A stochastic Boolean approach to enhance immunogenic cell death

Immunogenic cell death (ICD) has raised some interest over the past years for its role in triggering an antitumor immune response following cytotoxic interventions, such as chemotherapies. However, many uncertainties remain about the succession of events that are involved in ICD, the components that favor it or not, and how to intervene throughout the process to improve the immune response.

The experimental models needed for studying ICD are based on in vivo mouse models that are complicated and costly.  We propose here a mathematical approach to better characterize these events and suggest ways to enhance ICD. For that purpose, we have constructed a mathematical model using a software, UPMaBoSS, that simulates interactions between the key cell types of ICD, namely tumor cells, dendritic cells, CD4+ and CD8+ T cells. With this model, we were not only able to reproduce the timing of ICD events, but also to suggest two possible interventions: one that completely switches off ICD and another one that enhances it. These candidates will then be evaluated in breast cancer mice models.

Javiera Cortés-Ríos, Ricardo Leiva-Guerra, Cain Acuña-Benavides, Maria Rodriguez-Fernandez and Pedro A. Saa

Overflow metabolism refers to the phenomenon in which cells use fermentative pathways instead of respiration to generate energy in the presence of oxygen. The latter is highly unintuitive as cellular respiration is a more energy-efficient process. Interestingly, this process has been observed in many bacteria, yeasts and even mammalian cells since many years, and it was not until recently that a fundamental modelling framework based on resource (protein) allocation was established to describe it. Protein allocation models explain overflow metabolism based on the higher proteome cost for energy biogenesis of respiration compared to fermentation at high growth rates. To enable predictions, coarse-grained allocation models incorporate cross-sectioned proteome constraints such as respiratory, fermentation, and biomass fractions. While the latter have been effective for reproducing the observed data, they lack mechanistic detail. In this work, we developed a large metabolic model of E. coli (ECC2) provided with enzymatic constraints for representing overflow metabolism in greater detail. To this goal, turnover numbers from BRENDA and the typical total amount of enzyme in the cell (0.26 g/g) were mapped onto the model and used to constrain reaction fluxes. A sensitivity analysis was performed to identify the turnover constants with the highest contribution to overflow metabolism, which were then tuned using global optimization to reproduce the observations. The calibrated model accurately predicted acetate production (fermentation product) in the presence of oxygen from 0.52 1/h up to a maximum growth rate of 0.73 1/h. Notably, the maximum occupancy of the enzyme pool was reached just before acetate production began, highlighting the presence of an enzymatic bottleneck. Overall, this work sheds light on the metabolic mechanisms governing overflow metabolism.

Franz-Georg Wieland, Matias D. Zurbriggen, Wilfried Weber and Jens Timmer

Controlling cellular processes with light by introducing gene switches sensitive to different light conditions allows spatio-temporally precise manipulation of these processes. We have introduced these optogenetic switches in two recent applications. PULSE, the plant-usable light-switch elements allows for the use of optogenetic switches in plants under ambient white light, which has so far not been possible. In a further study, we showed how phase separation in the cell through intrinsically disordered regions can increase gene expression many-fold. For both applications, we created an ordinary differential equation model to accurately describe the system, assess its uncertainties, make accurate predictions, and to validate the results with independent measurements. The mathematical modelling allowed us to quantitatively characterize PULSE in protoplasts and to predict the systems behaviour for a wide range of stimulations. It furthermore allowed us to assess the effect of phase-separation on gene expression and the resulting fold-change of the transcription activation for two distinctly different systems with a joint approach.


1. Ochoa-Fernandez R, Abel NB, Wieland F-G, Schlegel J, Koch L-A, Miller JB, Engesser R, Giuriani G, Brandl SM, Timmer J, et al.: Optogenetic control of gene expression in plants in the presence of ambient white light. Nat Methods 2020, 17:717–725.

2. Schneider N, Wieland F-G, Kong D, Fischer AAM, Hörner M, Timmer J, Ye H, Weber W: Liquid-liquid phase separation of light-inducible transcription factors increases transcription activation in mammalian cells and mice. Sci Adv 2021, 7:eabd3568.

Fabian Froehlich, Luca Gerosa and Peter Sorger

The modular assembly of protein complexes enables cells to respond to external stimuli in a context-dependent manner. Signal transduction can be rewired by mutations or in response to perturbations such as small molecules, which may lead to inherent and adaptive resistance to targeted therapies. Conformational coupling between post translational modifications that transduce signaling and binding events makes it difficult to understand and predict rewiring. Thermodynamic kinetic models that can describe conformational coupling have been developed, but respective models are large and complex, complicating the comprehension of model simulations and predictions.
Here we demonstrate the use of a programmatic, thermodynamic rule-based formalism in PySB to describe conformational coupling of interactions. To make these models comprehensible, we introduce a novel causal deconvolution approach that facilitates the analysis of intertwined signaling channels. We combine this with a novel quantification of signaling gain in transient signals to provide simple and intuitive visualizations of drug mediated pathway rewiring.
We apply these methods to an ordinary differential equation model of adaptive resistance in melanoma (EGFR and ERK pathways, >1k molecular species, >10k reactions), accounting for paradoxical activation. We trained the model on absolute proteomic and phospho-proteomic, time-resolved transcriptomic as well as time-resolved immunofluorescence data, all in dose-response to small molecule inhibitors. We deconvolve oncogenic and physiological reaction channels to derive simple explanations for complex dose-response relationships, explain how synergy and antagonism can arise without direct drug interaction and establish a link between adaptive and genetic resistance in melanoma.

Stochastic biological systems in compartmentalized environments

Sebastian Persson

Advances in single-cell imaging has made multi-individual single-cell time-lapse data more common. Ideally, such single-cell data can be used to calibrate single-cell level dynamic models. However, calibrating a model to single-cell time-lapse data is challenging due to 1) cell-to-cell variability and 2) the fact that the measured output typically corresponds to fluorescent intensity. More specifically, factor 1) complicates parameter estimation, while factor 2) complicates model construction if the measured output corresponds to gene-expression. This is because most fluorescent proteins have a maturation time of several minutes. Hence, an increase in gene-expression will not manifest in the data until several minutes after said change.

Recently, we encountered both mentioned challenges when modelling the nutrient signalling network (SNF1-pathway) in budding yeast. To account for cell-to-cell variability in parameter estimation, we choose to compare the standard-two-stage (STS) approach and non-linear mixed effects modelling for our data, which is rich in both individuals and observations per individual. Overall, NLME proved superior to STS. To account for fluorescent maturation, we employed time-delays via delay differential equations (DDE:s). Overall, by using DDE:s in a NLME-framework we were able to proposes a negative feedback mechanism that works through the SNF1 complex, and thus further the understanding of nutrient sensing.

Oleksii Rukhlenko, Alfonso Bolado-Carrancio, Elena Nikonova, Mikhail Tsyganov, Anne Wheeler, Amaya Garcia Munoz, Walter Kolch, Alex von Kriegsheim and Boris Kholodenko

Cell migration relies on the coordination of actin dynamics at the leading and the trailing edges. During the mesenchymal type of migration, protrusive filamentous actin (F-actin) is cyclically polymerised/depolymerised at the cell’s leading edge, whereas the contractile, actomyosin-enriched trailing edge forms the rear. How these different dynamics coexist and get coordinated within the cell is unclear.

We have elucidated the spatial profiles of RhoA-Rac1 interactions in motile MDA-MB-231 breast cancer cells. Using proximity ligation assays, we showed that the concentration of complexes formed by RhoA and its downstream effectors DIA and ROCK depends on the spatial location along the longitudinal axis of polarized cells. RhoA primarily interacts with DIA at the cell leading edge, whereas RhoA - ROCK interactions are the strongest at the cell rear. Based on these findings, we have built a mathematical model to analyse RhoA-Rac1 signalling in space and time. The model predicted and the experiments confirmed that at the cell front the GTPase network exhibits oscillatory behavior with high average Rac1-GTP, whereas at the cell rear there is a (quasi)steady state with high RhoA-GTP and low Rac. The front and rear are connected by periodic, propagating GTPase waves. When the wave reaches the rear, RhoA-GTP transiently oscillates and then, following the rear retraction, the GTPase network dynamic pattern returns to the original state. Our modelling and experimental results showed how different GTPase dynamics at the leading edge and the trailing edge can govern distinct cytoskeleton processes and how moving cells reconcile these different dynamics. In addition, the model correctly described network responses to different inhibitor perturbations. The RhoA-Rac1 interaction network model defines minimal, autonomous biochemical machinery that is necessary and sufficient for biologically observed modes of cell movement.

Cemal Erdem, Sean M. Gross, Laura M. Heiser and Marc R. Birtwistle

A key missing capability in current cancer research is the ability to predict how a particular single cancer cell will respond to microenvironmental cues or a drug cocktail. Yet, it is not even possible to perform this task well for normal healthy cells. This work builds on the hypothesis that first principles, mechanistic models of how cells respond to different perturbagens will ultimately improve drug combination response predictions. However, building such single-cell models of complex, large-scale, and incompletely understood systems remains an extremely challenging task. Here, we defined an open-source pipeline for scalable, single-cell mechanistic modeling that converts simple, annotated input files (structured lists of species, parameters, reaction types) into an SBML model file. Using this pipeline, (i) we re-created one of the largest pan-cancer signaling models in the literature (774 species, 141 genes, 2400 reactions), (ii) enlarged the model to include Interferon-γ (IFNγ) signaling pathway (950 species, 150 genes, 2500 reactions), and (iii) re-parametrized the model to test and prioritize candidate mechanisms. Specifically, we used the enlarged model to test alternative mechanistic hypotheses for the experimental observations that IFNγ inhibits epidermal growth factor (EGF)-induced cell proliferation. Our single-cell-simulation-based analysis suggested, and experiments support that these observations are better explained by IFNγ-induced SOCS1 expression sequestering activated EGF receptors, thereby downregulating AKT activity, as opposed to direct IFNγ-induced upregulation of p21 expression. Finally, our new modeling format is available online and compatible with high performance (Kubernetes) computing platforms, enabling us to study virtual cell population responses. Overall, our new model enables easy modification of large mechanistic models and simulation of thousands of single-cell responses to multiple ligands and drug combinations.