Cell signaling networks ontology

Although databases for cell signaling pathways include numbers of reaction data of the pathways, the reaction data cannot be used yet to deduce biological functions from them. For the deduction, we need systematic and consistent interpretation of biological functions of reactions in cell signaling pathways in the context of "information transmission". To address this issue, we have developed a functional ontology for cell signaling pathways, Cell Signaling Network Ontology (CSN-Ontology), which provides framework for the functional interpretation presenting some important concepts as information, selectivity, movability, and signaling rules including passage of time.     

Exploring mechanisms of diet-colon cancer associations through candidate molecular interaction networks

Background

Epidemiological studies in the recent years have investigated the relationship between dietary habits and disease risk demonstrating that diet has a direct effect on public health. Especially plant-based diets -fruits, vegetables and herbs- are known as a source of molecules with pharmacological properties for treatment of several malignancies. Unquestionably, for developing specific intervention strategies to reduce cancer risk there is a need for a more extensive and holistic examination of the dietary components for exploring the mechanisms of action and understanding the nutrient-nutrient interactions. Here, we used colon cancer as a proof-of-concept for understanding key regulatory sites of diet on the disease pathway.

Results

We started from a unique vantage point by having a database of 158 plants positively associated to colon cancer reduction and their molecular composition (~3,500 unique compounds). We generated a comprehensive picture of the interaction profile of these edible and non-edible plants with a predefined candidate colon cancer target space consisting of ~1,900 proteins. This knowledge allowed us to study systematically the key components in colon cancer that are targeted synergistically by phytochemicals and identify statistically significant and highly correlated protein networks that could be perturbed by dietary habits.

Conclusion

We propose here a framework for interrogating the critical targets in colon cancer processes and identifying plant-based dietary interventions as important modifiers using a systems chemical biology approach. Our methodology for better delineating prevention of colon cancer by nutritional interventions relies heavily on the availability of information about the small molecule constituents of our diet and it can be expanded to any other disease class that previous evidence has linked to lifestyle.     

Exploring local structural organization of metabolic networks using subgraph patterns

Metabolic networks of many cellular organisms share global statistical features. Their connectivity distributions follow the long-tailed power law and show the small-world property. In addition, their modular structures are organized in a hierarchical manner. Although the global topological organization of metabolic networks is well understood, their local structural organization is still not clear. Investigating local properties of metabolic networks is necessary to understand the nature of metabolism in living organisms. To identify the local structural organization of metabolic networks, we analysed the subgraphs of metabolic networks of 43 organisms from three domains of life. We first identified the network motifs of metabolic networks and identified the statistically significant subgraph patterns. We then compared metabolic networks from different domains and found that they have similar local structures and that the local structure of each metabolic network has its own taxonomical meaning. Organisms closer in taxonomy showed similar local structures. In addition, the common substrates of 43 metabolic networks were not randomly distributed, but were more likely to be constituents of cohesive subgraph patterns.     

Exploring spike transfer through the thalamus using hybrid artificial-biological neuronal networks

We use dynamic clamp to construct "hybrid" thalamic circuits by connecting a biological neuron in situ to silicon- or software-generated "neurons" through artificial synapses. The purpose is to explore cellular sensory gating mechanisms that regulate the transfer efficiency of signals during different sleep-wake states. Hybrid technology is applied in vitro to different paradigms such as: (1) simulating interactions between biological thalamocortical neurons, artificial reticular thalamic inhibitory interneurons and a simulated sensory input, (2) grafting an artificial sensory input to a wholly biological thalamic network that generates spontaneous sleep-like oscillations, (3) injecting in thalamocortical neurons a background synaptic bombardment mimicking the activity of corticothalamic inputs. We show that the graded control of the strength of intrathalamic inhibition, combined with the membrane polarization and the fluctuating synaptic noise in thalamocortical neurons, is able to govern functional shifts between different input/output transmission states of the thalamic gate.     

Exploring the flexibility of ribosome recycling factor using molecular dynamics

Ribosome recycling factor is proposed to be flexible, and that flexibility is believed to be important to its function. Here we use molecular dynamics to test the flexibility of Escherichia coli RRF (ecRRF) with and without decanoic acid bound to a hydrophobic pocket between domains 1 and 2, and Thermus thermophilus RRF (ttRRF) with and without a mutation in the hinge between domains 1 and 2. Our simulations show that the structure of ecRRF rapidly goes from having an interdomain angle of 124 degrees to an angle of 98 degrees independently of the presence of decanoic acid. The simulations also show that the presence or absence of decanoic acid leads to changes in ecRRF flexibility. Simulations of wild-type and mutant ttRRF (R32G) show that mutating Arg-32 to glycine decreases RRF flexibility. This was unexpected because the range of dihedral angles for arginine is limited relative to glycine. Furthermore, the interdomain angle of wild-type T. thermophilus goes from 81 degrees to 118 degrees whereas the R32G mutant remains very close to the crystallographic angle of 78 degrees . We propose that this difference accounts for the fact that mutant ttRRF complements an RRF deficient strain of E. coli whereas wild-type ttRRF does not. When the ensemble of RRF structures is modeled into the ribosomal crystal structure, a series of overlaps is found that corresponds with regions where conformational changes have been found in the cryoelectron microscopic structure of the RRF/ribosome complex, and in the crystal structure of a cocomplex of RRF with the 50S subunit. There are also overlaps with the P-site, suggesting that RRF flexibility plays a role in removing the deacylated P-site tRNA during termination of translation.     

Self-organizing molecular networks.

Strong diffusional mixing and short delivery times typical for micrometer and sub-micrometer reaction volumes lead to a special situation where the turnover times of individual enzyme molecules become the largest characteristic time scale of the chemical kinetics. Under these conditions, populations of cross-regulating allosteric enzymes form molecular networks that exhibit various kinds of self-organized coherent collective dynamics.     

Analyzing molecular reaction networks

Pathways are typically the central concept in the analysis of biochemical reaction networks. A pathway can be interpreted as a chain of enzymatical reactions performing a specific biological function. A common way to study metabolic networks are minimal pathways that can operate at steady state called elementary modes. The theory of chemical organizations has recently been used to decompose biochemical networks into algebraically closed and self-maintaining subnetworks termed organizations. The aim of this paper is to elucidate the relation between these two concepts. Whereas elementary modes represent the boundaries of the potential behavior of the network, organizations define metabolite compositions that are likely to be present in biological feasible situations. Hence, steady state organizations consist of combinations of elementary modes. On the other hand, it is possible to assign a unique (and possibly empty) set of organizations to each elementary mode, indicating the metabolites accompanying the active pathway in a feasible steady state.     

Exploring ligand dissociation pathways from aminopeptidase N using random acceleration molecular dynamics simulation

Aminopeptidase N (APN) is a zinc-dependent ectopeptidase involved in cell proliferation, secretion, invasion, and angiogenesis, and is widely recognized as an important cancer target. However, the mechanisms whereby ligands leave the active site of APN remain unknown. Investigating ligand dissociation processes is quite difficult, both in classical simulation methods and in experimental approaches. In this study, random acceleration molecular dynamics (RAMD) simulation was used to investigate the potential dissociation pathways of ligand from APN. The results revealed three pathways (channels A, B and C) for ligand release. Channel A, which matches the hypothetical channel region, was the most preferred region for bestatin to dissociate from the enzyme, and is probably the major channel for the inner bound ligand. In addition, two alternative channels (channels B and C) were shown to be possible pathways for ligand egression. Meanwhile, we identified key residues controlling the dynamic features of APN channels. Identification of the dissociation routes will provide further mechanistic insights into APN, which will benefit the development of more promising APN inhibitors.

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Graphical Abstract The release pathways of bestatin inside active site of aminopeptidase N were simulated using RAMD simulation

     

Exploring the structure and dynamics of nano-confined water molecules using molecular dynamics simulations

Confinement effects can lead to drastic changes in the structural and dynamical properties of water molecules. In this work, we have performed classical molecular dynamics simulations of endohedral fullerenes of type (H₂O)_n@C_m (n = 1, 12, 21, 62, 108 and m = 60, 180, 240, 500 and 720) to explore the effects of spherical confinement on water properties. It is shown that these confined water molecules can form distinct solvation pattern depending upon the available space inside the fullerene cavity. For the systems with smaller diameter, cage-like structure is predominant whereas bulk-like structure is observed for larger fullerenes. The orientational relaxation of these confined water molecules showed slower relaxation as the cavity diameter increases except for the (H₂O)₂₁@C₂₄₀. In this case, stable cage-like structure hinders the overall dynamics of the trapped water molecules. Finally, we have calculated the hydrogen bond lifetimes from the hydrogen bond time correlation functions and compared with that of bulk water.     

JNets: Exploring networks by integrating annotation

Background  

A common method for presenting and studying biological interaction networks is visualization. Software tools can enhance our ability to explore network visualizations and improve our understanding of biological systems, particularly when these tools offer analysis capabilities. However, most published network visualizations are static representations that do not support user interaction.     

Exploring the diversity of complex metabolic networks

MOTIVATION: Metabolism, the network of chemical reactions that make life possible, is one of the most complex processes in nature. We describe here the development of a computational approach for the identification of every possible biochemical reaction from a given set of enzyme reaction rules that allows the de novo synthesis of metabolic pathways composed of these reactions, and the evaluation of these novel pathways with respect to their thermodynamic properties. RESULTS: We applied this framework to the analysis of the aromatic amino acid pathways and discovered almost 75,000 novel biochemical routes from chorismate to phenylalanine, more than 350,000 from chorismate to tyrosine, but only 13 from chorismate to tryptophan. Thermodynamic analysis of these pathways suggests that the native pathways are thermodynamically more favorable than the alternative possible pathways. The pathways generated involve compounds that exist in biological databases, as well as compounds that exist in chemical databases and novel compounds, suggesting novel biochemical routes for these compounds and the existence of biochemical compounds that remain to be discovered or synthesized through enzyme and pathway engineering. AVAILABILITY: Framework will be available via web interface at http://systemsbiology.northwestern.edu/BNICE (site under construction). CONTACT: vassily@northwestern.edu or broadbelt@northwestern.edu SUPPLEMENTARY INFORMATION: http://systemsbiology.northwestern.edu/BNICE/publications.     

Exploring genetic interactions and networks with yeast

The development and application of genetic tools and resources has enabled a partial genetic-interaction network for the yeast Saccharomyces cerevisiae to be compiled. Analysis of the network, which is ongoing, has already provided a clear picture of the nature and scale of the genetic interactions that robustly sustain biological systems, and how cellular buffering is achieved at the molecular level. Recent studies in yeast have begun to define general principles of genetic networks, and also pave the way for similar studies in metazoan model systems. A comparative understanding of genetic-interaction networks promises insights into some long-standing genetic problems, such as the nature of quantitative traits and the basis of complex inherited disease.     

Exploring multiplicity conditions in enzymatic reaction networks

In this work, a novel algorithmic approach to detect multiplicity of steady states in enzymatic reaction networks is presented. The method exploits the structural properties of networks derived from the Chemical Reaction Network Theory. In first instance, the space of parameters is divided in different regions according to the qualitative behavior induced by the parameters in the long term dynamics of the network. Once the regions are identified, a condition for the appearance of multiplicities is checked in the different regions by solving a given optimization problem. In this way, the method allows the characterization of the whole parameter space of biochemical networks in terms of the appearance or not of multistability. The approach is illustrated through a well‐known case of enzymatic catalysis with substrate inhibition. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009