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Topic Review
Molecular Dynamics Simulations for DNAs
DNA carries the genetic information required for the synthesis of RNA and proteins and plays an important role in many processes of biological development. Understanding the three-dimensional (3D) structures and dynamics of DNA is crucial for understanding their biological functions and guiding the development of novel materials. Molecular dynamics (MD) simulations can generally reproduce the behavior of DNAs in a computer, providing detailed structural and dynamical insights that enhancing our comprehension of relevant experimental data. MD simulations using classical force fields such as AMBER and CHARMM have provided highly detailed and flexible descriptions of DNA dynamics, including structural transformations, stability of non-canonical conformations, salt ion cohesion effects, twist-stretch coupling of stress, flexibility under methylation modifications, and interactions with other macromolecules. It is always fascinating to obtain microscopic insights into DNA dynamics through MD simulations. However, the innumerable degrees of freedom, interconnected in complex ways, can make it practically impossible to detect DNA dynamics on biologically relevant time scales and length scales using currently available computer hardware
  • 275
  • 28 Jun 2023
Topic Review
A Taxonomic Survey of Physics-Informed Machine Learning
Physics-informed machine learning (PIML) refers to the emerging area of extracting physically relevant solutions to complex multiscale modeling problems lacking sufficient quantity and veracity of data with learning models informed by physically relevant prior information.
  • 253
  • 20 Jun 2023
Topic Review
NNetEn Entropy
NNetEn is the first entropy measure that is based on artificial intelligence methods. The method modifies the structure of the LogNNet classification model so that the classification accuracy of the MNIST-10 digits dataset indicates the degree of complexity of a given time series. The calculation results of the proposed model are similar to those of existing methods, while the model structure is completely different and provides considerable advantages.
  • 626
  • 19 Jun 2023
Topic Review
Real-World Driver Stress Recognition and Diagnosis
Mental stress is known as a prime factor in road crashes. The devastation of these crashes often results in damage to humans, vehicles, and infrastructure. Likewise, persistent mental stress could lead to the development of mental, cardiovascular, and abdominal disorders. Preceding research in this domain mostly focuses on feature engineering and conventional machine learning approaches.
  • 265
  • 19 Jun 2023
Topic Review
Hydrotropism
Hydrotropism is the movement or growth of a plant towards water. It is a type of tropism, or directional growth response, that is triggered by water. Plants are able to detect water through various stimuli, including changes in moisture levels and changes in water potential. 
  • 305
  • 23 Feb 2023
Topic Review
Unique Properties of the Immune System
The human body is unquestionably one of the most complex systems known to humanity. There are three main regulation systems in the human body (the nervous system, the endocrine system and the immune system). These three systems are integrated into one ultimate information communication network within the human body. However, each regulation system has its specific roles and unique properties. Consequently, each of these regulation systems has served as inspiration for computational models to efficiently solve real-world problems. An overview of these models and their applications is presented.
  • 256
  • 01 Feb 2023
Topic Review
Genome-Scale Metabolic Modelling
Genome-scale metabolic models (GEMs) aim to systematically encode knowledge of the metabolism of an organism. GEMs are composed of different layers of information and are constructed with a combination of automated approaches and manual curation based on the available literature and experimental data. These models not only encode existing knowledge about an organism, but can also generate new knowledge through various analytical methods. The latter are mostly focused on the assessment of reaction fluxes through the metabolic network in different conditions.
  • 430
  • 28 Jan 2023
Topic Review
Post-Stroke Movement with Motion Capture and Musculoskeletal Modeling
Research of post-stroke locomotion via musculoskeletal (MSK) modeling has offered an unprecedented insight into pathological muscle function and its interplay with skeletal geometry and external stimuli. Advances in solving the dynamical system of post-stroke effort and the generic MSK models used have triggered noticeable improvements in simulating muscle activation dynamics of stroke populations.
  • 477
  • 09 Dec 2022
Topic Review
Computational Biology in Drug Design
Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, researchers propose a methodology for integrating various computational techniques into new drug discovery and design.
  • 386
  • 28 Nov 2022
Topic Review
Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution
Cancer treatment and pharmaceutical development require targeted treatment and less toxic therapeutic intervention to achieve real progress against this disease. In this scenario, nanomedicine emerged as a reliable tool to improve drug pharmacokinetics and to translate to the clinical biologics based on large molecules. However, the ability of body to recognize foreign objects together with carrier transport heterogeneity derived from the combination of particle physical and chemical properties, payload and surface modification, make the designing of effective carriers very difficult. In this scenario, physiologically based pharmacokinetic modeling can help to design the particles and eventually predict their ability to reach the target and treat the tumor. This effort is performed by scientists with specific expertise and skills and familiarity with artificial intelligence tools such as advanced software that are not usually in the “cords” of traditional medical or material researchers. 
  • 543
  • 04 Nov 2022
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