Large-scale quantum effects in biological systems

2005 ◽  
Vol 102 (6) ◽  
pp. 1116-1130 ◽  
Author(s):  
Marcus V. Mesquita ◽  
�urea R. Vasconcellos ◽  
Roberto Luzzi ◽  
Sergio Mascarenhas
Keyword(s):  
Large Scale ◽  
Biological Systems ◽  
Quantum Effects

THE BUBBLING UNIVERSE

2003 ◽  
Vol 12 (09) ◽  
pp. 1783-1786
Author(s):  
TANMAY VACHASPATI
Keyword(s):  
20Th Century ◽  
Large Scale ◽  
Quantum Effects ◽  
Expanding Universe

The scenario of an expanding universe envisioned in the early part of the 20th century has been replaced by one where we live in a chaotic, eternally bubbling universe. Every bubble has the potential for developing into a new habitable universe. In this essay I describe how large-scale quantum effects could be driving the chaotic bubbling universe.

scholarly journals Some challenges and opportunities for Russia and regions in terms of the global decarbonization trend

Georesursy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 8-16
Author(s):  
Danis K. Nurgaliev ◽  
Svetlana Yu. Selivanovskaya ◽  
Maria V. Kozhevnikova ◽  
Polina Yu. Galitskaya
Keyword(s):  
Carbon Sequestration ◽  
Large Scale ◽  
Oil And Gas ◽  
Hydrogen Generation ◽  
Biological Systems ◽  
Energy Transition ◽  
Gas Production ◽  
High Tech ◽  
Oil And Gas Production ◽  
Testing Area

This article discusses a possible scenario of energy transition in Russia, taking into account the economic structure, presence of huge oil and gas infrastructure and unique natural resources. All this allows to consider global trends of energy and economic decarbonization not only as a challenge, but also as a new opportunity for the country. Considering developed oil and gas production, transportation, refining and petrochemical infrastructure, as well as the vast territory, forest, water and soil resources, our country has unique opportunities for carbon sequestration using both biological systems and the existing oil and gas infrastructure. It is proposed to use the existing oil and gas production facilities for hydrogen generation in the processes of hydrocarbon catalytic transformation inside the reservoir. It is suggested to create and use large-scale technologies for CO2 sequestration using existing oil and gas production infrastructure. Considering high potential of the Russian Federation for carbon sequestration by biological systems, a network of Russian carbon testing areas is being developed, including one at Kazan Federal University (KFU), – the “Carbon-Povolzhye” testing area. The creation of carbon farms based on the applications at such testing areas could become a high-demand high-tech business. A detailed description of the KFU carbon testing area and its planned objectives are given.

scholarly journals Biomining – Biotechnological Systems for the Extraction and Recovery of Metals from Secondary Sources

2018 ◽  
Vol 20 (4) ◽  
pp. 737-742 ◽  
Keyword(s):  
Large Scale ◽  
Low Cost ◽  
Biological Systems ◽  
Bottom Ash ◽  
Precious Metals ◽  
Small Scale ◽  
Secondary Sources ◽  
Hydrometallurgical Processing ◽  
Pre Treatment ◽  
Pyrometallurgical Processing

<p>Biomining is the common term used to define processes that utilize biological systems to facilitate the extraction of metals from ores. Nowadays, a biomining concept can be defined as a two stage combined biological systems (1st stage bioleaching and 2nd stage biosorption) in order to perform the extraction and recovery of the metals from secondary sources such as industrial and mining waste, waste electrical and electronic equipment (WEEE), bottom ash and end of life vehicles. Overwhelming demand and limited sources of metals have resulted in searching new sources so that attentions have been shifted from mining process towards recycling of secondary resources for the recovery of metals. There are several metallurgical processes for metal recovery from the secondary sources such as pyrometallurgical processing, hydrometallurgical and bio/hydrometal-lurgical processing. Biomining processes are estimated to be relatively low-cost, environmentally friendly and suitable for both large scale as well as small scale applications under the bio/hydrometallurgical processing. Thus, the process involves physical separation (pre-treatment) and biomining (bioleaching and biosorption) and hydrometallurgical processes for recovery of base metals, rare earth elements (REEs) and precious metals from e-waste was evaluated.</p>

scholarly journals StructureMan: A Structure Manipulation Tool to Study Large Scale Biomolecular Interactions

2021 ◽  
Vol 7 ◽  
Author(s):  
Yuejiao Xian ◽  
Yixin Xie ◽  
Sebastian Miki Silva ◽  
Chitra B. Karki ◽  
Weihong Qiu ◽  
...  
Keyword(s):  
Large Scale ◽  
Molecular Motor ◽  
Biological Systems ◽  
Biomolecular Interactions ◽  
Viral Capsid ◽  
Alternative Approach ◽  
Binding Mechanisms

Studying biomolecular interactions is a crucial but challenging task. Due to their large scales, many biomolecular interactions are difficult to be simulated via all atom models. An effective approach to investigate the biomolecular interactions is highly demanded in many areas. Here we introduce a Structure Manipulation (StructureMan) program to operate the structures when studying the large-scale biomolecular interactions. This novel StructureMan tool provides comprehensive operations which can be utilized to study the interactions in various large biological systems. Combining with electrostatic calculation programs such as DelPhi and DelPhiForce, StructureMan was implemented to reveal the detailed electrostatic features in two large biological examples, the viral capsid and molecular motor-microtubule complexes. Applications on these two examples revealed interesting binding mechanisms in the viral capsid and molecular motor. Such applications demonstrated that the StructureMan can be widely used when studying the biomolecular interactions in large scale biological problems. This novel tool provides an alternative approach to efficiently study the biomolecular interactions, especially for large scale biology systems. The StructureMan tool is available at our website: http://compbio.utep.edu/static/downloads/script-for-munipulation2.zip.

scholarly journals Heterogeneous CPU+GPU-Enabled Simulations for DFTB Molecular Dynamics of Large Chemical and Biological Systems

2019 ◽  
Author(s):  
Sarah I. Allec ◽  
Yijing Sun ◽  
Jianan Sun ◽  
Chia-En A. Chang ◽  
Bryan Wong
Keyword(s):  
Molecular Dynamics ◽  
Large Scale ◽  
Heterogeneous Computing ◽  
Biological Systems ◽  
Hiv Protease ◽  
Hamiltonian Matrix ◽  
Large Complex System ◽  
Molecular Dynamics Trajectory ◽  
Chemical And Biological Systems ◽  
Computational Bottleneck

We introduce a new heterogeneous CPU+GPU-enhanced DFTB approach for the routine and efficient simulation of large chemical and biological systems. Compared to homogenous computing with conventional CPUs, heterogeneous computing approaches exhibit substantial performance with only a modest increase in power consumption, both of which are essential to upcoming exascale computing initiatives. We show that DFTB-based molecular dynamics is a natural candidate for heterogeneous computing since the computational bottleneck in these simulations is the diagonalization of the Hamiltonian matrix, which is performed several times during a single molecular dynamics trajectory. To thoroughly test and understand the performance of our heterogeneous CPU+GPU approach, we examine a variety of algorithmic implementations, benchmarks of different hardware configurations, and applications of this methodology on several large chemical and biological systems. Finally, to demonstrate the capability of our implementation, we conclude with a large-scale DFTB MD simulation of explicitly solvated HIV protease (3,974 atoms total) as a proof-of-concept example of an extremely large/complex system which, to the best of our knowledge, is the first time that an entire explicitly-solvated protein has been treated at a quantum-based MD level of detail.

scholarly journals A modified Ising model of Barabási–Albert network with gene-type spins

2020 ◽  
Vol 81 (3) ◽  
pp. 769-798
Author(s):  
Jeyashree Krishnan ◽  
Reza Torabi ◽  
Andreas Schuppert ◽  
Edoardo Di Napoli
Keyword(s):  
Phase Transitions ◽  
Ising Model ◽  
Large Scale ◽  
Biological Systems ◽  
Ising Models ◽  
Network Size ◽  
Living Systems ◽  
Computational Techniques ◽  
Solid State Physics ◽  
Gene Type

Abstract The central question of systems biology is to understand how individual components of a biological system such as genes or proteins cooperate in emerging phenotypes resulting in the evolution of diseases. As living cells are open systems in quasi-steady state type equilibrium in continuous exchange with their environment, computational techniques that have been successfully applied in statistical thermodynamics to describe phase transitions may provide new insights to the emerging behavior of biological systems. Here we systematically evaluate the translation of computational techniques from solid-state physics to network models that closely resemble biological networks and develop specific translational rules to tackle problems unique to living systems. We focus on logic models exhibiting only two states in each network node. Motivated by the apparent asymmetry between biological states where an entity exhibits boolean states i.e. is active or inactive, we present an adaptation of symmetric Ising model towards an asymmetric one fitting to living systems here referred to as the modified Ising model with gene-type spins. We analyze phase transitions by Monte Carlo simulations and propose a mean-field solution of a modified Ising model of a network type that closely resembles a real-world network, the Barabási–Albert model of scale-free networks. We show that asymmetric Ising models show similarities to symmetric Ising models with the external field and undergoes a discontinuous phase transition of the first-order and exhibits hysteresis. The simulation setup presented herein can be directly used for any biological network connectivity dataset and is also applicable for other networks that exhibit similar states of activity. The method proposed here is a general statistical method to deal with non-linear large scale models arising in the context of biological systems and is scalable to any network size.

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