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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
  • 282
  • 28 Jun 2023
Topic Review
Approaches to Cardiovascular and Respiratory Systems Modelling
'Medicine in silico' has been strongly encouraged due to ethical and legal limitations related to animal experiments and investigations conducted on patients. Computer models, particularly the very complex ones (virtual patients—VP), can be used in medical education and biomedical research as well as in clinical applications. Simpler patient-specific models may aid medical procedures. However, computer models are unfit for medical devices testing. Hybrid (i.e., numerical–physical) models do not have this disadvantage.
  • 276
  • 20 Jun 2022
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.
  • 271
  • 19 Jun 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.
  • 263
  • 01 Feb 2023
Topic Review
Kinases/Protein Phosphatases in Signaling Pathways Activation
Optimizing physical training regimens to increase muscle aerobic capacity requires an understanding of the internal processes that occur during exercise that initiate subsequent adaptation. During exercise, muscle cells undergo a series of metabolic events that trigger downstream signaling pathways and induce the expression of many genes in working muscle fibers. There are a number of studies that show the dependence of changes in the activity of AMP-activated protein kinase (AMPK), one of the mediators of cellular signaling pathways, on the duration and intensity of single exercises. The activity of various AMPK isoforms can change in different directions, increasing for some isoforms and decreasing for others, depending on the intensity and duration of the load.
  • 263
  • 11 Jul 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.
  • 258
  • 20 Jun 2023
Topic Review
Deep Learning for Protein-Protein Interaction
Deep learning is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein–Protein Interactions (PPIs), key components governing a wide array of biological functionalities.
  • 213
  • 04 Jul 2023
Topic Review
Deep Learning in Whole Slide Imaging for Cancer
The significant progress made in the field of cancer prognosis using whole slide images (WSIs) is encouraging, indicating a promising future for cancer diagnosis and management. The ability to accurately predict survival rates and recurrence risk using deep learning methods has significant implications for clinical practice and patient care. As more sophisticated models and techniques are developed, the potential to revolutionize the field of oncology is immense.
  • 211
  • 02 Aug 2023
Topic Review
Application of GANs in Gene Expression Data Augmentation
A generative adversarial network (GAN) is essentially a two-player game composed of a generator and a discriminator. The generator’s role is to create synthetic data, while the discriminator’s task is to distinguish between real and generated data. During the training process, the generator strives to produce data that the discriminator cannot differentiate from the real data, whereas the discriminator continually improves its ability to distinguish real from generated data. This adversarial training regimen imbues GANs with the capability to model complex data distributions and produce high-quality synthetic data. Notably, their application to gene expression data systems is a fascinating and rapidly growing focus area.
  • 206
  • 21 Jul 2023
Topic Review
State-of the-Art Constraint-Based Modeling of Microbial Metabolism
Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. 
  • 169
  • 03 Jan 2024
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