Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number

Abstract

Identifying the sources of cell-to-cell variability in signaling dynamics is essential to understand drug response variability and develop effective therapeutics. However, it is challenging because not all signaling intermediate reactions can be experimentally measured simultaneously. This can be overcome by replacing them with a single random time delay, but the resulting process is non-Markovian, making it difficult to infer cell-to-cell heterogeneity in reaction rates and time delays. To address this, we developed an efficient and scalable moment-based Bayesian inference method (MBI) with a user-friendly computational package that infers cell-to-cell heterogeneity in the non-Markovian signaling process. We applied MBI to single-cell expression profiles from promoters responding to antibiotics and discovered a major source of cell-to-cell variability in antibiotic stress response: the number of rate-limiting steps in signaling cascades. This knowledge can help identify effective therapies that destroy all pathogenic or cancer cells, and the approach can be applied to precision medicine.

Spontaneous vortex formation by microswimmers with retarded attractions

“…Importantly, the cascades of complex biochemical/biophysical processes 17,18 needed to transform signal perception into a navigational reaction inevitably result in retarded interactions upon coarsegraining 19 (cf. supplementary Table S1).…”

Inferring delays in partially observed gene regulatory networks

“…A simulation-based MCMC method can be developed for other dynamical models as long as a likelihood function is available. While we used a continuous-time Markov Chain, which efficiently explains a biochemical reaction network with a low copy number of molecules, one can also use a stochastic differential equation (Calderazzo et al ., 2018; Ruttor and Opper, 2009) which is accurate when the copy numbers are higher, an agent-based model (Grazzini et al, 2017), or a delay differential equation (Kim et al ., 2022). Thus, we expect that our framework can be extended to various stochastic models of non-Markovian GRNs, and thus characterize the dynamics of a variety of systems from partial observations.…”

“…Recently, inference methods have been proposed based on the assumption that the unobserved processes are sequential, and thus can be modeled by introducing a delay (Jiang et al ., 2021; Heron et al ., 2007; Calderazzo et al ., 2018; Choi et al ., 2020; Cortez et al ., 2022; Barrio et al ., 2013; Leier et al ., 2014; Gomez et al ., 2016; Kim et al ., 2022). The resulting models are non-Markovian, as system dynamics depends not only on the present, but also past states.…”

“…Recently, inference methods have been proposed based on the assumption that the unobserved processes are sequential, and thus can be modeled by introducing a delay (Jiang et al, 2021;Heron et al, 2007;Calderazzo et al, 2018;Choi et al, 2020;Cortez et al, 2022; Barrio i i "output" -2022/11/27 -13:34 -page 2 -#2 i i i i i i 2 Hong et al et al, 2013;Leier et al, 2014;Gomez et al, 2016;Kim et al, 2022).…”

Spatially coordinated collective phosphorylation filters spatiotemporal noises for precise circadian timekeeping

“…Furthermore, since directly simulating spatiotemporal dynamics during the cytoplasmic trafficking of thousands of PER molecules is computationally intractable, we describe the process with a distributed time delay (𝜏) [48][49][50][51] . This time delay 𝜏 is assumed to be gammadistributed, which is similar to the distribution of time spent during the cytoplasmic trafficking of proteins 33 .…”