Attractor Stability in Finite Asynchronous Biological System Models

TL;DR

This paper develops mathematical techniques to systematically analyze the long-term dynamics and attractor configurations of asynchronous biological system models, significantly reducing computational complexity.

Contribution

It extends the notion of κ-equivalence to support exhaustive analysis of attractor structures under asynchronous updates, applying combinatorial methods to biological network models.

Findings

Identified 4 distinct attractor structures in the lac operon model using only 344 representative update orders.

Discovered 125 distinct attractor structures in the C. elegans vulva precursor cell model.

Demonstrated the effectiveness of the method in reducing computational costs from over 3.6 million to a few hundred cases.

Abstract

We present mathematical techniques for exhaustive studies of long-term dynamics of asynchronous biological system models. Specifically, we extend the notion of $\kappa$-equivalence developed for graph dynamical systems to support systematic analysis of all possible attractor configurations that can be generated when varying the asynchronous update order (Macauley and Mortveit (2009)). We extend earlier work by Veliz-Cuba and Stigler (2011), Goles et al. (2014), and others by comparing long-term dynamics up to topological conjugation: rather than comparing the exact states and their transitions on attractors, we only compare the attractor structures. In general, obtaining this information is computationally intractable. Here, we adapt and apply combinatorial theory for dynamical systems to develop computational methods that greatly reduce this computational cost. We give a detailed algorithm and apply it to ($i$) the lac operon model for Escherichia coli proposed by Veliz-Cuba and Stigler (2011), and ($ii$) the regulatory network involved in the control of the cell cycle and cell differentiation in the Caenorhabditis elegans vulva precursor cells proposed by Weinstein et al. (2015). In both cases, we uncover all possible limit cycle structures for these networks under sequential updates. Specifically, for the lac operon model, rather than examining all $10! > 3.6 \cdot 10^6$ sequential update orders, we demonstrate that it is sufficient to consider $344$ representative update orders, and, more notably, that these $344$ representatives give rise to $4$ distinct attractor structures. A similar analysis performed for the C. elegans model demonstrates that it has precisely $125$ distinct attractor structures. We conclude with observations on the variety and distribution of the models' attractor structures and use the results to discuss their robustness.