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We present an overview of the current status of the European collaborative
project PAEON. The challenge of PAEON is to provide specialists in reproduc-
tive medicine with a computerised model of the menstrual cycle under normal
and various pathological conditions, which will allow them to get further in-
sight in fertility dynamics. This model also enables the simulation of treatment
protocols, which were used within in vitro fertilization. By the definition of
virtual patients through biologically admissible parametrizations our approach
allows not only the evaluation of a given treatment strategy in silico, but also
the design and optimization of such protocols. Once a protocol is formalized
in the virtual hospital, the success can be controlled by a treatment execution
monitor, which works then as a clinical decision support system. All these tools
will be combined in a virtual hospital environment, enabling the access to the
PAEON services through the web.
In this paper, a down-to-earth approach to purely data-based modelling
of unknown dynamical systems is presented. Starting from a classical, explicit ODE
formulation y=f(t,y) of a dynamical system, a method determining the unknown
right-hand side f(t,y) from some trajectory data y_k(t_j), possibly very sparse, is given.
As illustrative examples, a semi-standard predator-prey model is reconstructed from a
data set describing the population numbers of hares and lynxes over a period of twenty
years [1], and a simple damped pendulum system with a highly non-linear right-hand
side is recovered from some artificial but very sparse data [2].
In this paper, a down-to-earth approach to purely data-based modelling of unknown dynamical systems is presented. Starting from a classical, explicit ODE formulation y=f(t,y) of a dynamical system, a method determining the unknown right-hand side f(t,y) from some trajectory data y_k(t_j), possibly very sparse, is given. As illustrative examples, a semi-standard predator-prey model is reconstructed from a data set describing the population numbers of hares and lynxes over a period of twenty years, and a simple damped pendulum system with a highly non-linear right-hand side is recovered from some artificial but very sparse data.
Modelling, parameter identification, and simulation play an important rôle in Systems Biology. In recent years, various software packages have been established for scientific use in both licencing types, open source as well as commercial. Many of these codes are based on inefficient and mathematically outdated algorithms. By introducing the package BioPARKIN recently developed at ZIB, we want to improve this situation significantly. The development of the software BioPARKIN involves long standing mathematical ideas that, however, have not yet entered the field of Systems Biology, as well as new ideas and tools that are particularly important for the analysis of the dynamics of biological networks. BioPARKIN originates from the package PARKIN, written by P.Deuflhard and U.Nowak, that has been applied successfully for parameter identification in physical chemistry for many years.
In this article we present a new approach to estimate the change of the present value of a given cashflow pattern caused by an interest rate shift. Our approximation is based on analysing the evolution of the present value function through a linear differential equation. The outcome is far more accurate than the standard approach achieved by a Taylor expansion. Furthermore, we derive an approximation formula of second order that produces nearly accurate results. In particular, we prove that our method is superior to any known alternative approximation formula based on duration. In order to demonstrate the power of this improved approximation we apply it to coupon bonds, level annuities, and level perpetuities. We finally generalise the approach to a non-flat term structure. As for applications in insurance, we estimate the change of the discounted value of future liabilities due to a proportional shift in the set of capital accumulation factors. These findings are of particular importance to capital adequacy calculations with respect to interest rate stress scenarios that are part of regulatory solvency requirements.