On Characterization of Inverse Data in the Boundary Control Method

We deal with a dynamical system \begin{align*} & u_{tt}-\Delta u+qu=0 && {\rm in}\,\,\,\Omega \times (0,T)\\ & u\big|_{t=0}=u_t\big|_{t=0}=0 && {\rm in}\,\,\,\overline \Omega\\ & \partial_\nu u = f && {\rm in}\,\,\,\partial\Omega \times [0,T]\,, \end{align*} where $\Omega \subset {\mathbb R}^n$ is a bounded domain, $q \in L_\infty(\Omega)$ a real-valued function, $\nu$ the outward normal to $\partial \Omega$, $u=u^f(x,t)$ a solution. The input/output correspondence is realized by a response operator $R^T: f \mapsto u^f\big|_{\partial\Omega \times [0,T]}$ and its relevant extension by hyperbolicity $R^{2T}$. Ope\-rator $R^{2T}$ is determined by $q\big|_{\Omega^T}$, where $\Omega^T:=\{x \in \Omega\,|\,\,{\rm dist\,}(x,\partial \Omega)<T\}$. The inverse problem is: Given $R^{2T}$ to recover $q$ in $\Omega^T$. We solve this problem by the boundary control method and describe the {\it ne\-ces\-sary and sufficient} conditions on $R^{2T}$, which provide its solvability.Comment: 33 pages, 1 figur

Tropical geometries and dynamics of biochemical networks. Application to hybrid cell cycle models

We use the Litvinov-Maslov correspondence principle to reduce and hybridize networks of biochemical reactions. We apply this method to a cell cycle oscillator model. The reduced and hybridized model can be used as a hybrid model for the cell cycle. We also propose a practical recipe for detecting quasi-equilibrium QE reactions and quasi-steady state QSS species in biochemical models with rational rate functions and use this recipe for model reduction. Interestingly, the QE/QSS invariant manifold of the smooth model and the reduced dynamics along this manifold can be put into correspondence to the tropical variety of the hybridization and to sliding modes along this variety, respectivelyComment: conference SASB 2011, to be published in Electronic Notes in Theoretical Computer Scienc

Flexible and robust networks

We consider networks with two types of nodes. The v-nodes, called centers, are hyper- connected and interact one to another via many u-nodes, called satellites. This central- ized architecture, widespread in gene networks, possesses two fundamental properties. Namely, this organization creates feedback loops that are capable to generate practically any prescribed patterning dynamics, chaotic or periodic, or having a number of equilib- rium states. Moreover, this organization is robust with respect to random perturbations of the system.Comment: Journal of Bioinformatics and Computational Biology, in pres