The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a "middle-out" strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from "-omics" signatures are identified as key elements of a successful systems biology modeling approach in nutrition research--one that integrates physiological mechanisms and data at multiple space and time scales.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2777333PMC
http://dx.doi.org/10.1371/journal.pcbi.1000554DOI Listing

Publication Analysis

Top Keywords

space time
12
systems biology
8
biology modeling
8
nutritional questions
8
modeling
6
nutritional
4
nutritional systems
4
modeling molecular
4
molecular mechanisms
4
mechanisms physiology
4

Similar Publications

Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning.

Beilstein J Org Chem

January 2025

Institute of Materials Research and Engineering (IMRE), Agency for Science Technology and Research (A*STAR), 2 Fusionopolis Way, Singapore 138634, Republic of Singapore.

The discovery of the optimal conditions for chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically, the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and through the design of experiments where reaction variables are modified one at a time to find the optimal conditions for a specific reaction outcome. Recently, a paradigm change in chemical reaction optimization has been enabled by advances in lab automation and the introduction of machine learning algorithms.

View Article and Find Full Text PDF

Objective This in vitro study evaluated the impact of different time intervals on the color stability of glass ionomer cement (GIC) and composite materials bonded to teeth treated with silver diamine fluoride (SDF). Specifically, the study sought to determine if immediate or delayed application of these restorative materials affects the degree of staining caused by SDF. Materials and methods Twenty-eight extracted primary molars with cavitated lesions were randomly divided into four groups, each comprising seven samples.

View Article and Find Full Text PDF

Despite the evident demand and promising potential of disulfide-functionalized amino acids and peptides in linker chemistry and peptide drug discovery, those disulfurated specifically at the α-position constitute a unique yet rather highly underexplored chemical space. In this study, we have developed a method for preparing -linked amino acid/peptide derivatives through a base-catalyzed disulfuration reaction of azlactones, followed by the ring-opening functionalization. The disulfuration reaction proceeds under mild conditions, yielding disulfurated azlactones in excellent yields across a variety of -dithiophthalimides and diverse azlactones derived from various amino acids and peptides.

View Article and Find Full Text PDF

Purpose: Treating stage II endometrial cancer involves total hysterectomy, bilateral salpingo-oophorectomy, and risk-adapted adjuvant therapy. Professional guidelines support various adjuvant treatments, but high-level data supporting specific options are conflicting. We sought to evaluate adjuvant radiation therapy (RT) trends for these patients, hypothesizing increased utilization of pelvic external beam RT (EBRT) over time.

View Article and Find Full Text PDF

Non-stationary Domain Generalization: Theory and Algorithm.

Uncertain Artif Intell

January 2025

Department of Computer Science and Engineering, The Ohio State University, USA.

Although recent advances in machine learning have shown its success to learn from independent and identically distributed (IID) data, it is vulnerable to out-of-distribution (OOD) data in an open world. Domain generalization (DG) deals with such an issue and it aims to learn a model from multiple source domains that can be generalized to unseen target domains. Existing studies on DG have largely focused on stationary settings with homogeneous source domains.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!