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Modelling and understanding of biological systems, using physics as a tool

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Living systems are complex, and are based on the basic building blocks of genes, proteins, chemical reactions, and physical forces. Yet, out of this complexity, with remarkable robustness and precision, cells orchestrate the cooperative action of thousands of specific molecular reactions and interactions to carry out mechanical tasks. For example, 100 billion heart cells can synchronise their oscillation to generate a single pulse.

On the other hand, as an individual species, living system exhibits robust adaptation to changing environment. For example, social insects show well organised distribution of tasks via mutual interactions in order to maintain activities as a whole colony such as taking care of eggs and juveniles, searching for food, and so on.

To understand the fundamental principles that govern cooperative processes in living systems, it is of critical importance to develop an understanding of the underlying physio-chemical processes and individual behaviour, because living systems are also subject to laws in physical systems.

We believe that these amazing phenomena in living system can be understood by a concept of ‘Self-organization’ which is a process under non-equilibrium where overall order arises from local interactions between components. From thermodynamics points of view, we are interested in how random fluctuations, amplified by positive feedback, can lead to the ordered structure and a certain dynamics.

In this project, based on experimental results of biological systems, we aim to understand fundamental mechanisms of living systems, using a concept of self-organisation. Individual projects are open for discussion and can be shaped according to individual interest of the PhD candidates.

For informal inquiries please contact Dr Yoshikatsu Hayashi, email:.

Funding Notes

Eligibility requirements:

Applicants should have a bachelors (at least 2.1 or equivalent) or masters degree in physics, statistical physics
applied mathematics, computing or a related discipline.

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