Browse
Submit Data
Databases
API
Help

Dataset Information

6 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Morphisms of reaction networks that couple structure to function.

ABSTRACT:

Background

The mechanisms underlying complex biological systems are routinely represented as networks. Network kinetics is widely studied, and so is the connection between network structure and behavior. However, similarity of mechanism is better revealed by relationships between network structures.

Results

We define morphisms (mappings) between reaction networks that establish structural connections between them. Some morphisms imply kinetic similarity, and yet their properties can be checked statically on the structure of the networks. In particular we can determine statically that a complex network will emulate a simpler network: it will reproduce its kinetics for all corresponding choices of reaction rates and initial conditions. We use this property to relate the kinetics of many common biological networks of different sizes, also relating them to a fundamental population algorithm.

Conclusions

Structural similarity between reaction networks can be revealed by network morphisms, elucidating mechanistic and functional aspects of complex networks in terms of simpler networks.

SUBMITTER: Cardelli L

PROVIDER: S-EPMC4236760 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Morphisms of reaction networks that couple structure to function.

Cardelli Luca L

BMC systems biology 20140815

<h4>Background</h4>The mechanisms underlying complex biological systems are routinely represented as networks. Network kinetics is widely studied, and so is the connection between network structure and behavior. However, similarity of mechanism is better revealed by relationships between network structures.<h4>Results</h4>We define morphisms (mappings) between reaction networks that establish structural connections between them. Some morphisms imply kinetic similarity, and yet their properties c ...[more]

PMID: 25128194

Similar Datasets

Derivation of stationary distributions of biochemical reaction networks via structure transformation.

Project description:Long-term behaviors of biochemical reaction networks (BRNs) are described by steady states in deterministic models and stationary distributions in stochastic models. Unlike deterministic steady states, stationary distributions capturing inherent fluctuations of reactions are extremely difficult to derive analytically due to the curse of dimensionality. Here, we develop a method to derive analytic stationary distributions from deterministic steady states by transforming BRNs to have a special dynamic property, called complex balancing. Specifically, we merge nodes and edges of BRNs to match in- and out-flows of each node. This allows us to derive the stationary distributions of a large class of BRNs, including autophosphorylation networks of EGFR, PAK1, and Aurora B kinase and a genetic toggle switch. This reveals the unique properties of their stochastic dynamics such as robustness, sensitivity, and multi-modality. Importantly, we provide a user-friendly computational package, CASTANET, that automatically derives symbolic expressions of the stationary distributions of BRNs to understand their long-term stochasticity.

| S-EPMC8144570 | biostudies-literature

Excess heat production in the redox couple reaction of ferricyanide and ferrocyanide.

Project description:In order to establish the universality of the excess heat production in electrochemical reaction, under a high magnetic field, as one of the most fundamental electrochemical reactions, the case of ferricyanide-ferrocyanide redox reaction was examined, where ionic vacancies with ± 1 unit charge were collided by means of magnetohydrodynamic (MHD) flow. As a result, from the pair annihilation of the vacancies with opposite signs, beyond 7 T, excess heat production up to 25 kJ·mol-1 in average at 15 T was observed, which was attributed to the liberation of the solvation energy stored in a pair of the vacancy cores with a 0.32 nm radius, i.e., 112 kJ·mol-1. Difference between the observed and expected energies comes from the small collision efficiency of 0.22 due to small radius of the vacancy core. Ionic vacancy initially created as a by-product of electrode reaction is unstable in solution phase, stabilized by releasing solvation energy. Ionic vacancy utilizes the energy to enlarge the core and stores the energy in it. As a result, solvated ionic vacancy consists of a polarized free space of the enlarged core surrounded by oppositely charged ionic cloud. The accuracy and precision of the measured values were ascertained by in situ standard additive method.

| S-EPMC7674507 | biostudies-literature

Structure-based protein function prediction using graph convolutional networks.

Project description:The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures. It outperforms current leading methods and sequence-based Convolutional Neural Networks and scales to the size of current sequence repositories. Augmenting the training set of experimental structures with homology models allows us to significantly expand the number of predictable functions. DeepFRI has significant de-noising capability, with only a minor drop in performance when experimental structures are replaced by protein models. Class activation mapping allows function predictions at an unprecedented resolution, allowing site-specific annotations at the residue-level in an automated manner. We show the utility and high performance of our method by annotating structures from the PDB and SWISS-MODEL, making several new confident function predictions. DeepFRI is available as a webserver at https://beta.deepfri.flatironinstitute.org/ .

| S-EPMC8155034 | biostudies-literature

Genetic networks regulating structure and function of the maize shoot apical meristem

Project description:The shoot apical meristem (SAM) of plants, a specialized stem cell niche at the growing shoot tip, integrates developmental and environmental signals to direct the initiation and patterning of new organs such as leaves. Its activity throughout the plant's lifetime is tightly controlled. To gain insight into gene regulatory networks behind stem cell maintenance and organogenesis, we generated a high-resolution gene expression atlas of distinct domains and cell types within the vegetative shoot apex of 14 day-old maize B73 seedlings using laser microdissection and RNA deep sequencing. Data for 9 domains was previously released under SRA: SRP101301. This release adds data for biological duplicate samples collected for the SAM central zone, and the adaxial and abaxial sides of leaf primordia.

2019-10-02 | GSE137715 | GEO

Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series.

Project description:Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the reactionet lasso, a computational procedure that derives a stepwise sparse regression approach on the basis of the Chemical Master Equation, enabling large-scale structure learning for reaction networks by implicitly accounting for billions of topology variants. We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only < 1% false discoveries, the reactionet lasso is able to recover 45% of all true reactions ab initio among > 6000 possible reactions and over 102000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso.

| S-EPMC5140059 | biostudies-literature

DNA-Based Dynamic Reaction Networks.

Project description:Deriving from logical and mechanical interactions between DNA strands and complexes, DNA-based artificial reaction networks (RNs) are attractive for their high programmability, as well as cascading and fan-out ability, which are similar to the basic principles of electronic logic gates. Arising from the dream of creating novel computing mechanisms, researchers have placed high hopes on the development of DNA-based dynamic RNs and have strived to establish the basic theories and operative strategies of these networks. This review starts by looking back on the evolution of DNA dynamic RNs; in particular' the most significant applications in biochemistry occurring in recent years. Finally, we discuss the perspectives of DNA dynamic RNs and give a possible direction for the development of DNA circuits.

| S-EPMC6522144 | biostudies-literature

Exploring the structure and function of temporal networks with dynamic graphlets.

Project description:With increasing availability of temporal real-world networks, how to efficiently study these data? One can model a temporal network as a single aggregate static network, or as a series of time-specific snapshots, each being an aggregate static network over the corresponding time window. Then, one can use established methods for static analysis on the resulting aggregate network(s), but losing in the process valuable temporal information either completely, or at the interface between different snapshots, respectively. Here, we develop a novel approach for studying a temporal network more explicitly, by capturing inter-snapshot relationships.We base our methodology on well-established graphlets (subgraphs), which have been proven in numerous contexts in static network research. We develop new theory to allow for graphlet-based analyses of temporal networks. Our new notion of dynamic graphlets is different from existing dynamic network approaches that are based on temporal motifs (statistically significant subgraphs). The latter have limitations: their results depend on the choice of a null network model that is required to evaluate the significance of a subgraph, and choosing a good null model is non-trivial. Our dynamic graphlets overcome the limitations of the temporal motifs. Also, when we aim to characterize the structure and function of an entire temporal network or of individual nodes, our dynamic graphlets outperform the static graphlets. Clearly, accounting for temporal information helps. We apply dynamic graphlets to temporal age-specific molecular network data to deepen our limited knowledge about human aging.http://www.nd.edu/?cone/DG.

| S-EPMC4765862 | biostudies-other

Function prediction from networks of local evolutionary similarity in protein structure.

Project description:Annotating protein function with both high accuracy and sensitivity remains a major challenge in structural genomics. One proven computational strategy has been to group a few key functional amino acids into templates and search for these templates in other protein structures, so as to transfer function when a match is found. To this end, we previously developed Evolutionary Trace Annotation (ETA) and showed that diffusing known annotations over a network of template matches on a structural genomic scale improved predictions of function. In order to further increase sensitivity, we now let each protein contribute multiple templates rather than just one, and also let the template size vary.Retrospective benchmarks in 605 Structural Genomics enzymes showed that multiple templates increased sensitivity by up to 14% when combined with single template predictions even as they maintained the accuracy over 91%. Diffusing function globally on networks of single and multiple template matches marginally increased the area under the ROC curve over 0.97, but in a subset of proteins that could not be annotated by ETA, the network approach recovered annotations for the most confident 20-23 of 91 cases with 100% accuracy.We improve the accuracy and sensitivity of predictions by using multiple templates per protein structure when constructing networks of ETA matches and diffusing annotations.

| S-EPMC3584919 | biostudies-literature

Development of structure-function coupling in human brain networks during youth.

Project description:The protracted development of structural and functional brain connectivity within distributed association networks coincides with improvements in higher-order cognitive processes such as executive function. However, it remains unclear how white-matter architecture develops during youth to directly support coordinated neural activity. Here, we characterize the development of structure-function coupling using diffusion-weighted imaging and n-back functional MRI data in a sample of 727 individuals (ages 8 to 23 y). We found that spatial variability in structure-function coupling aligned with cortical hierarchies of functional specialization and evolutionary expansion. Furthermore, hierarchy-dependent age effects on structure-function coupling localized to transmodal cortex in both cross-sectional data and a subset of participants with longitudinal data (n = 294). Moreover, structure-function coupling in rostrolateral prefrontal cortex was associated with executive performance and partially mediated age-related improvements in executive function. Together, these findings delineate a critical dimension of adolescent brain development, whereby the coupling between structural and functional connectivity remodels to support functional specialization and cognition.

| S-EPMC6955327 | biostudies-literature

Neural networks for protein structure and function prediction and dynamic analysis.

Project description:Hardware and software advancements along with the accumulation of large amounts of data in recent years have together spurred a remarkable growth in the application of neural networks to various scientific fields. Machine learning based on neural networks with multiple (hidden) layers is becoming an extremely powerful approach for analyzing data. With the accumulation of large amounts of protein data such as structural and functional assay data, the effects of such approaches within the field of protein informatics are increasing. Here, we introduce our recent studies based on applications of neural networks for protein structure and function prediction and dynamic analysis involving: (i) inter-residue contact prediction based on a multiple sequence alignment (MSA) of amino acid sequences, (ii) prediction of protein-compound interaction using assay data, and (iii) detection of protein allostery from trajectories of molecular dynamic (MD) simulation.

| S-EPMC7242519 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data