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To maintain normal functionality, it is necessary for a multicellular organism to generate robust responses to external temporal signals. However, the underlying mechanisms to coordinate the collective dynamics of cells remain poorly understood. Here, we study the calcium activity of biological neuron networks excited by periodic ATP stimuli. We use micropatterning to control the cells' physical connectivity. We find that whereas isolated cells become more synchronized in their calcium activity at long driving periods, connected cells become less synchronized, despite expressing more gap junctions which enable calcium exchange. To understand this result, we use a mathematical model in which a bifurcation analysis has previously shown coupling-induced desynchronization in an oscillatory network. Using parameters close to this bifurcation but in the excitable regime, we find that this desynchronization persists and can explain the experimental observations. The model further predicts that co-culturing with gap-junction-deficient cells should restore synchronization, which experiments confirm. Combining quantitative experiments, the physical and biological manipulation of cells, and mathematical modeling, our results suggest that cell-to-cell connectivity significantly affects how populations encode an external temporal signal as it slows down: Sparse networks synchronize due to longer entrainment, whereas highly connected networks can desynchronize due to dynamic frustration. Published by the American Physical Society2025 

Cells sense various environmental cues and subsequently process intracellular signals to decide their migration direction in many physiological and pathological processes. Although several signaling molecules and networks have been identified in these directed migrations, it still remains ambiguous to predict the migration direction under multiple and integrated cues, specifically chemical and fluidic cues. Here, we investigated the cellular signal processing machinery by reverse-engineering directed cell migration under integrated chemical and fluidic cues. We imposed controlled chemical and fluidic cues to cells using a microfluidic platform and analyzed the extracellular coupling of the cues with respect to the cellular detection limit. Then, the cell's migratory behavior was reverse-engineered to build a cellular signal processing system as a logic gate, which is based on a “selection” gate. This framework is further discussed with a minimal intracellular signaling network of a shared pathway model. The proposed framework of the ternary logic gate suggests a systematic view to understand how cells decode multiple cues and make decisions about the migration direction. 

Abstract Cell signaling networks are complex and often incompletely characterized, making it difficult to obtain a comprehensive picture of the mechanisms they encode. Mathematical modeling of these networks provides important clues, but the models themselves are often complex, and it is not always clear how to extract falsifiable predictions. Here we take an inverse approach, using experimental data at the cell level to deduce the minimal signaling network. We focus on cells’ response to multiple cues, specifically on the surprising case in which the response is antagonistic: the response to multiple cues is weaker than the response to the individual cues. We systematically build candidate signaling networks one node at a time, using the ubiquitous ingredients of (i) up- or down-regulation, (ii) molecular conversion, or (iii) reversible binding. In each case, our method reveals a minimal, interpretable signaling mechanism that explains the antagonistic response. Our work provides a systematic way to deduce molecular mechanisms from cell-level data.