scholarly journals Using Simulation to Interpret a Discrete Time Survival Model in a Complex Biological System: Fertility and Lameness in Dairy Cows

PLoS ONE ◽  
2014 ◽  
Vol 9 (8) ◽  
pp. e103426 ◽  
Author(s):  
Christopher D. Hudson ◽  
Jonathan N. Huxley ◽  
Martin J. Green
Keyword(s):  
Dairy Cows ◽  
Discrete Time ◽  
Biological System ◽  
Survival Model ◽  
Complex Biological System

Integrative Data Analysis for Biological Discovery

2011 ◽  
pp. 1058-1065
Author(s):  
Sai Moturu
Keyword(s):  
Molecular Biology ◽  
Biological System ◽  
Molecular Level ◽  
Biological Systems ◽  
Integrated Analysis ◽  
John Muir ◽  
Complex Biological System ◽  
Protein Levels ◽  
Lofty Goal ◽  
Complex Relationships

As John Muir noted, “When we try to pick out anything by itself, we find it hitched to everything else in the Universe” (Muir, 1911). In tune with Muir’s elegantly stated notion, research in molecular biology is progressing toward a systems level approach, with a goal of modeling biological systems at the molecular level. To achieve such a lofty goal, the analysis of multiple datasets is required to form a clearer picture of entire biological systems (Figure 1). Traditional molecular biology studies focus on a specific process in a complex biological system. The availability of high-throughput technologies allows us to sample tens of thousands of features of biological samples at the molecular level. Even so, these are limited to one particular view of a biological system governed by complex relationships and feedback mechanisms on a variety of levels. Integrated analysis of varied biological datasets from the genetic, translational, and protein levels promises more accurate and comprehensive results, which help discover concepts that cannot be found through separate, independent analyses. With this article, we attempt to provide a comprehensive review of the existing body of research in this domain.

Heterogeneous information network and its application to human health and disease

2019 ◽  
Vol 21 (4) ◽  
pp. 1327-1346 ◽  
Author(s):  
Pingjian Ding ◽  
Wenjue Ouyang ◽  
Jiawei Luo ◽  
Chee-Keong Kwoh
Keyword(s):  
Biological System ◽  
Functional Module ◽  
Network Motif ◽  
Biological Entity ◽  
Information Network ◽  
Human Interactome ◽  
Complex Biological System ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
The Individual

Abstract The molecular components with the functional interdependencies in human cell form complicated biological network. Diseases are mostly caused by the perturbations of the composite of the interaction multi-biomolecules, rather than an abnormality of a single biomolecule. Furthermore, new biological functions and processes could be revealed by discovering novel biological entity relationships. Hence, more and more biologists focus on studying the complex biological system instead of the individual biological components. The emergence of heterogeneous information network (HIN) offers a promising way to systematically explore complicated and heterogeneous relationships between various molecules for apparently distinct phenotypes. In this review, we first present the basic definition of HIN and the biological system considered as a complex HIN. Then, we discuss the topological properties of HIN and how these can be applied to detect network motif and functional module. Afterwards, methodologies of discovering relationships between disease and biomolecule are presented. Useful insights on how HIN aids in drug development and explores human interactome are provided. Finally, we analyze the challenges and opportunities for uncovering combinatorial patterns among pharmacogenomics and cell-type detection based on single-cell genomic data.

Computational approaches for the prediction of protein function in the mitochondrion

2006 ◽  
Vol 291 (6) ◽  
pp. C1121-C1128 ◽  
Author(s):  
Toni Gabaldón
Keyword(s):  
Biological System ◽  
Protein Function ◽  
Research Field ◽  
Computational Techniques ◽  
Complex Biological System ◽  
Computational Approaches ◽  
Functional Interactions ◽  
Physical Interactions ◽  
Computational Research

Understanding a complex biological system, such as the mitochondrion, requires the identification of the complete repertoire of proteins targeted to the organelle, the characterization of these, and finally, the elucidation of the functional and physical interactions that occur within the mitochondrion. In the last decade, significant developments have contributed to increase our understanding of the mitochondrion, and among these, computational research has played a significant role. Not only general bioinformatics tools have been applied in the context of the mitochondrion, but also some computational techniques have been specifically developed to address problems that arose from within the mitochondrial research field. In this review the contribution of bioinformatics to mitochondrial biology is addressed through a survey of current computational methods that can be applied to predict which proteins will be localized to the mitochondrion and to unravel their functional interactions.

Predicting the probability of abortion in dairy cows: a hierarchical Bayesian logistic-survival model using sequential pregnancy data

2005 ◽  
Vol 68 (2-4) ◽  
pp. 223-239 ◽  
Author(s):  
M.C. Thurmond ◽  
A.J. Branscum ◽  
W.O. Johnson ◽  
E.J. Bedrick ◽  
T.E. Hanson
Keyword(s):  
Dairy Cows ◽  
Survival Model ◽  
Hierarchical Bayesian

scholarly journals Assembly and Regulation of the Human RNA Polymerase II Preinitiation Complex: A Single-Molecule Approach to a Complex Biological System

2011 ◽  
Vol 100 (3) ◽  
pp. 10a
Author(s):  
Andrey Revyakin ◽  
Zhengjian Zhang ◽  
Robert Coleman ◽  
Yu-Chih Tsai ◽  
Yan Li ◽  
...  
Keyword(s):  
Rna Polymerase ◽  
Rna Polymerase Ii ◽  
Single Molecule ◽  
Biological System ◽  
Preinitiation Complex ◽  
Complex Biological System

A complex biological system: the fly's visual module

2007 ◽  
Vol 366 (1864) ◽  
pp. 345-357 ◽  
Author(s):  
Murilo S Baptista ◽  
Lirio O.B de Almeida ◽  
Jan F.W Slaets ◽  
Roland Köberle ◽  
Celso Grebogi
Keyword(s):  
Probability Measure ◽  
Complex Systems ◽  
Visual System ◽  
Lyapunov Exponents ◽  
Biological System ◽  
Dimensional Space ◽  
Point Of View ◽  
Two Dimensional ◽  
Complex Biological System

Is the characterization of biological systems as complex systems in the mathematical sense a fruitful assertion? In this paper we argue in the affirmative, although obviously we do not attempt to confront all the issues raised by this question. We use the fly's visual system as an example and analyse our experimental results of one particular neuron in the fly's visual system from this point of view. We find that the motion-sensitive ‘H1’ neuron, which converts incoming signals into a sequence of identical pulses or ‘spikes’, encodes the information contained in the stimulus into an alphabet composed of a few letters. This encoding occurs on multilayered sets, one of the features attributed to complex systems . The conversion of intervals between consecutive occurrences of spikes into an alphabet requires us to construct a generating partition . This entails a one-to-one correspondence between sequences of spike intervals and words written in the alphabet. The alphabet dynamics is multifractal both with and without stimulus, though the multifractality increases with the stimulus entropy. This is in sharp contrast to models generating independent spike intervals, such as models using Poisson statistics, whose dynamics is monofractal. We embed the support of the probability measure, which describes the distribution of words written in this alphabet, in a two-dimensional space, whose topology can be reproduced by an M-shaped map. This map has positive Lyapunov exponents, indicating a chaotic-like encoding.

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