Biological networks have been considered a suitable formalism to extend evolutionary and comparative biology. In this paper we present GASOLINE, an algorithm for multiple local network alignment based on statistical iterative sampling in connection to a greedy strategy. GASOLINE overcomes the limits of current approaches by producing biologically significant alignments within a feasible running time, even for very large input instances. The method has been extensively tested on a database of real and synthetic biological networks. A comprehensive comparison with state-of-the art algorithms clearly shows that GASOLINE yields the best results in terms of both reliability of alignments and running time on real biological networks and results comparable in terms of quality of alignments on synthetic networks. GASOLINE has been developed in Java, and is available, along with all the computed alignments, at the following URL:  The structure and the dynamic of biological networks arise from interactions among molecules within the cell. Biological functions are obtained by the collaborative action of a number of cellular constituents, such as proteins, DNA, RNA and other small molecules degree ratio, increases. The algorithm iterates the above steps producing a set of local networks alignments . At the end of the process, each set of local networks alignments is ranked according to an index called Index of Structural Conservation. We extensively tested GASOLINE on: (i) a set of 25 biological networks drawn from STRING In this paper, we present GASOLINE , a novel algorithm for protein networks alignment based on iterative sampling We compared our system with a selected list of state-of-the-art methods such as SMETANA Given Such a problem is related to subgraph isomorphism, which is known to be NP-complete, therefore heuristics are needed. GASOLINE is able to produce an approximate solution through a stochastic-greedy strategy consisting of two phases.bootstrap phase, we look for orthologous proteins across the networks and build a set of seeds. The set of seeds initially consists of During the first step called iterative phase, repeatedly either adds (extension step) or removes  nodes in the network alignment, trying to maximize the final alignment score. Each extension step adds, in each network, a single node to the corresponding seed. During the extension step the seeds grow up producing a set of The second step, called Each removal step replaces from the current alignment the set of proteins (one from each network) providing the minimum score.The initial phase and each extension step are performed through an iterative sampling. Consequently, different iterations of the algorithm may produce different local alignments. GASOLINE iterates the above steps producing a set of local networks alignments. Those local alignments are ranked according to an Index of Structural Conservation (ISC) score combining topology and sequence similarity.GASOLINE implements preprocessing and post-processing steps. During preprocessing, the search space for potential seeds is reduced. This is obtained by marking only proteins having orthologs in all aligning networks and with a significant interaction degree in each network.All marked nodes in each network The computational complexity analysis assumes that all networks have the same number Let At the end of the iterative phase the aligned seeds are removed from the sets The search for an initial set of seeds is performed by a Monte Carlo Markov Chain in connection with a Gibbs Sampling algorithm. The Gibbs sampling builds a chain, where each state represents a combination  of Similarity Score. Given two proteins The transition probability is defined on top of a Finally, the alignment score is defined as the sum-of-pairs of similarity scores between the aligned proteins:At the end of the bootstrap phase the alignment of seeds maximizing the sum-of-pairs score over all the iterations of the Gibbs sampling is chosen.Let Each extension step is performed through an iterative sampling similar to the one described above, where a state of the Markov chain represents an alignment of Again, the initial state of the chain is randomly selected. Then, a series of transitions from a state to another one is made, by replacing a randomly chosen protein of the current alignment with a node of the same network in the corresponding adjacent set. The transition probabilities are computed by considering sequence similarity in connection with neighborhood similarity.Let The Similarity Score of The orthology score of topology vector, storing the weights of the edges linking Concerning the topology similarity, we build a vector Given two proteins The alignment score is calculated in terms of orthology and topology similarity by the sum-of-pair of the pairwise alignments:At the end of Gibbs sampling, the alignment with highest sum-of-pair score is selected for the extension of subgraphs in The extension of subgraphs mainly depends on a degree ratio of the alignment which evaluates the local density and the modularity of aligned subgraphs with respect to their neighborhood. Given an aligned subgraph oodness score.In the removal step, we discard from the current alignment a set of mapped proteins which give a minimal contribution to alignment quality. Such a step tries to refine the topology of the aligned subgraphs and therefore does not take into account the sequence similarity. The reason behind this choice is that during the extension steps the subgraph topology conservation intrinsically decreases since no backtracking is performed. Such a step deals with such an issue by making use of a measure called Goodness of a generic protein oodness of column oodness scores of all its proteins:Let oodness score. However, such proteins could be added again to the alignment, in some future extension steps. In Each removal step deletes from the current alignment the nodes corresponding to the column with the minimum GNotice that, the topology similarity score between proteins in the Gibbs sampling algorithm of the extension process is defined in order to reward structural conservation, edge weights and density of the aligning subgraphs. So, as long as the extension process continues, the degree ratio increases. However, it tends to reach local maxima, so the goal of the refinement phase is to try to shift from these local maxima, in order to reach a better approximation of the global maximum.Index of Structural Conservation  Three different case studies have been examined:Pairwise and multiple alignments of synthetic networks;6-way alignment of real PPI eukaryotic networks;25-way alignment of real PPI vertebrata networks.In case studies a) and b) we compared our method against three different global and local multiple network alignment algorithms: SMETANA We used several measures to evaluate the specificity, the sensitivity and the functional consistency of the alignment algorithms, both for synthetic and for real biological networks, following the methodology described in In case study c) we tested the ability of GASOLINE to find highly conserved complexes across many species in reasonable time. Starting from the information about orthologous groups  obtained from the STRING database, we computed the Jaccard similarity coefficient The algorithm needs a few parameters to be set out:Some of these parameters were established experimentally:Notice that, some parameters are strictly related to the stochastic nature of the algorithm . Such parameters have been determined in connection to the convergence of the algorithm on the network instances tested. Therefore, we suggest these default parameters since are enough to yield good alignment results.The threshold parameter (As regards the duplication-mutation-complementation model (DMC) duplication with random mutations model (DMR) crystal growth model (CG) We first assessed the performance of the proposed method on different datasets of synthetic similar PPI networks generated with NAPAbench The first partition is formed by families of 2 closely related networks, evolved from a common ancestor with To measure the overall accuracy of the proposed methods, we used functional groups associated by NAPAbench to each protein of the aligning networks. We call equivalence class a set of proteins of different species (one or more for each network), which are mapped together by a given algorithm. An equivalence class is claimed as correct if all the included nodes belong to the same functional group. For each method we computed three different quality measures:Specificity (SPE): the relative number of correct equivalence classes;Correct nodes (CN): the total number of proteins assigned to correct equivalence classes;Mean normalized entropy (MNE): the mean normalized entropy of the predicted equivalence classes. Given an equivalence class CN reflects the sensitivity of the method, while MNE measures the consistency of the predicted alignments. For SMETANA and IsoRankN we considered only equivalence classes that contain at least one node from each species.Next, we investigated the effects of sequence similarities on the performances of the algorithms. Following the approach used in Finally, we tested the scalability of our method, based on the size of aligning networks. We generated 7 different families, by varying the number of nodes of the ancestral network, In the second case study, we compared the four algorithms on real biological networks of 6 species . To asses the performance of the algorithms, we computed specificity (SPE) and number of correct nodes (CN), and we replaced the mean normalized entropy with a different measure, the mean group consistency (MGC), defined as follows:where We decided to change the consistency measure because a protein of a real biological network may be associated to more than one groups, while in the previous case study a protein was always associated to at most one group, assigned by NAPAbench during the generation of synthetic networks.Such results are confirmed for the alignment of 4, 5 and 6 species . It is wTo sum up, the performance results of GASOLINE in the context of real biological networks are superior to those of synthetic networks, with respect to both specificity and number of correct nodes, which is related to the sensitivity of the algorithm. Moreover, the values of CN are very close to or even higher than SMETANA, though the latter is a global alignment method.A further comparison between GASOLINE and NetworkBlast-M was made to assess the statistical and biological significance of complexes found by both methods in the alignment of 6 species. We annotated aligned proteins with GO terms , taken from BioDBNet GASOLINE found more complexes than NetworkBlast-M (46 vs 45). However, most of the results are common to both methods. Nine small complexes (5–7 proteins) have been identified only by GASOLINE and eight small complexes (5–10 proteins) have been recovered only by NetworkBlast-M.Some of the complexes are correctly split by GASOLINE and wrongly joined in NetworkBlast-M, while other complexes in GASOLINE are actually smaller than the corresponding ones in NetworkBlast-M. This is probably due to the different scoring functions used by the two methods.All complexes returned by NetworkBlast-M can include non 1-to-1 mapping between proteins of different networks. However, these have a fixed maximum size of 15 proteins. This is a serious limitation in the context of local alignment of biological networks since real biological complexes can be actually bigger In In the last case study, we collected a dataset of 25 vertebrata biological networks. Most of the complexes found by GASOLINE in the second case study are also present in this third one. However they are smaller here (i.e. spliceosome), due to (i) the higher number of aligned networks; (ii) incompleteness of PPI networks data in some species. GASOLINE took 2250 seconds Click here for additional data file.


To understand the molecular mechanisms that give rise to a protein's function, biologists often need to (i) find and access all related atomic-resolution 3D structures, and (ii) map sequence-based features  onto these structures.To streamline these processes we recently developed Aquaria, a resource offering unprecedented access to protein structure information based on an all-against-all comparison of SwissProt and PDB sequences. In this work, we provide a requirements analysis for several frequently occuring tasks in molecular biology and describe how design choices in Aquaria meet these requirements. Finally, we show how the interface can be used to explore features of a protein and gain biologically meaningful insights in two case studies conducted by domain experts.The user interface design of Aquaria enables biologists to gain unprecedented access to molecular structures and simplifies the generation of insight. The tasks involved in mapping sequence features onto structures can be conducted easier and faster using Aquaria. The number of protein sequences collected in public databases such as UniProt  has beenstructure to refer to atomic-resolution three-dimensional models of protein molecules, or of proteins in complex with other macromolecules) adopted by a protein across a range of physiologically relevant conditions. Where available, such structures can give detailed insight into the molecular mechanisms underlying a protein's function. Unfortunately, the experimental determination of protein structures lags significantly behind sequencing; currently, the protein data bank (PDB) [Proteins are involved in nearly every biological process and can be viewed as the molecular machinery of life. To fully understand the biological functions of a protein, however, life scientists need to know much more than just its amino acid sequence -- one very rich source of additional knowledge are the three-dimensional (3D) structures   holds slhomology modelling or comparative modelling methods [ab initio from amino acid sequence -- sometimes considered as the "holy grail" in bioinformatics [ab initio models. The quality of a model depends on details of the often very complex method used to derive it -- it can be difficult to communicate this information clearly to end-users unfamiliar with modelling methodologies, with the very real danger that incorrect conclusions may be drawn by inexpert users.However, for many of the > 99% of proteins that lack any experimentally determined structures, some structure information can usually be inferred via  methods . These m methods -6. For tormatics . When usfeature or annotation such as SNPs or post-translational modifications (PTMs) in their spatial context, which often helps in understanding of the effects of such features on a protein's function.Avoiding such misinterpretations was a key motivation behind the development of a related approach taken by the SRS 3D system , which wThere are many approaches to query for and visualise protein molecular structures, many accessible from the PDB ,3; only The Protein Model Portal  systematAlthough the models presented in Aquaria are not as refined as those derived from modelling , the uncWhile the Aquaria resource and the details of the underlying database are described in detail elsewhere , this paThe development of the Aquaria user interface was an iterative, user-centred design process, based upon understanding of users, tasks, and context. Visual mock-ups were created in Adobe Illustrator and Adobe Photoshop for each design phase and for each state of interactive user interfaces. Colours were soft-proofed in Photoshop for the two most common types of colour blindness, protanopia (reduced sensitivity to red) and deuteranopia (reduced sensitivity to green). Users of different levels of expertise, skills, and experience were involved throughout design and development, and the design was driven and defined by user-centred evaluation. Here, we describe the requirements and design decisions as a result of this process.In this section, we provide a requirements analysis for designing a visual interface to the database underlying Aquaria. This includes a characterisation of the problem domain and a list of common questions biologists seek to answer in order to gain insight from protein structures.Structural biologists can spend months and years working on a single structure, and hence are often very knowledgable about structures for a relatively small set of proteins; in contrast, most other biologists and molecular biologists require much less frequent access to structural data, and, when they do view structures, will usually ask less specialised questions. Hence, Aquaria is targeted mainly at life scientists who do not have expert knowledge about the structural biology of a particular protein. These target users are typically interested in first gaining an initial overview of which related structures are available for a given protein, then selecting which structures are most relevant to the biological phenomena they are most interested in understanding.To understand more precisely which key questions biologists in this situation will need to address, we conducted detailed discussions with two structural biologists, a cell biologist, a chemist, and two bioinformaticians - they showed us how they currently answer these with existing systems, and provided us with questions they are frequently facing Table . This liWhile we found that most of these questions can be addressed using a combination of the existing systems and public databases described in the previous section, doing so can be difficult or even impossible due to a lack of integration of the required resources. One important issue we found in using the existing tools to address the questions listed in Table In this section, we describe the design decisions made for the Aquaria user interface to help biologists answer the questions listed in Table Since the primary design goal was to create an accessible and easy-to-use interface to protein structures for a wide range of users, the first design decision we made was to build our tool as a web-based resource, for two reasons: first, to facilitate easy access to the databases which contain related information about protein structure and features , fully opaque structures would hinder the view on the chain associated with the specified protein. 'Autofocus' can be disabled via the menu bar at the top of the 3D view.If the user selects one of the semi-transparent residues in the other chains, then the chain of the selected residue will become opaque and the centre of rotation, as it is now the selected chain, and the pre-existing selected chain is made transparent. In order to allow the user to explore the binding partners of a molecule (cf. Question 21), the specified protein will be replaced by the sequence of the newly selected chain and all other panels will be updated accordingly.The SRS3D viewer supported a number of feature annotations that would be displayed below the 3D model as selectable rows, and the features would be mapped to the 3D object when selected by the user. However, nowadays annotations for a protein may number in the thousands, which would not scale well when displayed within the view frame. Aquaria limits the number of features that can be loaded into the view to one and provides a separate interface for navigating and selecting features from a list to be sent to the view.Questions 1 to 8 in Table However, structural matches as reported by the Aquaria database range from several dozen to thousands of results for a specified protein. While the position of the Matching Structures panel at the bottom of the layout provides the option to expand the panel to fit any amount of content, having to scroll too much would be an obvious impairment of the workflow. Hence, we used aggregation on two levels to get a good trade-off between the overview required for Questions 1 to 6 and the details required to answer Questions 7 and 8:At the sequence level, functional elements occupy one or more positions, defined by start- and endpoints. Search results are aggregated at the first level by position in the alignment. Within each group, sequences are ordered by identity relative to the query sequence, whereas the visual representative is the top-ranked structure within each group. Next to the sequence image, which shows the secondary structure and indicates the degree of similarity with the query sequence, we placed a numeric label stating the size of the group. To the right side of the panel, a thumbnail of the structure image from PDB provides useful information for those familiar with proteins. This allows one to visually assess the quantity, quality, and position of the best structural matches for the specified protein.At the next level of aggregation, sequences are grouped by molecular configuration (Questions 7 and 8): conformation , symmetry , and binding partners the structure includes. Within this grouping, structures are ordered by identity and crystallographic resolution, with NMR structures last.To navigate inside the group, a click on the numeric label opens a collapsible tree structure, showing the next level of grouping. For large groups, this poses the problem how to manage the display of potentially thousands of nodes. We employed a paging-based strategy:The tree is drawn, centred vertically on the first-level group, limiting the number of nodes to what can comfortably fit in the available space. We use pagination to accomodate for larger sets of nodes.To avoid spacing problems arising from the position of nodes expanded inside the tree, all nodes in the level undergoing selection are collapsed and the chosen node moves to the centre, while the next level of the tree is expanded. This results in a straight line of parent nodes, which can also be navigated in reverse: when clicked, the child nodes collapse and the nodes on the parent level are made accessible again.On the second level of aggregation, again, each node has a numeric label indicating how many structures it contains, which acts as the trigger to show the next level of the tree. This third level is where the user can select a structure to load into the 3D view by clicking on the thumbnail image.features or annotations are a rich source of information available on the web. These features, however, can not be easily mapped onto a spatial context, as they naturally occur in proteins. Questions 17 to 20 are about visualising features on one of the matching structures.Sequence-based The features panel Figures , and 4 sOne phenomenon of protein features is that there can be multiple annotations for the same region of a protein sequence. For single-residue features, such as mutations, one option is to display feature density, which uses less space by displaying all features of one category in one track. For multi-residue features, overlaps could be made visible by employing transparency. Both approaches, however, create ambiguity when trying to use them in an interactive context: when hovering over an area with overlapping features, meta-information for many instances would need to be displayed at the same time. This kind of information includes additional information about a single feature occurring at a particular position of the specified sequence.lanes, which give users access to metadata for each feature. For feature types with a large number of annotations, as highly-studied proteins often have, this method produces a histogram-like display with the most frequently varied residues shown in the lower lanes , shown diminished in saturation and brightness• Not conserved , shown in dark grey• Insertion, shown in light grey• The resulting display of structures allows users to judge the quality of a structure model intuitively by its chromatic quality: the less saturated and bright the colours in a structure appear, the less similar its sequence is to that of the query protein. This effect is particularly noticeable in the Matching Structures panel, where structures are ordered by percent identity, with the highest-ranked structures displayed at the top.However, in order not to rely on colour coding alone, we also display a precise percentage of identity for each structure .In this section, we present two case studies that demonstrate the effectiveness of Aquaria for using structures to learn about protein function.For this case study, we focused on BLK, a tyrosine kinase involved in B-lymphocyte development, differentiation and signaling (Uniprot id P51451). To gain information about a protein in Aquaria, the first step is typically to query using the protein name, synonym, or primary accession. In this example, the user first needs to confirm that the organism is set to "Human", then enters "BLK" into the query field. A list of 3, 261 matching structures is displayed -- a wealth of structural data that can provide insight into the molecular mechanisms occurring in related structures. These structures are clustered by homology into 33 groups through finding all sequences of structures that match a particular range of the given protein Figure  that givThe structure that is displayed initially in the 3D viewer is from the third cluster; while this structure has has a lower sequence identity to BLK that structures in the top two clusters (70% compared with 82 and 75%), it has a large total number of identical residues, when aligned onto the full-length BLK sequence. The width of each cluster shows the length of the alignment, while the color is used to communicate the quality of the top ranked structure in each cluster. Clicking on a cluster group loads the best matched protein structure of that cluster into the 3D viewer. When a new structure is loaded, the text on the right -- containing bibliographic information about the structure -- is updated accordingly. The default selected cluster contains 30 similar PDB structures based on homology matching for that group. By clicking on any of the clusters, a tree appears showing the respective PDB structures grouped by macromolecular assembly and binding partners into sub-clusters , and other experimental information. Clicking on the top feature loads the feature track into the 3D viewer highlighting the different domains in the 3D structure , a molecule which is considered important in the development of Alzheimer's Disease. This model was designed to be used in an animated visualisation which explores the process of Alzheimer's disease associated neural plaque formation, and therefore was intended for a general audience. The work was conducted by one of the authors (CJH), a biomedical animator trained in medical science.The elements which were to be visualised included the overall structure of the APP protein, its localisation within the cell membrane and the enzymatic degradation of the protein.An investigation into the structure of APP was conducted in order to build a model which was consistent with the up-to-date understanding of the known structure of the protein. Aquaria shows that no single structure spanning the entire APP protein has yet been resolved Figure , howeverThe modelling process involved using Aquaria to assemble the relevant data from the protein data bank (PDB), reading a series of reviews into the overall structure and cellular distribution of the APP protein and piecing together PDB structures using Blender and the embedded python molecular viewer (ePMV) to create the model and animate it for a final video.We searched Aquaria for related structures by using the keyword "APP" and selecting "human" as the organism. Aquaria's top match result is UniProt entry P05067 which was the appropriate result in this case Figure .In Aquaria this identifier currently yields 221 structures which span most of the full sequence  on the N-terminal end (Table • Mapping the Region "polypeptide region" onto structure 2lmp gave insight into the precise location of enzyme activity on APP via alpha and gamma secretase, which are known to cleave APP Table , Q13. Al• Examining the molecule processing feature lane titled "Mature Protein Region" gave insight into the precise location of enzymatic cleavages that occur within and flanking the intermembrane domain Table , Q13.From the Aquaria interface, by tracking the literature for each structure observed, we found a recent literature review that discussed the current state of knowledge regarding the overall structure and subcellular location of APP . This gaThe key benefits we found using Aquaria for this case study were:• Clarity as to which regions of the protein did and did not have resolved structures• Access to an organised and coherent assembly of all PDB entries relevant to the protein of interest• Clear insight into the subcellular location of APP, and into which residues contact interaction partners• An indication of the integrity and match identity of the available structuresHaving evolved over years with a continuous feedback loop between developers and users of both SRS3D and the Aquaria pre-release, the system gradually improved in many ways. In this paper, we describe the major design decisions that have been made with respect to the visualisations and the user interface to the Aquaria database. Some of our visual mappings - such as the colour scheme and the representation of secondary structures - have been prototyped and discussed with users prior to being implemented in the system. For the interaction with the 3D view, a user study has been conducted  that proMany of the design decisions implemented in Aquaria could also be helpful for the visualisation of molecular dynamics. For example, the ability to focus on residues of a particular functional annotation and then inspect their range of motion within the structure could help illuminate their mechanism of action. Conversely, it would be useful to observe a particular mode of dynamics and then find the associated residues in the amino acid sequence. The tight integration of sequence and structure in the Aquaria user interface makes the required selection processes very easy.By employing a consistent visual vocabulary for data integrated from disparate sources, Aquaria provides a comprehensive experience for the user that invites exploration with a minimum of cognitive load.As demonstrated in both case studies, the visual design of the user interface enables users to address the key questions identified in the requirements analysis Table ; these sFor future work, we would like to adapt Aquaria for mobile devices by implementing the entire user-interface in JavaScript. We are exploring the potential of newly available input devices -- such as depth- sensing cameras -- to simplify 3D control of molecular graphics . We alsoprotein data bank, NGS: next generation sequencing, SNP: single-nucleotide polymorphism, PTM: post-translational modification, PMP: protein model portal, APP: amyloid precursor protein, 3D: three-dimensional, ePMV: embedded python molecular viewerPDB: The authors declare that they have no competing interests.CS led the interface design with contributions from SIOD. SIOD, AS, KSS and JH conducted the requirements analysis. The manuscript was written by CS, JH, SIOD, KSS and CJH. All figures were prepared by CS with contributions from SIOD and CJH, who also prepared the case studies. SIOD and AS conceived of the project, and participated in its design and coordination. All authors read and approved the final manuscript.


The only subgroups of patients with heart failure and atrial fibrillation in which the efficacy of cardiac resynchronization therapy has been scientifically proven are patients with indications for right ventricular pacing and patients after atrioventricular junction ablation. However it is unlikely that atrioventricular junction ablation would be a standard procedure in the majority of the heart failure patients with cardiac resynchronization therapy and concomitant atrial fibrillation due to the irreversible character of the procedure and a spontaneous sinus rhythm resumption that occurs in about 10% of these patients.Pilot-CRAfT is the first randomized controlled trial evaluating the efficacy of a rhythm control strategy in atrial fibrillation patients with cardiac resynchronization therapy devices. The aim of this prospective, single center randomized controlled pilot study is to answer the question whether the patients with cardiac resynchronization therapy and permanent atrial fibrillation would benefit from a strategy to restore and maintain sinus rhythm (that is ‘rhythm control’ strategy) in comparison to rate control strategy. The study population consists of 60 patients with heart failure and concomitant long-standing persistent or permanent atrial fibrillation who underwent a cardiac resynchronization therapy device implantation at least 3 months before qualification. Study participants are randomly assigned to the rhythm control strategy  or to the rate control group whose goal is to control ventricular rate. The follow-up time is 12 months. The primary endpoint is the ratio of effectively captured biventricular beats. The secondary endpoints include peak oxygen consumption, six-minute walk test distance, heart failure symptom escalation, reverse remodelling of the heart on echo and quality of life.NCT01850277 registered on 22 April 2013  The heart failure (HF) patients with concomitant atrial fibrillation (AF) make up a significant subgroup of patients eligible for cardiac resynchronization therapy (CRT) but their treatment remains problematic.The prevalence of AF as well as chronic HF is significant and it is still growing. It has been estimated that 1 to 2% of population suffers from AF - the most common form of sustained arrhythmia , 4. AbouNevertheless, the CRT in patients with AF encounters many problems and its beneficial effect in this group of patients is not fully established. The indications for CRT arise from the evidence coming from the studies where AF patients were absent or barely present. The only subgroup of patients with AF in which beneficial effect of CRT is scientifically proven (that is proven in randomized controlled trails (RCTs)) are the patients with concomitant indications for right ventricular pacing , 7. As aContrary to the widening range of patients with HF suitable for CRT, the evidence is growing that the benefit of resynchronization in the subgroup of patients with AF is reduced. The initial meta-analyses of the studies comparing the efficacy of CRT in patients with AF versus SR suggested similar effect of response to CRT in the AF group in terms of NYHA functional class or ejection fraction and lower effect on the exercise tolerance improvement measured by means of six-minute walk test (6MWT) , 9. The The lower effectiveness of CRT in AF patients is explained by the irregular and unpredictable character of ventricular beats in this arrhythmia which lowers the ratio of effectively biventricular paced captured beats (BiVp%). This hypothesis is corroborated by studies of AF patients on CRT undergoing atrioventricular junction ablation (AVJA), which enables the BiVp% to be close to 100%. According to the registries, the CRT efficacy in patients with AF undergoing AVJA is similar to that in patients in SR, whereas the patients with AF but no AVJA seem not to benefit from resynchronization at all, even if BiVp% exceeds 85% , 11. As Although AVJA seems to provide the efficacy of CRT , 7, 14, et al. [To the authors’ best of knowledge, a randomized, prospective trial assessing the influence of rhythm control strategy on CRT efficacy in AF patients has not yet been performed. The only trial upon this issue was the non-randomized, cohort study of Turco et al. , which hablate and pace strategy (that is performing an AVJA + CRT-D (CRT with defibrillator function) implantation) in HF patients with paroxysmal or persistent/permanent AF [The promising results of the rhythm control strategy were also achieved in the PABA-CHF trial which compared pulmonary vein isolation (PVI) versus the anent AF . During The aim of the study is to answer the question whether the patients with permanent or long-standing persistent AF on cardiac resynchronization therapy due to heart failure will benefit from the treatment strategy to resume and maintain sinus rhythm in terms of achievement of adequate BiVp%. An additional goal of the study is the identification of potential factors predicting the efficacy/the lack of benefit of the rhythm control strategy. As it is a pilot study, its other goal is to collect data which would enable the authors to design another, larger study, which would have a power to examine the influence of a rhythm control strategy versus rate control strategy on the mortality and morbidity of HF in this group of patients.The Pilot-CRAfT study is a single-center, open, prospective, randomized controlled trial. The trial enrolls 60 patients with HF and concomitant permanent or long-standing persistent AF who underwent CRT implantation at least three months before the enrollment in the Second Department of Coronary Artery Disease of the Institute of Cardiology in Warsaw, Poland. The inclusion and exclusion criteria are listed in the Table The enrolled patients are randomly assigned to one of the two groups (in a 1:1 ratio). In the first group, a strategy to restore and maintain SR is implemented (that is rhythm control strategy - the active treatment group). In the second group, a strategy to slow ventricular rate will be implemented (that is rate control strategy - the control group).The basic method to restore and maintain sinus rhythm comprises a combination of pharmacological antiarrhythmic treatment and external electrical cardioversion (EEC). The attempts to restore SR must not be performed unless the patient has been treated effectively with oral anticoagulants for at least three weeks.The pharmacological treatment consists of amiodarone administered orally. The loading dose of amiodarone is up to 600 mg daily for the first 4 weeks. Then, a maintenance dose of 200 mg/daily is administered (a dose may be adjusted at discretion of treating physician). The loading dose may be modified or omitted, especially, if a patient had been given amiodarone before the enrollment. If any contraindications to amiodarone occur during the treatment, it must be discontinued promptly. The use of other antiarrhythmic agents is possible unless they are contraindicated.rhythm control is discontinued and a rate control strategy is implemented.A basic tool to restore SR in the trial is an EEC. The first EEC should be performed after the complete loading dose of amiodarone has been administered (unless SR is restored spontaneously). Up to three shocks during one cardioversion are allowed. The amount of the energy delivered is left to discretion of a physician performing the EEC. If AF recurs, the patient should undergo the next EEC as soon as possible but preserving the safety time margins of effective anticoagulation period (at least three weeks). The maximal number of EEC procedures is three and the third one is possible only if AF recurs no earlier than three weeks after the second EEC and SR resumption is related to alleviation of HF symptoms. If SR resumption or its maintenance is impossible or AF recurs after the third EEC, a strategy of rhythm control strategy treatment, a procedure of ablation of AF substrate (that is PVI) is possible. The decision to offer the patient an ablation must be made collectively at the discretion of the therapeutic team. Routinely in the trial, a PVI procedure is allowed and offered to patients responding to EEC after the second recurrence of AF.In patients undergoing the rate control group (that is the control group) is to slow and control ventricular rate by means of pharmacological antiarrhythmic agents and AVJA as to achieve satisfactory BiVp%. The pharmacotherapy should include negative chronotropes and negative dromotropic drugs such as beta-blockers, digitalis and amiodarone . It should be consistent with the current guidelines of treatment of HF and AF. The choice of the agents and its’ dosage is left to discretion of the treating physician. An AVJA procedure in this group of patients is possible but not obligatory. The decision to perform AVJA is left to the discretion of the treating physician. An effective anticoagulant treatment is essential in both groups.The treatment goal in the http://www.mapi-trust.org and University of Minnesota. Email: mlhfq@umn.edu - Internet http://www.mlhfq.org). Moreover, a thyroid-stimulating hormone (TSH) level control is performed on the qualification visit and during visits after six and twelve months.The follow-up period is 12 months. The patients are scheduled for control visits every three months. Every control visit consists of: a standard review of a device and treatment; an assessment of the actual BiVp%; the questions concerning the occurrence of the analyzed endpoints and a standard 12-lead ECG. Additionally, on the qualification visit, and control visits after 3 and 12 months, a 6MWT, an echocardiograph and a cardiopulmonary exercise test (CPX) are performed and the patients are asked to fill the questionnaire about their quality of live, that is the Minnesota Living with Heart Failure Questionnaire (MLHFQ), © University of Minnesota , 20, . The ‘triggered’ paced beats are not treated as effective and are excluded from the analysis.The secondary endpoints are listed in the Table rhythm control strategy.Based on the results of the primary endpoint, the authors plan to perform a preliminary identification of the factors predicting the response to the Device interrogation is performed on every control visit. It should include: a standard battery and lead control, analysis of the histograms, alerts , history of the ‘mode switch’ episodes  and BiVp% (measured from the last control visit). In case of CRT-D devices, the episodes of arrhythmia as well as the number of adequate and inadequate high-voltage intervention should be registered. If required, necessary modifications in the identification and treatment of ventricular arrhythmias should be made.rhythm control for the rate control or a decision to perform AVJA should be made. If an intervention of a device occurs, the additional device control should be performed.Moreover, during the visit a review of pharmacological treatment is performed, including analysis of its efficacy, validity, and occurrence of adverse effects. If required, necessary modifications in the pharmacological treatment, an eventual decision to change the Participants will undergo an echo test  performed with a general assessment of morphology and function of the heart.Echocardiography will include the evaluation of:Cardiac dimensions Left ventricular global systolic function , left ventricular regional function  and left ventricle diastolic functionType of diastolic dysfunction, diastolic pressure in the left ventriclePresence and severity of valvular diseases Evaluation of cardiac dyssynchronyIntraventricular dyssynchrony parameters  delay)Atrioventricular dyssynchrony: diastolic filling ratio (LVFT/RR)Interventricular dyssynchrony In patients with sinus rhythm, an AV interval will be optimized (unless optimization had been performed previously).CPXs on a treadmill  with standardized modified Bruce or Naughton exercise protocols will be applied. Breath-to-breath respiratory gas analysis will be carried out with Vmax Encore 29c equipment . An exercise test will be carried out until maximal patient’s exertion, maximal heart rate or dangerous ventricular arrhythmia occurs. The following parameters will be monitored during CPX:Heart rate, arterial blood pressure, ST segment changes2, ml/kg/minute, l/minute)Peak oxygen uptake Peak carbon dioxide exhalation Oxygen pulse Breathing reserve (BR): maximal voluntary ventilation/maximal exertional ventilation × 100%Heart rate reserve (HRR): maximal HR/peak exertional HR × 100%Anaerobic threshold level (ml/kg/minute)2 equivalent: VE/VCO2 maxMaximal CO2 slope and VE/VO2 slopeVE/VCOSubjective dyspnea level Exertional level will be assessed subjectively by patients in Borg scale .t-test; 5) drop-out rate: 8%.The calculation of a sample size was made based on the estimated results in the groups for the primary endpoint (BiVp%) and the following assumptions: 1) the mean difference of BiVp% between groups would be 8% and standard deviation (SD): 15%; 2) probability of a type I error: 0.05; 3) probability of a type II error: 20%; 4) the type of test: Student’s two-sample A power analysis revealed that a minimum of 30 patients per group will be required to assure at least 80% power for detecting the anticipated between-group differences in BiVp% and to compensate for the expected drop-out rate.Randomization will be provided by independent statistician using SAS 9.2 software . Permuted block randomization will be used (blocks AB and BA). Once an eligible patient gives his informed consent, he will be given a specific, unique identifier. The statistician will monitor the enrollment process.During the study, the investigators will regularly fill in the case report forms (CRF). Database management and quality control for the study will be performed by an independent statistician. Structured data elements from the CRFs will be entered into the database and reviewed using double data entry for verification. Information entered into the database will be systematically checked and evident errors will be corrected. All of the omissions and/or questions concerning the data will be discussed with the investigator in order to preserve the completeness of the database.Baseline demographic characteristic and clinical variables will be summarized for each arm of the study. Continuous data will be presented as arithmetic means and SD for normally distributed variables or as medians and inter-quartile ranges (25 to 75th) for abnormally distributed variables. Normality will be tested by the Shapiro-Wilk test.t-test, or Cochran-Cox test or non-parametric Mann–Whitney test as appropriate. Student’s paired t- or Wilcoxon’s test will be used to compare continuous variable differences between baseline and the end of observation period.Comparison of two groups will be based on parametric Student’s two-sample Categorical data will be given as absolute and relative frequencies (percentages). The differences in proportions between them will be examined using the chi-square - test with Yates’s correction or Fisher’s exact test as appropriate.A logistic regression model will be used to identify independent predictors of response to therapy. For the longitudinal analysis, curves of cumulative probability of events will be constructed according to the Kaplan-Meier method, and the cumulative events rates will be compared by the log-rank test. A Cox proportional hazards regression analysis will be performed to assess for any potential influence of covariates. The assumptions of the Cox proportional hazards model will be checked by visual inspection of the log-log survival function by time curve.Due to the possibility of the treatment crossovers, all major treatment comparisons between the randomized group in this trial will be performed according to both the intention-to-treat and the per protocol principles.All null hypothesis will be two-tailed with a 0.05 type I error rate.Statistical analysis will be performed using the SAS 9.2 statistical package .The investigation conforms with the principles outlined in the Declaration of Helsinki . The locrhythm control strategy would be at least as good as the rate control strategy in terms of BiVp%. The achievement of SR and satisfactory BiVp% in the rhythm control group may strengthen the underestimated position of cardioversion in the group of HF patients on CRT with concomitant permanent AF.Based on the current knowledge, the authors assume that the et al., this is highly probable. In the latter work it has been proven that - by means of a single cardioversion - SR is resumed in 82% of the patients and maintained for a 1 year in about 60% of the former AF-CRT patients [It may seem disputable whether the CRT patients with permanent AF will resume and maintain SR. As has been already mentioned, HF and AF are diseases that can negatively influence each other. In at least some of the patients, an occurrence of AF seems to be a marker of HF severity. We hypothesize that restoration of sinus rhythm, which would enhance the performance of CRT and enable better treatment of HF, may have beneficial effect on AF progression. Based on the evidence of the SR resumption phenomenon  and the patients . We assupatients .The authors are fully aware that during the progression of HF, irreversible fibrous changes within the atria occur which promote AF and its maintenance , so it irate control group arises from its beneficial effect on AV node in this group of patients [The standard use of amiodarone in both of the groups may seem disputable in the potential complications of this treatment and limited evidence of its efficacy. However, following the literature the use of amiodarone should facilitate sinus rhythm maintenance in the rhythm control group , 23, 24.patients . Moreovepatients , amiodarOne of the limitations of the trial is the number of the participants which surely has an impact on the statistical strength of the study. It would enable the researchers to assess the efficacy of the two treatment strategies on the primary endpoint only. The statistical difference within the clinical secondary endpoints of this study will be difficult to achieve. However, the lack of sufficient data about the CRT in the patients with concomitant AF necessitates the undertaking of an initial pilot study upon this issue.The incoming results of the Pilot-CRAfT would enable the authors to prepare a parallel larger, well-designed multi-center study assessing the influence of both treatment strategies on ‘harder’ clinical endpoints, such as mortality and morbidity, as well as further analysis of the factors determining the response to the rhythm control strategy. The assessment of the efficacy of the rhythm control strategy in CRT and AF patients and the identification of the subgroup of patients who benefit from it the most would create an alternative to the currently recommended rate control strategy which has some considerable limitations.Whatever the results of the trial, performing a prospective, randomized study on CRT patients with concomitant persistent forms of AF would finally provide strong scientific evidence data about the efficacy of CRT in this specific subgroup of patients. This urgently needed data has been long awaited.Ongoing - currently recruiting.


Candida albicans is the most common causative agent of human fungal infections and better drugs or drug combination strategies are urgently needed. Here, we present an agent-based model of the interplay of C. albicans with the host immune system and with the microflora of the host. We took into account the morphological change of C. albicans from the yeast to hyphae form and its dynamics during infection. The model allowed us to follow the dynamics of fungal growth and morphology, of the immune cells and of microflora in different perturbing situations. We specifically focused on the consequences of microflora reduction following antibiotic treatment. Using the agent-based model, different drug types have been tested for their effectiveness, namely drugs that inhibit cell division and drugs that constrain the yeast-to-hyphae transition. Applied individually, the division drug turned out to successfully decrease hyphae while the transition drug leads to a burst in hyphae after the end of the treatment. To evaluate the effect of different drug combinations, doses, and schedules, we introduced a measure for the return to a healthy state, the infection score. Using this measure, we found that the addition of a transition drug to a division drug treatment can improve the treatment reliability while minimizing treatment duration and drug dosage. In this work we present a theoretical study. Although our model has not been calibrated to quantitative experimental data, the technique of computationally identifying synergistic treatment combinations in an agent based model exemplifies the importance of computational techniques in translational research.The fungus  Candida albicans, the most common causative agent of human fungal infections  according to a set of rules , the so-called patches. In our model, we assigned to each MC variables that could influence the agent dynamics:C. albicans yeast and hyphal agents, and the three mobile agents including microflora, PMN and macrophage agents. All the interactions of agents and patches that we implemented in the model are depicted in Figure C. albicans yeast and hypha agents, microflora, PMN and macrophage agents were randomly distributed over the world at the beginning of each simulation  are able to escape macrophages  and the end of the simulation time (8000 ts), respectively, and tstart is a time point where a successful treatment is considered to have returned the system to the basal state (here: 7000 ts). Agent indicates the number of the respective agents under the respective simulation conditions at the specified time point.The last term, “C. albicans and its interactions with the host cells during the commensal colonization of the epithelium. The topology of the model is depicted in Figure C. albicans and immune cells were randomly distributed over the grid at the beginning of every ABM simulation . The host immune response was incorporated in the model in the form of PMN and macrophage agents. Immune cells appeared in the system upon sensing inflammatory signals diffusing through the epithelium due to higher hyphal load. In the ABM, inflammatory signals reflected the cytokine release by each MC and represented the hyphal burden on the MC. Immune cells could enter the system through nine MCs, referred to as portals providing access to the blood stream  to analyze the population dynamics of n Figure . During C. albicans population during a commensal state and C. albicans was maintained on low levels with a predominant yeast form . We applied the MD treatment only after an initial commensal state had established, i.e., at time s Figure . A consts Figure . The decs Figure . Saturats Figure . Hyphal s Figure . Accordis Figure .t = 1500 ts, 2000 ts, or 2500 ts are depicted in Figures C. albicans agents and the slower the regeneration of the microflora. Moreover, the regeneration process showed a higher variability in the host response , the amount of hyphae at the same reference interval , and the amount of hyphae during the whole simulation period . Since we always compared the infected and treated case to the uninfected, healthy case, an IStotal value of 1 indicated that the considered system has an intact microflora without C. albicans infection. A treatment was considered as optimal if the resulting score was reliably close to 1 depicting a successful cure of the C. albicans infection. A score smaller or greater than 1 indicated a disruption in the microfloral balance, having either more or less C. albicans agents in the treated system than in the healthy reference system, respectively  or prevented the transition from yeast to hyphal state . We tested the efficiency of both drugs in clearing the fungal infection. The drugs were applied separately or in combination for different durations and dosages, after the initial onset of MD  increased as treatment duration and dosage increased  led to an increased amount of energy required for a yeast agent to divide. For instance, a DD drug dosage of 2 means that the yeast cell needed twice as much t Figure . High dod Figure . AlthougThe transition drug (TD), the second drug that we tested, inhibited hyphal formation and was motivated by studies that proposed this morphogenic switch as a target for novel drugs , indicating a higher predictive value of these treatments. The tested treatment scenarios contained two cases with particularly low deviations from the optimal score  and inhibiting hyphae formation (TD). In our model, TD alone leads to an adverse state as yeast cells can accumulate and form hyphae upon treatment termination. The synergism emerges when TD is applied early on to prevent hyphae formation while an addition of DD to the treatment will prevent yeast cells to accumulate—a state otherwise deleterious upon the end of TD treatment. Comparison of treatment 5 with treatment 6 as well as treatment 8 with treatment 9 indicates the overall improvement in IStotal from the untreated value  while minimizing treatment duration and drug dosage. Minimizing treatment dosage and duration reduces both treatment cost and occurrence of potential side effects. Models of host-pathogen interactions used in the context of evaluation of drug treatment strategies, here assessed by evaluating IStotal, will be of special use especially for establishing scenarios for treating hospital-acquired fungal infections. Patients struggling with hospital-derived illnesses often suffer from a weakened immune system and are administered other drugs in parallel. With host-pathogen models combined with pharmacokinetic models, one could examine the risks of combinatorial drug treatments.Our results indicated that TD drugs which prevent the switch from yeast to hyphae [e.g., a farnesol-derived treatment or Allium sativum .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Two-component signal transduction systems, where the phosphorylation state of a regulator protein is modulated by a sensor kinase, are common in bacteria and other microbes. In many of these systems, the sensor kinase is bifunctional catalyzing both, the phosphorylation and the dephosphorylation of the regulator protein in response to input signals. Previous studies have shown that systems with a bifunctional enzyme can adjust the phosphorylation level of the regulator protein independently of the total protein concentrations – a property known as concentration robustness. Here, I argue that two-component systems with a bifunctional enzyme may also exhibit ultrasensitivity if the input signal reciprocally affects multiple activities of the sensor kinase. To this end, I consider the case where an allosteric effector inhibits autophosphorylation and, concomitantly, activates the enzyme's phosphatase activity, as observed experimentally in the PhoQ/PhoP and NRII/NRI systems. A theoretical analysis reveals two operating regimes under steady state conditions depending on the effector affinity: If the affinity is low the system produces a graded response with respect to input signals and exhibits stimulus-dependent concentration robustness – consistent with previous experiments. In contrast, a high-affinity effector may generate ultrasensitivity by a similar mechanism as phosphorylation-dephosphorylation cycles with distinct converter enzymes. The occurrence of ultrasensitivity requires saturation of the sensor kinase's phosphatase activity, but is restricted to low effector concentrations, which suggests that this mode of operation might be employed for the detection and amplification of low abundant input signals. Interestingly, the same mechanism also applies to covalent modification cycles with a bifunctional converter enzyme, which suggests that reciprocal regulation, as a mechanism to generate ultrasensitivity, is not restricted to two-component systems, but may apply more generally to bifunctional enzyme systems. Bacteria often use two-component systems to sense and respond to environmental changes, which involves autophosphorylation of a sensor kinase and phosphotransfer to a cognate response regulator. However, despite conservation of this ‘classical’ scheme there exist substantial variations in the mechanism of phosphotransfer among systems. Also, many sensor kinases exhibit phosphatase activity raising the question whether such a bifunctional architecture enables special regulatory properties in the response behavior to input signals. According to previous studies, classical two-component systems are unlikely to produce sigmoidal response curves (ultrasensitivity) if the sensor protein is bifunctional. Here, I argue that this is not necessarily true if the input stimulus  reciprocally affects multiple activities of the sensor kinase, as it seems to be common for bifunctional enzymes. To this end, I propose and analyze an extension of the experimentally well-supported Batchelor-Goulian model which shows that ultrasensitivity requires a high-affinity effector and saturation of the phosphatase activity. The underlying mechanism involves sequestration of the effector by the sensor kinase which restricts the occurrence of ultrasensitivity to sufficiently low effector concentrations. Hence, this operating regime might be useful to sense effector limitations or to amplify weak input signals. Two-component systems (TCSs) are modular signal transduction systems which are utilized by bacteria and other microbes to respond to intracellular or environmental stimuli Even though the overall signal flow from the sensor kinase to the response regulator is well-conserved between different systems there exist substantial variations in the particular mechanism through which the phosphoryl group is transferred to the regulator protein In contrast, systems with a bifunctional design are expected to generate graded responses to input stimuli In the present study, I wish to argue that ultrasensitivity may still occur in two-component systems with a bifunctional enzyme kinase if the input signal reciprocally affects multiple activities of the sensor kinase. Reciprocal regulatory patterns have been observed in the PhoQ/PhoP system which mediates adaption in response to  protein  [20], [2in vitro experiments suggests that in the NRII/NRI system the occurrence of ultrasensitivity is (partly) suppressed by the intrinsic autophosphatase activity of NRI.In a first step, the impact of reciprocal regulation is analyzed in covalent modification cycles with a bifunctional converter enzyme, which will serve as a ‘toy’ model that allows for an intuitive understanding of the potential mechanism for the generation of ultrasensitivity. In a second step, it will be shown that the same mechanism may also generate ultrasensitivity in classical TCSs with a bifunctional sensor kinase. To this end, an extension of the experimentally well-supported Batchelor-Goulian model (see below) is proposed which assumes that autokinase and phosphatase activities of the HK are reciprocally regulated by an allosteric effector . AnalysiE. coli, Batchelor and Goulian proposed a simple mathematical model based on the three activities of the bifunctional EnvZ (denoted by HK in SI Text S1)To rationalize the occurrence of concentration robustness in the EnvZ/OmpR system of SI Text S1)Interestingly, .e.  if   or  total QSSA may be advantageous If the substrate concentration is much larger than that of the converter enzyme (Using the QSSA condition elations  and 7(11effector  and 12 aIf the relative binding affinity of the enzyme-effector complex is low (SI Text S1).If the relative binding affinity of the enzyme-effector complex is high  . In contnchanged .phoP gene). In contrast, under limiting Evidence for stimulus-dependent concentration robustness came from experiments with the PhoQ/PhoP system where Miyashiro and Goulian investigated the effect of genetic autoregulation on the expression level of PhoP-regulated genes at different These findings were rationalized based on is level , increased model , where iAlthough Miyashiro and Goulian did not measure the concentration of PhoP-P directly they observed a gradual (rather than switch-like) increase in the transcript levels of PhoP-regulated genes as the ion  see Methods If the dissociation constant of the enzyme-effector complex is much smaller than the total enzyme concentration (d by see Methods(s at cf. (38)Hencezyme cf. . Howeverzyme cf.  and 19, ies  in  . Thus, tsitivity . Howeversitivity  and may,sitivity .SI Text S1)Under the condition, stated in for  cf. .In the mechanism depicted in eactions , but wit binding .As can be seen in ly large . This suin vitroCompared to PhoQ, which is a transmembrane sensor kinase, NRII is located in the cytosol where it controls the expression of nitrogen-regulated genes through reversible phosphorylation of NRI. The PII protein binds to the kinase-domain of NRII which inhibits autophosphorylation, but increases the phosphatase activity of NRII in vitro conditions. However, even under saturating substrate levels  with respect to PII at different levels of total NRI  are satisfied in a particular system The occurrence of ultrasensitivity also requires saturation of the sensor kinase's phosphatase activity which means that the Michaelis-Menten constant, associated with that activity, has to be smaller than the total concentration of the response regulator. Measurements in the EnvZ/OmpR system have shown that the dissociation constant for the EnvZ-OmpR complex is 5-fold smaller than the total OmpR concentration which indicates that enzyme saturation is, in principle, possible under physiological conditions Under steady state conditions, the right-hand sides of Using the expressions from Similar as in the case of a covalent modification cycle with a bifunctional enzyme the type of steady state solution, that is obtained from In contrast, if the affinity of the effector is sufficiently high (omes cf. (51)UsiSimilar to the case of covalent modification cycles it is straightforward to show using  and 51 tFor TCSs with a transmembrane sensor kinase autophosphorylation, phosphotransfer and dephosphorylation occur in the cytosol whereas signal-sensing typically takes place in the periplasm (for gram-negative bacteria) or directly in the extracellular space . Hence, Instead, to evaluate the impact of compartmentalization on the conditions for the occurrence of ultrasensitivity and concentration robustness it seems reasonable to consider (as a first approximation) a simplified model where the reactions describing the catalytic activities of the sensor kinase occur in the cytosol  whereas binding of the effector to the regulatory site of the sensor kinase occurs either in the periplasm or in the extracellular space. Because effector-binding does not involve mass transfer between the extracellular space (or the periplasm) and the cytoplasm the equations for such a two-compartment model are essentially the same as those for a single compartment –30 if thSpecifically, ultrasensitivity is predicted to occur if the amount of response regulator is much larger than that of the sensor kinase Click here for additional data file.


Current generation wireless sensor routing algorithms and protocols have been designed based on a myopic routing approach, where the motes are assumed to have the same sensing and communication capabilities. Myopic routing is not a natural fit for the IoT, as it may lead to energy imbalance and subsequent short-lived sensor networks, routing the sensor readings over the most service-intensive sensor nodes, while leaving the least active nodes idle. This paper revisits the issue of energy efficiency in sensor networks to propose a clustering model where sensor devices’ service delivery is mapped into an energy awareness model, used to design a clustering algorithm that finds service-aware clustering (SAC) configurations in IoT settings. The performance evaluation reveals the relative energy efficiency of the proposed SAC algorithm compared to related routing algorithms in terms of energy consumption, the sensor nodes’ life span and its traffic engineering efficiency in terms of throughput and delay. These include the well-known low energy adaptive clustering hierarchy (LEACH) and LEACH-centralized (LEACH-C) algorithms, as well as the most recent algorithms, such as DECSA and MOCRN. Next generation sensor networks are predicted to be deployed in the Internet-of-Things (IoT) using heterogeneous sensor fabrics and following a service architecture where the sensor nodes/motes equipped with different sensing, identification and communication capabilities are tasked to deliver different services to different users anytime, anywhere and using anything to reach some of the areas of our life that we could not fathom without the advances made in the sensor technology. However, current generation wireless sensor systems have only scarcely exploited the integration of services, such as detecting and locating an object within an environment  while giad hoc network that has similar properties to wireless sensor networks (WSNs). The issue with such an approach is that the resulting network will also present similar energy limitations to WSNs. In [Researchers ,7,8,9 haWSNs. In , on-demaThe issue of energy efficiency for wireless sensor networks has attracted much interest in the research community, as these networks are built around tiny nodes with limited processing, minimum memory footprint and low communication capabilities. Sensor network clustering is an energy-aware traffic engineering method, which aims to maximize the lifetime of a sensor network, by structuring the sensor network into clusters and assigning “cluster head” and “cluster member” roles to the sensor nodes. This enables multi-hop communication, data aggregation and fusion within a cluster, in order to decrease the number of messages routed by the sensor network and essentially reducing the number of hops traversed by the sensor readings from the source to the sink node. This is implemented by subdividing a sensor network into a hierarchy of sensor clusters, each containing a cluster head. The sensor nodes are organized into cluster members and cluster heads depending on different performance parameters, such as their residual node’s energies. The cluster heads play the role of collecting, aggregating and processing data, while the cluster members are assigned the minimal functioning roles of sensing and forwarding. By producing several points of data aggregation and processing in the core of the network, in-network processing is promoted, and the network edge is left light. This model moves away from the Internet paradigm, which uses intelligent edges to produce data, which are forwarded by a dumb core.a priori knowledge of the geographical position, residual energy and neighbor information of all sensor nodes to select the cluster heads. However, both LEACH and LEACH-C are built around a homogeneous service architecture, where all sensor motes are assumed to be made of the same sensor fabric and are expected to deliver equal service and exhibit the same power consumption profile for all nodes. This discounts the energy required to deliver these services, thereby leaving the energy consumption equation unsolved.Low energy adaptive clustering hierarchy (LEACH)  and LEACDECSA  is an imMOCRN  is anothAs described above, LEACH, LEACH-C, DECSA and MOCRN are energy-efficient protocols, which have been designed based on an energy-aware, but service-blind routing approach, where the sensor nodes/motes are assumed to have the same sensing and communication capabilities and also provide the same services. These routing algorithms are not fit for the next generation IoT, where the heterogeneity of services will lead to heterogeneous devices with different energy consumption profiles. Such a diversity of energy consumption profiles should be accounted for when designing next generation energy-efficient IoT platforms.It is widely recognized that clustering techniques may help alleviate the energy issue in sensor networks. However, the application of traditional “service-blind clustering (SBC)” algorithms to the IoT may lead to energy imbalance, arising from electing the most service-demanding nodes as cluster heads and aggregating and forwarding most of a cluster’s sensor readings to the base station, while the least service-demanding nodes are used as cluster members, which remain idle most of the time. “Service-aware clustering (SAC)” is a natural fit for the IoT platforms where the heterogeneity and diversity of service profiles of the smart objects demand new ways of engineering the multitude of interconnected sensor networks islands. However, to the best of our knowledge, traffic engineering techniques that account for such service profiles have not yet been addressed by either the research or the practitioner communities. This paper’s main contribution is to propose and assess the relevance of using a traffic engineering model that builds upon clustering techniques and sensor nodes service profiles to improve the lifetime of a wireless sensor network. The proposed TEmodel is built around four main ideas.We consider a model where different energy levels are associated with different roles the sensor nodes can play in a clustering process and where the sensor nodes are allocated different roles in the network based on their residual energy: energy-depleted nodes are allocated lower energy-demanding roles as edge nodes playing the role of cluster member, while fully-energized nodes are allocated high energy-demanding roles as core nodes playing the role of cluster heads.We consider a model where the sensor motes/nodes are classified into service classes depending on their service profile and fabric and are then assigned corresponding roles. The least service-demanding nodes have a higher probability of becoming cluster heads in the core of the network tasked for collecting, aggregating and forwarding data to the base station, while the most service-intensive ones are left as member nodes at the edge of the network tasked for collecting data and forwarding to the cluster head.We consider an approach where the clustering model is mapped into a centralized routing process where path finding and routing table computations are performed by the gateway, where sufficient computation resources are availed for centralized processing with the expectation of achieving an optimized routing configuration. The model uses information collected from the nodes by an energy-frugal distributed signaling protocol.The centralized routing model described above is complemented by a distributed signaling process where a signaling tree rooted at the sink/gateway is used to: (1) transport the nodes’ information to the sink for topology discovery, path finding and routing table computation; and (2) disseminate the network provisioning information from the gateway to the nodes for routing table configuration. The signaling tree is built using an energy-frugal distributed algorithm.The works most closely related to ours are found in ,12,13,14The remainder of this paper is organized as follows: As illustrated by The SAC model is based on a system architecture where the STN nodes are assumed to be ultra high frequency (UHF) RFID passive tags, which are outfitted with sensors. These nodes allow tags to be interrogated at a range of up to 10 meters , but canThe service-aware clustering (SAC) model proposed in this paper is based on a software architecture that maps the sensor nodes’ service delivery into energy awareness and network roles defined by Depending on their service delivery, the different types of nodes and roles may be assigned different energy consumption profiles and levels used to determine the role to be assigned to these nodes during the traffic engineering process. They may be tasked to perform the high-energy consuming roles of the network, for instance, being a cluster head, or the least-energy consuming, such as cluster slave, depending on these profiles and associated energy levels in order to balance the energy dissipation in the network.k bits of data between a transmitter and a receiver node separated by a distance d meters is given by Equation E=ETGiven a threshold transmission distance eter ξFS  is emplogiven by .(2)EampA CSN sensor node acting as CM spends energy on sensing and transmitting data to its cluster head.A CSN sensor node acting as CH dissipates energy for receiving, processing and transmitting data from its cluster members, as well as relaying data from other cluster heads.An HSN sensor node acting as CM spends energy for sensing and transmitting data to its cluster head, in addition to interrogating sensor tags and relaying data from other HSNs.An HSN acting as CH also spends energy for receiving, processing and transmitting data from its cluster members, as well as on interrogating sensor tags and relaying data from other HSNs.The role that each node performs in the network has a significant impact on the total energy dissipation. Therefore, in order to assign roles fairly, a good understanding of the energy consumption of the different types of nodes is required. This is provided by a radio energy model that describes the energy consumption of each of the hardware components involved in radio communication. Furthermore, a cluster energy budget calculation that considers the energy associated with the different roles played by the system is required. The total energy dissipated by each node is the sum of the energy dissipated for its functions and its role in the network.S that represents the set of sensor tags randomly distributed within the reading area of t sensor tags. Each sensor tag generates k bits of data. Assuming that t sensor tags, then by following the principle in Equation , we , we 4), Equation . Note thThe efficiency of an IoT traffic engineering model depends on its routing configuration, which usually reveals the actual routing paths followed by the sensor readings from their environment to the base station during operation. An efficient routing configuration is obtained by engineering the traffic flows carrying the sensor readings to route these readings from their controlling locations to the base station with the expectation of optimizing an objective function  subject to some constraints on routing metrics, such as the delay, reliability or energy consumption.s is unique and selected as the network base station. Assume that s and normal nodes, respectively, while x is allocated a service delivery level x and j and x; x; and Let us consider a wireless sensor network represented by a directed graph Energy-frugal routing expressed by the routing objective Equation , where tEnergy-aware clustering expressed by the constraint Equation a used inService-aware clustering expressed by the constraint Equation  used in the service-aware pre-selection phase of the cluster head pre-election process described in Neighbor selection on the multi-hop backbone network of cluster heads as expressed by the distance minimization constraint in the Equation d and theNote that, as formulated above, the SAC problem expresses:By using a probabilistic approach where service and energy awareness are expressed by probabilities of nodes becoming cluster heads or members depending on their service levels and residual energy, the SAC formulation enables striking a good balance between service awareness and energy awareness: e.g., while HSN nodes should naturally be excluded from becoming CH because they are service intensive, there might be cases during a WSN operation where they could still qualify to become the CH when they are more battery energized than CSN nodes.As formulated above, many of the constraints of the service-aware clustering problem and its objective function involve global parameters that may be processed by only a centralized processing entity that has a global view of the sensor network. Therefore, the SAC formulation falls in the category of centralized problems, which require a centralized algorithmic solution, like LEACH-C, MOCRN and DECSA. However, SAC differs from these algorithms by the service awareness constraints expressed by Equation . In a WSN, the gateway is a natural fit for such centralized processing, as it is usually attached to a stable power supply and has enough processing and storage capabilities for a centralized process. However, the centralized process proposed in this paper needs to be complemented by a distributed network provisioning process enabling local routing tables on sensor nodes to be configured to reflect the efficient routing configuration The integration of centralized clustering and distributed provisioning resulting from the IoT network management model of Centralized clustering: The clustering process is led by the gateway attached to the base station rather than the sensor nodes for reasons described above and in order to avoid the energy wastage associated with a distributed model, enabling the sensor nodes to communicate amongst themselves to elect their cluster heads and build their clusters. Distributed provisioning: As suggested above, a distributed process is needed for the sensor network provisioning. It is implemented with the objective of building a signaling tree for the sensor nodes’ information discovery by the gateway and sensor nodes’ role confirmation by the gateway. Optimal number of clusters: The algorithm works with a number of predetermined clusters to control the size of messages routed between and within clusters. Such a number might be related to the size of the sensor network, as well as some geo-spatial constraints and/or based on an optimal number of clusters’ computation dictated by energy efficiency requirements.Energy-aware routing: The algorithm assumes that the initial and residual node energy might be known at any moment by using the signaling tree to propagate topology information to the gateway for path computation and network provisioning information to the nodes for routing table construction.Service-aware clustering: As assumed earlier, the energy profiles of nodes are related to their types and their service delivery types: service-intensive nodes will consume much energy during operation, while energy-frugal nodes will consume less energy during operation. This is taken into consideration by the algorithm through service differentiation.The efficiency of the SAC solution proposed in this paper is based on a good distribution of routing roles to sensor nodes in order to minimize the overall energy dissipation in the sensor network. The SAC solution adopted in this paper is built around the following key features:Initialization: The routing process starts with an initial network depicted by the graph in s) that meet the constraint s and any of its neighbors j should be inferior to the wireless coverage range Beaconing process: As shown in Acknowledgment and relaying: In the steps in Weight: The least interference paradigm maps the acknowledgment process into a weight setting by: (1) having each node receiving only one acknowledgment from each of its potential children; and (2) the sum of received acknowledgment messages representing a weight on a node expressing the interference on the node of the paths carrying the sensor readings from sensing locations to the sink.Termination criteria: The beaconing process is repeated recursively using a breadth-first approach, resulting in the formation of a spanning tree when the beacons reach the leaf nodes, which are marked with weight zero, as they do not receive any acknowledgment message from any node, as expressed by A modified version of the LIBPprotocol ,20 is usThe service-aware clustering proposed in this paper consists of mapping a physical network consisting of normal and hybrid nodes into a logical topology containing cluster heads and cluster slave/member nodes. It follows the five steps described belowStep 1: This step consists of applying the distributed provisioning described above to allow the gateway to get the necessary information from the sensor nodes in order to achieve global computation. The node discovery process is piggy backed on the signaling tree construction, which will produce a routing tree used later in the network provisioning phase.Step 2: In this step, clusters are constructed based on the information received from the sensor nodes. This step is executed following three main sub-steps: (i) average residual network energy calculation used to achieve energy-aware pre-selection of cluster heads, while the sensor nodes’ service types reported to the gateway are used to achieve service-aware pre-selection; (ii) cluster head election achieved by comparing the number of pre-selected cluster heads to the required number of cluster heads; (iii) cluster head-slave association performed using an optimization method that considers both the geographical location of nodes and their residual energy; and (iv) TDMA schedule generation to preclude an interference-free transmission of the information (sensor readings).Step 3: A node’s role confirmation is achieved by using the signaling tree built by making use of the distributed provisioning process described earlier. It aims at informing the nodes of their roles, the next hop to the gateway and also their TDM slots.Step 4: During this step, the sensor backbone of cluster heads is constructed by using the highest signal strength - an approach that translates into increasing the probability of selecting the nearest neighbor’s node and, thus, leading to the shortest delays on the backbone.Step 5: This step uses the TDMA schedule resulting from Step 2 to enable sensor readings’ dissemination from the nodes to the gateway.T seconds. The five steps are described below.Our proposed routing algorithm operates in five steps to elect the best cluster head. Each step is then repeated after a round of During this step, each sensor node (HSN and CSN) receives a beacon message carrying a query from the base station and signals its presence in the network by selecting a potential parent (next hop to the base station) and replying to the query by sending to its parent node a hello packet containing its identification number  to check if the selected node is an HSN. This is because, following the constraint Equation c of the Cluster head election: Finally, the election process is concluded by checking if the number of nodes that have met the previous requirements are less than the pre-determined number of cluster heads. If this is the case, then the base station re-examines each node in the network and selects any node whose energy is greater than the average.k cluster heads and clusters that optimize the Equation costThe two functions TDMA transmission schedule: After cluster formation, the base station allocates time slots to each node within a cluster using time division multiple access (TDMA) while code division multiple access (CDMA) is used to reduce collision between cluster heads.Finally, the base station informs the nodes about their roles in the network by using the signaling tree built during node discovery to broadcast roles and association messages. Using this process, each node is assigned its role in the network (CH or CM) and its next hop to the base station. The message content will be of the form In order to optimize the routing process, each cluster head uses the closest cluster head as the next hop to the base station, as determined by the signal strength emitted by all neighboring cluster heads, including the base station.Each cluster head selects the strongest signal strength neighbor as its next hop to the gateway and adjusts its routing table accordingly by recording the IDs of its pre-hop node and next-hop node. Note that as currently designed, this backbone could lead to a tree-like or mesh topology.This stage determines how the multi-hop routes are set up by the cluster heads to create a sensor network backbone. The cluster heads use their radio to create the sensor backbone as follows.In this paper, we assumed that each sensor node senses data at a fixed rate and always has data to send to its cluster head during its allocated time slot. Then, the node switches into sleep mode until the next transmission slot. Once data is received from cluster members, it is aggregated by the cluster head and then forwarded to the next cluster head along the path to the base station. The HSNs use the 915-MHz channel periodically to interrogate sensor tags within their range, while the 2.4-GHz channel is used for intra-cluster communication. The RFID reader operates differently, that is once the information is collected, it is routed to the next hop along the HSN-to-HSN path.Different experiments were conducted to compare the performance achieved by the SAC protocol/algorithm to the family of LEACH protocols/algorithms (LEACH and LEACH-C) and more recent clustering protocols/algorithms, DECSA and MOCRN. The experiments were conducted in a realistic sensor network environment where distributed signaling using Contiki/Cooja and centralized clustering were run in two different threads, but communicating through a sink interface: a SAC thread tasked to execute the SAC algorithm and report to the Cooja thread for routing table set up and a Cooja thread tasked to achieve distributed signaling and packet forwarding. It then reports to the SAC thread for routing configuration computation. Therefore, the experimental sensor network environment used in this paper mimics the IoT network management model in Using the Contiki/Cooja environment, build the signaling tree.Upload topology to the gateway through the sink interface.Compute the efficient routing configuration Download the routing information in the sensor nodes through the signaling interface.Build forwarding tables on the nodes based on the provisioning information received through distributed signaling.Emulate data forwarding in the Contiki/Cooja environment and record statistics.For each epoch:Objective: This experiment was conducted to assess the relevance of using the SAC model in IoT settings by comparing it to a breadth-first search (BFS) and a service-blind clustering (SBC) algorithm. The BFS algorithm is a non-hierarchical routing algorithm that finds routing paths for the sensor readings by considering a flat network topology, while the SBC is a hierarchical routing algorithm that is based on only signal strength for cluster selection and cluster head-member association, discounting energy and service awareness. This experiment was complemented by another experiment aiming at comparing two different versions of the SAC algorithm: (1) the SAC algorithm where over the experimental time, energy awareness and service awareness are combined to find efficient routing paths; and (2) a dual-mode SAC algorithm called SAC-MIX, where energy awareness is implemented during the first half of the experiment when all nodes are assumed to have enough residual energy. Service awareness is implemented during the second half of the experiment when the nodes are assumed to have depleted enough energy.Network: Two networks were considered: (1) a hybrid network consisting of 100 nodes randomly distributed within the sensing area; and (2) a conventional sensor network consisting of 100 CSN nodes. The hybrid sensor network consisted of 80 CSN nodes and 20 HSN nodes, where each HSN node consumes 0.5 J more than the energy of a CSN node. The hybrid sensor network running SAC had a total initial energy of 110 joules, while the normal sensor network running the BFS and SBC had a total initial energy of 100 joules. This is because, in practice, the HSN nodes are initially more energized than the CSN nodes.Results: The results presented by Objective: This experiment was conducted to assess the relevance of service awareness by comparing the SAC algorithm with the family of energy-aware LEACH algorithms in terms of energy dissipation per round of the experiment and total energy dissipation for the whole experiment. LEACH and LEACH-C were considered for these experiments.Network: Two network models were used in this experiment: a 50-node network and a 250-node network each with 80% of CSN nodes and 20% of HSN nodes randomly distributed in a 100 m × 100 m sensing area. The initial energy of each node was 1 J, and the simulation time (round) varied depending on the experiment. The energy dissipation of the nodes was taken every 10 s.Results: The results presented in Objective: This experiment was conducted to evaluate the network lifetime when defined in terms of the first node to die (FND) and the last node to die (LND) and the relative efficiency of the different protocols in terms of the number of nodes alive during the simulation period.Network: The network model used consisted of 100 nodes randomly distributed in a 100 m × 100 m sensing area with 80 normal nodes and 20 hybrid nodes, where each hybrid node consumes 0.5 J more than the energy of a normal node. The initial energy of each node is 1 J, and the simulation time (round) runs for 1000 s. The average energy consumption of the nodes is taken every 100 s. The receiving and sending power of each node is 0.360 W and 0.395 W. The same parameters are used for all of the protocols. Two network configurations with the base station located differently were considered in order to evaluate the impact of the communication distance between the nodes and the base station on the network lifetime: one configuration with the base station located at the center of the network area and another one with the base station located outside the network area.Results: The results are presented in It can be seen from the table above that SAC runs more rounds before the first and last node dies. The reason is that our algorithm considered parameters such as the current energy of the sensor nodes, service differentiation and the distance between the nodes during the election of new cluster heads. Hence, it performs better than the other two protocols.The sensor network lifetime Objective: We conducted a set of experiments to evaluate the average lifetime of the sensor networks of different sizes ranging from 100 nodes–500 nodes. We also evaluated the time till the last node dies (LND) for the same network configurations and the scalability of the algorithms when applied to a 500-node network and when scalability is expressed in terms of the optimal number of clusters.Network: Sensor networks of different sizes ranging from 100–500 nodes were considered for these experiments with the base station located at the center of the network.Results: The results presented in K. In our case, the energy consumed in a cluster consists of the energy consumed by the cluster head The optimal number of clusters is another important parameter, which can be determined by deriving the total energy consumed in the whole network The optimal number of clusters is determined by setting the derivative of is gives:K2=VϵfsM2Objective: We conducted another set of experiments to evaluate the traffic engineering performance of the proposed SAC algorithm by looking at the energy frugality resulting from the distributed signaling process and the routing efficiency of the centralized clustering process. The energy frugality of our distributed signaling model using the LIBP-based signaling was compared to those of the RPL- and CTP-based signaling in terms of the average energy consumption and the radio duty cycle. The routing efficiency resulting from the centralized clustering process was measured by looking at the end-to-end packet delays and the packet delivery ratio resulting from running the different algorithms.Network: The energy consumption was measured using different network sizes ranging from 10–100 nodes, while the radio duty cycle was considered for a 100-node network.Results: The results depicted by A hybrid network is a network that integrates conventional wireless sensor nodes, sensor tags, hybrid RFID and a base station into the same network in order to provide different services to users in IoT environments. In such settings, sensors are designed to offer different services and, therefore, have different energy patterns, which may lead to network energy imbalance if not managed carefully. This paper proposed the SAC routing protocol to improve energy management in a hybrid network designed to identify objects and sense their environments by using hybrid sensor/RFID devices. The proposed routing protocol uses a centralized mechanism to address the energy imbalance problem by letting the base station select cluster heads depending on the residual energy of nodes, energy properties, the node’s type and service to be delivered and the position of nodes in the network. Experiments were conducted in a hybrid emulated/simulated IoT environment to compare the efficiency of the proposed SAC algorithm to the LEACH and LEACH-C protocols and the more recent DECSA and MOCRN protocols. It was observed, from these experiments, that the SAC protocol achieved the best energy management and better traffic engineering performance, leading to the longest life span.Following the model presented in , experim