arXiv subjects

S. A. Kauffman

Publications and source records attributed to S. A. Kauffman.

5 recordsLinked to original sources

arXiv2024

Extrinsic and intrinsic effects setting viscosity in complex fluids and life processes: the role of fundamental physical constants

Understanding the values and origin of fundamental physical constants, one of the grandest challenges in modern science, has been discussed in particle physics, astronomy and cosmology. More recently, it was realised that fundamental constants have a bio-friendly window set by life processes involving motion and flow. This window is related to intrinsic fluid properties such as energy and length scales in condensed matter set by fundamental constants. Here, we discuss important extrinsic factors governing the viscosity of complex fluids operating in life processes due to collective effects. We show that both extrinsic and intrinsic factors affecting viscosity need to be taken into account when estimating the bio-friendly range of fundamental constants from life processes, and our discussion provides a straightforward recipe for doing this. We also find that the relative role of extrinsic and intrinsic factors depends on the range of variability of these intrinsic and extrinsic factors. Remarkably, the viscosity of a complex fluid such as blood with significant extrinsic effects is not far from the intrinsic viscosity calculated using the fundamental constants only, and we discuss the reason for this in terms of dynamics of contact points between cells.

cond-mat.soft↗

arXiv2010

Phase transition in a class of non-linear random networks

We discuss the complex dynamics of a non-linear random networks model, as a function of the connectivity k between the elements of the network. We show that this class of networks exhibit an order-chaos phase transition for a critical connectivity k = 2. Also, we show that both, pairwise correlation and complexity measures are maximized in dynamically critical networks. These results are in good agreement with the previously reported studies on random Boolean networks and random threshold networks, and show once again that critical networks provide an optimal coordination of diverse behavior.

nlin.AO↗

arXiv2009

Maximal Information Transfer and Behavior Diversity in Random Threshold Networks

Random Threshold Networks (RTNs) are an idealized model of diluted, non symmetric spin glasses, neural networks or gene regulatory networks. RTNs also serve as an interesting general example of any coordinated causal system. Here we study the conditions for maximal information transfer and behavior diversity in RTNs. These conditions are likely to play a major role in physical and biological systems, perhaps serving as important selective traits in biological systems. We show that the pairwise mutual information is maximized in dynamically critical networks. Also, we show that the correlated behavior diversity is maximized for slightly chaotic networks, close to the critical region. Importantly, critical networks maximize coordinated, diverse dynamical behavior across the network and across time: the information transmission between source and receiver nodes and the diversity of dynamical behaviors, when measured with a time delay between the source and receiver, are maximized for critical networks.

q-bio.QM↗

arXiv2006

Mean Field Model of Genetic Regulatory Networks

In this paper, we propose a mean-field model which attempts to bridge the gap between random Boolean networks and more realistic stochastic modeling of genetic regulatory networks. The main idea of the model is to replace all regulatory interactions to any one gene with an average or effective interaction, which takes into account the repression and activation mechanisms. We find that depending on the set of regulatory parameters, the model exhibits rich nonlinear dynamics. The model also provides quantitative support to the earlier qualitative results obtained for random Boolean networks.

q-bio.QM↗

arXiv2006

Network growth models and genetic regulatory networks

We study a class of growth algorithms for directed graphs that are candidate models for the evolution of genetic regulatory networks. The algorithms involve partial duplication of nodes and their links, together with innovation of new links, allowing for the possibility that input and output links from a newly created node may have different probabilities of survival. We find some counterintuitive trends as parameters are varied, including the broadening of indegree distribution when the probability for retaining input links is decreased. We also find that both the scaling of transcription factors with genome size and the measured degree distributions for genes in yeast can be reproduced by the growth algorithm if and only if a special seed is used to initiate the process.

q-bio.MN↗