2013/03/20 by Thomas Dierkes, Susanna Röblitz, Dierkes, Thomas +5
Biochemistry, Genetics and Molecular Biology · Chemistry · Computer Science · #65C20 #65L04 #65L09 (Primary) 49M15 #65L80 #92C42 (Secondary) #Computational Drug Discovery Methods #Computational Engineering #FOS: Biological sciences #FOS: Computer and information sciences #Finance #G.1.6 #G.1.7 #Gene Regulatory Network Analysis #J.3 #Mathematical Software (cs.MS) #Quantitative Methods (q-bio.QM) #and Science (cs.CE) #thermodynamics and calorimetric analyses
paper · pdf · doi:10.48550/arxiv.1303.4928
openalex publication_date 2013/03/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Modelling, parameter identification, and simulation play an important role in systems biology. Usually, the goal is to determine parameter values that minimise the difference between experimental measurement values and model predictions in a least-squares sense. Large-scale biological networks, however, often suffer from missing data for parameter identification. Thus, the least-squares problems are rank-deficient and solutions are not unique. Many common optimisation methods ignore this detail because they do not take into account the structure of the underlying inverse problem. These algorithms simply return a "solution" without additional information on identifiability or uniqueness. This can yield misleading results, especially if parameters are co-regulated and data are noisy.