Henry Chan, Mathew Cherukara, Troy D. Loeffler
et al.
AbstractWe introduce an unsupervised machine learning (ML) based technique for the identification and characterization of microstructures in three-dimensional (3D) samples obtained from molecular dynamics simulations, particle tracking data, or experiments. Our technique combines topology classification, image processing, and clustering algorithms, and can handle a wide range of microstructure types including grains in polycrystalline materials, voids in porous systems, and structures from self/directed assembly in soft-matter complex solutions. Our technique does not require a priori microstructure description of the target system and is insensitive to disorder such as extended defects in polycrystals arising from line and plane defects. We demonstrate quantitively that our technique provides unbiased microstructural information such as precise quantification of grains and their size distributions in 3D polycrystalline samples, characterizes features such as voids and porosity in 3D polymeric samples and micellar size distribution in 3D complex fluids. To demonstrate the efficacy of our ML approach, we benchmark it against a diverse set of synthetic data samples representing nanocrystalline metals, polymers and complex fluids as well as experimentally published characterization data. Our technique is computationally efficient and provides a way to quickly identify, track, and quantify complex microstructural features that impact the observed material behavior.
Quantitatively mapping three-dimensional (3D) flow, diffusion, and particle density in crowded living cells remains challenging because most dynamic optical microscopy measurements are effectively planar and existing analysis methods struggle with dense, noisy volumetric data. We introduce volumetric spatio-temporal image correlation spectroscopy (vSTICS), a framework that recovers voxel-resolved flow, diffusion coefficients, and particle densities from 3D fluorescence time series. Growing Camellia japonica pollen tubes were imaged with field-synthesis lattice light-sheet microscopy, and localized 3D spatio-temporal correlation analysis was applied to overlapping volumetric samples to generate maps of velocity, diffusion, and density. Validation with synthetic flow-diffusion simulations showed accurate recovery of seeded transport parameters, including velocities near $3$ $μ$m s$^{-1}$ and diffusion near $10^{-3}$ $μ$m$^2$ s$^{-1}$. Fluorescent microsphere experiments verified particle number and point spread function readouts and measured diffusion coefficients of $0.3 \pm 0.1$ $μ$m$^2$ s$^{-1}$ in gel, consistent with imaging-FCS measurements of $0.5 \pm 0.2$ $μ$m$^2$ s$^{-1}$. Applied to mitochondria in pollen tubes, vSTICS resolved a bidirectional reverse-fountain pattern with slower anterograde transport ($0.1$-$1$ $μ$m s$^{-1}$) and faster retrograde motion peaking near $3$ $μ$m s$^{-1}$, plus a retrograde corridor about $2$ $μ$m wide. Density and diffusion maps indicated a denser, more advective core and higher peripheral diffusion. High-density sub-diffraction vesicle mapping produced similar velocity landscapes with about ten-fold higher particle densities. These results establish vSTICS as a practical method for quantitative 3D mapping of intracellular transport and refines the reverse-fountain model by revealing asymmetric, predominantly transverse circulation.
Hannah G. Scanlon, Gibarni Mahata, Anna C. Nelson
et al.
The microtubule cytoskeleton is comprised of dynamic, polarized filaments that facilitate transport within the cell. Polarized microtubule arrays are key to facilitating cargo transport in long cells such as neurons. Microtubules also undergo dynamic instability, where the plus and minus ends of the filaments switch between growth and shrinking phases, leading to frequent microtubule turnover. Although microtubules often completely disassemble and new filaments nucleate, microtubule arrays have been observed to both maintain their biased orientation throughout the cell lifetime and to rearrange their polarity as an adaptive response to injury. Motivated by cytoskeleton organization in neurites, we propose a spatially-explicit stochastic model of microtubule arrays and investigate how nucleation of new filaments could generate biased polarity in a simple linear domain. Using a continuous-time Markov chain model of microtubule growth dynamics, we model and parameterize two experimentally-validated nucleation mechanisms: nucleation feedback, where the direction of filament growth depends on existing microtubule content, and a checkpoint mechanism, where microtubules that nucleate in a direction opposite to the majority experience frequent catastrophe. When incorporating these validated mechanisms into the spatial model, we find that nucleation feedback is sufficient to establish biased polarity in neurites of different lengths, and that the emergence and maintenance of biased polarity is relatively stable in spite of stochastic fluctuations. This work provides a framework to study the relationship between microtubule nucleation and polarity, and could extend to give insights into mechanisms that drive the formation of polarized filament arrays in other biological settings.
Andrei Kramer, Federica Milinanni, Jeanette Hellgren Kotaleski
et al.
Biochemical reaction models describing subcellular processes generally come with a large uncertainty. To be able to account for this during the modeling process, we have developed the R-package UQSA, performing uncertainty quantification and sensitivity analysis in an integrated fashion. UQSA is designed for fast sampling of complicated multi-dimensional parameter distributions, using efficient Markov chain Monte Carlo (MCMC) sampling techniques and Vine-copulas to model complicated joint distributions. We perform MCMC sampling both from stochastic and deterministic models, in either likelihood-free or likelihood-based settings. In the likelihood-free case, we use Approximate Bayesian Computation (ABC), while for likelihood-based sampling we provide different algorithms, including the fast geometry-informed algorithm SMMALA (Simplified Manifold Metropolis-Adjusted Langevin Algorithm). The uncertainty quantification can be followed by a variance decomposition-based global sensitivity analysis. We are aiming for biochemical models, but UQSA can be used for any type of reaction networks. The use of Vine-copulas allows us to describe, evaluate, and sample from complicated parameter distributions, as well as adding new datasets in a sequential manner without redoing the previous parameter fit. The code is written in R, with C as a backend to improve speed. We use the SBtab table format for Systems Biology projects for the model description as well as the experimental data. An event system allows the user to model complicated transient input, common within, e.g., neuroscience. UQSA has an extensive documentation with several examples describing different types of models and data. The code has been tested on up to 2000 cores on several nodes on a computing cluster, but we also include smaller examples that can be run on a laptop. Source code: https://github.com/icpm-kth/uqsa
The applicability of computational and dynamical systems models to organisms is scrutinized, using examples from developmental biology and cognition. Developmental morphogenesis is dependent on the inherent material properties of developing tissues, a non-computational modality, but cell differentiation, which utilizes chromatin-based revisable memory banks and program-like function-calling, via the developmental gene co-expression system unique to metazoans, has a quasi-computational basis. Multi-attractor dynamical models are argued to be misapplied to global properties of development, and it is suggested that along with computationalism, dynamicism is similarly unsuitable to accounting for cognitive phenomena. Proposals are made for treating brains and other nervous tissues as novel forms of excitable matter with inherent properties which enable the intensification of cell-based basal cognition capabilities present throughout the tree of life.
Supersymmetric extensions of the standard model generically contain stable non-topological solitons, Q-balls, which carry baryon or lepton number. We show that large Q-balls can be copiously produced in the early universe, can survive until the present time, and can contribute to dark matter.
Iman Habibi, Effat S Emamian, Osvaldo Simeone
et al.
Due to structural and functional abnormalities or genetic variations and mutations, there may be dysfunctional molecules within an intracellular signaling network that do not allow the network to correctly regulate its output molecules, such as transcription factors. This disruption in signaling interrupts normal cellular functions and may eventually develop some pathological conditions. In this paper, computation capacity of signaling networks is introduced as a fundamental limit on signaling capability and performance of such networks. The computation capacity measures the maximum number of computable inputs, that is, the maximum number of input values for which the correct functional output values can be recovered from the erroneous network outputs, when the network contains some dysfunctional molecules. This contrasts with the conventional communication capacity that measures instead the maximum number of input values that can be correctly distinguished based on the erroneous network outputs. The computation capacity is higher than the communication capacity, if the network response function is not a one-to-one function of the input signals. By explicitly incorporating the effect of signaling errors that result in the network dysfunction, the computation capacity provides more information about the network and its malfunction. Two examples of signaling networks are studied here, one regulating caspase3 and another regulating NFkB, for which computation and communication capacities are analyzed. Higher computation capacities are observed for both networks. One biological implication of this finding is that signaling networks may have more capacity than that specified by the conventional communication capacity metric. The effect of feedback is also studied. In summary, this paper reports findings on a new fundamental feature of the signaling capability of cell signaling networks.
Rebecca L. Brouillette, Élie Besserer-Offroy, Christine E. Mona
et al.
Pepducins are cell-penetrating, membrane-tethered lipopeptides designed to target the intracellular region of a G protein-coupled receptor (GPCR) in order to allosterically modulate the receptor's signaling output. In this proof-of-concept study, we explored the pain-relief potential of a pepducin series derived from the first intracellular loop of neurotensin receptor type 1 (NTS1), a class A GPCR that mediates many of the effects of the neurotensin (NT) tridecapeptide, including hypothermia, hypotension and analgesia. We used BRET-based biosensors to determine the pepducins' ability to engage G protein signaling pathways associated with NTS1 activation. We observed partial Gq and G13 activation at a 10 μM concentration, indicating that these pepducins may act as allosteric agonists of NTS1. Additionally, we used surface plasmon resonance (SPR) as a label-free assay to monitor pepducin-induced responses in CHO-K1 cells stably expressing hNTS1. This whole-cell integrated assay enabled us to subdivide our pepducin series into three profile response groups. In order to determine the pepducins' antinociceptive potential, we then screened the series in an acute pain model (tail-flick test) by measuring tail withdrawal latencies to a thermal nociceptive stimulus, following intrathecal pepducin administration (275 nmol/kg). We further evaluated promising pepducins in a tonic pain model (formalin test), as well as in neuropathic (Chronic Constriction Injury) and inflammatory (Complete Freund's Adjuvant) chronic pain models. We report one pepducin, PP-001, that consistently reduced rat nociceptive behaviors, even in chronic pain paradigm. Altogether, these results suggest that NTS1-derived pepducins may represent a promising strategy in pain-relief.
Chronic fetal hypoxia and infection are examples of adverse conditions during complicated pregnancy, which impact cardiac myogenesis and increase the lifetime risk of heart disease. However, the effects that chronic hypoxic or inflammatory environments exert on cardiac pacemaker cells are poorly understood. Here, we review the current evidence and novel avenues of bench-to-bed research in this field of perinatal cardiogenesis as well as its translational significance for early detection of future risk for cardiovascular disease.
It is possible that there are post-translational circadian oscillators that continue functioning in the absence of negative feedback transcriptional repression in many cell types from diverse organisms. Apart from the KaiABC system from cyanobacteria, the molecular components and interactions required to create in-vitro ("test-tube") circadian oscillations in different cell types are currently unknown. Inspired by the KaiABC system, I provide "proof-of-principle" mathematical models that a protein with 2 (or more) modification sites which selectively sequesters an effector/cofactor molecule can function as a circadian time-keeper. The 2-site mechanism can be implemented using two relatively simple coupled non-linear ODEs in terms of site occupancy; the models do not require overly special fine-tuning of parameters for generating stable limit cycle oscillations.
Biosyntheses of proteins, nucleotides and fatty acids, are essential for the malignant proliferation and survival of cancer cells. Cumulating research findings show that amino acid restrictions are potential strategies for cancer interventions. Meanwhile, dietary strategies are popular among cancer patients. However, there is still lacking solid rationale to clarify what is the best strategy, why and how it is. Here, integrated analyses and comprehensive summaries for the abundances, signalling and functions of amino acids in proteomes, metabolism, immunity and food compositions, suggest that, intermittent fasting or intermittent dietary lysine restriction with normal maize as an intermittent staple food for days or weeks, might have the value and potential for cancer prevention or therapy. Moreover, dietary supplements were also discussed for cancer cachexia including dietary immunomodulatory.
Oscillatory gene circuits are ubiquitous to biology and are involved in fundamental processes of cell cycle, circadian rhythms and developmental systems. The synthesis of small, non-natural oscillatory genetic circuits have been increasingly used to test fundamental principles of genetic network dynamics. A recently developed fast, tunable genetic oscillator by Stricker et al.[23] has demonstrated robustness and tunability of oscillatory behavior by combining positive and negative feedback loops. This oscillator combining lacI (negative) and araC (positive) feedback loops, was however modeled using multiple layers of differential equations to capture the molecular complexity of regulation, in order to explain the experimentally measured oscillations. We have developed a reduced model based on delay differential equations (DDEs) of this dual feedback loop oscillator, that reproduces the tunability of oscillator period and amplitude based on the concentration of the two inducers isopropyl b-D-1-thiogalactopyranoside (IPTG) and arabinose. Previous work had predicted a need for an asymmetry in copy numbers of activator (araC) and repressor (lacI) genes encoded on plasmids. We use our reduced model to redesign the network by comparing the effect of asymmetry in gene expression at the level of (a) DNA copy numbers and the rates of (b) mRNA translation and (c) degradation. We find the minimal period of the oscillator is sensitive to DNA copy number asymmetry, but translation rate asymmetry has an identical effect as plasmid copy numbers, while modulating the asymmetry in mRNA degradation can improve the tunability of period of the oscillator, together with increased robustness to replication 'noise' and influence of the host cell cycle. Thus, our model predicts experimentally testable principles to redesign a potentially more robust oscillatory genetic network.