Li, Zonglun; (2024) Modelling and revealing intelligence in complex biological systems: a regard from genetic circuits to neuronal networks. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Numerous real-world phenomena and challenges require a new way of thinking from a systems perspective and this new approach is broadly called complex sys- tems. Instead of having to break down a system into its individual components and studying their respective dynamics and contribution to the entire system, complex systems adopt a more collective approach with the emphasis on the whole rather than the sum of it, while sometimes the individual components may still be of in- terest. Among them, biological systems have attracted growing attention in recent years and are reckoned able to demonstrate a wealth of intelligence of different types which has remained untapped by humans. Therefore, the thesis is dedicated to the advancement that has been made during my PhD in revealing the intelligence that the systems can exhibit, ranging from molecular circuits to neuronal networks with the aid of mathematical and computational models. We will first leverage Hill equations to investigate the two advanced properties for associative learning that we newly proposed in the context of genetic circuits. Later, we will focus on the various forms of intelligence that neuronal networks can enable. It comprises the investi- gation of short-term memory in the presence of astrocytes, information processing and the disorder of the network, and classifying time series inputs.

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