CDEV Subgroup Contributed Talks

Nathan Smyers

University of North Carolina at Chapel Hill

"From Data to Dynamics: Uncovering Cell Signaling Networks with Physics-Informed Machine Learning"

Cell signaling is governed by complex networks of biochemical interactions. These networks are critical for a wide range of cellular functions, including detecting environmental changes and cellular motility. Modeling these processes with reaction-diffusion equations (RDEs) requires prior knowledge of protein-protein interactions for constructing the underlying network. The complex nature of signaling pathways means many relevant interactions may be unknown. To address this challenge, we developed a deep learning-based method to infer reaction networks from data. By integrating a physics-informed neural network (PINN) with a neural network for symbolic regression, this method learns interpretable RDE models from spatiotemporal data, effectively learning the biochemical reactions driving dynamics. To develop and validate our approach, we applied it to data generated from a model of cell polarity establishment. This approach has the potential to overcome limitations from incomplete knowledge of protein-protein interactions, serving as a powerful tool for uncovering how cells regulate complex behaviors.

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Anna Nelson

University of New Mexico

"Modeling mechanisms of microtubule dynamics and polarity in neurons"

The stability and polarity of the microtubule cytoskeleton is required for long-range, sustained transport within neuronal cellsl. In particular, the healthy microtubule cytoskeleton is comprised of tubulin protein and is stable with a particular orientation. However, when injured, these microtubules are dynamic, rearrange their orientation, and the appearance of microtubules is upregulated. It is unknown what mechanisms are involved in this balance between dynamic rearrangement and sustained function. Using a stochastic mathematical model that incorporates experimental data, we seek to understand how nucleation can impact microtubule dynamics in dendrites of fruit fly neurons. In the stochastic model, we assume two mechanisms limit microtubule growth: limited tubulin availability and the dependence of shrinking events on microtubule length. To better understand our stochastic model, we develop a partial differential equation (PDE) model that describes microtubule growth and nucleation dynamics, and we compare analytical results to results from the complex stochastic model. Insights from these models can then be used to understand what mechanisms are used organize into polarized structures in neurons, and how microtubule dynamics, like nucleation, may impact cargo localization post-injury.

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Dietmar Oelz

The University of Queensland

"Mechanochemical pattern formation in Hydra"

Tissue morphogenesis involves the self-organized creation of patterns and shapes. In many cases details of underlying mechanisms are elusive, yet an increasing amount of experimental data suggests that chemical morphogens and mechanical processes are strongly coupled. Here, we develop and simulate a minimal model for the emergence of asymmetry in aggregates of the Hydra polyp based on mechanochemical coupling of surface stiffness and a morphogen concentration. We contrast this model with the classical morphogen patterning mechanisms based on Turing type reaction diffusion systems. In analogy to this classical mechnism, we carry out the stability analysis of the lower dimensional toy model and identify minimal conditions for symmetry breaking. Our results suggest that mechanochemical pattern formation underlies symmetry breaking in Hydra.

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Katrin Schröder

Goethe University

"mRNA Translation Stalling in Single-Codon Resolution Monte-Carlo Ribosome Flow Model Simulations"

Ribosomal stalling during translation of mRNA can result for example from oxidative conditions surrounding the site of translation. It impacts the cellular protein production machinery and therefore decrease cell proliferation. Accordingly, the rate of protein synthesis (R) can be considered as a hallmark of ribosomal stalling. In vivo experiments can determine protein content of a cell and differences in ribosomal density for different stalling scenarios. We employ the Ribosome Flow Model (RFM) coupled with Monte Carlo simulations to quantitatively establish the implications of three stalling patterns motivated by biological processes: We consider (1) the overall frequency of stalling sides as defined by harmful mRNA modification, (2) the degree of the reduction of the translocation rate λ reflecting the severity of mRNA transcriptional impairments, as well as (3) the effect of clustering, chain and gap impairments, as well as cluster locality of these anomalies. Each of these stalling patterns impacts protein synthesis rate and ribosomal density differently. We show how quantitative prediction of the impact of each and combinations of these patterns can be used as to study and predict mRNA stalling. Major findings of our analysis are, that for a given severity of mRNA damage, the equilibrium rate of protein synthesis R* does not depend on impairment locality, and is not related to the ribosomal density. In contrast, ribosomal density is strongly dependent on the locality of impairment clustering.

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Adriana Zanca

The University of Melbourne

"Cell fate through the lens of random dynamical systems"

How pluripotent cells give rise to progressively more specialised cells over multiple cell divisions, known as cell fate, remains one of the mysteries of systems biology. During development, it is of the utmost importance that cells uphold certain division regimes for an organism to survive and thrive. Beyond development, cell fate perturbations can result in cancer and other pathological conditions. The theoretical and mathematical biology community has been making contributions to our understanding of cell fate including by quantifying Waddington’s seminal landscape using dynamical systems, performing statistical trajectory inference on single-cell sequencing data, or considering geometric and algebraic approaches to cell fate. In this talk, I will present a random dynamical systems interpretation of cell fate. This approach is, arguably, a generalisation of existing models of cell fate that may be able to provide new perspectives into cell fate.

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