Impact of measurement noise, experimental design, and estimation methods on Modular Response Analysis based network reconstruction

Abstract

Modular Response Analysis (MRA) is a method to reconstruct signalling networks from steady-state perturbation data which has frequently been used in different settings. Since these data are usually noisy due to multi-step measurement procedures and biological variability, it is important to investigate the effect of this noise onto network reconstruction. Here we present a systematic study to investigate propagation of noise from concentration measurements to network structures. Therefore, we design an in silico study of the MAPK and the p53 signalling pathways with realistic noise settings. We make use of statistical concepts and measures to evaluate accuracy and precision of individual inferred interactions and resulting network structures. Our results allow to derive clear recommendations to optimize the performance of MRA based network reconstruction: First, large perturbations are favorable in terms of accuracy even for models with non-linear steady-state response curves. Second, a single control measurement for different perturbation experiments seems to be sufficient for network reconstruction, and third, we recommend to execute the MRA workflow with the mean of different replicates for concentration measurements rather than using computationally more involved regression strategies.

An R package for generic modular response analysis and its application to estrogen and retinoic acid receptor crosstalk

“…Lastly, one million matrices were generated sampling normal distributions, one distribution for each with mean equal to the average of the 6 raw data replicates and specific variance obtained by SPC. Note that, in practice, different displayed very different variances in some cases on the contrary to Thomaseth et al study 27 . The 95% CIs were obtained from the 2.5th and 97.5th percentiles.…”

“…SPC can be used for normally distributed data, which will be the case here, but also in a nonparametric manner. In addition to SPC, our bootstrap procedure was also influenced by recent results discussing the propagation of noise in the data across MRA calculations 27 . These results showed that CI estimations on MRA parameters are best obtained by considering the noise on global response coefficients.…”

A systematic analysis of signaling reactivation and drug resistance

“…The local responses ðr Xj XjÀ1 , r X1T Þ are also referred to as connection coefficients, because they quantify the strengths of direct connections between network nodes (Bastiaens et al, 2015;Kholodenko, 2007;Thomaseth et al, 2018). The connection coefficients r XjXjÀ1 are positive because each kinase is activated by its immediately preceding kinase.…”

Model-based identification of the crosstalks and feedbacks that determine the doxorubicin response dynamics of the JNK-p38-p53 network

“…For example, the observed impairment of the p53 response in response to the JNK inhibitor could be due to a direct JNK-p53 interaction or an indirect JNK-p38-p53 interaction loop. To detangle direct from indirect effects, formal network reconstruction techniques have to be applied 24–28 . Here, we use the constructed model and parameter estimation to identify the interactions parameters.…”