It is an interesting time to get into the study of theoretical physics, especially research driven by statistical and non equilibrium physics. This is, in part, due to the beautiful melding of biology and physics and revolutions in quantitative biology over the last few years. Advances in genetic sequencing tools have increasingly brought data science into the study of biology. We now have access to a wealth of information, at multiple scales and unprecedented depth.
One project that utilizes these new avenues of information at multiple scales is a project attempting to understand plasticity using the Polistes Canadensis (Red Paper wasps) as a model system. ‘Plasticity’ is loosely defined as the adaptability of an organism to changes in its habitat. This work is being done by the Statistical Physics of Living Systems Group (led by Dr. Steffen Rulands) at the Max Planck Institute for Physics of Complex Systems, Dresden, Germany. I was an intern with the group for two and studied certain aspects of this project. The project aims to explore how biological systems achieve plasticity and robust specialisation at the same time. The question of plasticity has far ranging implications across multiple questions and subfields. Multiple systems in biology exhibit this behavior, ranging from basal epithelial cells to stem cells.
Polistes Canadensis as a Model System:
The red paper wasp lives in small nests of workers and a single queen. Despite the same genetic information, the physical manifestation of these genes, or ‘phenotype’, differs between the workers and the queen, leading to a social order. A set of ‘queen genes’ are expressed at higher levels in the queen wasp, leading to only one queen that can viably reproduce. If a queen is removed from the nest (an external perturbation), the nest undergoes ‘reprogramming’. There are interactions and competitions between the remaining worker wasps till a new queen is established. In essence, these worker wasps ‘genetically reprogram’ themselves such that after a period of relaxation, the nest ends up with another singular queen. This phenomenon is what we call ‘plasticity’.
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This process to study this system experimentally was developed and executed by an experimental collaborator, Dr. Solenn Patalano. Using her tools and findings, the group at Dresden studies this relaxation process at three different scales; the macroscopic population level at the nest with videos, at the individual wasp level using physiological and behavioral parameters, and at the molecular levels using genetic and transcriptome data from the brains of the individuals during the relaxation process.
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These studies and measurements provide insights into the underlying biology of the system. As an example, we learn that after queen removal, all workers start expressing the queen genes at a higher level, before the population settles down into a single queen again. However, this genetic symmetry between workers in these states must be broken in order to attain a singular queen. Surprisingly this happens at a population level due to antagonistic physical interactions between the workers (often including aggressive behaviors) as well as interplays of signalling molecules (to repress gene expression in the surrounding wasps). A collective process at the population level is influencing molecular level gene expression in individuals!
These insights at all three levels also allows us to write a simple model to understand how the plasticity and social order is regulated. The group can now use certain probability equations called “Master Equations” to model the relevant parameters (gene expression and signalling molecules) and their time evolution. These master equations are ubiquitous in statistical non-equilibrium physics, and talk about how probability evolves in time. The group uses tools from stochastic dynamics, stochastic simulations and numerical solutions of partial difference equations to solve this master equation and understand how these reprogramming dynamics work. The group is also able to identify a range of behaviors through very informative ‘phase diagrams’.
These phase diagrams serve as one test to verify the ‘correctness’ of the model. However, tools from theoretical physics also allow us to gain a deeper understanding into the “How” and “Why” of this reprogramming. The group uses an approximation method called the “Mean Field Approach” to simplify the Master Equation. Further, tools from non-linear dynamics like steady states and phase portraits help understand, in greater detail, how a worker wasp can become a queen wasp. The group also studies perturbations to understand how the worker wasps respond to internal perturbations (thermal noise, genetic variety) and external perturbations (queen removal). Stability to these intrinsic perturbations allows our wasps to gain robust specialisation AND plasticity, simultaneously!
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This work is a stark example of how biology and physics come together to offer insights into such fascinating systems. Advances and experiments in biology help gain the understanding into ‘WHAT’ is happening during the phenomenon. In this case, as in many others, this understanding helps form the basis for physical theories that answer the ‘HOW’ and ‘WHY’ this is happening. Without understanding the biology, writing the theory would not only be much more difficult, it may also not be fruitful. The theory helps us gain an understanding into questions that experimental biology may struggle to answer (or it may be impractical to answer those questions experimentally). Such works are also informed by tools from a wide variety of fields, be it data visualisation to understand the molecular level workings or advances in numerical solution techniques to be able to solve the systems we set up.
Interestingly, much older, seemingly unconnected physical theories have also been surprisingly useful in understanding biological systems. Yet, there is a need for new theories to be developed. Biological systems differ from standard physical systems; there are a plethora of complications like operating near criticality or breaking microscopic symmetry. Even describing such systems in mathematical terms is a difficult, yet intriguing challenge.
Since this is a highly interdisciplinary field, practitioners range from biologists and physicists to computer scientists. Everyone brings something unique from their background and the interdisciplinary fields are richer for everyone’s unique inputs!
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