اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدPrevalence of deficiency zero reaction networks in an Erdos-Renyi framework
124
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Reaction networks are commonly used within the mathematical biology and mathematical chemistry communities to model the dynamics of interacting species. These models differ from the typical graphs found in random graph theory since their vertices are constructed from elementary building blocks, i.e., the species. In this paper, we consider these networks in an ErdH os-Renyi framework and, under suitable assumptions, derive a threshold function for the network to have a deficiency of zero, which is a property of great interest in the reaction network community. Specifically, if the number of species is denoted by $n$ and if the edge probability is denote by $p_n$, then we prove that the probability of a random binary network being deficiency zero converges to 1 if $frac{p_n}{r(n)}to 0$, as $n to infty$, and converges to 0 if $frac{p_n}{r(n)}to infty$, as $n to infty$, where $r(n)=frac{1}{n^3}$.
قيم البحث
اقرأ أيضاً
Deficiency zero is an important network structure and has been the focus of many celebrated results within reaction network theory. In our previous paper textit{Prevalence of deficiency zero reaction networks in an ErdH os-Renyi framework}, we provid
ed a framework to quantify the prevalence of deficiency zero among randomly generated reaction networks. Specifically, given a randomly generated binary reaction network with $n$ species, with an edge between two arbitrary vertices occurring independently with probability $p_n$, we established the threshold function $r(n)=frac{1}{n^3}$ such that the probability of the random network being deficiency zero converges to 1 if $frac{p_n}{r(n)}to 0$ and converges to 0 if $frac{p_n}{r(n)}toinfty$, as $n to infty$. With the base ErdH os-Renyi framework as a starting point, the current paper provides a significantly more flexible framework by weighting the edge probabilities via control parameters $alpha_{i,j}$, with $i,jin {0,1,2}$ enumerating the types of possible vertices (zeroth, first, or second order). The control parameters can be chosen to generate random reaction networks with a specific underlying structure, such as closed networks with very few inflow and outflow reactions, or open networks with abundant inflow and outflow. Under this new framework, for each choice of control parameters ${alpha_{i,j}}$, we establish a threshold function $r(n,{alpha_{i,j}})$ such that the probability of the random network being deficiency zero converges to 1 if $frac{p_n}{r(n,{alpha_{i,j}})}to 0$ and converges to 0 if $frac{p_n}{r(n,{alpha_{i,j}})}to infty$.
We consider stochastically modeled chemical reaction systems with mass-action kinetics and prove that a product-form stationary distribution exists for each closed, irreducible subset of the state space if an analogous deterministically modeled syste
m with mass-action kinetics admits a complex balanced equilibrium. Feinbergs deficiency zero theorem then implies that such a distribution exists so long as the corresponding chemical network is weakly reversible and has a deficiency of zero. The main parameter of the stationary distribution for the stochastically modeled system is a complex balanced equilibrium value for the corresponding deterministically modeled system. We also generalize our main result to some non-mass-action kinetics.
Chemical reaction networks describe interactions between biochemical species. Once an underlying reaction network is given for a biochemical system, the system dynamics can be modelled with various mathematical frameworks such as continuous time Mark
ov processes. In this manuscript, the identifiability of the underlying network structure with a given stochastic system dynamics is studied. It is shown that some data types related to the associated stochastic dynamics can uniquely identify the underlying network structure as well as the system parameters. The accuracy of the presented network inference is investigated when given dynamical data is obtained via stochastic simulations.
Graph shotgun assembly refers to the problem of reconstructing a graph from a collection of local neighborhoods. In this paper, we consider shotgun assembly of ErdH{o}s-Renyi random graphs $G(n, p_n)$, where $p_n = n^{-alpha}$ for $0 < alpha < 1$. We
consider both reconstruction up to isomorphism as well as exact reconstruction (recovering the vertex labels as well as the structure). We show that given the collection of distance-$1$ neighborhoods, $G$ is exactly reconstructable for $0 < alpha < frac{1}{3}$, but not reconstructable for $frac{1}{2} < alpha < 1$. Given the collection of distance-$2$ neighborhoods, $G$ is exactly reconstructable for $0 < alpha < frac{1}{2}$, but not reconstructable for $frac{3}{4} < alpha < 1$.
Given an unlabeled graph $G$ on $n$ vertices, let ${N_{G}(v)}_{v}$ be the collection of subgraphs of $G$, where for each vertex $v$ of $G$, $N_{G}(v)$ is the subgraph of $G$ induced by vertices of $G$ of distance at most one from $v$. We show that th
ere are universal constants $C,c>0$ with the following property. Let the sequence $(p_n)_{n=1}^infty$ satisfy $n^{-1/2}log^C nleq p_nleq c$. For each $n$, let $Gamma_n$ be an unlabeled $G(n,p_n)$ Erdos-Renyi graph. Then with probability $1-o(1)$, any unlabeled graph $tilde Gamma_n$ on $n$ vertices with ${N_{tilde Gamma_n}(v)}_{v}={N_{Gamma_n}(v)}_{v}$ must coincide with $Gamma_n$. This establishes $p_n= tilde Theta(n^{-1/2})$ as the transition for the density parameter $p_n$ between reconstructability and non-reconstructability of Erdos-Renyi graphs from their $1$--neighborhoods, answering a question of Gaudio and Mossel.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
