Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session JJ05: V: Tissues and Disease |
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Sponsoring Units: DBIO Chair: Jonathan Colen, University of Chicago Room: Virtual Room 5 |
Monday, March 20, 2023 3:00PM - 3:12PM |
JJ05.00001: Receptor promiscuity and feedback control mediate accurate and robust encoding and decoding of positional information during morphogenesis Krishnan S Iyer, Madan Rao Spatial profiles of morphogens provide positional information in a developing tissue during morphogenesis. The cells read the local morphogen concentrations via receptors and process this information via signalling mechanisms before inferring their position in terms of a transcriptional readout. However, the processes of binding to receptors and their internalisation by signalling mechanisms also mediate the dynamics of the morphogen profile through the effect on transport and degradation/removal of morphogen molecules. In this study, we show how receptor promiscuity and feedback control on receptors can act as local cell autonomous control mechanisms that allow for sculpting of morphogen profiles in tandem with accurate and robust positional inference. In effect, the same cellular processes provide robustness to encoding and decoding of positional information in morphogens simultaneously. |
Monday, March 20, 2023 3:12PM - 3:24PM |
JJ05.00002: Machine-learning Drosophila embryogenesis Jonathan Colen, Noah P Mitchell, Nikolas H Claussen, Marion Raich, Sebastian J Streichan, Vincenzo Vitelli Hydrodynamic theories use symmetries and conservation laws to build effective descriptions of many-body systems. This approach can break down in living and active matter, where less is known about the relevant collective variables or the relationships between them. Such problems arise in embryogenesis, where genetic control, force-generating motor proteins, and the system geometry drive dramatic rearrangements of tissue layers. Here, we show that deep neural networks can learn to predict such tissue flows in developing Drosophila embryos using cytoskeletal protein distributions. Our machine learning models are capable of determining the instantaneous tissue velocity as well as how the tissue will deform for several minutes into the future. Using a geometric data-driven approach, we can identify spatial regions whose protein distributions are crucial for determining embryo behavior. Our study incorporates neural networks as an integral part of a framework for constructing predictive phenomenological models in biology and biophysics. |
Monday, March 20, 2023 3:24PM - 3:36PM |
JJ05.00003: Topological defects in the nematic order of actin fibres as organization centres of Hydra regeneration Yonit Maroudas-Sacks, Lital Shani-Zerbib, Liora Garion, Anton Livhsits, Erez Braun, Kinneret Keren Morphogenesis is a process in which a well-defined pattern of functional tissues emerges during the development. One of the great challenges in morphogenesis research is understanding the organizational principles responsible for the robust convergence of the process, across scales, to form viable organisms under variable conditions. Here we utilize the fresh water animal Hydra as a model system, focusing on the dynamics of the actin cytoskeleton during a regeneration process which is akin to development. We show that the nematic order of the supra-cellular actin fibres in regenerating Hydra defines a slowly-varying field, whose dynamics provide an effective description of the morphogenesis process. The nematic orientation field contains defects constrained by the topology of the regenerating tissue. These nematic topological defects are long-lived, yet display rich dynamics that can be related to the major morphological events during regeneration. In particular, we show that these defects act as organizing centres, with the main functional morphological features developing at defect sites. These results suggest a system of self-organization involving the actin cytoskeleton in the regenerating tissue that is based on mechanical feedback. We therefore view the nematic orientation field as a “mechanical morphogen”, whose interaction with other mechanical and biochemical morphogen fields leads to the robust development of the body plan of the regenerating Hydra. |
Monday, March 20, 2023 3:36PM - 3:48PM |
JJ05.00004: Degradable X-Ca-Alginate Aerogels as neuronal scaffolds-investigation of pc12 cells-substrate interface Martina Rodriguez Sala, Firouzeh Sabri, Omar Skalli, Grigorios Raptopoulos, Patrina Paraskevopoulou Aerogels are a special class of nanostructured materials derived by sol-gel chemistry with tunable properties including extreme low density and high porosity. Applications of aerogels have a wide range from water treatment, thermal insulation and aircrafts. The biomedical applications of aerogels include drug delivery, regenerative medicine, wound healing, and biosensing. This group has focused on the use of aerogels as neuronal scaffold due to their 3-D structure which mimics the topography of tissues. Previously, the ideal material properties to tailor aerogels for peripheral nerve repair was systematically investigated by correlating the in vitro neural response to surface properties of aerogels such as stiffness, surface roughness, and conductivity as well as to external electrical bias. The degradation behavior of polyurea crosslinked calcium alginate (X-Ca-Alg) aerogels over time have been previously investigated. In this study, the suitability of X-Ca-Alg aerogels as neuronal scaffolds is examined. The in vitro evaluation of PC12 neuronal cells is analyzed over time when using a degradable scaffold. The use of molecular biology and material science is mixed to analyze and understand how degradable scaffolds affects neuronal tissue regeneration. |
Monday, March 20, 2023 3:48PM - 4:00PM |
JJ05.00005: Impairment of entropy regulation in humans associated with age and illness: a calorimetric approach Nicolas Brodeur Complex living systems such as the human body are characterized by their self-organized and dissipative behavior where irreversible processes continuously produce entropy internally as energy is metabolized, and exported to the environment. We hypothesize that entropy regulation acts as a driver for the maintenance of physiological stability. In this presentation, we will introduce our experimental approach for the continuous measurement of entropy flows in the human body, here considered as a non-equilibrium and non-stationary system. The experimental protocols involved participants of different age groups (young/middle-age/old), fitness level (trained/untrained) and with or without Type 2 diabetes that performed exercise at varying intensity under heat stress in a calorimetry chamber. Metabolic heat production rates and total heat dissipation rates were recorded continuously using indirect and direct calorimetry, respectively, from which were calculated the rates of internal entropy production and external dissipation of the body. For fixed internal entropy production rates, we observed a progressive inability to externally dissipate entropy in older, untrained, or ill subjects, thus leading to greater entropy accumulation during exercise/heat stress, which we hypothesize is a measure of vulnerability. This research aims for novel insights and understanding of human regulation based on the study of the non-equilibrium thermodynamics. |
Monday, March 20, 2023 4:00PM - 4:12PM |
JJ05.00006: Disease Prediction by Detecting and Integrating Connectomic Networks and Marginally Weak Signals Yanming Li Many contemporary studies use individual genomic or imaging profiles for early prediction of cancer or neuropsychological outcomes, such as cancer subtypes and Alzheimer's disease stages. Current approaches ignore the connection structures of the genome and the brain (e.g. gene pathways or brain networks). Many genetic and imaging markers, despite having marginally weak effects, may exude strong predictive effects once considered together with their connected biomarkers. To find such weak signals, the inter-feature connectomic structure of the genome or brain must be explored first. However, given the ultrahigh-dimensional characteristic of genomic/neuroimaging profiles, identifying the whole genome/brain connectomic features is computationally prohibitive. This is also an impediment to detecting weak signals. In this work, we hypothesize that a large portion of the predictiveness of disease outcomes attributes to inter-marker connections as well as marginally weak signals. By detecting and integrating them, prediction accuracy can be significantly improved. We develop novel statistical/machine-learning algorithms for detecting network-based biomarkers for cancer or AD-related outcome prediction. The identified network signatures and weak signals will also enhance our understanding of the underlying mechanisms of disease development and progression. |
Monday, March 20, 2023 4:12PM - 4:24PM |
JJ05.00007: An updated computer model of Type 1 Diabetes Mellitus in neonatal patients. Robert J Goshen, Harriet D Papernick In the original computer model (Fall 2021 NES 66,9 B01.00003) a mutant pathway of four steps leads to the demise of the pancreatic beta cell and permanent neonatal diabetes disease. The study focused on a DNA triplet involved in the translation of the preproinsulin precursor peptide mRNA into the mature bioactive insulin protein. Failure of this start codon to properly initiate the reading frame for the translation led to simulation model predictions of translocation defects. Variants such as truncated amino acid peptide chains, misfolded mutant proteins, severe cytosolic and ER stress, were all likely factors in the death of the host beta cell. The updated simulation has recently identified another factor. |
Monday, March 20, 2023 4:24PM - 4:36PM |
JJ05.00008: Atomic level fluctuations of connectivity in the structure network of SARS-CoV2-Human ACE2 receptor complex Saraswathi Vishveshwara, Varsha Subramanyan, Arinnia Anto, Moitrayee Bhattacharyya, Smitha Vishveshwara In this work, we study the non-covalent connectivity of the SARS-CoV2-Human ACE2 receptor complex, as obtained from molecular dynamics simulations, through graph spectral analysis and show the effects of connectivity strength and dynamic stability on the interface region. We also compare these features with those of the less debilitating SARS-CoV1-ACE2 receptor complex. Our analyses of the networks indicate that the Covid-19 complex forms a more robust interface seen at both the bond level as well as in measures of higher order connectivity like cliques, communities, and clusters. We also analyse individual snapshots of the molecular dynamics simulations to show persistent core level connectivity in most of the structures, with exciting differences being exhibited by various key conformations. The utility of such a map is highlighted by locating the mutated residues in the variants of SARS-CoV2, thus providing a tool to rationalize the effect of mutations through rearrangements of the local as well as global environments. |
Monday, March 20, 2023 4:36PM - 4:48PM |
JJ05.00009: Particle-Resolved Holographic Molecular Binding Assays Kaitlynn Snyder, David G Grier Holographic molecular binding assays use holographic video microscopy to monitor changes in the light-scattering properties of functionalized probe beads as target molecules bind to their surfaces. This label-free assay relies on the ability of holographic microscopy to resolve nanometer-scale changes in the diameter of a micrometer-scale colloidal sphere. Previous implementations have relied on large statistical samples obtained with thousands of beads to monitor population shifts with subnanometer precision. Recent advances in high-speed adaptive aberration correction bring the same level of precision to single-particle measurements and make possible complete binding assays with a single probe bead. We demonstrate this technique with probe beads functionalized with protein A that are immobilized with holographic optical traps while being incubated with target antibodies. The success of this approach not only creates new opportunities for medical diagnostic testing, but also illustrates the growing range of applications for holographic particle characterization that are made possible by recent advances in the technique. |
Monday, March 20, 2023 4:48PM - 5:00PM |
JJ05.00010: An elastic lattice polymer model of bacterial chromosomes Elham Ghobadpour, Ralf Everaers, Ivan Junier Bacterial DNA often adopts tree-like double-folded branching configurations due to DNA supercoiling. In this context, we propose a framework to generate expected bacterial chromosome structures at multiple scales. To this end, we extend our previous model of elastic polymer chains on an FCC lattice for tightly double-folded ring polymers [1] to include the degree of freedom associated with the length of branches. Namely, the model includes the spontaneous creation and deletion of side branches, which move along the tree graph structure due to local mass transport diffusion, and a chemical potential to control the length of branches. The model parameters can then be adjusted to simulate conditions similar to the environment of bacterial chromosomes in terms of DNA concentration, self-avoiding interactions and cylindrical confinement. By introducing a real-space renormalization of branching tree melts, we were able to coarse-grain our model to capture the universal properties of the DNA from 10 kb to 4 Mb scale. As a result, we are able to rationalize from first principles contact properties between bacterial chromosomal loci as measured from high-throughput conformation capture (Hi-C) methods. |
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