Trading the micro-world of combinatorial complexity for the macro-world of protein interaction domains

Membrane receptors and proteins involved in signal transduction display numerous binding domains and operate as molecular scaffolds generating a variety of parallel reactions and protein complexes. The resulting combinatorial explosion of the number of feasible chemical species and, hence, different states of a network greatly impedes mechanistic modeling of signaling systems. Here we present novel general principles and identify kinetic requirements that allow us to replace a mechanistic picture of all possible micro-states and transitions by a macro-description of states of separate binding sites of network proteins. This domain-oriented approach dramatically reduces computational models of cellular signaling networks by dissecting mechanistic trajectories into the dynamics of macro- and meso-variables. We specify the conditions when the temporal dynamics of micro-states can be exactly or approximately expressed in terms of the product of the relative concentrations of separate domains. We prove that our macro-modeling approach equally applies to signaling systems with low population levels, analyzed by stochastic rather than deterministic equations. Thus, our results greatly facilitate quantitative analysis and computational modeling of multi-protein signaling networks

Domain-Oriented Reduction of Rule-Based Network Models

The coupling of membrane-bound receptors to transcriptional regulators and other effector functions is mediated by multi-domain proteins that form complex assemblies. The modularity of protein interactions lends itself to a rule-based description, in which species and reactions are generated by rules that encode the necessary context for an interaction to occur, but also can produce a combinatorial explosion in the number of chemical species that make up the signaling network. We have shown previously that exact network reduction can be achieved using hierarchical control relationships between sites/domains on proteins to dissect multi-domain proteins into sets of non-interacting sites, allowing the replacement of each “full” (progenitor) protein with a set of derived auxiliary (offspring) proteins. The description of a network in terms of auxiliary proteins that have fewer sites than progenitor proteins often greatly reduces network size. We describe here a method for automating domain-oriented model reduction and its implementation as a module in the BioNetGen modeling package. It takes as input a standard BioNetGen model and automatically performs the following steps: 1) detecting the hierarchical control relationships between sites; 2) building up the auxiliary proteins; 3) generating a raw reduced model; and 4) cleaning up the raw model to provide the correct mass balance for each chemical species in the reduced network. We tested the performance of this module on models representing portions of growth factor receptor and immunoreceptor-mediated signaling networks, and confirmed its ability to reduce the model size and simulation cost by at least one or two orders of magnitude. Limitations of the current algorithm include the inability to reduce models based on implicit site dependencies or heterodimerization, and loss of accuracy when dynamics are computed stochastically

Features of feeding for pacific herring <i>Clupea pallasii,</i> walleye pollock <i>Theragra chalcogramma</i>, and arabesque greenling <i>Pleurogrammus azonus</i> in Peter the Great Bay (Japan Sea) in summer season

Qualitative and quantitative indices of feeding and competitive relationships are investigated for pacific herring Clupea pallasii , walleye pollock Theragra chalcogramma , and arabesque greenling Pleurogrammus azonus on the data of bottom trawl surveys conducted over the shelf and upper continental slope of Peter the Great Bay in June-September of 2002-2009. Daily rations of these species in summer are estimated as 4.5, 3.0 and 4.3 % of body weight on average, respectively. The diet depends on their bathymetric distribution and structure of prey: generally, the portion of plankton and nektobenthos reflects the species ability to dwell beyond the bottom and changes from 66.9 and 32.1 % for herring and 45.2 and 46.9 % for pollock to 30.2 and 31.8 % for greenling. The rest of the greenling diet is presented by benthic invertebrates (24.8 %) and nekton (13.2 %). Mysids are the main common diet component for all three species, their portion in the herring and pollock diets is extremely high in Peter the Great Bay comparing with other habitats of these species, obviously because of high abundance of mysid shrimps and low concentrations of large-sized zooplankton (particularly euphausiids) in the Bay. Role of the considered species in demersal ichthyocenosis of Peter the Great Bay is not high enough for food competition between them, taking into account their active seasonal migrations and low stocks of herring and pollock in recent times

Formality theorems for Hochschild complexes and their applications

We give a popular introduction to formality theorems for Hochschild complexes and their applications. We review some of the recent results and prove that the truncated Hochschild cochain complex of a polynomial algebra is non-formal.Comment: Submitted to proceedings of Poisson 200

Novel robust biomarkers for human bladder cancer based on activation of intracellular signaling pathways

Sherpa Romeo blue journal. Open access article. Creative Commons Attribution 3.0 License (CC BY 3.0) applies.We recently proposed a new bioinformatic algorithm called OncoFinder for quantifying the activation of intracellular signaling pathways. It was proved advantageous for minimizing errors of high-throughput gene expression analyses and showed strong potential for identifying new biomarkers. Here, for the first time, we applied OncoFinder for normal and cancerous tissues of the human bladder to identify biomarkers of bladder cancer. Using Illumina HT12v4 microarrays, we profiled gene expression in 17 cancer and seven non-cancerous bladder tissue samples. These experiments were done in two independent laboratories located in Russia and Canada. We calculated pathway activation strength values for the investigated transcriptomes and identified signaling pathways that were regulated differently in bladder cancer (BC) tissues compared with normal controls. We found, for both experimental datasets, 44 signaling pathways that serve as excellent new biomarkers of BC, supported by high area under the curve (AUC) values. We conclude that the OncoFinder approach is highly efficient in finding new biomarkers for cancer. These markers are mathematical functions involving multiple gene products, which distinguishes them from “traditional” expression biomarkers that only assess concentrations of single genes.Ye

Switches, Excitable Responses and Oscillations in the Ring1B/Bmi1 Ubiquitination System

In an active, self-ubiquitinated state, the Ring1B ligase monoubiquitinates histone H2A playing a critical role in Polycomb-mediated gene silencing. Following ubiquitination by external ligases, Ring1B is targeted for proteosomal degradation. Using biochemical data and computational modeling, we show that the Ring1B ligase can exhibit abrupt switches, overshoot transitions and self-perpetuating oscillations between its distinct ubiquitination and activity states. These different Ring1B states display canonical or multiply branched, atypical polyubiquitin chains and involve association with the Polycomb-group protein Bmi1. Bistable switches and oscillations may lead to all-or-none histone H2A monoubiquitination rates and result in discrete periods of gene (in)activity. Switches, overshoots and oscillations in Ring1B catalytic activity and proteosomal degradation are controlled by the abundances of Bmi1 and Ring1B, and the activities and abundances of external ligases and deubiquitinases, such as E6-AP and USP7

Cell-signalling dynamics in time and space

The specificity of cellular responses to receptor stimulation is encoded by the spatial and temporal dynamics of downstream signalling networks. Computational models provide insights into the intricate relationships between stimuli and responses and reveal mechanisms that enable networks to amplify signals, reduce noise and generate discontinuous bistable dynamics or oscillations. These temporal dynamics are coupled to precipitous spatial gradients of signalling activities, which guide pivotal intracellular processes, but also necessitate mechanisms to facilitate signal propagation across a cell