Health recommender systems (HRSs) enhance prognostication by leveraging clinical information. Existing HRSs often fail to capture the intrinsic correlations between patient phenotypes with similar clinical profiles, necessitating approaches that incorporate patient similarity into prognostic modeling. This work explores such correlations within three classes of biomedical information: diagnosis, procedure, and medication, and the clinical temporality during patient visits to improve diagnostic accuracy. Our approaches include both static and dynamic scenarios. In the static scenario, we propose SIM-PR, which integrates patient similarity and PageRank centrality on a personalized patient graph, and can operate with or without sequential hospital visit information. In the dynamic setting, we develop temporal prediction models based on a multilayer perceptron and a long short-term memory network to learn evolving diagnostic patterns from longitudinal visit histories. Standard supervised machine learning, including logistic regression, random forest, and support vector machines, is employed as comparative baselines. Experiments on the MIMIC-III database demonstrate that the proposed static and temporal models effectively reduce false positives and negatives and achieve superior predictive accuracy compared to existing HRS approaches.

Conventional drug discovery is expensive, time-consuming, and prone to failure. Artificial intelligence has become a potent substitute over the last decade, providing strong answers to challenging biological issues in this field. Among these difficulties, drug-target binding (DTB) is a key component of drug discovery techniques. In this context, drug-target affinity and drug-target interaction are complementary and essential frameworks that work together to improve our comprehension of DTB dynamics. In this work, we thoroughly analyze the most recent deep learning models, popular benchmark datasets, and assessment metrics for DTB prediction. We look at the paradigm shift in the development of drug discovery research since researchers started using deep learning as a potent tool for DTB prediction. In particular, we examine how methodologies have evolved, starting with early heterogeneous network-based approaches, progressing to graph-based approaches that were widely accepted, followed by modern attention-based architectures, and finally, the most recent multimodal approaches. We also provide case studies utilizing an extensive compound library against specific protein targets implicated in critical cancer pathways to demonstrate the usefulness of these approaches. In addition to summarizing the latest developments in DTB prediction models, this review also identifies their drawbacks. It also highlights the outlook for the DTB prediction domain and future research directions. Combined, these studies present a more comprehensive view of how deep learning offers a quantitative framework for researching drug-target relationships, speeding up the identification of new drug candidates and making it easier to identify possible DTBs.

The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence, it have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared with individual algorithm implementations on bulk RNA-seq and microarray data. In an effort to extend this approach to scRNA-seq data, we present COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting-based consensus algorithm that integrates information from 10 established GRN inference methods. We conclude that COFFEE has improved performance across synthetic, curated, and experimental datasets when compared with baseline methods. Additionally, we show that a modified version of COFFEE can be leveraged to improve performance on newer cell-type specific GRN inference methods. Overall, our results demonstrate that consensus-based methods with pertinent modifications continue to be valuable for GRN inference at the single cell level. While COFFEE is benchmarked on 10 algorithms, it is a flexible strategy that can incorporate any set of GRN inference algorithms according to user preference. A Python implementation of COFFEE may be found on GitHub: https://github.com/lodimk2/coffee.

Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell-type identification), a wisdom of crowds approach for scRNAseq clustering. CHAI presents two competing methods which aggregate the clustering results from seven state-of-the-art clustering methods: CHAI-AvgSim and CHAI-SNF. CHAI-AvgSim and CHAI-SNF demonstrate superior performance across several benchmarking datasets. Furthermore, both CHAI methods outperform the most recent consensus clustering method, SAME-clustering. We demonstrate CHAI's practical use case by identifying a leader tumor cell cluster enriched with CDH3. CHAI provides a platform for multiomic integration, and we demonstrate CHAI-SNF to have improved performance when including spatial transcriptomics data. CHAI overcomes previous limitations by incorporating the most recent and top performing scRNAseq clustering algorithms into the aggregation framework. It is also an intuitive and easily customizable R package where users may add their own clustering methods to the pipeline, or down-select just the ones they want to use for the clustering aggregation. This ensures that as more advanced clustering algorithms are developed, CHAI will remain useful to the community as a generalized framework. CHAI is available as an open source R package on GitHub: https://github.com/lodimk2/chai.

Abstract As manipulating the self-assembly of supramolecular and nanoscale constructs at the single-molecule level increasingly becomes the norm, new theoretical scaffolds must be erected to replace the thermodynamic and kinetics based models used to describe traditional bulk phase syntheses. Like the statistical mechanics underpinning these latter theories, the framework we propose uses state probabilities as its fundamental objects; but, contrary to the Gibbsian paradigm, our theory directly models the transition probabilities between the initial and final states of a trajectory, foregoing the need to assume ergodicity. We leverage these probabilities in the context of molecular self-assembly to compute the overall likelihood that a specified experimental condition leads to a desired structural outcome. We demonstrate the application of this framework to a toy model in which N identical molecules can assemble into oligomers of different lengths and conclude with a discussion of how the high computational cost of such a fine-grained model can be overcome through approximation when extending it to larger, more complex systems.

Vaccines have proven useful in curbing contagion from new strains of the SARS-CoV-2 virus. However, equitable vaccine allocation continues to be a significant challenge worldwide, necessitating a comprehensive allocation strategy incorporating heterogeneity in epidemiological and behavioral considerations. In this paper, we present a hierarchical allocation strategy that assigns vaccines to zones and their constituent neighborhoods cost-effectively, based on their population density, susceptibility, infected count, and attitude towards vaccinations. Moreover, it includes a module that tackles vaccine shortages in certain zones by locally transferring vaccines from zones with surplus vaccines. We leverage the epidemiological, socio-demographic, and social media datasets from Chicago and Greece and their constituent community areas to show that the proposed allocation approach assigns vaccines based on the chosen criteria and captures the effects of disparate vaccine adoption rates. We conclude the paper with a lowdown on future efforts to extend this study to design models for effective public policies and vaccination strategies that curtail vaccine purchase costs.

COVID-19 is a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2. While swift vaccine development and distribution have arrested the infection spread rate, it is necessary to design public policies that inform human mobility to curb outbreaks from future strains of the virus. While existing non-pharmaceutical approaches employing network science and machine learning offer promising travel policy solutions, they are guided by epidemiological and economic considerations alone and not human itineraries. We introduce an evolutionary algorithm (EA) based mobility scheduler that incorporates the personalized itineraries of individuals to determine the ideal timing of their mobility. We mathematically analyze the computational efficiency versus the optimality trade-off of the mobility scheduler. Through extensive simulations, we demonstrate that the EA-based mobility scheduler can balance the trade-off between (1) optimality and computational cost and (2) fair and preferential human mobility while reducing contagion under lockdown and no-lockdown as well as even and uneven human mobility traffic scenarios. We show that for two human mobility models, the scheduler exhibits lower infection numbers than a baseline trip-planning approach that directs human traffic along the least congested route to minimize contagion. We discuss that the EA scheduler lends itself to intricate mobility schedules of multiple destination choices with varying priorities and socioeconomic and demographic considerations.

Rapid development and distribution of vaccines have been a hallmark of the battle against COVID-19. While the efficacy, clinical trials, adverse health effects, and sociodemographic and clinical factors determining the distribution of vaccines have been studied extensively, there has been little effort to design cost-effective vaccine provisioning schemes. We introduce a vaccine provisioning scheme that leverages coalitional game theory to improve the cost of vaccines while meeting the epidemiological demand of neighboring zones. The proposed approach incentivizes bulk purchases by groups (or coalitions) of zones at lower prices while penalizing large coalitions to avoid logistical challenges. Moreover, it enables the policymaker to model the vaccine demand of zones based on their epidemiological profiles, such as susceptible, infected numbers or population density, or a combination thereof. We carry out experiments using the SEIRD (susceptible, exposed, infected, recovered, death) epidemic model as well as the daily confirmed cases in the five boroughs of New York City to show the efficacy of the approach.

Internet of Things (IoT) enables the collection of large volumes of data by billions of pervasive intelligent devices and sharing them with remote cloud servers for processing, resulting in increasing network congestion and server response times. The advent of edge computing has addressed these challenges by introducing an intermediate edge layer comprising networked fog nodes that provide on-demand computation, caching, and communication services to meet critical Quality-of-Service (QoS) requirements. However, in a challenging environment brought by disaster and aftershocks, the QoS is hampered as several fog nodes in the edge layer are damaged. Nevertheless, the existing network infrastructure should still support the uninterrupted flow of time-critical contextual data between the survivors and rescuers for quick recovery operations. In this work, we envision that the IoT devices and existing fog nodes will collaborate to form ad-hoc networks for emergency message delivery under disaster situations. We present a distributed routing mechanism, termed motif centrality-based routing ($MCR$), that leverages the concept of network motifs (subgraphs) seen in social and biological networks. Specifically, the proposed mechanism addresses three QoS requirements of an ad-hoc network: 1) robustness against component failures; 2) low latency; and 3) energy efficiency. We experimentally show that the$MCR$-based routing ensures high data delivery, low latency, and comparable efficiency in energy usage. Finally, an extensive simulation-based study shows that$MCR$outperforms the related benchmarks in terms of the three QoS requirements.

Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. With their superior performance, transformers have found their way in the field of hyperspectral image classification and achieved promising results. In this article, we harness the power of transformers to conquer the task of hyperspectral unmixing and propose a novel deep neural network-based unmixing model with transformers. A transformer network captures nonlocal feature dependencies by interactions between image patches, which are not employed in CNN models, and hereby has the ability to enhance the quality of the endmember spectra and the abundance maps. The proposed model is a combination of a convolutional autoencoder and a transformer. The hyperspectral data is encoded by the convolutional encoder. The transformer captures long-range dependencies between the representations derived from the encoder. The data are reconstructed using a convolutional decoder. We applied the proposed unmixing model to three widely used unmixing datasets, i.e., Samson, Apex, and Washington DC mall and compared it with the state-of-the-art in terms of root mean squared error and spectral angle distance. The source code for the proposed model will be made publicly available at https://github.com/preetam22n/DeepTrans-HSU.

Vaccines have proved to be highly effective in preventing severe outcomes in COVID-19 patients. Despite swift vaccine development, the policymakers are still struggling to meet the global challenges in the availability, cost and distribution of vaccines. With the emergence of new vaccine types and boosters to beat the newer strains of the virus, it is necessary to design effective vaccine distribution strategies. In this paper, we present generalizable, multi-vaccine distribution measures that allocate vaccines based on the socio-economic, epidemiological and demographic profiles of different zones. The proposed approach incorporates myriad features, whereby it can assign vaccines based on a subset of the chosen criteria, minimize or fix the number of assigned vaccines and balance the trade-off between cost and criteria. Through simulation experiments, we demonstrate the ability of the optimizer to capture the variable vaccine adoption rates among zones and reward lower vaccine hesitancy with reduced contagion.

Wireless Sensor Networks (WSNs) enable promising solutions to large-scale industrial, medical and environmental tracking and monitoring applications. Software Defined Networking (SDN) is a new paradigm that decouples the network control and data forwarding functionalities and may potentially improve data sensing in a highly dynamic environment. The networking community is directing its efforts towards ensuring that the software defined WSNs (SD-WSN) carry out the data sensing even in a hostile environment characterized by node or link failures. In this work, we present an adaptive topology control strategy based on reinforcement learning for mobile SD-WSN. The approach employs the notion of statistically significant subgraphs, called motifs, that have been shown to render graph robustness to biological networks. Our simulation experiments on the map of New York City shows that this approach is capable of modulating network parameters to achieve varying goals such as high data delivery, low latency and energy efficiency.

Software Defined Wireless Sensor Networks (SD-WSN) is a promising paradigm in wireless communication that offers high flexibility in network management by enabling dynamic and programmable network control. SDN controller has a centralized global view of the network, making it an ideal choice for data sensing in a highly dynamic sensing environment. We proposed a reinforcement learning (RL) based adaptive topology control approach (Roy et al., IEEE CCNC 2020) that employs periodic node mobility to meet diverse network objectives, such as data delivery, latency, and energy efficiency. We also demonstrated that erratic mobility can considerably hamper the learning of the RL module resulting in poor overall quality of service. In this work, we present a customized network simulation environment that captures the variations in the performance of the proposed SD-WSN framework. Finally, we present a new approach based on supervised machine learning that can identify periodic mobility and mitigate the ill-effects of erratic mobility.

Bridging Centrality (BriCe) is a popular measure that combines the Betweenness centrality and Bridging coefficient metrics to characterize nodes acting as a bridge among clusters. However, there were no implementations of the BriCe plugin that can be readily used in the GEPHI software or any other software dedicated to graph-based studies. In this paper, we present the BriCe plugin for GEPHI. It is available as a third-party functionality from the native GEPHI interface as a handy plugin to add; hence, no additional download and installation process is necessary. The BriCe plugin for GEPHI is open-source, and one can access the code through the GEPHI GitHub repository. As a use case of the BriCe plugin, we analyzed the genome of Mycobacterium tuberculosis H37Rv to identify biological explanations on why some proteins were ranked with top BriCe values? For instance, we were able to formulate a new hypothesis combining the predicted sub cellular localization and high BriCe values concerning lipopolysaccharides (LPS) exportation. Our hypothesis provides a possible link among proteins of a glycosyltransferase group and the type VII Secretion System. The Bridging Centrality plugin for GEPHI is an easy to use tool for analyzing complex graphs and draw novel insights from graphical data.

Aggregation of the amyloid-β (Aβ) protein has been implicated in Alzheimer's disease (AD). Since, low molecular weight Aβ aggregates are hypothesized to serve as the primary toxic species in AD pathogenesis, significant research has been conducted to understand the mechanistic details of the aggregation process. We previously demonstrated that heterotypic interactions between Aβ and fatty acids (FAs) can lead to competing pathways of Aβ aggregation, termed as the off-pathway; this off-pathway kinetics can also be modulated by FA concentrations as captured by mass action models. We employed ensemble kinetics simulations which uses a system of Ordinary Differential Equations to model the competing on- and off-pathways of Aβ aggregation that were trained and validated by biophysical experiments. However, these models had several rate constants, treated as free parameters to be estimated, which resulted in over-fitting of the model. Hence, in this paper, we present a global fitting based method to accurately identify the rate constants involved in the complex competing pathway model of Aβ aggregation. We additionally employ detailed parameter identifiability tests for uncertainty quantification using the profile likelihood method. Since, the emergence of off- or on-pathway aggregates are typically controlled by a narrow set of rate constants, it is imperative to rigorously identify the proper rate constants involved in these pathways. These rate constants serve as a basis for future experiments on modulating the aggregation pathways to populate a particular possibly less toxic oligomeric species. The obtained rate constants also motivate new biophysical experiments to better understand the mechanisms of amyloid aggregation in other neurodegenerative diseases.

Gene Regulatory Networks (GRNs) are biological networks that have been widely studied for their ability to regulate protein synthesis in cells by robust signal propagation. The innate biological robustness of GRN is attributed to the occurrence of statistically significant subgraphs, called motifs. While Wireless Sensor Network (WSN) topologies designed using GRN graphs, called bio-WSNs, have been proven to exhibit significant improvement in packet delivery and network latency over random graph-based WSNs, it is still not clear what role motifs play in the observed performance improvement of bio-WSNs. This work explores why a dominant 3-node motif, called Feed Forward Loop (FFL), typifies the robustness of GRN motifs. We also employ graph centrality metrics to corroborate biological studies that have shown motifs to provide pathways for signal propagation in GRNs. Finally, we perform graph-theoretic and simulation experiments on GRN subgraphs and their corresponding bio-WSNs to demonstrate that nodes with high FFL motif participation offer multiple short and robust communication pathways, despite the failure of random and targeted nodes and links.

Given a social network, the Influence Maximization (InfMax) problem seeks a seed set of k people that maximizes the expected influence for a viral marketing campaign. However, a solution for a particular seed size k is often not enough to make an informed choice regarding budget and cost-effectiveness. In this article, we propose the computation of Influence Spectrum (InfSpec), the maximum influence at each possible seed set size k within a given range [ k lower , k upper ], thus providing optimal decision making for any availability of budget or influence requirements. As none of the existing methods for InfMax are efficient enough for the task in large networks, we propose LISA (sub-Linear Influence Spectrum Approximation), an efficient approximation algorithm for InfSpec (and also InfMax) with the best-known worst-case guarantees for billion-scale networks. LISA returns an (1-1/e -ϵ)-approximate influence spectrum with high probability (1-δ), where ϵ, δ are precision parameters provided by users. Using statistical decision theory, LISA has an asymptotic optimal running time (in addition to optimal approximation guarantee). In practice, LISA surpasses the state-of-the-art InfMax methods, taking less than 15 minutes to process a network of 41.7 million nodes and 1.5 billions edges.

Given an aftermath of a cascade in the network, i.e. a set VI of "infected" nodes after an epidemic outbreak or a propagation of rumors/worms/viruses, how can we infer the sources of the cascade? Answering this challenging question is critical for computer forensic, vulnerability analysis, and risk management. Despite recent interest towards this problem, most of existing works focus only on single source detection or simple network topologies, e.g. trees or grids. In this paper, we propose a new approach to identify infection sources by searching for a seed set S that minimizes the symmetric difference between the cascade from S and VI, the given set of infected nodes. Our major result is an approximation algorithm, called SISI, to identify infection sources without the prior knowledge on the number of source nodes. SISI, to our best knowledge, is the first algorithm with provable guarantee for the problem in general graphs. It returns a 2/((1-ε)2 Δ-approximate solution with high probability, where Δ denotes the maximum number of nodes in VI that may infect a single node in the network. Our experiments on real-world networks show the superiority of our approach and SISI in detecting true source(s), boosting the F1-measure from few percents, for the state-of-the-art NETSLEUTH, to approximately 50%.

Robustness in wireless sensor networks (WSNs) is a critical factor that largely depends on their network topology and on how devices can react to disruptions, including node and link failures. This article presents a novel solution to obtain robust WSNs by exploiting principles of biological robustness at nanoscale. Specifically, we consider Gene Regulatory Networks (GRNs) as a model for the interaction between genes in living organisms. GRNs have evolved over millions of years to provide robustness against adverse factors in cells and their environment. Based on this observation, we apply a method to build robust WSNs, called bio-inspired WSNs, by establishing a correspondence between the topology of GRNs and that of already-deployed WSNs. Through simulation in realistic conditions, we demonstrate that bio-inspired WSNs are more reliable than existing solutions for the design of robust WSNs. We also show that communications in bio-inspired WSNs have lower latency as well as lower energy consumption than the state of the art.

BACKGROUND: The evolution of Next-Generation Sequencing (NGS) has considerably reduced the cost per sequenced-base, allowing a significant rise of sequencing projects, mainly in prokaryotes. However, the range of available NGS platforms requires different strategies and software to correctly assemble genomes. Different strategies are necessary to properly complete an assembly project, in addition to the installation or modification of various software. This requires users to have significant expertise in these software and command line scripting experience on Unix platforms, besides possessing the basic expertise on methodologies and techniques for genome assembly. These difficulties often delay the complete genome assembly projects. RESULTS: In order to overcome this, we developed SIMBA (SImple Manager for Bacterial Assemblies), a freely available web tool that integrates several component tools for assembling and finishing bacterial genomes. SIMBA provides a friendly and intuitive user interface so bioinformaticians, even with low computational expertise, can work under a centralized administrative control system of assemblies managed by the assembly center head. SIMBA guides the users to execute assembly process through simple and interactive pages. SIMBA workflow was divided in three modules: (i) projects: allows a general vision of genome sequencing projects, in addition to data quality analysis and data format conversions; (ii) assemblies: allows de novo assemblies with the software Mira, Minia, Newbler and SPAdes, also assembly quality validations using QUAST software; and (iii) curation: presents methods to finishing assemblies through tools for scaffolding contigs and close gaps. We also presented a case study that validated the efficacy of SIMBA to manage bacterial assemblies projects sequenced using Ion Torrent PGM. CONCLUSION: Besides to be a web tool for genome assembly, SIMBA is a complete genome assemblies project management system, which can be useful for managing of several projects in laboratories. SIMBA source code is available to download and install in local webservers at http://ufmg-simba.sourceforge.net .

Gene Regulatory Networks (GRNs) represent the interactions of genes in living organisms, which have evolved over millions of years to provide a near-optimal structure for rapid adaptation to the environment. On the other hand, robustness in wireless sensor networks (WSNs) is a critical factor that largely depends on their topology and how quickly the network can recover from node and link failures. This article proposes a novel approach to design robust WSNs by exploiting GRNs. Specifically, we build bio-inspired WSNs based on the topology of GRNs. Our approach embeds the physical communication graph of the WSN into the GRN graph under the optimization criterion of minimizing the interference between different nodes. Furthermore, we propose an algorithm to identify data collection points (i.e., sinks) and improve robustness by maximizing the expansion of the network. Through an analytical evaluation, we show that our bio-inspired graph embedding approach leads to robust WSNs which preserve the structural properties of GRNs.

Structural analysis over well studied transcriptional regulatory networks indicates that these complex networks are made up of small set of reoccurring patterns called motifs. While information theoretic approaches have been immensely popular, these approaches rely on inferring the regulatory networks by aggregating pair-wise interactions. In this paper, we propose novel structure based information theoretic approaches to infer transcriptional regulatory networks from the microarray expression data. The core idea is to go beyond pair-wise interactions and consider more complex structures as found in motifs. While this increases the network inference complexity over pair-wise interaction based approaches, it achieves much higher accuracy and yet is scalable to genome-level inference. Detailed performance analyses based on benchmark precision and recall metrics on the known Escherichia coli's transcriptional regulatory network indicates that the accuracy of the proposed algorithms is consistently higher in comparison to popular algorithms such as context likelihood of relatedness (CLR), relevance networks (RN) and GEneNetwork Inference with Ensemble of trees (GENIE3). In the proposed approaches the size of structures was limited to three node cases (any node and its two parents). Analysis on a smaller network showed that the performance of the algorithm improved when more complex structures were considered for inference, although such higher level structures may be computationally challenging to infer networks at the genome scale.

Biological networks (specifically, genetic regulatory networks) exhibit an optimized sparse topology and are known to be robust to various external perturbations. We have earlier utilized such networks, particularly, the gene regulatory network of E. coli, for constructing smart communication structures in bio-inspired Wireless Sensor Networks (WSNs) having high packet transmission efficiency. In this paper, we present machine learning approaches to relate the graph topology based characteristics of such bio-inspired WSNs to their network-level robustness in terms of average packet transmission efficiency. In particular, we generate a support vector regression model using the graph metric features as input data. The model predicts the percentage of packets received by the highest degree sink node and a theoretical estimate for the overall network robustness.

Proteins search along DNA for targets (e.g. transcription initiation sequences) through a combination of sliding, jumping, and intersegment transfers, wherein the sliding process proceeds until the protein-DNA complex dissociates. As such, we propose a diffusion-reaction model of the sliding phase of a total search process, and study the effect of varying reaction rate on the detailed search kinetics. With increasing dissociation rate, only the "fastest" proteins survive to explore downstream sequences, but at the price of increasing rarity, allowing for fluctuations to dominate the behavior at late times and long distances. Predictions of this model for the total search time agree with experimental estimates across an order of magnitude, providing bounds on dissociation rates that suggest a balance is established between maximizing downstream exploration of the DNA while minimizing the number of rare trajectories to guarantee the greatest chance of a successful search.

Most of the popular approaches towards gene regulatory networks inference e.g., Dynamic Bayesian Networks, Probabilistic Boolean Networks etc. are computationally complex and can only be used to infer small networks. While high-throughput experimental methods to monitor gene expression provide data for thousands of genes, these methods cannot fully utilize the entire spectrum of generated data. With the advent of information theoretic approaches in the last decade, the inference of larger regulatory networks from high throughput microarray data has become possible. Not all information theoretic approaches are scalable though; only methods that infer networks considering pair-wise interactions between genes such as, relevance networks, ARACNE and CLR to name a few, can be scaled upto genome-level inference. ARACNE and CLR attempt to improve the inference accuracy by pruning false edges, and do not bring in newer true edges. REVEAL is another information theoretic approach, which considers mutual information between multiple genes. As it goes beyond pair wise interactions, this approach was not scalable and could only infer small networks. In this paper, we propose two algorithms to improve the scalability of REVEAL by utilizing a transcription factor list (that can be predicted from the gene sequences) as prior knowledge and implementing time lags to further reduce the potential transcription factors that may regulate a gene. Our proposed S-REVEAL algorithms can infer larger networks with higher accuracy than the popular CLR algorithm.

BACKGROUND: In all amyloid diseases, protein aggregates have been implicated fully or partly, in the etiology of the disease. Due to their significance in human pathologies, there have been unprecedented efforts towards physiochemical understanding of aggregation and amyloid formation over the last two decades. An important relation from which hydrodynamic radii of the aggregate is routinely measured is the classic Stokes-Einstein equation. Here, we report a modification in the classical Stokes-Einstein equation using a mixture theory approach, in order to accommodate the changes in viscosity of the solvent due to the changes in solute size and shape, to implement a more realistic model for Aβ aggregation involved in Alzheimer's disease. Specifically, we have focused on validating this model in protofibrill lateral association reactions along the aggregation pathway, which has been experimentally well characterized. RESULTS: The modified Stokes-Einstein equation incorporates an effective viscosity for the mixture consisting of the macromolecules and solvent where the lateral association reaction occurs. This effective viscosity is modeled as a function of the volume fractions of the different species of molecules. The novelty of our model is that in addition to the volume fractions, it incorporates previously published reports on the dimensions of the protofibrils and their aggregates to formulate a more appropriate shape rather than mere spheres. The net result is that the diffusion coefficient which is inversely proportional to the viscosity of the system is now dependent on the concentration of the different molecules as well as their proper shapes. Comparison with experiments for variations in diffusion coefficients over time reveals very similar trends. CONCLUSIONS: We argue that the standard Stokes-Einstein's equation is insufficient to understand the temporal variations in diffusion when trying to understand the aggregation behavior of Aβ42 proteins. Our modifications also involve inclusion of improved shape factors of molecules and more appropriate viscosities. The modification we are reporting is not only useful in Aβ aggregation but also will be important for accurate measurements in all protein aggregation systems.

BACKGROUND: Proteins search along the DNA for targets, such as transcription initiation sequences, according to one-dimensional diffusion, which is interrupted by micro- and macro-hopping events and intersegmental transfers that occur under close packing conditions. RESULTS: A one-dimensional diffusion-reaction model in the form of difference-differential equations is proposed to analyze the nonequilibrium protein sliding kinetics along a segment of bacterial DNA. A renormalization approach is used to derive an expression for the mean first-passage time to arrive at sites downstream of the origin from the occupation probabilities given by the individual transport equations. Monte Carlo simulations are employed to assess the validity of the proposed approach, and all results are interpreted within the context of bacterial transcription. CONCLUSIONS: Mean first-passage times decrease with increasing reaction rates, indicating that, on average, surviving proteins more rapidly locate downstream targets than their reaction-free counterparts, but at the price of increasing rarity. Two qualitatively different screening regimes are identified according to whether the search process operates under "small" or "large" values for the dissociation rate of the protein-DNA complex. Lower bounds are placed on the overall search time for varying reactive conditions. Good agreement with experimental estimates requires the reaction rate reside near the transition between both screening regimes, suggesting that biology balances a need for rapid searches against maximum exploration during each round of the sliding phase.

BACKGROUND: A number of models and algorithms have been proposed in the past for gene regulatory network (GRN) inference; however, none of them address the effects of the size of time-series microarray expression data in terms of the number of time-points. In this paper, we study this problem by analyzing the behaviour of three algorithms based on information theory and dynamic Bayesian network (DBN) models. These algorithms were implemented on different sizes of data generated by synthetic networks. Experiments show that the inference accuracy of these algorithms reaches a saturation point after a specific data size brought about by a saturation in the pair-wise mutual information (MI) metric; hence there is a theoretical limit on the inference accuracy of information theory based schemes that depends on the number of time points of micro-array data used to infer GRNs. This illustrates the fact that MI might not be the best metric to use for GRN inference algorithms. To circumvent the limitations of the MI metric, we introduce a new method of computing time lags between any pair of genes and present the pair-wise time lagged Mutual Information (TLMI) and time lagged Conditional Mutual Information (TLCMI) metrics. Next we use these new metrics to propose novel GRN inference schemes which provides higher inference accuracy based on the precision and recall parameters. RESULTS: It was observed that beyond a certain number of time-points (i.e., a specific size) of micro-array data, the performance of the algorithms measured in terms of the recall-to-precision ratio saturated due to the saturation in the calculated pair-wise MI metric with increasing data size. The proposed algorithms were compared to existing approaches on four different biological networks. The resulting networks were evaluated based on the benchmark precision and recall metrics and the results favour our approach. CONCLUSIONS: To alleviate the effects of data size on information theory based GRN inference algorithms, novel time lag based information theoretic approaches to infer gene regulatory networks have been proposed. The results show that the time lags of regulatory effects between any pair of genes play an important role in GRN inference schemes.

BACKGROUND: The aggregates of a protein called, 'Aβ' found in brains of Alzheimer's patients are strongly believed to be the cause for neuronal death and cognitive decline. Among the different forms of Aβ aggregates, smaller aggregates called 'soluble oligomers' are increasingly believed to be the primary neurotoxic species responsible for early synaptic dysfunction. Since it is well known that the Aβ aggregation is a nucleation dependent process, it is widely believed that the toxic oligomers are intermediates to fibril formation, or what we call the 'on-pathway' products. Modeling of Aβ aggregation has been of intense investigation during the last decade. However, precise understanding of the process, pre-nucleation events in particular, are not yet known. Most of these models are based on curve-fitting and overlook the molecular-level biophysics involved in the aggregation pathway. Hence, such models are not reusable, and fail to predict the system dynamics in the presence of other competing pathways. RESULTS: In this paper, we present a molecular-level simulation model for understanding the dynamics of the amyloid-β (Aβ) peptide aggregation process involved in Alzheimer's disease (AD). The proposed chemical kinetic theory based approach is generic and can model most nucleation-dependent protein aggregation systems that cause a variety of neurodegenerative diseases. We discuss the challenges in estimating all the rate constants involved in the aggregation process towards fibril formation and propose a divide and conquer strategy by dissecting the pathway into three biophysically distinct stages: 1) pre-nucleation stage 2) post-nucleation stage and 3) protofibril elongation stage. We next focus on estimating the rate constants involved in the protofibril elongation stages for Aβ42 supported by in vitro experimental data. This elongation stage is further characterized by elongation due to oligomer additions and lateral association of protofibrils (13) and to properly validate the rate constants involved in these phases we have presented three distinct reaction models. We also present a novel scheme for mapping the fluorescence sensitivity and dynamic light scattering based in vitro experimental plots to estimates of concentration variation with time. Finally, we discuss how these rate constants will be incorporated into the overall simulation of the aggregation process to identify the parameters involved in the complete Aβ pathway in a bid to understand its dynamics. CONCLUSIONS: We have presented an instance of the top-down modeling paradigm where the biophysical system is approximated by a set of reactions for each of the stages that have been modeled. In this paper, we have only reported the kinetic rate constants of the fibril elongation stage that were validated by in vitro biophysical analyses. The kinetic parameters reported in the paper should be at least accurate upto the first two decimal places of the estimate. We sincerely believe that our top-down models and kinetic parameters will be able to accurately model the biophysical phenomenon of Aβ protein aggregation and identify the nucleation mass and rate constants of all the stages involved in the pathway. Our model is also reusable and will serve as the basis for making computational predictions on the system dynamics with the incorporation of other competing pathways introduced by lipids and fatty acids.

Data reporting strategy in heterogeneous wireless sensor networks can be differentiated based on task-specific requirements. In this paper, we propose a novel two-phase data reporting (TDR) scheme that supports class-based QoS to sensor nodes in different priority classes. In the first phase, time slots are divided into separate data reporting round defined for each class, while in the second phase, the sensor nodes in the same class are scheduled to particular time slots depending on the given number of slots calculated in the first phase. In TDR, sensor nodes compete with other nodes in the same class only, while nodes in different classes have differentiated channel access opportunity. TDR is performed in a single-hop cluster-based topology, and a cluster head acts as a node assignment manager (NAM). Sensor nodes wake up for their scheduled time slots; otherwise, they go into sleep mode to save energy. TDR supports both schedule- based and contention-based channel access mechanisms and is scalable due to its distributed nature.

In standard IEEE 802.11 based systems, when the wireless client migrates away from the radio range of the currently associated access point (AP), network applications temporarily loose connectivity till the client is able to re-associate itself with a new AP. The delay that occurs during the break-off interval can vary from a few hundreds of microseconds to a few seconds. However, delay sensitive applications such as Voice over IP (VoIP) or streaming multimedia applications usually are unable to tolerate such long connectivity delays that fall beyond the range of 50 - 200 ms. This results in dropped calls or frozen video frames. In this paper we describe the design, implementation, and evaluation of a software based framework that facilitates seamless and transparent handoff between different APs in standard IEEE 802.11 based wireless local area networks (WLANs). Although different solutions are available in the literature that seek to address the handoff latency, most of them propose changes that are outside the purview of the current 802.11 standards. We have specifically kept such compatibility restrictions in mind and have devised a software based client side solution that is capable of reducing handoff delays to an average value of 20 ms. It is available as a driver update to the client and requires no additional support from the network. As part of our solution, we have successfully implemented and tested our proposed solution framework on Atheros AR5212 chipsets using the open source MadWifi driver.

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.

In this paper, we present a node mobility prediction framework based on a generic mobile grid architecture. We show how this framework can be used to formulate a cost effective job scheduling scheme based on a predetermined pricing strategy at the wireless access point. The proposed scheme is for distributing grid computing jobs to the mobile nodes and considers the bandwidth constraints along with any internal job (e.g., call processing) arrival rate at the nodes. The simulation results point to the efficacy of our algorithm.

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

Opportunistic scheduling algorithms are effective in exploiting channel variations and maximizing system throughput in multi-rate wireless networks. However, most scheduling algorithms ignore the per-user quality of service (QoS) requirements and try to allocate resources (i.e., the time slots) among multiple users. This leads to a phenomenon commonly referred to as the exposure problem wherein the algorithms fail to satisfy the minimum slot requirements of the users due to substitutability and complementarity requirement of user slots. To eliminate this exposure problem, we propose a novel scheduling algorithm based on two phase combinatorial reverse auction with the primary objective to maximize the number of satisfied users in the system. We also consider maximizing the system throughput as a secondary objective. In the proposed scheme, multiple users bid to acquire the required number of time slots, and the allocations are done to satisfy the two objectives in a sequential manner. We provide an approximate solution to the proposed scheduling problem which is a NP-complete problem. We prove that our proposed algorithm is (1 + log m) times the optimal solution, where m is the number of slots in a schedule cycle. We also present an extension to this algorithm which can support more satisfied users at the cost of additional complexity. Numerical results are provided to compare the proposed scheduling algorithms with other competitive schemes.

The ever-growing demand of network capacity has resulted in the inception of optical burst switching (OBS), offering all-optical transmission, high-speed data rates, and format-transparent switching. However, the current OBS architecture is very complex, requiring costly fiber delay lines and quality-of-service (QoS) management techniques. In this paper, we propose a new OBS architecture based on photonic container switching to be deployed in the core network. We show that our architecture will solve most of the complexities of existing OBS mechanisms and, in fact, will make the core an all-optical zero-packet-loss network that will also guarantee equal QoS to all of the users. The packets are actually packed in fixed-size containers, which will be converted into an optical burst and transmitted through the network. Obviously, a major issue to solve in our architecture is the scheduler design that ensures zero packet loss and no optical-to-electrical switchings in the intermediate nodes. We devise a divide-and-conquer solution for the scheduler design problem and present some efficient algorithms for the same. We also analyze the performance of our algorithms under varying traffic conditions and network topologies to ascertain their efficiency and robustness.

Today's distributed computing systems incorporate different types of nodes with varied bandwidth constraints which should be considered while designing cost-optimal job allocation schemes for better system performance. In this paper, we propose a fair pricing strategy for job allocation in bandwidth-constrained distributed systems. The strategy formulates an incomplete information, alternating-offers bargaining game on two variables, such as price per unit resource and percentage of bandwidth allocated, for both single and multiclass jobs at each node. We present a cost-optimal job allocation scheme for single-class jobs that involve communication delay and, hence, the link bandwidth. For fast and adaptive allocation of multiclass jobs, we describe three efficient heuristics and compare them under different network scenarios. The results show that the proposed algorithms are comparable to existing job allocation schemes in terms of the expected system response time over all jobs.

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 ever-growing demand for network capacity has resulted in the inception of optical burst switching (OBS) offering all-optical transmission, high-speed data rates and format transparent switching. But the current OBS architecture is very complex requiring costly fiber delay lines and quality of service management (QoS) techniques. In this paper we propose a new OBS architecture based on photonic container switching to be deployed in the core network. We show that our architecture will solve most of the complexities of existing OBS mechanisms, and in fact will make the core an all-optical, zero packet loss network that will also guarantee equal QoS to all the users. The packets are actually packed in fixed size containers which will be converted into an optical burst and transmitted through the network. Obviously a major issue to solve in our architecture is the scheduler design that ensures zero packet loss and no optical-to-electrical switching in the intermediate nodes. We devise a divide and conquer solution for the scheduler design problem and present some efficient algorithms for the same. We also analyze the performance of our algorithms under varying traffic conditions and network topologies, to ascertain their efficiency and robustness.

In this paper, we propose an adaptive forward error correction (FEC) and rate control technique to improve service quality in a wireless gaming environment. In particular, we concentrate on a centralized server gaming architecture (our results can be extended to peer-to-peer architectures as well), where wireless users can also connect to the gaming service. Our models are well-suited for last-hop wireless link users, but can also guarantee service improvement for other wireless scenarios through small modifications. It is assumed that the game server and clients (mobile devices) can switch between different prediction levels having different data rates. We introduce a new scheme for estimating the packet loss rates due to congestion and wireless channel conditions and use this information in a cross-layer design to improve the overall service quality. The congestion packet loss probability is used to devise a simple TCP-friendly rate control algorithm for sending downlink data packets from the game server. We also propose a novel adaptive FEC and dynamic packetization algorithm to alleviate the effects of wireless channel packet losses based on this chosen data rate. The simulation results show the efficacy of our scheme in achieving higher throughput.

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.

Summary form only given. This article realizes the vision of mobile grid computing by proposing a fair pricing strategy and an optimal, static job allocation scheme. Mobile devices has not yet been integrated into grid computing platforms mainly due to their inherent limitations in processing and storage capacity, power and bandwidth shortages. However, millions of laptops, PDAs and other mobile devices remain unused most of the time and this huge resource repository can be potentially utilized in the grid environment. Here, we propose a game theoretic pricing model, to address load balancing issues in mobile grids. In particular, by drawing upon the Nash bargaining solution (NBS), we show that we can obtain an unified framework for addressing such issues as network efficiency, fairness, utility maximization, and pricing. The advantage of this framework is that we have a precise mathematical characterization of the solutions and their properties. Our current endeavor characterizes a two-player alternating-offer bargaining game between the wireless access point (WAP) server and the mobile devices to determine the pricing strategy. This pricing strategy is then made use of to effectively distribute jobs to the mobile devices. Our job allocation scheme maximizes the revenue of the grid user, and yet is comparable to the overall system response time of other load balancing schemes.