Complexity in Biotechnology: Cell Growth and Cell Division in Microorganisms

Applications of the complexity theory to biotechnology, and in particular, the study of growth of cells and microorganisms such as cancer cells, bacteria, virus and phytoplankton, is an increasingly important topic. Phytoplankton, given its importance as a clean source of food and energy, is likely going to be the dominant factor in influencing the economy in the future. However, a current problem in biotechnology is that a lot of the results are based on experiments carried out under laboratory conditions and lacks the complexity of a real-life system. This resonates well with the feedback from the industry: most academic studies fail in real life because there is a lack of real-life resemblance. Also, the theoretical models are largely based on laws of physics which are meant for systems at equilibrium. The use of the complexity theory approach can mitigate this problem by considering the emergent nature of the systems.

The main issue this Research Topic aims to provide insights on, is the impact of the use of the growth and addition features, which are prevalent in complex systems, on our understanding of how cells grow in general, albeit in various forms such as cancer cells, bacteria, viruses and phytoplankton. The understanding of the growth mechanism in all these organisms will have immense scientific, health, environmental and economic values. For the cancer, bacterial and viral studies, the results can be used to provide insights on the approach to tackle cancer, bacterial and viral spreading. For the phytoplankton study, it allows the optimization of the cultivation of the latter for clean sources of food and energy. Another goal we can strive to aim for is the convergence of all these results in various organisms to a single theory of complexity.

This Research Topic aims at developing realistic models for cells and microorganisms’ growth. This is cross-disciplinary research that involves both the fields of biotechnology and complex theory. The approach can be fully theoretical but works that involve theoretical models correlated with experimental results will be highly favoured. In either case, the models must demonstrate a reasonable level or realistic resemblance to a real-life system. Theoretical models can be solved with analytical and/or numerical methods.

The scope includes:

• Complex network

• Emergent behaviour

• Evolutionary behaviour

• Growth and addition patterns

• Nonlinear dynamics

• Self-organized criticality

The methods include:

• Agent-based modelling

• Analytical methods

• Numerical methods

• Evolutionary game theory

• Statistical methods and data science

• Statistical mechanics