Numerical framework to model temporally resolved multi-stage dynamic systems

• Published in: Computer methods and programs in biomedicine Vol. 108; no. 2; pp. 750 - 759
• Main Authors: Zakharov, M.N, Bhasin, S, Szafran, A.T, Mancini, M.A, Jasuja, R
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ireland Ltd 01.11.2012; Elsevier
• Subjects: Androgen; Androgens - physiology; Binding; Biological and medical sciences; Chemical kinetics; Computer programs; Computer simulation; Dynamical systems; Hormones; Humans; Internal Medicine; LabVIEW; Mathematical models; Medical sciences; Models, Theoretical; MWC; Other; Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects); Signaling; Simulation; Software; Technology. Biomaterials. Equipments. Material. Instrumentation; Transcription, Genetic; MWC; Signaling; Androgen; Simulation; LabVIEW; Chemical kinetics; Biomedical engineering
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2012.04.010

Abstract Numerical modeling of steroid hormone signaling presents an exciting challenge involving spatiotemporal coordination of multiple events. Ligand binding in cytoplasm triggers dissociation and/or association of coregulators which subsequently regulate DNA binding and transcriptional activity in nucleus. In order to develop a comprehensive multi-stage model, it is imperative to follow not only the transcriptional outcomes but also the intermediate protein complexes. Accordingly, we developed a software toolkit for simulating complex biochemical pathways as a set of non-linear differential equations in LabVIEW (Laboratory Virtual Instrumentation and Engineering Workbench, National Instruments, Austin, TX) environment. The toolkit is visual, highly modular, loosely coupled with the rest of LabVIEW, scalable and extensible. The toolkit can be used to develop and validate biochemical models and estimate model parameters from existing experimental data. We illustrate the application of the toolkit for simulation of steroid hormone response in cells, and demonstrate how the toolkit can be employed for other biological and chemical systems as well. The software module presented here can be used stand-alone as well as built into data collection and analysis applications.