Quantitative Systems Biology to decipher design principles of a dynamic cell cycle network: the “Maximum Allowable mammalian Trade–Off–Weight” (MAmTOW)

Abstract

Network complexity is required to lend cellular processes flexibility to respond timely to a variety of dynamic signals, while simultaneously warranting robustness to protect cellular integrity against perturbations. The cell cycle serves as a paradigm for such processes; it maintains its frequency and temporal structure (although these may differ among cell types) under the former, but accelerates under the latter. Cell cycle molecules act together in time and in different cellular compartments to execute cell type-specific programs. Strikingly, the timing at which molecular switches occur is controlled by abundance and stoichiometry of multiple proteins within complexes. However, traditional methods that investigate one effector at a time are insufficient to understand how modulation of protein complex dynamics at cell cycle transitions shapes responsiveness, yet preserving robustness. To overcome this shortcoming, we propose a multidisciplinary approach to gain a systems-level understanding of quantitative cell cycle dynamics in mammalian cells from a new perspective. By suggesting advanced experimental technologies and dedicated modeling approaches, we present innovative strategies (i) to measure absolute protein concentration in vivo, and (ii) to determine how protein dosage, e.g., altered protein abundance, and spatial (de)regulation may affect timing and robustness of phase transitions. We describe a method that we name “Maximum Allowable mammalian Trade–Off–Weight” (MAmTOW), which may be realized to determine the upper limit of gene copy numbers in mammalian cells. These aspects, not covered by current systems biology approaches, are essential requirements to generate precise computational models and identify (sub)network-centered nodes underlying a plethora of pathological conditions.

A Mechanistic Computational Model Reveals That Plasticity of CD4+ T Cell Differentiation Is a Function of Cytokine Composition and Dosage

“…The components (i.e., proteins and genes) in a logical model can have binary (0 or 1) states at any time t . The state of the network evolves stepwise based on the logical rules defined for each model component ( Helikar and Rogers, 2009 ; Helikar et al, 2012a , b , 2013 ; Naldi et al, 2015 ; Abou-Jaoudé et al, 2016 ; Barberis and Verbruggen, 2017 ; Linke et al, 2017 ).…”

“…Specifically, we and others propose that dosage- and timing-dependent impact of inputs, such as ILs, may impact the T cell differentiation ( Barberis et al, 2018 ; Martinez-Sanchez et al, 2018 ). This may be investigated by employing experimental methodologies that we have recently envisioned ( Barberis and Verbruggen, 2017 ). Furthermore, crosstalk between ILs and signaling cascades, such as the one governing the cell cycle, may impinge on a timely T cell-mediated protective response ( Barberis et al, 2018 ).…”

Simulation of Stimulation: Cytokine Dosage and Cell Cycle Crosstalk Driving Timing-Dependent T Cell Differentiation

“…Recently, we have devised a methodology to determine quantitatively the effects of gene dosage, thereby protein concentration, on in vivo cellular integrity, providing a detailed example for the eukaryotic cell cycle ( Barberis and Verbruggen, 2017 ). This methodology, which we coined “Maximum Allowable mammalian Trade-Off-Weight” (MAmTOW), relies on gene engineering strategies, such as the CRISPR/Cas9 technology, and may be combined with optogenetic tools that enable – upon light induction – the nuclear import and export of tagged proteins.…”

“…The goal of the methodology is to achieve a precise measurement of upper limit gene copy number (gene dosage) and microscopy-based visualization of protein spatiotemporal localization. Integrating this output with computer models provides information on cellular robustness ( Barberis and Verbruggen, 2017 ). Here, we propose that genetic engineering technologies such as the MAmTOW may also be successfully employed to investigate the weight of individual cytokines as well as components of TCR and CD28 pathways to tip the balance that modulates T cell activation, lineage decision and plasticity.…”

Systems biology: perspectives on multiscale modeling in research on endocrine-related cancers

“…Extracting such knowledge from large datasets by intuitive reasoning alone can be difficult or impossible and is often associated with a high risk of operator bias and/or error. Thus, new tools and approaches continue to emerge to deal with the challenges of working in high-dimensional data spaces and to enable integrating the spatial, temporal and cell context-specific nature of regulatory networks (Hoadley et al 2014, Leiserson et al 2015, Masoudi-Nejad et al 2015, Tape 2016, Barberis & Verbruggen 2017, Dimitrova et al 2017. New concepts, such as 'master regulator proteins' that may determine the transcriptional state of a cancer cell, also continue to arise (Califano & Alvarez 2017).…”