Dynamic pathway modeling: feasibility analysis and optimal experimental design.
A major challenge in systems biology is to evaluate the feasibility of a biological research project prior to its realization. Since experiments are animals-, cost- and time-consuming, approaches allowing researchers to discriminate alternative hypotheses with a minimal set of experiments are highly desirable. Given a null hypothesis and alternative model, as well as laboratory constraints like observable players, sample size, noise level, and stimulation options, we suggest a method to obtain a list of required experiments in order to significantly reject the null hypothesis model M0 if a specified alternative model MA is realized. For this purpose, we estimate the power to detect a violation of M0 by means of Monte Carlo simulations. Iteratively, the power is maximized over all feasible stimulations of the system using multi-experiment fitting, leading to an optimal combination of experimental settings to discriminate the null hypothesis and alternative model. We prove the importance of simultaneous modeling of combined experiments with quantitative, highly sampled in vivo measurements from the Jak/STAT5 signaling pathway in fibroblasts, stimulated with erythropoietin (Epo). Afterwards we apply the presented iterative experimental design approach to the Jak/STAT3 pathway of primary hepatocytes stimulated with IL-6. Our approach offers the possibility of deciding which scientific questions can be answered based on existing laboratory constraints. To be able to concentrate on feasible questions on account of inexpensive computational simulations yields not only enormous cost and time saving, but also helps to specify realizable, systematic research projects in advance.
Maiwald
Thomas
T
Freiburg Center for Data Analysis and Modeling, Freiburg University, Eckerstrasse 1, 79104 Freiburg, Germany. maiwald@fdm.uni-freiburg.de
Kreutz
Clemens
C
Pfeifer
Andrea C
AC
Bohl
Sebastian
S
Klingmüller
Ursula
U
Timmer
Jens
J
D000465:Algorithms; D001698:Biomedical Engineering / Q000379:methods; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D005240:Feasibility Studies; D015870:Gene Expression / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D012107:Research Design; D015398:Signal Transduction / Q000502:physiology*
D020543:Proteome