ADMIT (Analysis, Design and Model Invalidation Toolbox) is a MATLAB™ - based toolbox for model invalidation, state and parameter estimation using unknown-but-bounded quantitative data or qualitative information given as if-then statements.
Mathematical modeling and analysis have become, for the study of biological
and cellular processes, an important complement to experimental research.
However, the structural and quantitative knowledge available for such
processes is frequently limited, and measurements are often subject to
inherent and possibly large uncertainties or even only of qualitative
nature. This results in competing model hypotheses, whose kinetic
parameters may not be experimentally determinable. Discriminating among
these alternatives and estimating their kinetic parameters is crucial to
improve the understanding of the considered process, and to benefit from
the analytical tools at hand.
ADMIT implements novel algorithms for modeling and analysis of various types of biological networks such as signaling, metabolic or gene-regulation networks, and networks involving discrete variables. Nonlinear constraint satisfaction problems are easily constructed given the quantitative, qualitative information, and model descriptions, as illustrated in the tutorial and in the examples. A detailed understanding of the underlying mathematical concepts is not needed to run the examples or to produce own ones. Compared to approaches based on samples, the set-based approach allows definite statements on entire regions in the parameter space to be made. Furthermore, since only unknown- but-bounded uncertainties are assumed, no assumptions on statistics of measurements have to be made.
ADMIT was tested on Mac OS 10.6.x, Mac OS 10.7, Linux,
Windows XP, Windows 7.
ADMIT needs MatLab™ R2010b - R2017a and the SYMBOLIC Toolbox. Additionally Matlab's OPTIMIZATION Toolbox is recommended.
WARNING! MatLab™ versions newer than R2017a are incompatible with ADMIT due to changes in symbolic toolbox.
To improve estimation results, installation of Yalmip and a solver of your choice, e.g. SeDuMi, CPLEX, or Gurobi is recommended.
To be able to import SBML models or models developed with the SBToolbox2 (by Henning Schmidt et al.), please install the SBToolbox2 and libSBML.
Major release update: ADMIT 2.0 is available for download!
ADMIT is free for academic and non-commercial use. A commercial use of this free academic version is not permitted. For commercial use please contact us ().
|Licensing Conditions and Copyright Information|
|1.||The LICENSER (Otto-von-Guericke-Universitaet Magdeburg/Germany)
Institute for Automation Engineering, Chair of Systems Theory and
Automatic Control) authorizes the LICENSEE to use ADMIT without any fee
for academic and non-commercial purposes only.
|2.||The LICENSER does not adhere to any problems that arise directly
indirectly from using ADMIT. ADMIT is distributed in the hope that it
will be useful but WITHOUT ANY WARRANTY; without even the implied
warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
|3.||The copyright for ADMIT remains with the LICENSER.|
|4.||The LICENSEE has to ensure that ADMIT or parts of it are not
distributed outside the institution of the LICENSEE.
|5.||When publishing results gained by using ADMIT, the LICENSEE has to
mention ADMIT with appropriate citations (ask the LICENSER for an
appropriate list of references).
|6.||The LICENSER reminds the LICENSEE that for using ADMIT, the
commercial program MATLAB is required. In order to get a fully
functional version of ADMIT, the LICENSEE has to get the appropriate
For installation simply unpack the compressed file and run the installation script (installADMIT.m).
S. Streif, A. Savchenko, P. Rumschinski, S. Borchers, and R. Findeisen. ADMIT: a toolbox for guaranteed model invalidation, estimation and qualitative-quantitative modeling. Bioinformatics, 28(9):1290-1291, 2012.
P. Rumschinski, S. Borchers, S. Bosio, R. Weismantel and R. Findeisen. Set-based Parameter Estimation and Model Invalidation for Biochemical Reaction Networks. BMC Systems Biology 4:69, 2010.
S. Borchers, P. Rumschinski, S. Bosio, R. Weismantel and R. Findeisen. A set-based Framework for Coherent Model Invalidation and Parameter Estimation of Discrete-time Nonlinear Systems. Proc. 48th IEEE Conference on Decision and Control (CDC'09), Shanghai, China, 2009.
P. Rumschinski, S. Streif, and R. Findeisen. Combining qualitative information and semi-quantitative data for guaranteed invalidation of biochemical network models. Int. J. Robust Nonlin. Control, 22(10):1157-1173, 2012.
J. Hasenauer, P. Rumschinski, S. Waldherr, S. Borchers, F. Allgöwer and R. Findeisen. Guaranteed Steady State Bounds for Uncertain (Bio-)Chemical Processes using Infeasibility Certificates. Journal of Process Control 20(9):1076-1083, 2010.
J. A. Paulson, D. M. Raimondo, R. D. Braatz, R. Findeisen, and S. Streif. Guaranteed active fault diagnosis for uncertain nonlinear systems. In Proc. European Control Conference (ECC), Strasbourg, France, 2014. In press.
D. Hast, S. Streif, and R. Findeisen. Guaranteed parametric fault diagnosability for nonlinear systems. In Proc. 52nd IEEE Conference on Decision and Control (CDC), pages 5662-5667, Florence, Italy, 2013.
A. Savchenko, P. Rumschinski, S. Streif, and R. Findeisen. Complete diagnosability of abrupt faults using set-based sensitivities. In Proc. 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SafeProcess), pages 860-865, Mexico City, 2012.
A. Savchenko, P. Rumschinski and R. Findeisen. Fault Diagnosis for Polynomial Hybrid Systems. Proc. 18th IFAC World Congress (IFAC'11), Milano, Italy, 2011.
S. Streif, N. Strobel, and R. Findeisen. Inner approximations of consistent parameter sets via constraint inversion and mixed-integer linear programming. In Proc. 12th IFAC International Symposium on Computer Applications in Biotechnology (CAB), pages 326-331, Mumbai, India, 2013.
S. S. Borchers and R. Findeisen. Outlier detection for polynomial systems using semidefinite relaxations. In Proc. IFAC Symposium on Nonlinear Control Systems, NOLCOS'13, pages 761-766, Toulouse, France, 2013.
S. Streif, M. Karl, and R. Findeisen. Outlier analysis in set-based estimation for nonlinear systems using convex relaxations. In Proc. European Control Conference (ECC), pages 2921-2926, Zurich, Switzerland, 2013.
A. Savchenko, P. Andonov, S. Streif, and R. Findeisen. Guaranteed set-based controller parameter estimation for nonlinear systems - magnetic levitation platform as a case study. In Proc. 19th IFAC World Congress, Cape Town, South Africa, 2014. In press.
S. Streif, M. Kögel, T. Bäthge, and R. Findeisen. Robust nonlinear model predictive control with constraint satisfaction: A relaxation-based approach. In Proc. 19th IFAC World Congress, Cape Town, South Africa, 2014. Invited session paper. In press.