity (Expert et al 2010) and, surprisingly, probability distributions of the size of bursts of brain activity similar to the statics of rain showers. This suggests that Self-Organised Criticality may be of relevance to brain dynamics (Palmieri & Jensen 2020).
References:
P. Expert, R. Lambiotte, D. Chialvo, K. Christensen, H.J. Jensen, D.J. Sharp and F. Turkheimer, Self-similar correlation function in rest-state fMRI. J. R. Soc Interface(2010)
Lorenzo Palmieri and Henrik J Jensen, Investigating critical systems via the distribution of correlation lengths. Phys. Rev. Res. 2, 013199 (2020).
K. Christensen, K.A. Manani, and N.S. Peters, Simple Model for Identifying Critical Regions in Atrial Fibrillation. Phys. Rev. Lett. 114, 028104 (2015).
M. Falkenberg, A.J. Ford, A.C. Li, R. Lawrence, A. Ciacci, N.S. Peters, and K. Christensen, Unified mechanism of local drivers in a percolation model of atrial fibrillation. Phys. Rev. E 100, 062406 (2019).
N.R. Franks, A. Worley, M. Falkenberg, A.B. Sendova-Franks AB, and K. Christensen, Digging the optimum pit: Antlions, spirals and spontaneous stratification. Proceedings of the Royal Society B 286, 20190365 (2019).
K. Christensen, D. Papavassiliou, A. de Figueiredo, N.R. Franks, and A.B. Sendova-Franks, Universality on ant behaviour, J. Royal Society Interface 12, 20140985 (2015).
M.F. McGillivray, W. Cheng, N.S.Peters, and K. Christensen, Machine learning methods for locating re-entrant drivers from electrograms in a model of atrial fibrillation. Royal Socienty Open Science 5, 172434 (2018).