2208.11732
Attractor Stability in Finite Asynchronous Biological System Models
Henning S. Mortveit, Ryan Pederson
correctmedium confidence
- Category
- Not specified
- Journal tier
- Specialist/Solid
- Processed
- Sep 28, 2025, 12:56 AM
- arXiv Links
- Abstract ↗PDF ↗
Audit review
The paper explicitly states as Fact 4 that for any vertex v the set Acyc_v(G) is a complete set of κ-class representatives and that picking one linear extension per O ∈ Acyc_v(G) exhausts all possible cycle structures for F_π when varying the update order, citing prior work for the proof. It operationalizes this via Algorithm 1, which constructs Acyc_v(G) by enumerating acyclic orientations of G \ v and reattaching v as a unique source, thereby yielding one representative per κ-class. These claims and their intended use are consistent and correct in the paper’s framework . The candidate’s solution provides a different proof sketch: a constructive surjection from κ-classes to Acyc_v(G) via maximal sequences of clicks avoiding v, followed by a counting argument (|Acyc_v(G)| = κ(G)) to conclude bijectivity, and then the SDS cycle-equivalence step. Substantively, it agrees with the paper’s claims and is largely correct. One minor gap is the use of “maximal length” click sequences without ruling out repeats; this is easily repaired by restricting to repetition-free sequences (or giving a monotone potential function). The candidate also appropriately adds the connectedness hypothesis for the nonemptiness of Acyc_v(G), which the paper assumes implicitly. Overall, both are correct; the paper presents the result as a cited fact with an algorithmic construction, while the candidate offers a counting-plus-normal-form argument tying directly to κ-equivalence and SDS cycle-equivalence .
Referee report (LaTeX)
\textbf{Recommendation:} minor revisions \textbf{Journal Tier:} specialist/solid \textbf{Justification:} The manuscript correctly assembles established results on κ-equivalence and acyclic orientations to deliver an efficient algorithm for enumerating all SDS attractor structures under sequential updates, and it demonstrates the method on biologically relevant networks. The theoretical foundations are sound and properly cited. Minor revisions to make implicit assumptions explicit and to clarify a few algorithmic details will render the presentation fully self-contained and clearer to readers across disciplines.