Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i) a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii) TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks.

SUMMARY: Payao is a community-based, collaborative web service platform for gene-regulatory and biochemical pathway model curation. The system combines Web 2.0 technologies and online model visualization functions to enable a collaborative community to annotate and curate biological models. Payao reads the models in Systems Biology Markup Language format, displays them with CellDesigner, a process diagram editor, which complies with the Systems Biology Graphical Notation, and provides an interface for model enrichment (adding tags and comments to the models) for the access-controlled community members. AVAILABILITY AND IMPLEMENTATION: Freely available for model curation service at http://www.payaologue.org. Web site implemented in Seaser Framework 2.0 with S2Flex2, MySQL 5.0 and Tomcat 5.5, with all major browsers supported. CONTACT: [email protected]

Fluctuations in protein number (noise) caused by the stochasticity in gene expression plays a central role in the dynamic behavior of cellular pathways. Deterministic models capture average cell population behavior and are limited in their relevance in modeling stochastic deviations of gene expression in single cells. In this paper, we develop a birth and death Markov chain model to capture the discrete molecular events of transcription and translation in prokaryotic cells. We derive mathematical models for the expression `burst frequency' distribution as well as the number of protein molecules per burst. We validate our stochastic models with recent single cell experiments on the lacZ gene in Escherichia Coli. Further, we build a discrete-event stochastic simulation system to study the transient dynamics of lacZ gene expression, quantifying the role of promoters in controlling the `burstiness' of protein synthesis.

This paper presents a parametric model to estimate the DNA-protein binding time using the DNA and protein structures and details of the binding site. To understand the stochastic behavior of biological systems, we propose an "in silico" stochastic event based simulation that determines the temporal dynamics of different molecules. This paper presents a parametric model to determine the execution time of one biological function (i.e. simulation event): protein-DNA binding by abstracting the function as a stochastic process of microlevel biological events using probability measure. This probability is coarse grained to estimate the stochastic behavior of the biological function. Our model considers the structural configurations of the DNA, proteins and the actual binding mechanism. We use a collision theory based approach to transform the thermal and concentration gradients of this biological process into the probability measure of DNA-protein binding event. This information theoretic approach significantly removes the complexity of the classical protein sliding along the DNA model, improves the speed of computation and can bypass the speed-stability paradox. This model can produce acceptable estimates of DNA-protein binding time to be used by our event-based stochastic system simulator where the higher order (more than second order statistics) uncertainties can be ignored. The results show good correspondence with available experimental estimates. The model depends very little on experimentally generated rate constants

Quantum mechanics and molecular dynamic simulation provide important insights into structural configurations and molecular interaction data today. To extend this atomic/molecular level capability to system level understanding, we propose an "in silico" stochastic event based simulation technique. This simulation characterizes the time domain events as random variables represented by probabilities. This random variable is called the execution time and is different for different biological functions (e.g. the protein-ligand docking time). The simulation model requires fast computational speed and we need a simple transformation of the energy plane dynamics of the molecular behavior to the information plane. We use a variation of the collision theory model to get this transformation. The velocity distribution and energy threshold are the two parameters that capture the effects of the energy dynamics within the cell in our model. We use this technique to approximately determine the time required for the ligand-protein docking event. The model is parametric and uses the structural configurations of the ligands, proteins and the binding mechanism. The numerical results for the first moment show good correspondence with experimental results and demonstrate the efficacy of our model. The model is fast in computing and is less dependent on experimental data like rate constants

The design of the access networks of next generation broadband wireless systems requires special attention in the light of changing network characteristics. In this paper, we present a mesh-based distributed radio access network (RAN) framework for future wireless systems. Using short, high bandwidth optical wireless links to interconnect the various network elements, we identify a generic fractal or self similar structure in the network. A mathematical model for the framework is presented and the benefits of its scale-invariant properties on robustness, reliability and flexibility analyzed. We focus on three design parameters - carrier-class network reliability, network exposure due to failure conditions and system cost. The dynamics of these parameters on our proposed architecture are studied and compared against existing access network topologies like tree/ring and square-grid. The generality and recursive nature of our framework lends itself to be applied in interconnecting various heterogeneous broadband wireless access networks of the future

Wireless mesh based access networks are destined to play a pivotal role in next generation broadband systems. With the proliferation of mesh networks, a key issue for network designers is the design of an optimal mesh topology which minimizes cost while maintaining carrier-class features. In this paper, we formulate the design of an optimal mesh, taking network deployment cost, topological properties and carrier-grade reliability into account. Next, we present a genetic algorithm based algorithm (GaMa) for mesh topology design. We show that GaMa is capable of determining a generic mesh topology with carrier-class network features. The performance of the algorithm is compared with existing mesh topologies and gives improved results without the constraints of maintaining a regular topology.

For next generation wireless cellular networks, the radio access network (RAN) component is destined to evolve into a high-capacity, flexible and reconfigurable network architecture supporting smaller cell sites and newer technologies. Based on the well-known Petersen graph, in this paper we propose a RAN architecture, named PeterNet, that interconnects the network elements in a mesh topology. Focusing on optical wireless as the underlying transmission technology, we analyze our architecture on two key indices - carrier-class network reliability (99.999%) and infrastructure cost - providing comparative performance analysis with existing tree/star and regular grid networks. Furthermore, we generalize our architecture to what we call, the GPeterNet, which allows dynamic reconfiguration and redesign of the access network while leveraging the underlying salient features.