scholarly journals Biological Networks: An Introductory Review

2018 ◽  
Vol 2 (1) ◽  
pp. 41-111
Author(s):  
Mohammad Saad Zaghloul Salem
Keyword(s):  
Biological Networks ◽  
Wide Spectrum ◽  
Structural Complexity ◽  
Living Cells ◽  
Network Systems ◽  
Vast Number ◽  
Network Function ◽  
Functional Capabilities ◽  
Functional Disturbances ◽  
Functional Dynamics

All aspects of life activities in living cells are mediated/executed and regulated by a vast number of networks, comprising a wide spectrum of components, starting with simple biomolecules and ending with the whole organism, and functioning within a precisely organized tight framework. Proper mediation of cellular activities necessitates their inclusion within the context of structured and organized network systems capable of regulating/coordinating and synchronizing the countless numbers of biological processes occurring within living cells. The number of biological networks and pathways within the living cell is considerably huge, being dependent on the structural complexity and functional capabilities of the cell. Pathogenesis and progression of human diseases result from functional disturbances of biological networks within the cell as disturbed network function leads to deleterious effects on physiological processes dependent on, and mediated by, affected network(s). Ensuing pathological processes, defined by the nature of disturbed networks and the specific organs or tissues affected, pave the way for the development of pathognomonic and characteristic disease entities. As most network functions are dependent on relatively small number of key regulatory biomolecules, i.e. enzymes/proteins and signal transducing factors, it follows that functional disturbances of biological networks and pathogenesis of disease states can be attributed, in most instances, to quantitative and/or qualitative abnormalities of these key regulatory molecules. Study and analysis of the structural designs and the functional mechanisms of biological networks would have crucial and important impacts on many theoretical and applied aspects of biology, in general, and of medical sciences in particular. Meticulous study of biological networks represents an important and integral aspect in study of biology. Interpretation and analysis of key information deduced from observing and analyzing structural designs and functional characteristics and dynamics of biological networks discloses and defines the basic framework within which life activities in living cells are initiated, adapted to physiological requirements, maintained, and terminated upon completion of their aims. More important, however, is the contribution of this information to proper understanding of the different mechanisms responsible for regulating and synchronizing the functions and performances of the vast spectrum of different network categories within the cell. In addition to its vital scientific significance, discovering and defining the key pivotal structural and regulatory molecules within life-mediating networks, and along different pathways responsible for controlling functional dynamics of the network, represent an indispensable diagnostic approach insistent for designing proper therapeutic approaches to diseases caused by network defects.

scholarly journals Organisation of the transcriptional regulation of genes involved in protein transactions in yeast

2017 ◽  
Author(s):  
Duygu Dikicioglu ◽  
Daniel J H Nightingale ◽  
Valerie Wood ◽  
Kathryn S Lilley ◽  
Stephen G Oliver
Keyword(s):  
Biological Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Topological Analysis ◽  
Network Size ◽  
Transcriptional Level ◽  
Transcriptional Regulatory Networks ◽  
Network Function ◽  
Network Analyses ◽  
Transcriptional Regulatory

AbstractThe topological analyses of many large-scale molecular interaction networks often provide only limited insights into network function or evolution. In this paper, we argue that the functional heterogeneity of network components, rather than network size, is the main factor limiting the utility of topological analysis of large cellular networks. We have analysed large epistatic, functional, and transcriptional regulatory networks of genes that were attributed to the following biological process groupings: protein transactions, gene expression, cell cycle, and small molecule metabolism. Control analyses were performed on networks of randomly selected genes. We identified novel biological features emerging from the analysis of functionally homogenous biological networks irrespective of their size. In particular, direct regulation by transcription as an underrepresented feature of protein transactions. The analysis also demonstrated that the regulation of the genes involved in protein transactions at the transcriptional level was orchestrated by only a small number of regulators. Quantitative proteomic analysis of nuclear- and chromatin-enriched sub-cellular fractions of yeast provided supportive evidence for the conclusions generated by network analyses.

scholarly journals Two-tier supramolecular encapsulation of small molecules in a protein cage

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Thomas G. W. Edwardson ◽  
Stephan Tetter ◽  
Donald Hilvert
Keyword(s):  
Protein Design ◽  
Serum Proteins ◽  
Structural Complexity ◽  
Building Blocks ◽  
Water Soluble ◽  
Surfactant Mixtures ◽  
Protein Cages ◽  
Functional Capabilities ◽  
Molecular Building Blocks ◽  
And Function

Abstract Expanding protein design to include other molecular building blocks has the potential to increase structural complexity and practical utility. Nature often employs hybrid systems, such as clathrin-coated vesicles, lipid droplets, and lipoproteins, which combine biopolymers and lipids to transport a broader range of cargo molecules. To recapitulate the structure and function of such composite compartments, we devised a supramolecular strategy that enables porous protein cages to encapsulate poorly water-soluble small molecule cargo through templated formation of a hydrophobic surfactant-based core. These lipoprotein-like complexes protect their cargo from sequestration by serum proteins and enhance the cellular uptake of fluorescent probes and cytotoxic drugs. This design concept could be applied to other protein cages, surfactant mixtures, and cargo molecules to generate unique hybrid architectures and functional capabilities.

Thermodynamic-based computational profiling of cellular regulatory control in hepatocyte metabolism

2005 ◽  
Vol 288 (3) ◽  
pp. E633-E644 ◽  
Author(s):  
Daniel A. Beard ◽  
Hong Qian
Keyword(s):  
Biological Networks ◽  
Drug Targets ◽  
Metabolic Diseases ◽  
Cell Metabolism ◽  
Glucose Production ◽  
Glycogen Metabolism ◽  
Metabolic Model ◽  
Regulatory Control ◽  
Integrated Network ◽  
Network Function

Thermodynamic-based constraints on biochemical fluxes and concentrations are applied in concert with mass balance of fluxes in glycogenesis and glycogenolysis in a model of hepatic cell metabolism. Constraint-based modeling methods that facilitate predictions of reactant concentrations, reaction potentials, and enzyme activities are introduced to identify putative regulatory and control sites in biological networks by computing the minimal control scheme necessary to switch between metabolic modes. Computational predictions of control sites in glycogenic and glycogenolytic operational modes in the hepatocyte network compare favorably with known regulatory mechanisms. The developed hepatic metabolic model is used to computationally analyze the impairment of glucose production in von Gierke's and Hers' diseases, two metabolic diseases impacting glycogen metabolism. The computational methodology introduced here can be generalized to identify downstream targets of agonists, to systematically probe possible drug targets, and to predict the effects of specific inhibitors (or activators) on integrated network function.

scholarly journals Exploring community structure in biological networks with random graphs

2013 ◽  
Author(s):  
Pratha Sah ◽  
Lisa O. Singh ◽  
Aaron Clauset ◽  
Shweta Bansal
Keyword(s):  
Community Structure ◽  
Biological Networks ◽  
Biological Systems ◽  
Detection Algorithm ◽  
Global Scale ◽  
Clustering Coefficient ◽  
Network Function ◽  
Community Detection Algorithm ◽  
The Impact ◽  
Functional Components

A modular pattern, also called community structure, is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a system's functional components and the impact of local structures on dynamics at a global scale. Choosing an appropriate community detection algorithm to identify the community structure in an empirical network can be difficult, however, as the many algorithms available are based on a variety of cost functions and are difficult to validate. Even when community structure is identified in an empirical system, disentangling the effect of community structure from other network properties such as clustering coefficient and assortativity can be a challenge. Here, we develop a generative model to produce undirected, simple, connected graphs with a specified degrees and pattern of communities, while maintaining a graph structure that is as random as possible. Additionally, we demonstrate two important applications of our model: (a) to generate networks that can be used to benchmark existing and new algorithms for detecting communities in biological networks; and (b) to generate null models to serve as random controls when investigating the impact of complex network features beyond the byproduct of degree and modularity in empirical biological networks. Our model allows for the systematic study of the presence of community structure and its impact on network function and dynamics. This process is a crucial step in unraveling the functional consequences of the structural properties of biological systems and uncovering the mechanisms that drive these systems.

MUC glycoproteins: Potential biomarkers and molecular targets for cancer therapy

2020 ◽  
Vol 20 ◽  
Author(s):  
Chameli Ratan ◽  
Dalia Cicily K. D ◽  
Bhagyalakshmi Nair ◽  
Lekshmi R. Nath
Keyword(s):  
Cancer Therapy ◽  
Cancer Progression ◽  
Wide Spectrum ◽  
Primary Liver Cancer ◽  
Structural Complexity ◽  
Biochemical Properties ◽  
Therapeutic Interventions ◽  
Post Translational Modification ◽  
Cancer Cell Growth ◽  
Aberrant Expression

: MUC proteins have great significance as prognostic and diagnostic markers as well as a potential target for therapeutic interventions in most cancers of glandular epithelial origin. These are high molecular weight glycosylated proteins located in the epithelial lining of several tissues and ducts. Mucins belong to a heterogeneous group of large O-glycoproteins that can be either secreted or membrane-bound. Glycosylation, a post-translational modification affects the bio-physical, functional and biochemical properties and provides structural complexity for these proteins. Aberrant expression and glycosylation of mucins contribute to tumour survival and proliferation in many cancers, which in turn activates numerous signalling pathways such as NF-kB, ERα, HIF, MAPK, p53, c-Src, Wnt and JAK-STAT etc. This subsequently induces cancer cell growth, proliferation and metastasis. The present review mainly demonstrates the functional aspects of MUC glycoproteins along with its unique signalling mechanism and role of aberrant glycosylation in cancer progression and therapeutics. The importance of MUC proteins and its subtypes in a wide spectrum of cancers including but not limited to breast cancer, colorectal cancer, endometrial and cervical cancer, lung cancer, primary liver cancer, pancreatic cancer, prostate cancer and ovarian cancer have been exemplified with its significance in targeting the same. Several patents associated with the MUC proteins in the field of cancer therapy are also emphasized in the current review.

scholarly journals Applications of Ion Mobility-Mass Spectrometry in Carbohydrate Chemistry and Glycobiology

Molecules ◽  
2018 ◽  
Vol 23 (10) ◽  
pp. 2557 ◽  
Author(s):  
Yuqing Mu ◽  
Benjamin Schulz ◽  
Vito Ferro
Keyword(s):  
Mass Spectrometry ◽  
Ion Mobility ◽  
Drift Time ◽  
Structural Complexity ◽  
Ion Mobility Mass Spectrometry ◽  
Vast Number ◽  
Disease States ◽  
Coupled Technique ◽  
Analytical Tools

Carbohydrate analyses are often challenging due to the structural complexity of these molecules, as well as the lack of suitable analytical tools for distinguishing the vast number of possible isomers. The coupled technique, ion mobility-mass spectrometry (IM-MS), has been in use for two decades for the analysis of complex biomolecules, and in recent years it has emerged as a powerful technique for the analysis of carbohydrates. For carbohydrates, most studies have focused on the separation and characterization of isomers in biological samples. IM-MS is capable of separating isomeric ions by drift time, and further characterizing them by mass analysis. Applications of IM-MS in carbohydrate analysis are extremely useful and important for understanding many biological mechanisms and for the determination of disease states, although efforts are still needed for higher sensitivity and resolution.

scholarly journals Hierarchical Coordinate Systems for Understanding Complexity and its Evolution, with Applications to Genetic Regulatory Networks

2008 ◽  
Vol 14 (3) ◽  
pp. 299-312 ◽  
Author(s):  
Attila Egri-Nagy ◽  
Chrystopher L. Nehaniv
Keyword(s):  
Biological Networks ◽  
Regulatory Networks ◽  
Complexity Analysis ◽  
Structural Complexity ◽  
Evolutionary Process ◽  
Genetic Regulatory Networks ◽  
Lac Operon ◽  
Complexity Measures ◽  
Evolution Of Complexity ◽  
Hierarchical Complexity

Beyond complexity measures, sometimes it is worthwhile in addition to investigate how complexity changes structurally, especially in artificial systems where we have complete knowledge about the evolutionary process. Hierarchical decomposition is a useful way of assessing structural complexity changes of organisms modeled as automata, and we show how recently developed computational tools can be used for this purpose, by computing holonomy decompositions and holonomy complexity. To gain insight into the evolution of complexity, we investigate the smoothness of the landscape structure of complexity under minimal transitions. As a proof of concept, we illustrate how the hierarchical complexity analysis reveals symmetries and irreversible structure in biological networks by applying the methods to the lac operon mechanism in the genetic regulatory network of Escherichia coli.

scholarly journals Resonating neurons stabilize heterogeneous grid-cell networks

2020 ◽  
Author(s):  
Divyansh Mittal ◽  
Rishikesh Narayanan
Keyword(s):  
Neural Activity ◽  
Biological Networks ◽  
Spatial Information ◽  
Physiological Mechanism ◽  
Low Frequency ◽  
Two Dimensional ◽  
Attractor Network ◽  
Network Function ◽  
Frequency Components ◽  
The Impact

ABSTRACTGrid cells in the medial entorhinal cortex manifest multiple firing fields, patterned to tessellate external space with triangles. Although two-dimensional continuous attractor network (CAN) models have offered remarkable insights about grid-patterned activity generation, their functional stability in the presence of biological heterogeneities remains unexplored. In this study, we systematically incorporated three distinct forms of intrinsic and synaptic heterogeneities into a rate-based CAN model driven by virtual trajectories, developed here to mimic animal traversals and improve computational efficiency. We found that increasing degrees of biological heterogeneities progressively disrupted the emergence of grid-patterned activity and resulted in progressively large perturbations in neural activity. Quantitatively, grid score and spatial information associated with neural activity reduced progressively with increasing degree of heterogeneities, and perturbations were primarily confined to low-frequency neural activity. We postulated that suppressing low-frequency perturbations could ameliorate the disruptive impact of heterogeneities on grid-patterned activity. To test this, we formulated a strategy to introduce intrinsic neuronal resonance, a physiological mechanism to suppress low-frequency activity, in our rate-based neuronal model by incorporating filters that mimicked resonating conductances. We confirmed the emergence of grid-patterned activity in homogeneous CAN models built with resonating neurons and assessed the impact of heterogeneities on these models. Strikingly, CAN models with resonating neurons were resilient to the incorporation of heterogeneities and exhibited stable grid-patterned firing, through suppression of low-frequency components in neural activity. Our analyses suggest a universal role for intrinsic neuronal resonance, an established mechanism in biological neurons to suppress low-frequency neural activity, in stabilizing heterogeneous network physiology.SIGNIFICANCE STATEMENTA central theme that governs the functional design of biological networks is their ability to sustain stable function despite widespread parametric variability. However, several theoretical and modeling frameworks employ unnatural homogeneous networks in assessing network function owing to the enormous analytical or computational costs involved in assessing heterogeneous networks. Here, we investigate the impact of biological heterogeneities on a powerful two-dimensional continuous attractor network implicated in the emergence of patterned neural activity. We show that network function is disrupted by biological heterogeneities, but is stabilized by intrinsic neuronal resonance, a physiological mechanism that suppresses low-frequency perturbations. As low-frequency perturbations are pervasive across biological systems, mechanisms that suppress low-frequency components could form a generalized route to stabilize heterogeneous biological networks.

The automated interactive microscope (AIM) for investigating the functional dynamics of living cells: Status and vision

1994 ◽  
Vol 52 ◽  
pp. 160-161
Author(s):  
Lowell D. Harris ◽  
David W. Deerfield ◽  
Scott E. Fahlman ◽  
Albert H. Gough ◽  
Frederick Lanni ◽  
...  
Keyword(s):  
Light Microscope ◽  
High Speed ◽  
High Performance ◽  
Imaging System ◽  
Living Cells ◽  
Current Status ◽  
Cell Functions ◽  
Functional Dynamics ◽  
Experiment Control ◽  
High Speed Data

The current status and future development plans for the Automated Interactive Microscope (AIM), to better define the mechanisms of the function of living cells, is described. The development of AIM is fueled by biologists need to spatially and temporally correlate biochemical, molecular and genetic patterns to study cell functions such as division, locomotion and endocytosis. AIM will allow the investigator to use the cell as a “living microcuvette”.AIM redefines the meaning of the term “microscope” to include the functional capability of the microscope system provided by computerization, in addition to the more classical definition which emphasizes the opto-mechanical hardware. AIM is an next generation electronic light microscope imaging system which has grown out of our work in the development of the multimode light microscope. New functionality includes new experiment control facilities as well as related multidimensional image processing, image analysis and interactive data visualization. AIM is built around microscope hardware with high precision motion controls and a powerful host workstation computer linked, via a very high speed data gateway, with the high performance computing and communication (HPCC) facilities of the Pittsburgh Supercomputing Center (PSC).

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