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The Quest of Modelling Complex Systems

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A car is complicated. It consists of many components with known properties that work together in a predictable way, ensuring the car does what it is designed to do. A complex system is different in the sense that the behaviour of the system cannot be predicted from knowing the properties of the constituent parts alone. For example, the behaviour of the brain cannot be deduced from studying a single neuron in isolation nor can the behaviour of an ant colony be deduced from studying a single ant in isolation. These complex systems display emergent behaviour, e.g. intelligence or ability to find the shortest route to a food source, due to the interactions of the constituent parts. We will discuss why it is of paramount importance to develop new appropriate approaches and theories to investigate such ubiquitous complex systems.

We will review various examples of complex systems in nature before illustrating how one might model a complex system such as the heart with the emergent property of atrial fibrillation. Atrial fibrillation (AF) is the most common abnormal heart rhythm and the single biggest cause of stroke. We design a complexity science model of activation wave front propagation on an anisotropic structure mimicking the branching network of heart muscle cells. This integration of phenomenological dynamics and pertinent structure shows how AF emerges spontaneously when the transverse cell-to-cell coupling decreases, as occurs with age, beyond a threshold value. We identify critical regions responsible for the initiation and maintenance of AF, the ablation of which terminates AF. This insight may eventually lead to patient-specific therapy when it becomes possible to observe the cellular structure of a patient’s heart and thereby improve the currently empirically applied ablation procedure.

This talk is part of the Cambridge University Physics Society series.

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