Karl Thomaseth,



Concise graphical and/or textual notations are ubiquitously used to represent the physical structure of dynamical systems. Each different notation can be normally translated directly into mathematical expressions of system equations that allow further application of general system theoretical results and techniques for the analysis and exploitation of the studied systems, e.g. stability, controllability, sensitivity with respect to parameters, identifiability, optimization, open- and closed-loop control, etc.
The most frequently applied symbolic manipulation is differentiation, which is readily available in modern simulation programs, such as Matlab/Simulink, which do however seldom provide the analytic expressions of the simulated systems.
With the aim to overcome this limitation, i.e. to make available in symbolic form the dynamic model equations starting from a structural representation of a system, the presented software tool PANSYM has been developed over the years as a marginal project to support modeling research on biomedical systems. Although mostly still in prototypal form, the software may become useful also to others because of some interesting and unique features regarding automatic generation of: (1) ODE model equations from multidisciplinary system representations (compartmental, electrical, biochemical, bondgraphs) (DAE are transformed into ODE by computer algebra); (2) sensitivity differential equations for dependable calculations instead of numerical differentiation; (3) adjoint differential equations arising from optimal control problems; (4) source code for: (a) numerical simulation exploiting multiprocessor architectures (Fortran); (b) documentation (Latex); (c) model simulation and identification ( R ). The ad hoc coding of new formatting routines for other applications, e.g. extended Kalman filtering, and programming languages, e.g. Matlab, C++, is feasible and little time-consuming.
Latest developments include: (i) identification of population models using advanced statistical approaches, such as Nonlinear Mixed Effects Models and Bayesian inference based on Markov Chain Monte Carlo; (ii) analysis of metabolic maps with generation of dynamic equations for selected pathways.
Future plans include modeling of isotopomer dynamics for studying substrate recycling in intermediate metabolism.


Date: 2008-Jul-24     Time: 10:00:00     Room: 336

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