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1. Coupled Feedback Loops Involving PAGE4, EMT and Notch Signaling Can Give Rise to Non-Genetic Heterogeneity in Prostate Cancer Cells

   https://doi.org/10.3390/e23030288  

   Singh, Divyoj; Bocci, Federico; Kulkarni, Prakash; Jolly, Mohit Kumar (March 2021, Entropy)

   null (Ed.)

   Non-genetic heterogeneity is emerging as a crucial factor underlying therapy resistance in multiple cancers. However, the design principles of regulatory networks underlying non-genetic heterogeneity in cancer remain poorly understood. Here, we investigate the coupled dynamics of feedback loops involving (a) oscillations in androgen receptor (AR) signaling mediated through an intrinsically disordered protein PAGE4, (b) multistability in epithelial–mesenchymal transition (EMT), and (c) Notch–Delta–Jagged signaling mediated cell-cell communication, each of which can generate non-genetic heterogeneity through multistability and/or oscillations. Our results show how different coupling strengths between AR and EMT signaling can lead to monostability, bistability, or oscillations in the levels of AR, as well as propagation of oscillations to EMT dynamics. These results reveal the emergent dynamics of coupled oscillatory and multi-stable systems and unravel mechanisms by which non-genetic heterogeneity in AR levels can be generated, which can act as a barrier to most existing therapies for prostate cancer patients. 

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2. Synchronization-based model for turbulent thermoacoustic systems

   https://doi.org/10.1007/s11071-023-08368-z  

   Weng, Yue; Unni, Vishnu R.; Sujith, R. I.; Saha, Abhishek (March 2023, Nonlinear Dynamics)

   Abstract We present a phenomenological reduced-order model to capture the transition to thermoacoustic instability in turbulent combustors. Based on the synchronization framework, the model considers the acoustic field and the unsteady heat release rate from turbulent reactive flow as two nonlinearly coupled sub-systems. To model combustion noise, we use a pair of nonlinearly coupled second-order ODEs to represent the unsteady heat release rate. This simple configuration, while nonlinearly coupled to another oscillator that represents the independent sub-system of acoustics (pressure oscillations) in the combustor, is able to produce chaos. Previous experimental studies have reported a route from low amplitude chaotic oscillation (i.e., combustion noise) to periodic oscillation through intermittency in turbulent combustors. By varying the coupling strength, the model can replicate the route of transition observed and reflect the coupled dynamics arising from the interplay of unsteady heat release rate and pressure oscillations. 

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3. Inferring the structures of signaling motifs from paired dynamic traces of single cells

   https://doi.org/10.1371/journal.pcbi.1008657  

   Haggerty, Raymond A.; Purvis, Jeremy E. (February 2021, PLOS Computational Biology)

   Asthagiri, Anand R. (Ed.)

   Individual cells show variability in their signaling dynamics that often correlates with phenotypic responses, indicating that cell-to-cell variability is not merely noise but can have functional consequences. Based on this observation, we reasoned that cell-to-cell variability under the same treatment condition could be explained in part by a single signaling motif that maps different upstream signals into a corresponding set of downstream responses. If this assumption holds, then repeated measurements of upstream and downstream signaling dynamics in a population of cells could provide information about the underlying signaling motif for a given pathway, even when no prior knowledge of that motif exists. To test these two hypotheses, we developed a computer algorithm called MISC (Motif Inference from Single Cells) that infers the underlying signaling motif from paired time-series measurements from individual cells. When applied to measurements of transcription factor and reporter gene expression in the yeast stress response, MISC predicted signaling motifs that were consistent with previous mechanistic models of transcription. The ability to detect the underlying mechanism became less certain when a cell’s upstream signal was randomly paired with another cell’s downstream response, demonstrating how averaging time-series measurements across a population obscures information about the underlying signaling mechanism. In some cases, motif predictions improved as more cells were added to the analysis. These results provide evidence that mechanistic information about cellular signaling networks can be systematically extracted from the dynamical patterns of single cells. 

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4. A topological mechanism for robust and efficient global oscillations in biological networks

   https://doi.org/10.1038/s41467-024-50510-x  

   Zheng, Chongbin; Tang, Evelyn (July 2024, Nature Communications)

   Abstract Long and stable timescales are often observed in complex biochemical networks, such as in emergent oscillations. How these robust dynamics persist remains unclear, given the many stochastic reactions and shorter time scales demonstrated by underlying components. We propose a topological model that produces long oscillations around the network boundary, reducing the system dynamics to a lower-dimensional current in a robust manner. Using this to model KaiC, which regulates the circadian rhythm in cyanobacteria, we compare the coherence of oscillations to that in other KaiC models. Our topological model localizes currents on the system edge, with an efficient regime of simultaneously increased precision and decreased cost. Further, we introduce a new predictor of coherence from the analysis of spectral gaps, and show that our model saturates a global thermodynamic bound. Our work presents a new mechanism and parsimonious description for robust emergent oscillations in complex biological networks. 

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5. Bayesian Spectral Graph Denoising with Smoothness Prior

   https://doi.org/10.1109/CISS59072.2024.10480177  

   Leone, Sam; Sun, Xingzhi; Perlmutter, Michael; Krishnaswamy, Smita (March 2024, IEEE)

   Here we consider the problem of denoising features associated to complex data, modeled as signals on a graph, via a smoothness prior. This is motivated in part by settings such as single-cell RNA where the data is very high-dimensional, but its structure can be captured via an affinity graph. This allows us to utilize ideas from graph signal processing. In particular, we present algorithms for the cases where the signal is perturbed by Gaussian noise, dropout, and uniformly distributed noise. The signals are assumed to follow a prior distribution defined in the frequency domain which favors signals which are smooth across the edges of the graph. By pairing this prior distribution with our three models of noise generation, we propose Maximum A Posteriori (M.A.P.) estimates of the true signal in the presence of noisy data and provide algorithms for computing the M.A.P. Finally, we demonstrate the algorithms’ ability to effectively restore signals from white noise on image data and from severe dropout in single-cell RNA sequence data. 

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