Aperiodic crystals in biology

2021 ◽  
Author(s):  
Enrique Maciá
Keyword(s):  
Sea Urchins ◽  
Biological Systems ◽  
Structural Features ◽  
Modeling Framework ◽  
Topical Review ◽  
Symmetry Principles ◽  
Biological Structures ◽  
Cell Patterns ◽  
The Rich ◽  
Aperiodic Crystals

Abstract Biological systems display a broad palette of hierarchically ordered designs spanning over many orders of magnitude in size. Remarkably enough, periodic order, which profusely shows up in nonliving ordered compounds, plays a quite subsidiary role in most biological structures, which can be appropriately described in terms of the more general aperiodic crystal notion instead. In this Topical Review I shall illustrate this issue by considering several representative examples, including botanical phyllotaxis, the geometry of cell patterns in tissues, the morphology of sea urchins, or the symmetry principles underlying virus architectures. In doing so, we will realize that albeit the currently adopted quasicrystal notion is not general enough to properly account for the rich structural features one usually finds in biological arrangements of matter, several mathematical tools and fundamental notions belonging to the aperiodic crystals science toolkit can provide a useful modeling framework to this end.

Skeletal elements of crinoid echinoderms: High-resolution structural and microanalytical results

1983 ◽  
Vol 41 ◽  
pp. 194-195
Author(s):  
D. F. Blake ◽  
L. F. Allard ◽  
D. R. Peacor
Keyword(s):  
High Resolution ◽  
Marine Invertebrates ◽  
Sea Urchins ◽  
Structural Features ◽  
Sea Stars ◽  
High Resolution Tem ◽  
Relationship Of ◽  
The Relationship ◽  
Insight Into

Echinodermata is a phylum of marine invertebrates which has been extant since Cambrian time (c.a. 500 m.y. before the present). Modern examples of echinoderms include sea urchins, sea stars, and sea lilies (crinoids). The endoskeletons of echinoderms are composed of plates or ossicles (Fig. 1) which are with few exceptions, porous, single crystals of high-magnesian calcite. Despite their single crystal nature, fracture surfaces do not exhibit the near-perfect {10.4} cleavage characteristic of inorganic calcite. This paradoxical mix of biogenic and inorganic features has prompted much recent work on echinoderm skeletal crystallography. Furthermore, fossil echinoderm hard parts comprise a volumetrically significant portion of some marine limestones sequences. The ultrastructural and microchemical characterization of modern skeletal material should lend insight into: 1). The nature of the biogenic processes involved, for example, the relationship of Mg heterogeneity to morphological and structural features in modern echinoderm material, and 2). The nature of the diagenetic changes undergone by their ancient, fossilized counterparts. In this study, high resolution TEM (HRTEM), high voltage TEM (HVTEM), and STEM microanalysis are used to characterize tha ultrastructural and microchemical composition of skeletal elements of the modern crinoid Neocrinus blakei.

Therapeutic properties of organotin complexes with reference to their structural and environmental features

2017 ◽  
Vol 37 (2) ◽  
pp. 51-70 ◽  
Author(s):  
Muhammad Iqbal ◽  
Saqib Ali ◽  
Ali Haider ◽  
Nasir Khalid
Keyword(s):  
Mode Of Action ◽  
Therapeutic Potential ◽  
Biological Systems ◽  
Structural Features ◽  
Present Review ◽  
Environmental Features ◽  
Recent Advances ◽  
Target Sites ◽  
Therapeutic Properties ◽  
Modes Of Interaction

AbstractOrganotin complexes are being extensively studied and screened for their therapeutic potential. Although many recent advances and achievements in this field have been made, the exact mode of action of these complexes is yet to be unveiled. In the present review, an attempt has been made to correlate the therapeutic properties of organotin complexes with their structural features and the environment in which these interact with biological systems. The mechanism, various modes of interaction with biological systems, and physiological target sites of organotin complexes have been highlighted as well.

Iron Chelation by thiocytosine: Investigating electronic and structural features for describing tautomerism and metal chelation processes

2021 ◽  
pp. 1-8
Author(s):  
Azadeh Jafari Rad ◽  
Maryam Abbasi ◽  
Bahareh Zohrevand
Keyword(s):  
Density Functional Theory ◽  
Dft Calculations ◽  
Molecular Orbital ◽  
Density Functional ◽  
Biological Systems ◽  
Iron Chelation ◽  
Structural Features ◽  
Metal Chelation ◽  
Functional Theory

This work was performed regarding the importance of iron (Fe) chelation for biological systems. This goal was investigated by assistance of a model of thiocytosine (TC) for participating in Fe-chelation processes. First, formations of tautomeric conformations were investigated to explore existence of possible structures of TC. Next, Fe-chelation processes were examined for all four obtained tautomers of TC. The results indicated that thiol tautomers could be seen at higher stability than thio tautomers, in which one of such thiol tautomers yielded the strongest Fe-chelation process to build FeTC3 model. As a consequence, parallel to the results of original TC tautomers, Fe-chelated models were found to be achievable for meaningful chelation processes or sensing the existence of Fe in media. Examining molecular orbital features could help for sensing purposes. The results of this work were obtained by performing density functional theory (DFT) calculations proposing TC compounds suitable for Fe-chelation purposes.

A product architecture-based tool for bioinspired function-sharing

2020 ◽  
pp. 1-33 ◽  
Author(s):  
Devesh Bhasin ◽  
Daniel A. McAdams ◽  
Astrid Layton
Keyword(s):  
Engineering Design ◽  
Biological Function ◽  
Biological Systems ◽  
Product Architecture ◽  
Biological Structures ◽  
Structure Tree ◽  
Single Structure ◽  
Function Sharing ◽  
Architectural Characteristics

Abstract In this work, we show that bioinspired function-sharing can be effectively applied in engineering design by abstracting and emulating the product architecture of biological systems that exhibit function-sharing. Systems that leverage function-sharing enable multiple functions to be performed by a single structure. Billions of years of evolution has led to the development of function-sharing adaptations in biological systems. Currently, engineers leverage biological function-sharing by imitating serendipitously encountered biological structures. As a result, utilizing bioinspired function-sharing remains limited to some specific engineering problems. To overcome this limitation, we propose the Function-Behavior-Structure tree as a tool to simultaneously abstract both biological adaptations and the product architecture of biological systems. The tool uses information from an existing bioinspired design abstraction tool and an existing product architecture representation tool. A case study demonstrates the tool's ability to abstract the product architectural characteristics of function-sharing biological systems. The abstracted product architectural characteristics are then shown to facilitate problem-driven bioinspiration of function-sharing. The availability of a problem-driven approach may reduce the need to imitate biological structures to leverage biological function-sharing in engineering design. This work is a step forward in analyzing biological product architectures to inspire engineering design.

A hybrid model of cell cycle in mammals

2016 ◽  
Vol 14 (01) ◽  
pp. 1640001 ◽  
Author(s):  
Jonathan Behaegel ◽  
Jean-Paul Comet ◽  
Gilles Bernot ◽  
Emilien Cornillon ◽  
Franck Delaunay
Keyword(s):  
Cell Cycle ◽  
Hybrid Model ◽  
Biological Systems ◽  
Modeling Framework ◽  
Variable Model ◽  
The Core ◽  
Mammalian Cell Cycle ◽  
Hybrid Framework ◽  
Qualitative State ◽  
Core Idea

Time plays an essential role in many biological systems, especially in cell cycle. Many models of biological systems rely on differential equations, but parameter identification is an obstacle to use differential frameworks. In this paper, we present a new hybrid modeling framework that extends René Thomas’ discrete modeling. The core idea is to associate with each qualitative state “celerities” allowing us to compute the time spent in each state. This hybrid framework is illustrated by building a 5-variable model of the mammalian cell cycle. Its parameters are determined by applying formal methods on the underlying discrete model and by constraining parameters using timing observations on the cell cycle. This first hybrid model presents the most important known behaviors of the cell cycle, including quiescent phase and endoreplication.

scholarly journals Improved constraints increase the predictivity and applicability of a linear programming-based dynamic metabolic modeling framework

2021 ◽  
Author(s):  
Justin Y Lee ◽  
Mark P Styczynski
Keyword(s):  
Experimental Data ◽  
Linear Programming ◽  
Biological Systems ◽  
Metabolic Modeling ◽  
Training Data ◽  
Modeling Framework ◽  
Modeling Tools ◽  
Regulatory Constraints ◽  
Simplifying Assumptions ◽  
Optimal Approach

Background: Current metabolic modeling tools suffer from a variety of limitations, from scalability to simplifying assumptions, that preclude their use in many applications. We recently created a modeling framework, LK-DFBA, that addresses a key gap: capturing metabolite dynamics and regulation while retaining a potentially scalable linear programming structure. Key to this framework's success are the linear kinetics and regulatory constraints imposed on the system. Here, we present improvements to these constraints to improve the predictivity of LK-DFBA models and their applicability to biological systems. Method: Three new constraint approaches were created to better model interactions between metabolites and the reactions they regulate. These new approaches (and the original LK-DFBA approach) were tested on several synthetic and biological systems to determine their performance when using both noiseless and noisy data. To validate our framework, we compared experimental data to metabolite dynamics predicted by LK-DFBA. Results: There was no single optimal type of constraints across all synthetic and biological systems; rather, any one of the four approaches could perform best for a given system. The optimal approach for fitting to wildtype data of a given model was consistently the best approach when predicting new phenotypes for that model. Furthermore, many of LK-DFBA's predictions qualitatively agreed with experimental data. Conclusions: LK-DFBA can be improved by using several kinetics constraint approaches, with the ideal one selected based on wild-type training data. LK-DFBA's ability to predict metabolic trends in experimental data further supports its potential for modeling metabolite dynamics in systems of all sizes.

Introducing SEC–SANS for studies of complex self-organized biological systems

2018 ◽  
Vol 74 (12) ◽  
pp. 1178-1191 ◽  
Author(s):  
Nicolai Tidemand Johansen ◽  
Martin Cramer Pedersen ◽  
Lionel Porcar ◽  
Anne Martel ◽  
Lise Arleth
Keyword(s):  
Small Angle ◽  
Signal To Noise Ratio ◽  
Biological Systems ◽  
Size Exclusion ◽  
Contrast Variation ◽  
Saxs Data ◽  
Biological Structures ◽  
Exclusion Chromatography ◽  
Protein Components ◽  
Counting Statistics

Small-angle neutron scattering (SANS) is maturing as a method for studying complex biological structures. Owing to the intrinsic ability of the technique to discern between 1H- and 2H-labelled particles, it is especially useful for contrast-variation studies of biological systems containing multiple components. SANS is complementary to small-angle X-ray scattering (SAXS), in which similar contrast variation is not easily performed but in which data with superior counting statistics are more easily obtained. Obtaining small-angle scattering (SAS) data on monodisperse complex biological structures is often challenging owing to sample degradation and/or aggregation. This problem is enhanced in the D2O-based buffers that are typically used in SANS. In SAXS, such problems are solved using an online size-exclusion chromatography (SEC) setup. In the present work, the feasibility of SEC–SANS was investigated using a series of complex and difficult samples of membrane proteins embedded in nanodisc particles that consist of both phospholipid and protein components. It is demonstrated that SEC–SANS provides data of sufficient signal-to-noise ratio for these systems, while at the same time circumventing aggregation. By combining SEC–SANS and SEC–SAXS data, an optimized basis for refining structural models of the investigated structures is obtained.

TOWARDS ANALYZING COMPLEX SWARMING PATTERNS IN BIOLOGICAL SYSTEMS WITH THE HELP OF LATTICE-GAS CELLULAR AUTOMATA

1995 ◽  
Vol 03 (04) ◽  
pp. 947-955 ◽  
Author(s):  
ANDREAS DEUTSCH
Keyword(s):  
Random Walk ◽  
Lattice Gas ◽  
Temporal Dynamics ◽  
Pigment Cell ◽  
Biological Systems ◽  
Discrete Modeling ◽  
Cell Patterns ◽  
Lattice Gas Cellular Automata ◽  
Salamander Larvae ◽  
Swarming Behaviour

A new discrete modeling approach for analyzing swarming behaviour in biological systems is proposed, The model is formulated as a "lattice-gas cellular automaton" based on a piecewise straight random walk with constant speed of organisms (or cells), which solely interact with other members of the swarm. Putative applications are the swarming of myxobacteria and ants, slug pattern formation of Dictyostelium, as well as the formation of pigment cell patterns in salamander larvae. The temporal dynamics of the automaton are determined by rotation and propagation operators, together with a "biological operator" which describes local interactions of organisms within a well-defined perception area.

scholarly journals Structural Features of the Human Connectome That Facilitate the Switching of Brain Dynamics via Noradrenergic Neuromodulation

2021 ◽  
Vol 15 ◽  
Author(s):  
Carlos Coronel-Oliveros ◽  
Samy Castro ◽  
Rodrigo Cofré ◽  
Patricio Orio
Keyword(s):  
Structural Connectivity ◽  
Structural Features ◽  
Global Scale ◽  
Brain Dynamics ◽  
Connectivity Matrix ◽  
Rich Club ◽  
Spatial Features ◽  
Brain States ◽  
The Rich ◽  
Meso Scale

The structural connectivity of human brain allows the coexistence of segregated and integrated states of activity. Neuromodulatory systems facilitate the transition between these functional states and recent computational studies have shown how an interplay between the noradrenergic and cholinergic systems define these transitions. However, there is still much to be known about the interaction between the structural connectivity and the effect of neuromodulation, and to what extent the connectome facilitates dynamic transitions. In this work, we use a whole brain model, based on the Jasen and Rit equations plus a human structural connectivity matrix, to find out which structural features of the human connectome network define the optimal neuromodulatory effects. We simulated the effect of the noradrenergic system as changes in filter gain, and studied its effects related to the global-, local-, and meso-scale features of the connectome. At the global-scale, we found that the ability of the network of transiting through a variety of dynamical states is disrupted by randomization of the connection weights. By simulating neuromodulation of partial subsets of nodes, we found that transitions between integrated and segregated states are more easily achieved when targeting nodes with greater connection strengths—local feature—or belonging to the rich club—meso-scale feature. Overall, our findings clarify how the network spatial features, at different levels, interact with neuromodulation to facilitate the switching between segregated and integrated brain states and to sustain a richer brain dynamics.

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