activity
20062023
most citedA geometric approach to the Global Attractor Conjecture

111 citations · 273 across the 31 of their papers we have counts for

collaborators

47 papers

math.DS2023★ 4 cited

Absolute Concentration Robustness and Multistationarity in Reaction Networks: Conditions for Coexistence

Nidhi Kaihnsa, Tung Nguyen, Anne Shiu

Many reaction networks arising in applications are multistationary, that is, they have the capacity for more than one steady state; while some networks exhibit absolute concentrati…

math.DS2023★ 1 cited

Deficiency of chemical reaction networks: The effect of operations that preserve multistationarity and periodic orbits

Awildo Gutierrez, Elijah Leake, Caelyn Rivas-Sobie +2

We investigate six operations on chemical reaction networks, all of which have been proven to preserve important dynamical properties, namely, the capacity for nondegenerate multis…

math.DS2023★ 2 cited

On the connectedness of multistationarity regions of small reaction networks

Allison McClure, Anne Shiu

A multistationarity region is the part of a reaction network's parameter space that gives rise to multiple steady states. Mathematically, this region consists of the positive param…

math.PR2023

Prevalence of multistationarity and absolute concentration robustness in reaction networks

Badal Joshi, Nidhi Kaihnsa, Tung D. Nguyen +1

For reaction networks arising in systems biology, the capacity for two or more steady states, that is, multistationarity, is an important property that underlies biochemical switch…

math.CO2021

Wheels: A New Criterion for Non-convexity of Neural Codes

Laura Matusevich, Alexander Ruys de Perez, Anne Shiu

We introduce new geometric and combinatorial criteria that preclude a neural code from being convex, and use them to tackle the classification problem for codes on six neurons. Alo…

math.DS2021★ 7 cited

Identifiability of linear compartmental tree models and a general formula for input-output equations

Cashous Bortner, Elizabeth Gross, Nicolette Meshkat +2

A foundational question in the theory of linear compartmental models is how to assess whether a model is structurally identifiable -- that is, whether parameter values can be infer…