Dynamics Of Biological Systems Research Articles

Pixelated High-Q Metasurfaces for in Situ Biospectroscopy and Artificial Intelligence-Enabled Classification of Lipid Membrane Photoswitching Dynamics.

Nanophotonic devices excel at confining light into intense hot spots of electromagnetic near fields, creating exceptional opportunities for light-matter coupling and surface-enhanced sensing. Recently, all-dielectric metasurfaces with ultrasharp resonances enabled by photonic bound states in the continuum (BICs) have unlocked additional functionalities for surface-enhanced biospectroscopy by precisely targeting and reading out the molecular absorption signatures of diverse molecular systems. However, BIC-driven molecular spectroscopy has so far focused on end point measurements in dry conditions, neglecting the crucial interaction dynamics of biological systems. Here, we combine the advantages of pixelated all-dielectric metasurfaces with deep learning-enabled feature extraction and prediction to realize an integrated optofluidic platform for time-resolved in situ biospectroscopy. Our approach harnesses high-Q metasurfaces specifically designed for operation in a lossy aqueous environment together with advanced spectral sampling techniques to temporally resolve the dynamic behavior of photoswitchable lipid membranes. Enabled by a software convolutional neural network, we further demonstrate the real-time classification of the characteristic cis and trans membrane conformations with 98% accuracy. Our synergistic sensing platform incorporating metasurfaces, optofluidics, and deep learning reveals exciting possibilities for studying multimolecular biological systems, ranging from the behavior of transmembrane proteins to the dynamic processes associated with cellular communication.

Expanding the Palette of SWIR Emitting Nanoparticles Based on Au Nanoclusters for Single-Particle Tracking Microscopy.

Single-molecule localization microscopy has proved promising to unravel the dynamics and molecular architecture of thin biological samples down to nanoscales. For applications in complex, thick biological tissues shifting single-particle emission wavelengths to the shortwave infrared (SWIR also called NIR II) region between 900 to 2100nm, where biological tissues are more transparent is key. To date, mainly single-walled carbon nanotubes (SWCNTs) enable such applications, but they are inherently 1D objects. Here, 0D ultra-small luminescent gold nanoclusters (AuNCs, <3nm) and ≈25nm AuNC-loaded-polymeric particles that can be detected at the single-particle level in the SWIR are presented. Thanks to high brightness and excellent photostability, it is shown that the dynamics of the spherical polymeric particles can be followed at the single-particle level in solution at video rates for minutes. We compared single particle tracking of AuNC-loaded-polymeric particles with that of SWCNT diffusing in agarose gels demonstrating the specificity and complementarity of diffusion properties of these SWIR-emitting nano-objects when exploring a complex environment. This extends the library of photostable SWIR emitting nanomaterials to 0D nano-objects of variable size for single-molecule localization microscopy in the second biological window, opening unprecedented possibilities for mapping the structure and dynamics of complex biological systems.

Study of lump and periodic waves along with rogue waves and breathers for mathematical modeling of biological dynamical system

Globally, infectious diseases pose a significant threat. Notable cases include influenza, Hepatitis B and HIV. COVID-19, caused by the coronavirus, has been the subject of recent discussion due to its great transmissibility. This study explores the Susceptible–Infectious–Recovered (SIR) epidemic model, explaining several analytical approaches to comprehend the nonlinear incident rates and geographic dispersion. The model investigates phenomena such as M-shaped solitons, homoclinic breather-like solitons, lump waves (LWs), lump solution (LS) with one or two kinks, rogue waves (RWs), periodic waves (PWs) and periodic cross-kink waves. These solutions aid in understanding the disease spread and informing containment strategies by identifying optimal outbreak control methods. This work studies localized wave solutions in nonlinear wave equations, known as soliton solutions including the LS. RWs are characterized by unexpectedly large amplitude and rapid profile changes, drawing attention for their erratic features. PWs exhibit periodic repetitions over time and space. Besides, some type of solitons with special waveforms are the periodic cross-kink, M-shaped and homoclinic breather-like solitons. Graphical representation visualizes the behavior of these effective waves, providing insights into virus spread in specific regions over time. SIR models help identify optimal strategies for controlling outbreaks. The work adds to our understanding of epidemic dynamics by illuminating how the movement of susceptible and infected individuals affects the spread of disease.

Experimental two-dimensional infrared spectra of methyl thiocyanate in water and organic solvents.

Thiocyanates, nitriles, and azides represent a versatile set of vibrational probes to measure the structure and dynamics in biological systems. The probes are minimally perturbative, the nitrile stretching mode appears in an otherwise uncongested spectral region, and the spectra report on the local environment around the probe. Nitrile frequencies and lineshapes, however, are difficult to interpret, and theoretical models that connect local environments with vibrational frequencies are often necessary. However, the development of both more accurate and intuitive models remains a challenge for the community. The present work provides an experimentally consistent collection of experimental measurements, including IR absorption and ultrafast two-dimensional infrared (2D IR) spectra, to serve as a benchmark in the development of future models. Specifically, we catalog spectra of the nitrile stretching mode of methyl thiocyanate (MeSCN) in fourteen different solvents, including non-polar, polar, and protic solvents. Absorption spectra indicate that π-interactions may be responsible for the line shape differences observed between aromatic and aliphatic alcohols. We also demonstrate that a recent Kamlet-Taft formulation describes the center frequency MeSCN. Furthermore, we report cryogenic infrared spectra that may lead to insights into the peak asymmetry in aprotic solvents. 2D IR spectra measured in protic solvents serve to connect hydrogen bonding with static inhomogeneity. We expect that these insights, along with the publicly available dataset, will be useful to continue advancing future models capable of quantitatively describing the relation between local environments, line shapes, and dynamics in nitrile probes.

Photodynamic patterning of living bacterial biofilms with high resolutions for information encryption and antibiotic screening

AbstractControlling the growth of bacterial biofilms in a specific pattern greatly enhances the study of cell‐to‐cell interactions and paves the way for expanding their biological applications. However, the development of simple, cost‐effective, and highly resolved biopatterning approaches remains a persistent challenge. Herein, a pioneering photodynamic biopatterning technique for the creation of living bacterial biofilms with customized geometries at high resolutions is presented. First of all, an outstanding aggregation‐induced emission photosensitizer is synthesized to enable efficient photodynamic bacterial killing at a low concentration. By combining with custom‐designed photomasks featuring both opaque and transparent patterns, the viability of photosensitizer‐coated bacteria is successfully manipulated by controlling the degree of light transmittance. This process leads to the formation of living bacterial biofilms with specific patterns replicated from the photomask. Such an innovative strategy can be employed to generate living bacterial biofilms composed of either mono‐ or multispecies, with a spatial resolution of approximately 24 µm. Furthermore, its potential applications in information storage/encryption and antibiotic screening are explored. This study provides an alternative way to understand and investigate the intricate interactions among bacteria within 3D biofilms, holding great promise in the controlled fabrication of dynamic biological systems for advanced applications.

Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.

Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). This perspective aims to (1)justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2)recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

Chemogenetic Tools in Focus: Proximity, Conformation, and Sterics

AbstractChemogenetic tools are genetically encoded systems regulated by user‐defined chemicals. Their ability to temporally modulate protein functions in specific cell populations has facilitated in‐depth understanding of dynamic biological systems. Many chemogenetic domains have been developed for regulating a wide range of biological processes, ranging from cellular events to animal behaviors. These tools share some common mechanisms, including proximity regulation, conformational change and allosteric control, as well as steric hinderance control. Here in this review, we aim to provide an overview of different chemogenetic tool designs that utilize the above three common mechanisms to control cellular events.

Digital quantum simulation of non-perturbative dynamics of open systems with orthogonal polynomials

Classical non-perturbative simulations of open quantum systems&amp;apos; dynamics face several scalability problems, namely, exponential scaling of the computational effort as a function of either the time length of the simulation or the size of the open system. In this work, we propose the use of the Time Evolving Density operator with Orthogonal Polynomials Algorithm (TEDOPA) on a quantum computer, which we term as Quantum TEDOPA (Q-TEDOPA), to simulate non-perturbative dynamics of open quantum systems linearly coupled to a bosonic environment (continuous phonon bath). By performing a change of basis of the Hamiltonian, the TEDOPA yields a chain of harmonic oscillators with only local nearest-neighbour interactions, making this algorithm suitable for implementation on quantum devices with limited qubit connectivity such as superconducting quantum processors. We analyse in detail the implementation of the TEDOPA on a quantum device and show that exponential scalings of computational resources can potentially be avoided for time-evolution simulations of the systems considered in this work. We applied the proposed method to the simulation of the exciton transport between two light-harvesting molecules in the regime of moderate coupling strength to a non-Markovian harmonic oscillator environment on an IBMQ device. Applications of the Q-TEDOPA span problems which can not be solved by perturbation techniques belonging to different areas, such as the dynamics of quantum biological systems and strongly correlated condensed matter systems.

Interplay of structure and dynamics in reaction pathways, chemical reactivity and biological systems

Interplay of structure and dynamics in reaction pathways, chemical reactivity and biological systems

Trajectory inference across multiple conditions with condiments

In single-cell RNA sequencing (scRNA-Seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolution. In such dynamic biological systems, cellular states form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-Seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present condiments, a method for the inference and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the cell’s conditions along the trajectory’s path, we can detect large-scale changes, indicative of differential progression or fate selection. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.

A data-driven approach for timescale decomposition of biochemical reaction networks.

Understanding the dynamics of biological systems in evolving environments is a challenge due to their scale and complexity. Here, we present a computational framework for the timescale decomposition of biochemical reaction networks to distill essential patterns from their intricate dynamics. This approach identifies timescale hierarchies, concentration pools, and coherent structures from time-series data, providing a system-level description of reaction networks at physiologically important timescales. We apply this technique to kinetic models of hypothetical and biological pathways, validating it by reproducing analytically characterized or previously known concentration pools of these pathways. Moreover, by analyzing the timescale hierarchy of the glycolytic pathway, we elucidate the connections between the stoichiometric and dissipative structures of reaction networks and the temporal organization of coherent structures. Specifically, we show that glycolysis is a cofactor-driven pathway, the slowest dynamics of which are described by a balance between high-energy phosphate bond and redox trafficking. Overall, this approach provides more biologically interpretable characterizations of network dynamics than large-scale kinetic models, thus facilitating model reduction and personalized medicine applications. IMPORTANCE Complex interactions within interconnected biochemical reaction networks enable cellular responses to a wide range of unpredictable environmental perturbations. Understanding how biological functions arise from these intricate interactions has been a long-standing problem in biology. Here, we introduce a computational approach to dissect complex biological systems' dynamics in evolving environments. This approach characterizes the timescale hierarchies of complex reaction networks, offering a system-level understanding at physiologically relevant timescales. Analyzing various hypothetical and biological pathways, we show how stoichiometric properties shape the way energy is dissipated throughout reaction networks. Notably, we establish that glycolysis operates as a cofactor-driven pathway, where the slowest dynamics are governed by a balance between high-energy phosphate bonds and redox trafficking. This approach enhances our understanding of network dynamics and facilitates the development of reduced-order kinetic models with biologically interpretable components.

Quantifying the energy landscape in weakly and strongly disordered frictional media.

We investigate the "roughness" of the energy landscape of a system that diffuses in a heterogeneous medium with a random position-dependent friction coefficient α(x). This random friction acting on the system stems from spatial inhomogeneity in the surrounding medium and is modeled using the generalized Caldira-Leggett model. For a weakly disordered medium exhibiting a Gaussian random diffusivity D(x) = kBT/α(x) characterized by its average value ⟨D(x)⟩ and a pair-correlation function ⟨D(x1)D(x2)⟩, we find that the renormalized intrinsic diffusion coefficient is lower than the average one due to the fluctuations in diffusivity. The induced weak internal friction leads to increased roughness in the energy landscape. When applying this idea to diffusive motion in liquid water, the dissociation energy for a hydrogen bond gradually approaches experimental findings as fluctuation parameters increase. Conversely, for a strongly disordered medium (i.e., ultrafast-folding proteins), the energy landscape ranges from a few to a few kcal/mol, depending on the strength of the disorder. By fitting protein folding dynamics to the escape process from a metastable potential, the decreased escape rate conceptualizes the role of strong internal friction. Studying the energy landscape in complex systems is helpful because it has implications for the dynamics of biological, soft, and active matter systems.

Viscoelastic materials are most energy efficient when loaded and unloaded at equal rates.

Biological springs can be used in nature for energy conservation and ultra-fast motion. The loading and unloading rates of elastic materials can play an important role in determining how the properties of these springs affect movements. We investigate the mechanical energy efficiency of biological springs (American bullfrog plantaris tendons and guinea fowl lateral gastrocnemius tendons) and synthetic elastomers. We measure these materials under symmetric rates (equal loading and unloading durations) and asymmetric rates (unequal loading and unloading durations) using novel dynamic mechanical analysis measurements. We find that mechanical efficiency is highest at symmetric rates and significantly decreases with a larger degree of asymmetry. A generalized one-dimensional Maxwell model with no fitting parameters captures the experimental results based on the independently characterized linear viscoelastic properties of the materials. The model further shows that a broader viscoelastic relaxation spectrum enhances the effect of rate-asymmetry on efficiency. Overall, our study provides valuable insights into the interplay between material properties and unloading dynamics in both biological and synthetic elastic systems.

PyRates-A code-generation tool for modeling dynamical systems in biology and beyond.

The mathematical study of real-world dynamical systems relies on models composed of differential equations. Numerical methods for solving and analyzing differential equation systems are essential when complex biological problems have to be studied, such as the spreading of a virus, the evolution of competing species in an ecosystem, or the dynamics of neurons in the brain. Here we present PyRates, a Python-based software for modeling and analyzing differential equation systems via numerical methods. PyRates is specifically designed to account for the inherent complexity of biological systems. It provides a new language for defining models that mirrors the modular organization of real-world dynamical systems and thus simplifies the implementation of complex networks of interacting dynamic entities. Furthermore, PyRates provides extensive support for the various forms of interaction delays that can be observed in biological systems. The core of PyRates is a versatile code-generation system that translates user-defined models into "backend" implementations in various languages, including Python, Fortran, Matlab, and Julia. This allows users to apply a wide range of analysis methods for dynamical systems, eliminating the need for manual translation between code bases. PyRates may also be used as a model definition interface for the creation of custom dynamical systems tools. To demonstrate this, we developed two extensions of PyRates for common analyses of dynamic models of biological systems: PyCoBi for bifurcation analysis and RectiPy for parameter fitting. We demonstrate in a series of example models how PyRates can be used in combination with PyCoBi and RectiPy for model analysis and fitting. Together, these tools offer a versatile framework for applying computational modeling and numerical analysis methods to dynamical systems in biology and beyond.

Calcium imaging: a technique to monitor calcium dynamics in biological systems.

Calcium imaging: a technique to monitor calcium dynamics in biological systems.

Magic angle spinning effects on longitudinal NMR relaxation: 15N in L-histidine

Solid-state magnetic resonance is a unique technique that can reveal the dynamics of complex biological systems with atomic resolution. Longitudinal relaxation is a mechanism that returns longitudinal nuclear magnetization to its thermal equilibrium by incoherent processes. The measured longitudinal relaxation rate constant however represents the combination of both incoherent and coherent contributions to the change of nuclear magnetization. This work demonstrates the effect of magic angle spinning rate on the longitudinal relaxation rate constant in two model compounds: L-histidine hydrochloride monohydrate and glycine serving as proxies for isotopically-enriched biological materials. Most notably, it is demonstrated that the longitudinal N15 relaxation of the two nitrogen nuclei in the imidazole ring in histidine is reduced by almost three orders of magnitude at the condition of rotational resonance with the amine, while the amine relaxation rate constant is increased at these conditions. The observed phenomenon may have radical implications for the solid-state magnetic resonance in biophysics and materials, especially in the proper measurement of dynamics and as a selective serial transfer step in dynamic nuclear polarization.

Phosphorylated HP1α-Nucleosome Interactions in Phase Separated Environments.

In the nucleus, transcriptionally silent genes are sequestered into heterochromatin compartments comprising nucleosomes decorated with histone H3 Lys9 trimethylation and a protein called HP1α. This protein can form liquid-liquid droplets in vitro and potentially organize heterochromatin through a phase separation mechanism that is promoted by phosphorylation. Elucidating the molecular interactions that drive HP1α phase separation and its consequences on nucleosome structure and dynamics has been challenging due to the viscous and heterogeneous nature of such assemblies. Here, we tackle this problem by a combination of solution and solid-state NMR spectroscopy, which allows us to dissect the interactions of phosphorylated HP1α with nucleosomes in the context of phase separation. Our experiments indicate that phosphorylated human HP1α does not cause any major rearrangements to the nucleosome core, in contrast to the yeast homologue Swi6. Instead, HP1α interacts specifically with the methylated H3 tails and slows the dynamics of the H4 tails. Our results shed light on how phosphorylated HP1α proteins may regulate the heterochromatin landscape, while our approach provides an atomic resolution view of a heterogeneous and dynamic biological system regulated by a complex network of interactions and post-translational modifications.

OPUS-DSD: deep structural disentanglement for cryo-EM single-particle analysis

Cryo-electron microscopy (cryo-EM) captures snapshots of dynamic macromolecules, collectively illustrating the involved structural landscapes. This provides an exciting opportunity to explore the structural variations of macromolecules under study. However, traditional cryo-EM single-particle analysis often yields static structures. Here we describe OPUS-DSD, an algorithm capable of efficiently reconstructing the structural landscape embedded in cryo-EM data. OPUS-DSD uses a three-dimensional convolutional encoder–decoder architecture trained with cryo-EM images, thereby encoding structural variations into a smooth and easily analyzable low-dimension space. This space can be traversed to reconstruct continuous dynamics or clustered to identify distinct conformations. OPUS-DSD can offer meaningful insights into the structural variations of macromolecules, filling in the gaps left by traditional cryo-EM structural determination, and potentially improves the reconstruction resolution by reliably clustering similar particles within the dataset. These functionalities are especially relevant to the study of highly dynamic biological systems. OPUS-DSD is available at https://github.com/alncat/opusDSD.

Control of stochastic and inverse stochastic resonances in a liquid-crystal electroconvection system using amplitude and phase noises

Stochastic and inverse stochastic resonances are counterintuitive phenomena, where noise plays a pivotal role in the dynamics of various biological and engineering systems. Even though these resonances have been identified in various systems, a transition between them has never been observed before. The present study demonstrates the presence of both resonances in a liquid crystal electroconvection system using combined amplitude and phase noises, which correspond to colored noises with appropriate cutoff frequencies (i.e., finite correlation times). We established the emergence of both resonances and their transition through systematic control of the electroconvection threshold voltage using these two noise sources. Our numerical simulations were experimentally confirmed and revealed how the output performance of the system could be controlled by combining the intensity and cutoff frequency of the two noises. Furthermore, we suggested the crucial contribution of a usually overlooked additional phase noise to the advancements in various noise-related fields.

Biological systems - "Symphonies of Life": Reviving Friedrich Cramer's general resonance theory.

Understanding biological systems in terms of scientific materialism has arguably reached a frontier, leaving fundamental questions about their complexity unanswered. In 1998, Friedrich Cramer proposed a general resonance theory as a way forward. His theory builds on the extension of the quantum physical duality of matter and wave to the macroscopic world. According to Cramer' theory, agents constituting biological systems oscillate, akin to musical soundwaves, at specific eigenfrequencies. Biological system dynamics can be described as "Symphonies of Life" emerging from the resonance (and dissonance) of eigenfrequencies within the interacting collective. His theory has potential for studying biological problems of increasing complexity in a fast-changing Anthropocene from a new and transdisciplinary angle. Despite data becoming increasingly available for analyses, Cramer's theory remains ignored and therefore untested a quarter century after its publication. This paper discusses how the theory can move to quantitative assessments and application. Cramer's general resonance theory deserves revival.