Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session P13: Noise and Stochasticity in Biological NetworksLive
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Sponsoring Units: DBIO Chair: Brian Camley, Johns Hopkins University |
Wednesday, March 17, 2021 3:00PM - 3:12PM Live |
P13.00001: Sensing and making sense of fluctuating cellular states Felix Meigel, Lina Hellwig, Jörg Contzen, Philipp Mergenthaler, Steffen Rulands The maintenance of intact tissues relies on precise cellular decision-making despite strongly fluctuating extrinsic cues. These decisions involve processes on vastly different scales, from molecules to organelles and cells in tissues. How can cells manipulate the propagation of fluctuations across these scales to perform biological function? Here, we show how the non-equilibrium interplay between microscopic and mesoscopic dynamics leads to a kinetic low-pass filter facilitating precise sensing of fluctuating cellular states. Specifically, we find that the interplay between molecular and organelle dynamics gives rise to a single, collective degree of freedom. We show that this degree of freedom exhibits rich dynamical behaviour showing different kinetics on different temporal scales and thereby leading to the suppression of fast fluctuations. We demonstrate our findings in the context of the metabolic regulation of cell death via the interplay of Bax protein dynamics with rapid mitochondrial fusion and fission and find an order of magnitude effect on the error rate of the cell death decision. Our work shows paradigmatically how biological function relies on the non-equilibrium integration of processes on different spatial scales to control and respond to fluctuations. |
Wednesday, March 17, 2021 3:12PM - 3:24PM Live |
P13.00002: Maximizing Information from Noisy Measurements of Single-cell Gene Expression Distributions Huy Vo, Brian Munsky
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Wednesday, March 17, 2021 3:24PM - 3:36PM Live |
P13.00003: The Long and Short of Templated Copying Jenny Poulton Templated copying is the central operation by which biology produces complex molecules. Cells copy sequence information from DNA to RNA and into proteins, which are the molecules responsible for the function and regulation of cellular systems. In the templated copying process the template catalyses the formation of a second molecule carrying the same sequence. Traditionally, people have ignored the separation of the template and copy at the end of the process, but separation is both necessary and fundamentally changes the thermodynamics of the process. |
Wednesday, March 17, 2021 3:36PM - 3:48PM Live |
P13.00004: Apply granger causality test to determine connections within peridodics ATP stimulated KTaR-1 cell colony Guanyu Li Cells are constantly detecting their local chemical environment. It is important for cells to be able to detect the change of the environment and employ correct strategies to make correct response. During the chemosensing process, cell colony would be able to generate highly regulated, consistent response, but for individual cell, the response would be intrinsically stochastic. The highly regulated, consistent response is vital to maintain the normal functionality of organism or larger biological system. Here we aim to understand how communication would play a role in the chemosesing process. In particular, we study KTaR cells, an immortalized neuronal cell line, with periodic ATP stimulations. We systematically vary the concentration and period of the stimuli. By analyzing the resulted Ca2+ oscillation, we use Granger Causality test to determine the connection between cells in the cell colony and to reconstruct the communicational network with the cells. Our results show that the external stimuli modulates self-organized multicellular network in profound ways. |
Wednesday, March 17, 2021 3:48PM - 4:00PM Live |
P13.00005: Topology Control and Pruning in Intertwined Biological Flow Networks. Felix Kramer, Carl D Modes Recent work on self-organized remodeling of vasculature in slime-molds, leaf venation systems and vessel systems in vertebrates has put forward a plethora of potential adaptation mechanisms. All these share the underlying hypothesis of a flow-driven machinery, meant to alter rudimentary vessel networks in order to optimize the system's dissipation, flow uniformity, or more, with different versions of constraints. Nevertheless, the influence of environmental factors on the long-term adaptation dynamics as well as the networks structure and function have not been fully understood. |
Wednesday, March 17, 2021 4:00PM - 4:12PM Live |
P13.00006: Investigation of Allosteric Mechanism from an Evolutionary Perspective Riya Samanta, Calvin Muth, Neel Sanghvi, Dorothy Beckett, Silvina Matysiak Allostery is fundamental to biological regulation and, consequently, understanding its molecular mechanism has many potential practical applications. Investigation of the evolution of allostery offers one approach to elucidating this mechanism. Biotin protein ligases are essential for survival in all organisms. In bacteria the Class I ligases are non-allosteric whereas Class II ligases are allosteric. Both classes show significant structural and sequential similarity. So the question arises: what is the origin of allostery in Class II ligases? In this work, we used concepts from Network theory and applied bioinformatic tools to identify features that distinguish allosteric from non-allosteric biotin ligases. Energy-based network analysis performed on the MD trajectories of allosterically "inactive" and "active" forms of each protein class representative revealed distinct residue networks that show different responses to allosteric ligand binding. Phylogenetic Mutual Information analysis revealed markedly distinct residue coevolution patterns in the two ligase classes. The combined results reveal that allostery can evolve via changes in the composition of residue networks in a protein and the patterns of interaction among these networks. |
Wednesday, March 17, 2021 4:12PM - 4:24PM Live |
P13.00007: Can active hydrodynamic fluctuations affect barrier crossing during enzymatic catalysis? Ashwani Tripathi, Tamoghna Das, Govind Paneru, Hyuk Kyu Pak, Tsvi Tlusty The cellular milieu is teeming with biochemical nano-machines whose activity is a strong source of correlated non-thermal fluctuations termed ``active noise". Essential elements of this circuitry are enzymes, catalysts that speed up the rate of metabolic reactions by orders of magnitude, making life possible. Here, we examine the possibility that active noise in the cell affects enzymatic catalytic rate by accelerating or decelerating the crossing of energy barriers during a reaction. Considering hydrodynamic perturbations induced by biochemical activity as a source of active noise, we attempt to evaluate their plausible impact on the enzymatic cycle using a combination of analytic and numerical methods. Our estimate shows that the fast component of the active noise spectrum may enhance the rate of enzymes, by up to 50%, while reactions remain practically unaffected by the slow noise spectrum and are mostly governed by thermal fluctuations. Revisiting the physics of barrier crossing under the influence of active hydrodynamic fluctuations suggests that the internal mechanics and biochemical activity of macromolecules such as enzymes are coupled to active noise, with potential impact on metabolic networks and cascades in living and artificial systems alike. |
Wednesday, March 17, 2021 4:24PM - 4:36PM Live |
P13.00008: Non-genetic heterogeneity in prostate cancer through coupled dynamics of Androgen Receptor signalling, Epithelial-Mesenchymal Transition and Notch-Delta-Jagged signalling Divyoj Singh, Federico Bocci, Prakash Kulkarni, Mohit Kumar Jolly Non-genetic heterogeneity is emerging to be 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) signalling mediated through an intrinsically disordered protein PAGE4, b) multistability in epithelial-mesenchymal transition (EMT), and c) Notch-Delta-Jagged signalling 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 signalling can lead to possible bistability in levels of AR, which can be reinforced by Notch signalling. These results reveal how the emergent dynamics of coupling of oscillatory and multistable systems at an individual cell and a two-cell system and unravel various mechanisms through which AR signalling can generate non-genetic heterogeneity which can act as a barrier to most targeted therapies in the context of prostate cancer. |
Wednesday, March 17, 2021 4:36PM - 4:48PM Live |
P13.00009: RNA splicing and the renormalization group: why simple models can effectively describe complex and noisy gene networks John Vastola, William R. Holmes The regulatory networks governing gene expression are generally both complex (many chemical species interacting in nontrivial ways) and noisy (stochastic due to both intrinsic and extrinsic factors). A longstanding open question in the physics of living systems is: to what extent is this complexity irreducible? When can these apparently complex systems be described by simple models, and why might such reductions be possible? We introduce a framework for thinking about this question in the context of stochastic gene networks, and apply it to understanding RNA splicing and transcription. In that context, the operative question is: to what extent does the topology/time scale/complexity of the dynamics in between the production of nascent RNA and the appearance of fully processed RNA impact observable features (e.g. the probability distribution) of the processed RNA? More succinctly: when do the details of the intermediate dynamics matter? Our approach, which is inspired by renormalization group ideas, also makes use of other tools familiar from field theory, including path integrals and ladder operators. We comment on some conceptual links to other approaches, e.g. information-based approaches and time scale separation arguments. |
Wednesday, March 17, 2021 4:48PM - 5:00PM Live |
P13.00010: Use deep learning to infer the probability distributions for stochastic processes Shangying Wang, Simone Bianco Deep learning is revolutionizing biological and medical research in recent years. However, due to the intrinsic stochastic property of biological processes, it is often difficult to build reliable predictive machine learning models, which map underlying genetic and environmental conditions to phenotypic observations. Specifically, even genetically identical cells in identical environments display variable phenotypes. This imposes a big challenge in building traditional supervised models which can only predict a determined phenotype (or a set of determined phenotypes) per genetic and environmental condition. Furthermore, the intrinsic noise has been proved to play a crucial role in gene regulation mechanisms. Predicting only the average value of the outputs is not sufficient in studying the dynamics of biological systems. We developed a deep learning algorithm that can predict the probability distribution of the phenotypes based on only one noisy observation for each input condition, without the prior knowledge or assumption of the probability distributions. This study can facilitate the quantitative understanding of biological systems as well as the design of synthetic gene circuits. |
Wednesday, March 17, 2021 5:00PM - 5:12PM Live |
P13.00011: Precision of Protein Thermometry Michael Vennettilli, Soutick Saha, Ushasi Roy, Andrew Mugler
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Wednesday, March 17, 2021 5:12PM - 5:24PM Live |
P13.00012: Collective gradient sensing with limited positional information Emiliano Perez Ipina, Brian Camley Eukaryotic cells cooperate to sense chemical gradients more effectively. For individual cells, the accuracy of gradient sensing is limited by the cell size and the error in measuring chemoattractant concentration, set by the fluctuations of ligand-receptor binding. Clusters of cells improve this limit by sharing individual measurements and spanning over larger regions. However, in the simplest way of interpreting these measurements, cells must determine their location within the cluster - which is also a noisy measurement. Here, we apply the maximum likelihood estimation method (MLE) to study the accuracy of gradient sensing of a cluster of cells when there is limited positional information. We consider different models for how cells obtain their positional information and we compare our results with (1) the case of cells with perfect positional information and (2) a tug-of-war model where cells respond to the gradient by polarizing away from neighbors without relying on positional information. Our findings show that under certain conditions, as positional uncertainty increases, there is a trade-off where the tug-of-war model responds more accurately to the chemical gradient. |
Wednesday, March 17, 2021 5:24PM - 5:36PM Live |
P13.00013: Kinetic theory for structured populations: application to stochastic sizer-timer models of cell proliferation Tom Chou, Mingtao Xia Structured population models have been widely used to model cell |
Wednesday, March 17, 2021 5:36PM - 5:48PM Live |
P13.00014: Disentangling intrinsic and extrinsic gene expression noise in growing cells Jie Lin, Ariel Amir Gene expression is a stochastic process. Despite the increase of protein numbers in growing cells, the protein concentrations are often found to be confined within small ranges throughout the cell cycle. Generally, the noise in protein concentration can be decomposed into an intrinsic and an extrinsic component, where the former vanishes for high expression levels. Considering the time trajectory of protein concentration as a random walker in the concentration space, an effective restoring force (with a corresponding ”spring constant”) must exist to prevent the divergence of concentration due to random fluctuations. In this work, we prove that the magnitude of the effective spring constant is directly related to the fraction of intrinsic noise in the total protein concentration noise. We show that one can infer the magnitude of intrinsic, extrinsic, and measurement noises of gene expression solely based on time-resolved data of protein concentration, without any a priori knowledge of the underlying gene expression dynamics. We apply this method to experimental data of single-cell bacterial gene expression. The results allow us to estimate the average copy numbers and the translation burst parameters of the studied proteins. |
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