Complex physical properties of an adaptive, self-organizing biological system
arXiv:2107.13195 · doi:10.3390/biophysica3020015
Abstract
The physical interpretation of the functioning of the adaptive immune system, which has been thoroughly characterized on genetic and molecular levels, provides a unique opportunity to define an adaptive self-organizing biological system in its entirety. This paper describes a configuration space model of immune function, where directed chemical potentials of the system constitute a space of interactions. In the physical sense, the humoral adaptive immune system adjusts the chemical potential of all available antigenic molecules by tuning the chemical potential and organizing the network hierarchy of its sensor-effector molecules, antibodies. The coupling of sensors and effectors allows the system to adjust the thermodynamic activity of antigens and antibodies, while network organization helps minimize chemical potentials and maximize diversity. Mathematically the system couples the variance of Gaussian distributed interaction energies in its interaction space to the exponentially distributed chemical potentials of its effector molecules to maintain its stationary state. This process creates a scale-free network in interaction space, where absolute thermodynamic activity corresponds to node degree. In the thermodynamic interpretation, the system is an ensemble carrying out {mu}N work, adjusting chemical potentials according to the changes in the chemical potentials of the surroundings. The validity of the model is supported by identifying an interaction flexibility index, the corresponding variables in thermodynamics and network science, and by confirming its applicability to the humoral immune system. Overall, this statistical thermodynamics model of adaptive immunity describes how adaptive biological self-organization arises from the maintenance of a scale-free, directed interaction network with fractal topology.
arXiv comment22 pages, 8 figures
References in corpus (16)
- Power-law distributions in empirical data
- Power laws, Pareto distributions and Zipf's law
- The large-scale organization of metabolic networks
- Self-similarity of complex networks
- Are biological systems poised at criticality?
- Global organization of metabolic fluxes in the bacterium, Escherichia coli
- Origins of fractality in the growth of complex networks
- Maximum entropy models for antibody diversity
- How to calculate the fractal dimension of a complex network: the box covering algorithm
- Optimal Self-Organization
- Likelihood-based inference of B-cell clonal families
- Fluctuating fitness shapes the clone size distribution of immune repertoires
- How many different clonotypes do immune repertoires contain?
- How a well-adapting immune system remembers
- Physical epistatic landscape of antibody binding affinity
- Rank Distributions: Frequency vs. Magnitude