Hasil untuk "Biology (General)"

N. Zheng, N. Shabek

Marium Malik, M. Tariq, Maira Kamran et al.

M. Mahmoudi, I. Lynch, M. Ejtehadi et al.

M. Kéry, M. Schaub

Dan L. Nicolae, E. Gamazon, Wei Zhang et al.

Although genome-wide association studies (GWAS) of complex traits have yielded more reproducible associations than had been discovered using any other approach, the loci characterized to date do not account for much of the heritability to such traits and, in general, have not led to improved understanding of the biology underlying complex phenotypes. Using a web site we developed to serve results of expression quantitative trait locus (eQTL) studies in lymphoblastoid cell lines from HapMap samples (http://www.scandb.org), we show that single nucleotide polymorphisms (SNPs) associated with complex traits (from http://www.genome.gov/gwastudies/) are significantly more likely to be eQTLs than minor-allele-frequency–matched SNPs chosen from high-throughput GWAS platforms. These findings are robust across a range of thresholds for establishing eQTLs (p-values from 10−4–10−8), and a broad spectrum of human complex traits. Analyses of GWAS data from the Wellcome Trust studies confirm that annotating SNPs with a score reflecting the strength of the evidence that the SNP is an eQTL can improve the ability to discover true associations and clarify the nature of the mechanism driving the associations. Our results showing that trait-associated SNPs are more likely to be eQTLs and that application of this information can enhance discovery of trait-associated SNPs for complex phenotypes raise the possibility that we can utilize this information both to increase the heritability explained by identifiable genetic factors and to gain a better understanding of the biology underlying complex traits.

J. Goldstein, Michael S. Brown

D. Svergun, L. Feigin, G. Taylor

F. H. Adler

V. Devita, Samuel Hellman, S. Rosenberg

C. Hauert, M. Doebeli

S. Vogel

A. Cossins, K. Bowler

Guangchuang Yu, T. Lam, Huachen Zhu et al.

Ggtree is a comprehensive R package for visualizing and annotating phylogenetic trees with associated data. It can also map and visualize associated external data on phylogenies with two general methods. Method 1 allows external data to be mapped on the tree structure and used as visual characteristic in tree and data visualization. Method 2 plots the data with the tree side by side using different geometric functions after reordering the data based on the tree structure. These two methods integrate data with phylogeny for further exploration and comparison in the evolutionary biology context. Ggtree is available from http://www.bioconductor.org/packages/ggtree.

S. Hopkin

Abir Khazaal, Fatemeh Vafaee

Network science has become an essential interdisciplinary tool for understanding complex biological systems. However, because these systems undergo continuous, often stimulus-driven changes in both structure and function, traditional static network approaches frequently fall short in capturing their dynamic nature. Dynamic network analysis (DNA) addresses this limitation and offers a powerful framework to investigate these evolving relationships. This work focuses on temporal networks, a central paradigm within DNA, as an effective approach for modelling time-resolved changes in biological systems. While DNA has gained traction in domains like social and communication sciences, its integration in biology has been more gradual, hindered by data limitations and the need for domain-specific adaptations. Aimed at supporting researchers, particularly those new to the field, the review offers an integrative overview of the diverse and multidisciplinary landscape of DNA, with a focus on temporal networks in systems biology. I begin by clarifying foundational terminology and concepts, then present a multi-scale perspective spanning microscale (nodes and edges), mesoscale (motifs and communities), and macroscale (global topology) analyses. Finally, I explore analytical strategies and computational tools suited to various research objectives, including methods for detecting structural shifts, assessing network similarity, tracking module evolution, and predictive modelling of future network states.

Tomoyuki Araki, Naofumi Miwa

Abstract Objective Pancreatic cancer (PC) originates and progresses with genetic mutations in various oncogenes and suppressor genes, notably KRAS, CDKN2A, TP53, and SMAD4, prevalent across diverse PC cells. In addition to genetic mutations/deletions, persistent exposure to high-risk factors, including obesity, induces whole-genome scale epigenetic alterations contributing to malignancy. However, the impact of obesity on DNA methylation in the presymptomatic stage, particularly in genes prone to PC mutation, remains uncharacterized. Results We analyzed the methylation levels of 197 loci in six genes (KRAS, CDKN2A, TP53, SMAD4, GNAS and RNF43) using Illumina Mouse Methylation BeadChip array (280 K) data from pancreatic exocrine cells obtained from high-fat-diet (HFD) induced obese mice. Results revealed no significant differences in methylation levels in loci between HFD- and normal-fat-diet (NFD)-fed mice, except for RNF43, a negative regulator of Wnt signaling, which showed hypermethylation in three loci. These findings indicate that, in mouse pancreatic exocrine cells, high-fat dietary obesity induced aberrant DNA methylation in RNF43 but not in other frequently mutated PC-related genes.

Antón Pardo, Sandra Díaz Seoane, Dorin A. Ionescu et al.

Synthetic biology is a recent area of biological engineering, whose aim is to provide cells with novel functionalities. A number of important results regarding the development of control circuits in synthetic biology have been achieved during the last decade. A differential geometry approach can be used for the analysis of said systems, which are often nonlinear. Here we demonstrate the application of such tools to analyse the structural identifiability, observability, accessibility, and controllability of several biomolecular systems. We focus on a set of synthetic circuits of current interest, which can perform several tasks, both in open loop and closed loop settings. We analyse their properties with our own methods and tools; further, we describe a new open-source implementation of the techniques.

Le Song, Eran Segal, Eric Xing

We present an approach of using AI to model and simulate biology and life. Why is it important? Because at the core of medicine, pharmacy, public health, longevity, agriculture and food security, environmental protection, and clean energy, it is biology at work. Biology in the physical world is too complex to manipulate and always expensive and risky to tamper with. In this perspective, we layout an engineering viable approach to address this challenge by constructing an AI-Driven Digital Organism (AIDO), a system of integrated multiscale foundation models, in a modular, connectable, and holistic fashion to reflect biological scales, connectedness, and complexities. An AIDO opens up a safe, affordable and high-throughput alternative platform for predicting, simulating and programming biology at all levels from molecules to cells to individuals. We envision that an AIDO is poised to trigger a new wave of better-guided wet-lab experimentation and better-informed first-principle reasoning, which can eventually help us better decode and improve life.