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MACoRN Program

The MACoRN workshop program will include invited talks as detailed below. All workshop talks will be held in building 56, lecture room 230 at the University of Kaiserslautern.

Invited Talk 1
"Kronecker-based infinite level-dependent QBDs: Matrix analytic solution versus simulation"

Speaker Tugrul Dayar (Bilkent University, Turkey)
In this talk, we show how systems of stochastic chemical kinetics can be modeled using infinite level-dependent quasi-birth-and-death processes (LDQBDs), expressed in the form of Kronecker products, and analyzed for their steady-state probability distribution with the help of Lyapunov theory. Experiments are performed on systems having two or more countably infinite state space subsystems. Results indicate that, albeit more memory consuming, there are many cases where a matrix analytic solution coupled with Lyapunov theory yields a faster and more accurate steady-state measure compared to that obtained with simulation.

This is a joint work with Muhsin Can Orhan.
Time March 21, 2012 (Wednesday), 9:00h - 10:00h
Location Building 56, lecture room 230

Invited Talk 2
"The Monte Carlo EM method for the parameter estimation of biological models "

Speaker Andras Horvath (University of Turin, Italy)
It is often the case in modeling biological phenomena that the structure and the effect of the involved interactions are known but the rates of the interactions are neither known nor can easily be determined by experiments. This talk deals with the estimation of the rate parameters of reaction networks in a general and abstract context. In particular, we consider the case in which the phenomenon under study is stochastic and a continuous-time Markov chain (CTMC) is appropriate for its modeling. Further, we assume that the evolution of the system under study cannot be observed continuously but only at discrete sampling points between which a large amount of reactions can occur.
The parameter estimation of stochastic reaction networks is often performed by applying the principle of maximum likelihood. In this talk we describe how the Expectation-Maximisation (EM) method, which is a technique for maximum likelihood estimation in case of incomplete data, can be adopted to estimate kinetic rates of reaction networks. In particular, because of the huge state space of the underlying CTMC, it is convenient to use such a variant of the EM approach, namely the Monte Carlo EM (MCEM) method, which makes use of simulation for the analysis of the model. We show that in case of mass action kinetics the application of the MCEM method results in an efficient and surprisingly simple estimation procedure. We provide examples to illustrate the characteristics of the approach and show that it is applicable in case of systems of reactions involving several species.
Time March 21, 2012 (Wednesday), 10:15h - 11:15h
Location Building 56, lecture room 230

Invited Talk 3
"Robustness analysis for biological systems - from qualitative to quantitative models"

Speaker Frank Allgöwer (University of Stuttgart, Germany)
For most biological systems only models with large structural and parametric uncertainties are available. While for some signal transduction pathways rough estimates for kinetic parameters can be determined, for most gene regulation networks not even the interaction structure is fully understood. This complicates the already difficult problem of analyzing and predicting the often complex dynamical behavior of these systems and shows that there is a need for new analysis methods accounting for the respective degree of uncertainty.
In this talk, we present two methods which allow to study the dynamical robustness properties of an uncertain system. The first method is capable of assessing the ability of a gene regulation network to generate a desired multistable behavior in a maximally robust way. For this analysis, merely qualitative knowledge of the interaction structure is required. The second method was developed to study the existence of oscillations and bistability of systems with large parametric uncertainties. Therefore, variations in feedback circuit gains are studied. Both methods can be used to gain insight into highly uncertain systems using different levels of information.

This is a joint work with Steffen Waldherr, Christian Breindl and Daniella Schittler.
Time March 21, 2012 (Wednesday), 11:30h - 12:30h
Location Building 56, lecture room 230

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