scholarly journals A new computational model captures fundamental architectural features of diverse biological networks

2016 ◽  
Author(s):  
Bader Al-Anzi ◽  
Noah Olsman ◽  
Christopher Ormerod ◽  
Sherif Gerges ◽  
Georgios Piliouras ◽  
...  
Keyword(s):  
Neuronal Network ◽  
Biological Networks ◽  
Regulatory Protein ◽  
Structural Features ◽  
Path Selection ◽  
Selection Mechanism ◽  
Graph Models ◽  
C Elegans ◽  
Architectural Features ◽  
Complete Networks

ABSTRACTComplex biological systems are often represented by network graphs; however, their structural features are not adequately captured by existing computational graph models, perhaps because the datasets used to assemble them are incomplete and contain elements that lack shared functions. Here, we analyze three large, near-complete networks that produce specific cellular or behavioral outputs: a molecular yeast mitochondrial regulatory protein network, and two anatomical networks of very different scale, the mouse brain mesoscale connectivity network, and the C. elegans neuronal network. Surprisingly, these networks share similar characteristics. All consist of large communities composed of modules with general functions, and topologically distinct subnetworks spanning modular boundaries responsible for their more specific phenotypical outputs. We created a new model, SBM-PS, which generates networks by combining communities, followed by adjustment of connections by a ‘path selection’ mechanism. This model captures fundamental architectural features that are common to the three networks.

Networks

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Biological Networks ◽  
Generative Models ◽  
The Internet ◽  
Network Data ◽  
Computer Algorithms ◽  
Graph Models ◽  
Random Graph Models ◽  
The Social ◽  
Spectral Algorithms ◽  
Wide Availability

The study of networks, including computer networks, social networks, and biological networks, has attracted enormous interest in recent years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyse network data on an unprecendented scale, and the development of new theoretical tools has allowed us to extract knowledge from networks of many different kinds. The study of networks is broadly interdisciplinary and developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social science. This book brings together the most important breakthroughts in each of these fields and presents them in a unified fashion, highlighting the strong interconnections between work in different areas. Topics covered include the measurement of networks; methods for analysing network data, including methods developed in physics, statistics, and sociology; fundamentals of graph theory; computer algorithms, including spectral algorithms and community detection; mathematical models of networks such as random graph models and generative models; and models of processes taking place on networks.

scholarly journals Myosinome: A Database of Myosins from Select Eukaryotic Genomes to Facilitate Analysis of Sequence-Structure-Function Relationships

2012 ◽  
Vol 6 ◽  
pp. BBI.S9902 ◽  
Author(s):  
Divya P. Syamaladevi ◽  
Margaret S Sunitha ◽  
S. Kalaimathy ◽  
Chandrashekar C. Reddy ◽  
Mohammed Iftekhar ◽  
...  
Keyword(s):  
Conformational Changes ◽  
Atp Hydrolysis ◽  
Homo Sapiens ◽  
Relevant Literature ◽  
Myosin Ii ◽  
Coiled Coil ◽  
Structural Features ◽  
Model Organisms ◽  
Congenital Diseases ◽  
C Elegans

Myosins are one of the largest protein superfamilies with 24 classes. They have conserved structural features and catalytic domains yet show huge variation at different domains resulting in a variety of functions. Myosins are molecules driving various kinds of cellular processes and motility until the level of organisms. These are ATPases that utilize the chemical energy released by ATP hydrolysis to bring about conformational changes leading to a motor function. Myosins are important as they are involved in almost all cellular activities ranging from cell division to transcriptional regulation. They are crucial due to their involvement in many congenital diseases symptomatized by muscular malfunctions, cardiac diseases, deafness, neural and immunological dysfunction, and so on, many of which lead to death at an early age. We present Myosinome, a database of selected myosin classes (myosin II, V, and VI) from five model organisms. This knowledge base provides the sequences, phylogenetic clustering, domain architectures of myosins and molecular models, structural analyses, and relevant literature of their coiled-coil domains. In the current version of Myosinome, information about 71 myosin sequences belonging to three myosin classes (myosin II, V, and VI) in five model organisms ( Homo Sapiens, Mus musculus, D. melanogaster, C. elegans and S. cereviseae) identified using bioinformatics surveys are presented, and several of them are yet to be functionally characterized. As these proteins are involved in congenital diseases, such a database would be useful in short-listing candidates for gene therapy and drug development. The database can be accessed from http://caps.ncbs.res.in/myosinome .

scholarly journals STDP-driven networks and the C. elegans neuronal network

2010 ◽  
Vol 389 (18) ◽  
pp. 3900-3914 ◽  
Author(s):  
Quansheng Ren ◽  
Kiran M. Kolwankar ◽  
Areejit Samal ◽  
Jürgen Jost
Keyword(s):  
Neuronal Network ◽  
C Elegans

scholarly journals Soft-wired long-term memory in a natural recurrent neuronal network

2020 ◽  
Author(s):  
Miguel A. Casal ◽  
Santiago Galella ◽  
Oscar Vilarroya ◽  
Jordi Garcia-Ojalvo
Keyword(s):  
Neuronal Network ◽  
Neuronal Networks ◽  
Initial Conditions ◽  
Permutation Entropy ◽  
Long Term Memory ◽  
Term Memory ◽  
C Elegans ◽  
Living Organisms ◽  
Recurrent Connections

Neuronal networks provide living organisms with the ability to process information. They are also characterized by abundant recurrent connections, which give rise to strong feed-back that dictates their dynamics and endows them with fading (short-term) memory. The role of recurrence in long-term memory, on the other hand, is still unclear. Here we use the neuronal network of the roundworm C. elegans to show that recurrent architectures in living organisms can exhibit long-term memory without relying on specific hard-wired modules. A genetic algorithm reveals that the experimentally observed dynamics of the worm’s neuronal network exhibits maximal complexity (as measured by permutation entropy). In that complex regime, the response of the system to repeated presentations of a time-varying stimulus reveals a consistent behavior that can be interpreted as soft-wired long-term memory.A common manifestation of our ability to remember the past is the consistence of our responses to repeated presentations of stimuli across time. Complex chaotic dynamics is known to produce such reliable responses in spite of its characteristic sensitive dependence on initial conditions. In neuronal networks, complex behavior is known to result from a combination of (i) recurrent connections and (ii) a balance between excitation and inhibition. Here we show that those features concur in the neuronal network of a living organism, namely C. elegans. This enables long-term memory to arise in an on-line manner, without having to be hard-wired in the brain.

scholarly journals Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network of Caenorhabditis elegans

2018 ◽  
Vol 373 (1758) ◽  
pp. 20170377 ◽  
Author(s):  
Hexuan Liu ◽  
Jimin Kim ◽  
Eli Shlizerman
Keyword(s):  
Time Series ◽  
Nervous System ◽  
Caenorhabditis Elegans ◽  
Graphical Models ◽  
Neuronal Network ◽  
Graphical Model ◽  
Time Series Data ◽  
Series Data ◽  
Neuronal Responses ◽  
C Elegans

We propose an approach to represent neuronal network dynamics as a probabilistic graphical model (PGM). To construct the PGM, we collect time series of neuronal responses produced by the neuronal network and use singular value decomposition to obtain a low-dimensional projection of the time-series data. We then extract dominant patterns from the projections to get pairwise dependency information and create a graphical model for the full network. The outcome model is a functional connectome that captures how stimuli propagate through the network and thus represents causal dependencies between neurons and stimuli. We apply our methodology to a model of the Caenorhabditis elegans somatic nervous system to validate and show an example of our approach. The structure and dynamics of the C. elegans nervous system are well studied and a model that generates neuronal responses is available. The resulting PGM enables us to obtain and verify underlying neuronal pathways for known behavioural scenarios and detect possible pathways for novel scenarios. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.

scholarly journals Macrophage checkpoint blockade: results from initial clinical trials, binding analyses, and CD47-SIRPα structure–function

2020 ◽  
Vol 3 (2) ◽  
pp. 80-94 ◽  
Author(s):  
AbdelAziz R Jalil ◽  
Jason C Andrechak ◽  
Dennis E Discher
Keyword(s):  
Clinical Trials ◽  
Regulatory Protein ◽  
Clinical Results ◽  
Structural Features ◽  
Clinical Development ◽  
Combination Therapies ◽  
Syngeneic Mouse ◽  
Solid Malignancies ◽  
Anti Cancer ◽  
Tumor Types

Abstract The macrophage checkpoint is an anti-phagocytic interaction between signal regulatory protein alpha (SIRPα) on a macrophage and CD47 on all types of cells – ranging from blood cells to cancer cells. This interaction has emerged over the last decade as a potential co-target in cancer when combined with other anti-cancer agents, with antibodies against CD47 and SIRPα currently in preclinical and clinical development for a variety of hematological and solid malignancies. Monotherapy with CD47 blockade is ineffective in human clinical trials against many tumor types tested to date, except for rare cutaneous and peripheral lymphomas. In contrast, pre-clinical results show efficacy in multiple syngeneic mouse models of cancer, suggesting that many of these tumor models are more immunogenic and likely artificial compared to human tumors. However, combination therapies in humans of anti-CD47 with agents such as the anti-tumor antibody rituximab do show efficacy against liquid tumors (lymphoma) and are promising. Here, we review such trials as well as key interaction and structural features of CD47-SIRPα.

scholarly journals Proteomic analysis and molecular modelling characterize the iron-regulatory protein haemojuvelin/repulsive guidance molecule c

2013 ◽  
Vol 452 (1) ◽  
pp. 87-95 ◽  
Author(s):  
Mahta Nili ◽  
Larry David ◽  
Johannes Elferich ◽  
Ujwal Shinde ◽  
Peter Rotwein
Keyword(s):  
Ab Initio ◽  
Molecular Modelling ◽  
Electron Transfer Dissociation ◽  
Structural Information ◽  
Regulatory Protein ◽  
Structural Features ◽  
Single Chain ◽  
Disulfide Linkages ◽  
Repulsive Guidance Molecule ◽  
Guidance Molecule

HJV (haemojuvelin) plays a key role in iron metabolism in mammals by regulating expression of the liver-derived hormone hepcidin, which controls systemic iron uptake and release. Mutations in HJV cause juvenile haemochromatosis, a rapidly progressing iron overload disorder in humans. HJV, also known as RGMc (repulsive guidance molecule c), is a member of the three-protein RGM family. RGMs are GPI (glycosylphosphatidylinositol)-linked glycoproteins that share ~50% amino acid identity and several structural motifs, including the presence of 14 cysteine residues in analogous locations. Unlike RGMa and RGMb, HJV/RGMc is composed of both single-chain and two-chain isoforms. To date there is no structural information for any member of the RGM family. In the present study we have mapped the disulfide bonds in mouse HJV/RGMc using a proteomics strategy combining sequential MS steps composed of ETD (electron transfer dissociation) and CID (collision-induced dissociation), in which ETD induces cleavage of disulfide linkages, and CID establishes disulfide bond assignments between liberated peptides. The results of the present study identified an HJV/RGMc molecular species containing four disulfide linkages. We predict using ab initio modelling that this molecule is a single-chain HJV/RGMc isoform. Our observations outline a general approach using tandem MS and ab initio molecular modelling to define unknown structural features in proteins.

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