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Identifying parameter regions for multistationarity
Mathematical modelling has become an established tool for studying the
dynamics of biological systems. Current applications range from building models
that reproduce quantitative data to identifying systems with predefined
qualitative features, such as switching behaviour, bistability or oscillations.
Mathematically, the latter question amounts to identifying parameter values
associated with a given qualitative feature.
We introduce a procedure to partition the parameter space of a parameterized
system of ordinary differential equations into regions for which the system has
a unique or multiple equilibria. The procedure is based on the computation of
the Brouwer degree, and it creates a multivariate polynomial with parameter
depending coefficients. The signs of the coefficients determine parameter
regions with and without multistationarity. A particular strength of the
procedure is the avoidance of numerical analysis and parameter sampling.
The procedure consists of a number of steps. Each of these steps might be
addressed algorithmically using various computer programs and available
software, or manually. We demonstrate our procedure on several models of gene
transcription and cell signalling, and show that in many cases we obtain a
complete partitioning of the parameter space with respect to multistationarity.Comment: In this version the paper has been substantially rewritten and
reorganised. Theorem 1 has been reformulated and Corollary 1 adde
A unified view on bipartite species-reaction and interaction graphs for chemical reaction networks
The Jacobian matrix of a dynamic system and its principal minors play a
prominent role in the study of qualitative dynamics and bifurcation analysis.
When interpreting the Jacobian as an adjacency matrix of an interaction graph,
its principal minors correspond to sets of disjoint cycles in this graph and
conditions for various dynamic behaviors can be inferred from its cycle
structure. For chemical reaction systems, more fine-grained analyses are
possible by studying a bipartite species-reaction graph. Several results on
injectivity, multistationarity, and bifurcations of a chemical reaction system
have been derived by using various definitions of such bipartite graph. Here,
we present a new definition of the species-reaction graph that more directly
connects the cycle structure with determinant expansion terms, principal
minors, and the coefficients of the characteristic polynomial and encompasses
previous graph constructions as special cases. This graph has a direct relation
to the interaction graph, and properties of cycles and sub-graphs can be
translated in both directions. A simple equivalence relation enables to
decompose determinant expansions more directly and allows simpler and more
direct proofs of previous results.Comment: 27 pages. submitted to J. Math. Bio
A passivity-based stability criterion for a class of interconnected systems and applications to biochemical reaction networks
This paper presents a stability test for a class of interconnected nonlinear
systems motivated by biochemical reaction networks. One of the main results
determines global asymptotic stability of the network from the diagonal
stability of a "dissipativity matrix" which incorporates information about the
passivity properties of the subsystems, the interconnection structure of the
network, and the signs of the interconnection terms. This stability test
encompasses the "secant criterion" for cyclic networks presented in our
previous paper, and extends it to a general interconnection structure
represented by a graph. A second main result allows one to accommodate state
products. This extension makes the new stability criterion applicable to a
broader class of models, even in the case of cyclic systems. The new stability
test is illustrated on a mitogen activated protein kinase (MAPK) cascade model,
and on a branched interconnection structure motivated by metabolic networks.
Finally, another result addresses the robustness of stability in the presence
of diffusion terms in a compartmental system made out of identical systems.Comment: See http://www.math.rutgers.edu/~sontag/PUBDIR/index.html for related
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A delay-dependent LMI approach to dynamics analysis of discrete-time recurrent neural networks with time-varying delays
This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Elsevier Ltd.In this Letter, the analysis problem for the existence and stability of periodic solutions is investigated for a class of general discrete-time recurrent neural networks with time-varying delays. For the neural networks under study, a generalized activation function is considered, and the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. By employing the latest free-weighting matrix method, an appropriate Lyapunov–Krasovskii functional is constructed and several sufficient conditions are established to ensure the existence, uniqueness, and globally exponential stability of the periodic solution for the addressed neural network. The conditions are dependent on both the lower bound and upper bound of the time-varying time delays. Furthermore, the conditions are expressed in terms of the linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. Two simulation examples are given to show the effectiveness and less conservatism of the proposed criteria.This work was supported in part by the National Natural Science Foundation of China under Grant 50608072, an International Joint Project sponsored by the Royal Society of the UK and the National Natural Science Foundation of China, and the Alexander von Humboldt Foundation of Germany
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