The utility of 3D , haptic‐enabled , virtual reality technologies for student knowledge gains in the complex biological system of the human heart

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.

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Immersive Image Mining in Cardiology

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch111 ◽

2011 ◽

pp. 586-592

Author(s):

Xiaoqiang Liu ◽

Henk Koppelaar ◽

Ronald Hamers ◽

Nico Bruining

Keyword(s):

Heart Failure ◽

Virtual Reality ◽

Data Acquisition ◽

Human Body ◽

Human Heart ◽

Image Mining ◽

Computational Sciences ◽

Direct Inspection

Buried within the human body, the heart prohibits direct inspection, so most knowledge about heart failure is obtained by autopsy (in hindsight). Live immersive inspection within the human heart requires advanced data acquisition, image mining and virtual reality techniques. Computational sciences are being exploited as means to investigate biomedical processes in cardiology.

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Heterogeneous information network and its application to human health and disease

Briefings in Bioinformatics ◽

10.1093/bib/bbz091 ◽

2019 ◽

Vol 21 (4) ◽

pp. 1327-1346 ◽

Cited By ~ 1

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.

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Computational approaches for the prediction of protein function in the mitochondrion

AJP Cell Physiology ◽

10.1152/ajpcell.00225.2006 ◽

2006 ◽

Vol 291 (6) ◽

pp. C1121-C1128 ◽

Cited By ~ 14

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.

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

PLoS ONE ◽

10.1371/journal.pone.0103426 ◽

2014 ◽

Vol 9 (8) ◽

pp. e103426 ◽

Cited By ~ 9

Author(s):

Christopher D. Hudson ◽

Jonathan N. Huxley ◽

Martin J. Green

Keyword(s):

Dairy Cows ◽

Discrete Time ◽

Biological System ◽

Survival Model ◽

Complex Biological System

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

Biophysical Journal ◽

10.1016/j.bpj.2010.12.263 ◽

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

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A complex biological system: the fly's visual module

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2007.2093 ◽

2007 ◽

Vol 366 (1864) ◽

pp. 345-357 ◽

Cited By ~ 4

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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Electromagnetic Fields, Genomic Instability and Cancer: A Systems Biological View

Genes ◽

10.3390/genes10060479 ◽

2019 ◽

Vol 10 (6) ◽

pp. 479 ◽

Cited By ~ 2

Author(s):

Naarala ◽

Kolehmainen ◽

Juutilainen

Keyword(s):

Dynamical Systems ◽

Systems Theory ◽

Genomic Instability ◽

Electromagnetic Fields ◽

Biological System ◽

Dynamical Systems Theory ◽

The State ◽

Complex Biological System ◽

Environmental Perturbations ◽

Cell Phenotypes

This review discusses the use of systems biology in understanding the biological effectsof electromagnetic fields, with particular focus on induction of genomic instability and cancer. Weintroduce basic concepts of the dynamical systems theory such as the state space and attractors andthe use of these concepts in understanding the behavior of complex biological systems. We thendiscuss genomic instability in the framework of the dynamical systems theory, and describe thehypothesis that environmentally induced genomic instability corresponds to abnormal attractorstates; large enough environmental perturbations can force the biological system to leave normalevolutionarily optimized attractors (corresponding to normal cell phenotypes) and migrate to lessstable variant attractors. We discuss experimental approaches that can be coupled with theoreticalsystems biology such as testable predictions, derived from the theory and experimental methods,that can be used for measuring the state of the complex biological system. We also reviewpotentially informative studies and make recommendations for further studies.

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