Hasil untuk "Biology"

Maya Kansara, M. Teng, M. Smyth et al.

E. Kandel, Y. Dudai, M. Mayford

J. Hawley, M. Hargreaves, M. Joyner et al.

Exercise represents a major challenge to whole-body homeostasis provoking widespread perturbations in numerous cells, tissues, and organs that are caused by or are a response to the increased metabolic activity of contracting skeletal muscles. To meet this challenge, multiple integrated and often redundant responses operate to blunt the homeostatic threats generated by exercise-induced increases in muscle energy and oxygen demand. The application of molecular techniques to exercise biology has provided greater understanding of the multiplicity and complexity of cellular networks involved in exercise responses, and recent discoveries offer perspectives on the mechanisms by which muscle "communicates" with other organs and mediates the beneficial effects of exercise on health and performance.

G. Cramer, Kaoru Urano, S. Delrot et al.

The natural environment for plants is composed of a complex set of abiotic stresses and biotic stresses. Plant responses to these stresses are equally complex. Systems biology approaches facilitate a multi-targeted approach by allowing one to identify regulatory hubs in complex networks. Systems biology takes the molecular parts (transcripts, proteins and metabolites) of an organism and attempts to fit them into functional networks or models designed to describe and predict the dynamic activities of that organism in different environments. In this review, research progress in plant responses to abiotic stresses is summarized from the physiological level to the molecular level. New insights obtained from the integration of omics datasets are highlighted. Gaps in our knowledge are identified, providing additional focus areas for crop improvement research in the future.

Ahmad S. Khalil, J. Collins

Synthetic biology is bringing together engineers and biologists to design and build novel biomolecular components, networks and pathways, and to use these constructs to rewire and reprogram organisms. These re-engineered organisms will change our lives over the coming years, leading to cheaper drugs, 'green' means to fuel our cars and targeted therapies for attacking 'superbugs' and diseases, such as cancer. The de novo engineering of genetic circuits, biological modules and synthetic pathways is beginning to address these crucial problems and is being used in related practical applications.

R. Gutenkunst, J. Waterfall, F. Casey et al.

Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a “sloppy” spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.

Jennifer Y. Lin, D. Fisher

V. Gray-Schopfer, C. Wellbrock, R. Marais

C. Seeger, W. Mason

E. H. Harris

A. Hayward

S. Gelvin

M. Daëron

J. Mauchline

R. Herbst

R. Milo, R. Phillips

Robert Li, Z. Jia, M. Trush

Sonali Jathar, Vikram Kumar, Juhi Srivastava et al.

Kallol Polley, Sayed Mohammed Firdous

Abstract This narrative review summarises glioblastoma (GBM), a very frequent invasive kind of brain tumour in the elderly that is extremely aggressive, resistant to treatment, and has a bad prognosis because of its substantial genetic and cellular heterogeneity. With a median survival of about 15 months, GBM is still an incurable cancer. This review takes full 3 months for collect all relevant knowledge about PAX and SOX gene families, which have crucial roles in the biology of GBM, as shown by recent developments in molecular pathology. Depending on certain gene expression patterns, members of these transcription factor families have been shown to have both oncogenic and tumor suppressive properties. They are important regulators of brain development, stem cell maintenance, and tumor progression. While PAX6 functions as a tumor suppressor, preventing growth and angiogenesis, PAX3, PAX5, and PAX8 are increased in GBM, encouraging proliferation, stemness, and survival. Similarly, SOX7 and SOX11 act as suppressors, and their downregulation is associated with malignancy and a bad prognosis, while SOX2, SOX3, SOX4, and SOX9 increase tumor aggressiveness and resistance to treatment. Glioma cell migration, growth, and death inhibition are further fueled through the complex interactions between canonical and non-canonical WNT signaling that modulate PAX and SOX pathways. These results highlight how crucial thorough molecular profiling is for improved categorization, prognostication, and the creation of focused treatment plans in GBM. The discovery of the dual functions of the PAX and SOX genes in GBM highlights their potential as therapeutic targets and biomarkers, opening up new possibilities for more individualised and accurate treatment approaches.

Pengqiang Yu, Kejia Wu, Dongsheng Li et al.

This paper introduces an analytical method for passive earth pressure calculation based on a rigorous stress field analysis within the sliding wedge. Unlike traditional horizontal layer methods, this approach directly solves for the stress state at any point within the wedge by analyzing the equilibrium of 2D differential soil elements under appropriate boundary conditions, eliminating the need for a priori assumptions about the soil arch shape. The method yields the passive earth pressure distribution on the retaining structure and derives the soil arch shape analytically from major principal stress trajectories. This derived arch shape differs notably from conventional circular or parabolic assumptions, especially at higher soil–wall friction angles. Parametric studies show that the passive earth pressure coefficient increases with internal friction angle and surcharge. However, a key finding is the non-monotonic dependence of the passive earth pressure coefficient on the soil–wall friction angle, contrasting with many existing theories. Comparisons show predictions by the proposed method align well with experimental data, particularly offering a better representation of pressure distributions in the lower regions of retaining walls compared to Coulomb theory and other existing methods.