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Metabolic engineering uses genetic approaches to control microbial metabolism to produce desired compounds. Computational tools can identify new biological routes to chemicals and the changes needed in host metabolism to improve chemical production. Recent computational efforts have focused on exploring what compounds can be made biologically using native, heterologous, and/or enzymes with broad specificity. Additionally, computational methods have been developed to suggest different types of genetic modifications (e.g. gene deletion/addition or up/down regulation), as well as suggest strategies meeting different criteria (e.g. high yield, high productivity, or substrate co-utilization). Strategies to improve the runtime performances have also been developed, which allow for more complex metabolic engineering strategies to be identified. Future incorporation of kinetic considerations will further improve strain design algorithms. [ABSTRACT FROM AUTHOR]

Summary Biogenesis of high-density lipoproteins (HDL) is coupled to the transmembrane protein, ATP-binding cassette transporter A1 (ABCA1), which transports phospholipid (PL) from the inner to the outer membrane monolayer. Using a combination of computational and experimental approaches, we show that increased outer lipid monolayer surface density, driven by excess PL or membrane insertion of amphipathic helices, results in pleating of the outer monolayer to form membrane-attached discoidal bilayers. Apolipoprotein (apo)A-I accelerates and stabilizes the pleats. In the absence of apoA-I, pleats collapse to form vesicles. These results mimic cells overexpressing ABCA1 that, in the absence of apoA-I, form and release vesicles. We conclude that the basic driving force for nascent discoidal HDL assembly is a PL pump-induced surface density increase that produces lipid monolayer pleating. We then argue that ABCA1 forms an extracellular reservoir containing an isolated pressurized lipid monolayer decoupled from the transbilayer density buffering of cholesterol. [ABSTRACT FROM AUTHOR]

Structures of biomolecular systems are increasingly computed by integrative modeling that relies on varied types of experimental data and theoretical information. We describe here the proceedings and conclusions from the first wwPDB Hybrid/Integrative Methods Task Force Workshop held at the European Bioinformatics Institute in Hinxton, UK, on October 6 and 7, 2014. At the workshop, experts in various experimental fields of structural biology, experts in integrative modeling and visualization, and experts in data archiving addressed a series of questions central to the future of structural biology. How should integrative models be represented? How should the data and integrative models be validated? What data should be archived? How should the data and models be archived? What information should accompany the publication of integrative models? [ABSTRACT FROM AUTHOR]

Summary The human von Hippel-Lindau (VHL) tumor suppressor is a marginally stable protein previously used as a model substrate of eukaryotic refolding and degradation pathways. When expressed in the absence of its cofactors, VHL cannot fold and is quickly degraded by the quality control machinery of the cell. We combined computational methods with in vivo experiments to examine the basis of the misfolding propensity of VHL. By expressing a set of randomly mutated VHL sequences in yeast, we discovered a more stable mutant form. Subsequent modeling suggested the mutation had caused a conformational change affecting cofactor and chaperone interaction, and this hypothesis was then confirmed by additional knockout and overexpression experiments targeting a yeast cofactor homolog. These findings offer a detailed structural basis for the modulation of quality control fate in a model misfolded protein and highlight burial mode modeling as a rapid means to detect functionally important conformational changes in marginally stable globular domains. [ABSTRACT FROM AUTHOR]

Industrial cell culture processes are inherently expensive, time-consuming, and variable. These limitations have become a critical bottleneck for the industrial translation of human cell and tissue biomanufacturing, as few human cell culture products deliver sufficient benefit, value, and consistency to offset their high manufacturing costs and produce useful clinical or biomedical solutions. Recent advances in biomedical image analysis and computational modelling can enhance the design and operation of high-efficiency tissue biomanufacturing platforms, as well as the high-content characterisation and monitoring of culture performance, to enable bioprocess control, optimisation, and automation. These computational technologies aim to maximize culture outcomes while minimizing variability and process development expense. In this review, we outline current resources and approaches which harness biomedical imaging and image-based computational models to design and operate efficient and robust human tissue biomanufacturing platforms. [ABSTRACT FROM AUTHOR]

A deep insight into the 3D structures of the proteins is essential to understand their functions and will be helpful in drug design and making decisions for the treatment of diseases. The 3D structure for less than one-thousandth of the protein sequences has been determined so far due to the slow structure elucidation procedures with experimental methods, such as X-ray. For this reason, different computational methods were used to determine the structure of the proteins, including template-based and de novo approaches. Much software has been developed to facilitate the computational approaches in protein modeling, some of which need user expertise and extensive knowledge in bioinformatics. The present study attempted to provide a user-friendly environment to motivate the researchers in computational biology, a Python-based interface based on MODELLER software. PyProtModel can be used as a toolbox in structural bioinformatics for protein modeling from sequence to the prediction of 3D structure. In addition, the users can take benefits of PyProtModel in order to prepare and evaluate protein PDB files prior to other in silico experiments. PyProtModel allows the user to apply different MODELLER options, automates, and speeds up time-consuming homology modeling steps. Different aspects of PyProtModel and a comprehensive view of the application will be discussed in this paper. The application is available for download and use at: https://github.com/msisakht/pyprotmodel.git. [Display omitted] • PyProtModel is a new interface to handle different homology modeling tasks. • Different features of PyProtModel with other interfaces were discussed. • A workflow to all modules of PyProtModel was depicted for the users. [ABSTRACT FROM AUTHOR]

Leprosy is a significant universal health problem that is remarkably still a concern in developing countries due to infection frequency. New therapeutic molecules and next-generation vaccines are urgently needed to accelerate the leprosy-free world. In this direction, the present study was performed using two routes: proteome-mediated therapeutic target identification and mapping as well as multi-epitopic peptide-based novel vaccine development using state of the art of computational biology for the TN strain of M. leprae. The TN strain was selected from 65 Mycobacterium strains, and TN strain proteome mediated 83 therapeutic protein targets were mapped and characterized according to subcellular localization. Also, drug molecules were mapped with respect to protein targets localization. The Druggability potential of proteins was also evaluated. For multi-epitope peptide-based vaccine development, the four common types of B and T cell epitopes were identified (SLFQSHNRK, VVGIGQHAA, MMHRSPRTR, LGVDQTQPV) and combined with the suitable peptide linker. The vaccine component had an acceptable protective antigenic score (0.9751). The molecular docking of vaccine components with TLR4/MD2 complex exhibited a low ACE value (−244.12) which signifies the proper binding between the two molecules. The estimated free Gibbs binding energy ensured accurate protein-protein interactions (−112.46 kcal/mol). The vaccine was evaluated through adaptive immunity stimulation as well as immune interactions. The molecular dynamic simulation was carried out by using CHARMM topology-based parameters to minimize the docked complex. Subsequently, the Normal Mode Analysis in the internal coordinates showed a low eigen-value (1.3982892e−05), which also signifies the stability of molecular docking. Finally, the vaccine components were adopted for reverse transcription and codon optimization in E. coli strain K12 for the pGEX-4T1 vector, which supports in silico cloning of the vaccine components against the pathogen. The study directs the experimental study for therapeutics molecules discovery and vaccine candidate development with higher reliability. • Proteome mediated therapeutic target mapping for probable drug discovery in M. leprae • Prediction of common (B-cell and T-cell) epitopes and development of multi-epitopic peptide-based vaccine against M. leprae • Structure refinement, antigenic properties assessment, physicochemical characteristics and validation of vaccine construct • Molecular interaction and dynamics study of vaccine construct and human TLR4/ MD2 protein complex [ABSTRACT FROM AUTHOR]

Forces arising from contractile actomyosin filaments help shape tissue form during morphogenesis. Developmental events that result from actomyosin contractility include tissue elongation, bending, budding, and collective migration. Here, we highlight recent insights into these morphogenetic processes from the perspective of actomyosin contractility as a key regulator. Emphasis is placed on a range of results obtained through live imaging, culture, and computational methods. Combining these approaches in the future has the potential to generate a robust, quantitative understanding of tissue morphodynamics. [ABSTRACT FROM AUTHOR]

The Far From Most String Problem (FFMSP) is a string selection problem. The objective is to find a string whose distance to other strings in a certain input set is above a given threshold for as many of those strings as possible. This problem has links with some tasks in computational biology and its resolution has been shown to be very hard. We propose a memetic algorithm (MA) to tackle the FFMSP. This MA exploits a heuristic objective function for the problem and features initialization of the population via a Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic, intensive recombination via path relinking and local improvement via hill climbing. An extensive empirical evaluation using problem instances of both random and biological origin is done to assess parameter sensitivity and draw performance comparisons with other state-of-the-art techniques. The MA is shown to perform better than these latter techniques with statistical significance. [ABSTRACT FROM AUTHOR]

The computational detection of pulmonary lobes from CT images is a challenging segmentation problem with important respiratory health care applications, including surgical planning and regional image analysis. Several authors have proposed automated algorithms and we present a methodological review. These algorithms share a number of common stages and we consider each stage in turn, comparing the methods applied by each author and discussing their relative strengths. No standard method has yet emerged and none of the published methods have been demonstrated across a full range of clinical pathologies and imaging protocols. We discuss how improved methods could be developed by combining different approaches, and we use this to propose a workflow for the development of new algorithms. [ABSTRACT FROM AUTHOR]

Experimentation on the fast pyrolysis process has been primarily focused on the pyrolysis reactor itself, with less emphasis given to the liquid collection system (LCS). More importantly, the physics behind the vapour condensation process in LCSs has not been thoroughly researched mainly due to the complexity of the phenomena involved. The present work focusses on providing detailed information of the condensation process within the LCS, which consists of a water cooled indirect contact condenser. In an effort to understand the mass transfer phenomena within the LCS, a numerical simulation was performed using the Eulerian approach. A multiphase multi-component model, with the condensable vapours and non-condensable gases as the gaseous phase and the condensed bio-oil as the liquid phase, has been created. Species transport modelling has been used to capture the detailed physical phenomena of 11 major compounds present in the pyrolysis vapours. The development of the condensation model relies on the saturation pressures of the individual compounds based on the corresponding states correlations and assuming that the pyrolysis vapours form an ideal mixture. After the numerical analysis, results showed that different species condense at different times and at different rates. In this simulation, acidic components like acetic acid and formic acids were not condensed as it was also evident in experimental works, were the pH value of the condensed oil is higher than subsequent stages. In the future, the current computational model can provide significant aid in the design and optimization of different types of LCSs. [ABSTRACT FROM AUTHOR]

We performed an extensive computational study to obtain insight in the molecular mechanisms that take place prior to membrane fusion. We focused on membrane-anchored hybrid macromolecules (lipid–polymer–oligopeptide) that mimic biological SNARE proteins in terms of liposome fusion characteristics [H. Robson Marsden et al., 2009]; efficient micro-second simulation was enabled by combining validated MARTINI force fields for the molecular building blocks in coarse-grained molecular dynamics (CGMD). We find that individual peptide domains in the hybrid macromolecules bind and partially integrate parallel to the membrane surface, in agreement with experimental findings. By varying several experimental design parameters, we observe that peptide domains remain in the solvent phase only in two cases: (1) for solitary lipopeptides (low concentration), below a threshold area per lipid in the membrane, and (2) when the lipopeptide concentration is high enough for the peptide domains to self-assemble into tetrameric homo-complexes. The peptide–membrane binding is not affected by solvent-induced peptide unfolding, which we mimicked by relaxing the usual MARTINI helix constraints. Remarkably, in this case, a reverse transition to a helical secondary structure is observed after binding, highlighting the role of the membrane as a template (partitioning–folding coupling). Our findings undermine the current view of the initial stages towards fusion, in which membranes are thought to be kept in close apposition via dimerization of individual complementary peptides in the solvent phase. Although we did not study actual fusion, our simulations show that the formation of homomers, which is suppressed in experimental peptide-pair design and therefore believed to be insignificant for fusion, by peptides anchored to the same membrane does play a key role in this locking mechanism and potentially also in membrane destabilization that precede fusion. [ABSTRACT FROM AUTHOR]

The new sequencing technologies, called next-generation sequencing , provide a huge amount of data that can be used to reconstruct genomes. However, the methods applied to reconstruct genomes often are not able to reconstruct a complete genome and provide only an incomplete information. Here we consider two combinatorial problems that aim to reconstruct complete genomes by inserting a collection of missing genes. The first problem we consider, called One-sided scaffold filling , given an incomplete genome B and a complete genome A , asks for the insertion of missing genes into an incomplete genome B with the goal of maximizing the common adjacencies between genomes B ′ (resulting from the insertion of missing genes in B ) and A . The second problem, called Two-sided scaffold filling , given two incomplete genomes A , B , asks for the insertion of missing genes into both genomes so that the resulting genomes A ′ and B ′ have the same multiset of genes and the number of common adjacencies between A ′ and B ′ is maximized. Both problems were proved to be NP-hard, while their parameterized complexity, when the parameter is the number of common adjacencies of the resulting genomes, was left as an open problem. In this paper, we settle this open problem by presenting fixed-parameter algorithms for One-sided scaffold filling and Two-sided scaffold filling . [ABSTRACT FROM AUTHOR]

Integrated -omics approaches are quickly spreading across microbiology research labs, leading to (i) the possibility of detecting previously hidden features of microbial cells like multi-scale spatial organization and (ii) tracing molecular components across multiple cellular functional states. This promises to reduce the knowledge gap between genotype and phenotype and poses new challenges for computational microbiologists. We underline how the capability to unravel the complexity of microbial life will strongly depend on the integration of the huge and diverse amount of information that can be derived today from -omics experiments. In this work, we present opportunities and challenges of multi -omics data integration in current systems biology pipelines. We here discuss which layers of biological information are important for biotechnological and clinical purposes, with a special focus on bacterial metabolism and modelling procedures. A general review of the most recent computational tools for performing large-scale datasets integration is also presented, together with a possible framework to guide the design of systems biology experiments by microbiologists. [ABSTRACT FROM AUTHOR]

Biological networks are not operating in isolation, but are connected and interact with the other levels of the entire ecological hierarchy. It implies that both vertical and horizontal connections and interactions come into play and should be considered. The vertical effects and their integration with the horizontal networks are, however, a new challenge, as network theory up to now to a high extent has only considered the horizontal connections. It gives rise to the central questions, how two or more networks in two or more different levels of the hierarchy can work together and whether the interactions will change the network properties. The hierarchical levels in biology and ecology – molecules, cells, organs, organisms, populations, ecosystems, landscapes and the ecosphere – appears at first sight to be working two by two (molecules/cells, cells/organs, organs/organism, organisms/population, populations/ecosystems and landscapes/the ecosphere), with levels like molecules, organs, populations and landscapes forming competing horizontal network, which are driven by an energy input on the cell, organism, ecosystem and ecosphere level, respectively. A network model study of a landscape consisting of a network of ecosystems that are represented by networks – it means networks within a network – has been applied in these investigations to answer the raised questions. The study has shown that the ecosystem, which is able to use the resources with the highest efficiency in the landscape will win. Based on the obtained results from the model experiments it can be hypothesized that a “kind” of Darwinian selection may take place at all the levels of the hierarchy and that the winner is the one which is able under the prevailing conditions to obtain and exploit? most work energy. There are strong supports in the literature (see for instance Jørgensen, 2012 ) in the case for individuals and populations in ecosystems (formulated in the so called ELT, that have many observable supports; see Jørgensen, 2012 ). The model experiments presented here point toward an expansion of the ELT to ecosystems competing in the landscape, but a general expansion to all the hierarchical levels in ecology will require many more model experiments and more concrete observations (case studies). [ABSTRACT FROM AUTHOR]

MicroRNAs are small endogenous non-coding RNAs of ~ 19–24 nucleotides and perform regulatory roles in many plant processes. To identify miRNAs involved in regulatory networks controlling diverse biological processes including secondary metabolism in Catharanthus roseus , an important medicinal plant, we employed deep sequencing of small RNA from leaf tissue. A total of 88 potential miRNAs comprising of 81 conserved miRNAs belonging to 35 families and seven novel miRNAs were identified. Precursors for 16 conserved and seven novel cro-miRNAs were identified, and their stem-loop hairpin structures were predicted. Selected cro-miRNAs were analyzed by stem-loop qRT-PCR and differential expression patterns were observed in different vegetative tissues of C. roseus . Targets were predicted for conserved and novel cro-miRNAs, which were found to be involved in diverse biological role(s) including secondary metabolism. Our study enriches available resources and information regarding miRNAs and their potential targets for better understanding of miRNA-mediated gene regulation in plants. [ABSTRACT FROM AUTHOR]

Summary Characterization of lysine methylation has proven challenging despite its importance in biological processes such as gene transcription, protein turnover, and cytoskeletal organization. In contrast to other key posttranslational modifications, current proteomics techniques have thus far shown limited success at characterizing methyl-lysine residues across the cellular landscape. To complement current biochemical characterization methods, we developed a multistate computational protein design procedure to probe the substrate specificity of the protein lysine methyltransferase SMYD2. Modeling of substrate-bound SMYD2 identified residues important for substrate recognition and predicted amino acids necessary for methylation. Peptide- and protein- based substrate libraries confirmed that SMYD2 activity is dictated by the motif [LFM] −1 -K ∗ -[AFYMSHRK] +1 -[LYK] +2 around the target lysine K ∗ . Comprehensive motif-based searches and mutational analysis further established four additional substrates of SMYD2. Our methodology paves the way to systematically predict and validate posttranslational modification sites while simultaneously pairing them with their associated enzymes. [ABSTRACT FROM AUTHOR]

Grammatical inference – used successfully in a variety of fields such as pattern recognition, computational biology and natural language processing – is the process of automatically inferring a grammar by examining the sentences of an unknown language. Software engineering can also benefit from grammatical inference. Unlike these other fields, which use grammars as a convenient tool to model naturally occurring patterns, software engineering treats grammars as first-class objects typically created and maintained for a specific purpose by human designers. We introduce the theory of grammatical inference and review the state of the art as it relates to software engineering. [ABSTRACT FROM AUTHOR]

Grammatical inference – used successfully in a variety of fields such as pattern recognition, computational biology and natural language processing – is the process of automatically inferring a grammar by examining the sentences of an unknown language. Software engineering can also benefit from grammatical inference. Unlike these other fields, which use grammars as a convenient tool to model naturally occurring patterns, software engineering treats grammars as first-class objects typically created and maintained for a specific purpose by human designers. We introduce the theory of grammatical inference and review the state of the art as it relates to software engineering. [ABSTRACT FROM AUTHOR]

We present an image processing approach to automatically analyze duo-channel microscopic images of muscular fiber nuclei and cytoplasm. Nuclei and cytoplasm play a critical role in determining the health and functioning of muscular fibers as changes of nuclei and cytoplasm manifest in many diseases such as muscular dystrophy and hypertrophy. Quantitative evaluation of muscle fiber nuclei and cytoplasm thus is of great importance to researchers in musculoskeletal studies. The proposed computational approach consists of steps of image processing to segment and delineate cytoplasm and identify nuclei in two-channel images. Morphological operations like skeletonization is applied to extract the length of cytoplasm for quantification. We tested the approach on real images and found that it can achieve high accuracy, objectivity, and robustness. [ABSTRACT FROM AUTHOR]

Introduction Dynamic image acquisition protocols are increasingly used in emission tomography for drug development and clinical research. As such, there is a need for computational phantoms to accurately describe both the spatial and temporal distribution of radiotracers, also accounting for periodic and non-periodic physiological processes occurring during data acquisition. Methods A new 5D anthropomorphic digital phantom was developed based on a generic simulation platform, for accurate parametric imaging simulation studies in emission tomography. The phantom is based on high spatial and temporal information derived from real 4D MR data and a detailed multi-compartmental pharmacokinetic modelling simulator. Results The proposed phantom is comprised of three spatial and two temporal dimensions, including periodic physiological processes due to respiratory motion and non-periodic functional processes due to tracer kinetics. Example applications are shown in parametric [ 18 F]FDG and [ 15 O]H 2 O PET imaging, successfully generating realistic macro- and micro-parametric maps. Conclusions The envisaged applications of this digital phantom include the development and evaluation of motion correction and 4D image reconstruction algorithms in PET and SPECT, development of protocols and methods for tracer and drug development as well as new pharmacokinetic parameter estimation algorithms, amongst others. Although the simulation platform is primarily developed for generating dynamic phantoms for emission tomography studies, it can easily be extended to accommodate dynamic MR and CT imaging simulation protocols. [ABSTRACT FROM AUTHOR]

Although protein coding genes occupy only a small fraction of genomes in higher species, they are not randomly distributed within or between chromosomes. Clustering of genes with related function(s) and/or characteristics has been evident at several different levels. To study how common the clustering of functionally related genes is and what kind of functions the end products of these genes are involved, we collected gene ontology (GO) terms for complete genomes and developed a method to detect previously undefined gene clustering. Exhaustive analysis was performed for seven widely studied species ranging from human to Escherichia coli . To overcome problems related to varying gene lengths and densities, a novel method was developed and a fixed number of genes were analyzed irrespective of the genome span covered. Statistically very significant GO term clustering was apparent in all the investigated genomes. The analysis window, which ranged from 5 to 50 consecutive genes, revealed extensive GO term clusters for genes with widely varying functions. Here, the most interesting and significant results are discussed and the complete dataset for each analyzed species is available at the GOme database at http://bioinf.uta.fi/GOme . The results indicated that clusters of genes with related functions are very common, not only in bacteria, in which operons are frequent, but also in all the studied species irrespective of how complex they are. There are some differences between species but in all of them GO term clusters are common and of widely differing sizes. The presented method can be applied to analyze any genome or part of a genome for which descriptive features are available, and thus is not restricted to ontology terms. This method can also be applied to investigate gene and protein expression patterns. The results pave a way for further studies of mechanisms that shape genome structure and evolutionary forces related to them. [ABSTRACT FROM AUTHOR]

Protein–protein interactions mediate essentially all biological processes. Despite the quality of these data being widely questioned a decade ago, the reproducibility of large-scale protein interaction data is now much improved and there is little question that the latest screens are of high quality. Moreover, common data standards and coordinated curation practices between the databases that collect the interactions have made these valuable data available to a wide group of researchers. Here, I will review how protein–protein interactions are measured, collected and quality controlled. I discuss how the architecture of molecular protein networks has informed disease biology, and how these data are now being computationally integrated with the newest genomic technologies, in particular genome-wide association studies and exome-sequencing projects, to improve our understanding of molecular processes perturbed by genetics in human diseases. This article is part of a Special Issue entitled: From Genome to Function. [ABSTRACT FROM AUTHOR]

Orthologous introns have identical positions relative to the coding sequence in orthologous genes of different species. By analyzing the complete genomes of five plants we generated a database of 40,512 orthologous intron groups of dicotyledonous plants, 28,519 orthologous intron groups of angiosperms, and 15,726 of land plants (moss and angiosperms). Multiple sequence alignments of each orthologous intron group were obtained using the Mafft algorithm. The number of conserved regions in plant introns appeared to be hundreds of times fewer than that in mammals or vertebrates. Approximately three quarters of conserved intronic regions among angiosperms and dicots, in particular, correspond to alternatively-spliced exonic sequences. We registered only a handful of conserved intronic ncRNAs of flowering plants. However, the most evolutionarily conserved intronic region, which is ubiquitous for all plants examined in this study, including moss, possessed multiple structural features of tRNAs, which caused us to classify it as a putative tRNA-like ncRNA. Intronic sequences encoding tRNA-like structures are not unique to plants. Bioinformatics examination of the presence of tRNA inside introns revealed an unusually long-term association of four glycine tRNAs inside the Vac14 gene of fish, amniotes, and mammals. [ABSTRACT FROM AUTHOR]

Abstract: The Monte Carlo (MC) method for population balance modeling (PBM) has become increasingly popular because the discrete and stochastic nature of the MC method is especially suited for particle dynamics. However, for the two-particle events (typically, particle coagulation), the double looping over all simulation particles is required in normal MC methods, and the computational cost is O(N s 2), where N s is the simulation particle number. This paper proposes a fast random simulation scheme based on the differentially-weighted Monte Carlo (DWMC) method. The majorant of coagulation kernel was introduced to estimate the maximum coagulation rate by a single looping over all particles rather than the double looping. The acceptance–rejection process then proceeded to select successful coagulation particle pairs randomly, and meanwhile the waiting time (time-step) for a coagulation event was estimated by summing the coagulation kernels of rejected and accepted particle pairs. In such a way, the double looping is avoided and computational efficiency is greatly improved as expected. Five coagulation cases for which analytical solutions or benchmark solutions exist were simulated by the fast and normal DWMC, respectively. It is found the CPU time required is orders of magnitude lower and only increases linearly with N s; at the same time the computational accuracy is guaranteed very favorably. [Copyright &y& Elsevier]

Integrated whole-cell modeling is poised to make a dramatic impact on molecular and systems biology, bioengineering, and medicine--once certain obstacles are overcome. From our group's experience building a whole-cell model of Mycoplasma genitalium, we identified several significant challenges to building models of more complex cells. Here we review and discuss these challenges in seven areas: first, experimental interrogation; second, data curation; third, model building and integration; fourth, accelerated computation; fifth, analysis and visualization; sixth, model validation; and seventh, collaboration and community development. Surmounting these challenges will require the cooperation of an interdisciplinary group of researchers to create increasingly sophisticated whole-cell models and make data, models, and simulations more accessible to the wider community. [ABSTRACT FROM AUTHOR]

Structure-based antibody design and accurate predictions of antibody-antigen interactions remain major challenges in computational biology. By using molecular dynamics simulations, we show that a single static X-ray structure is not sufficient to identify determinants of antibody-antigen recognition. Here, we investigate antibodies that undergo substantial conformational changes upon antigen binding and have been classified as difficult cases in an extensive benchmark for antibody-antigen docking. We present thermodynamics and transition kinetics of these conformational rearrangements and show that paratope states can be used to improve antibody-antigen docking. By using the unbound antibody X-ray structure as starting structure for molecular dynamics simulations, we retain a binding competent conformation substantially different to the unbound antibody X-ray structure. We also observe that the kinetically dominant antibody paratope conformations are chosen by the bound antigen conformation with the highest probability. Thus, we show that paratope states in solution can improve antibody-antigen docking and structure prediction. [Display omitted] • Predicting antibody-antigen complexes is a major challenge in computational biology • Biophysical properties of antibodies are governed by their dynamic character • Dominant antibody paratope states coincide with the binding competent conformation • Paratope ensembles in solution improve structure prediction and docking Describing an antibody's binding site using only one single static structure limits the understanding and characterization of the antibody's function and binding properties. Fernández-Quintero et al., show that paratope ensembles in solution can be successfully used to improve structure prediction and enhance the predictivity of antibody-antigen docking. [ABSTRACT FROM AUTHOR]

This is a corrigendum for our paper [1] , as we have found that the first FPT algorithm for the Maximum-Duo Preservation String Mapping Problem we presented is incorrect. However, we show that, by slightly modifying the color-coding technique on which the algorithm is based, we can fix the error, thus giving a correct FPT algorithm for Maximum-Duo Preservation String Mapping Problem. [ABSTRACT FROM AUTHOR]

Highlights: [•] Identification of a potential Plk-1 inhibitor by virtual screening approach. [•] Calculated K d of identified compound CAP-53194 is higher than known inhibitors. [•] CAP-53194 is selective towards Plk1 over Plk2-4 and other kinases. [•] Modelling of its binding mode with Plk1-4 explains its selectivity. [•] Drug-like properties and selectivity for Plk1 makes it a promising lead molecule. [ABSTRACT FROM AUTHOR]

Abstract: Endometriosis is a complex disorder of the female reproductive system where endometrial tissue embeds and grows at extrauterine location leading to inflammation and pain. Hundreds of polymorphisms in several genes have been studied as probable risk factors of this debilitating disease. Bioinformatics tools have come a long way in augmenting the search for putative functional polymorphisms in human diseases. In this study we have explored 16 genes involved in the detoxification of xenobiotic chemicals that are implicated in endometriosis by utilising publically available programs like SIFT, Polyphen, Panther, FastSNP, SNPeffect and PhosSNP. The variations among different ethnic populations of the SNPs were studied. We then calculated the extent to which bioinformatics based predictions are concurrent with real world epidemiological, genotyping studies using a set of SNPs that have been studied in endometriosis case–control studies. Our study shows that there is a significant positive correlation (r=0.569, p<0.005) between the summary of the predicted scores taken from 4 different servers and the odds ratio found from epidemiological studies. This report has identified and catalogued various deleterious SNPs that could be important in endometriosis and could aid in further analysis by in vitro and in vivo methods for the better understanding of the disease pathophysiology. [Copyright &y& Elsevier]

Abstract: Objectives: The diagnosis of mental disorders is in most cases very difficult because of the high heterogeneity and overlap between associated cognitive impairments. Furthermore, early and individualized diagnosis is crucial. In this paper, we propose a methodology to support the individualized characterization and diagnosis of cognitive impairments. The methodology can also be used as a test platform for existing theories on the causes of the impairments. We use computational cognitive modeling to gather information on the cognitive mechanisms underlying normal and impaired behavior. We then use this information to feed machine-learning algorithms to individually characterize the impairment and to differentiate between normal and impaired behavior. We apply the methodology to the particular case of specific language impairment (SLI) in Spanish-speaking children. Methods and materials: The proposed methodology begins by defining a task in which normal and individuals with impairment present behavioral differences. Next we build a computational cognitive model of that task and individualize it: we build a cognitive model for each participant and optimize its parameter values to fit the behavior of each participant. Finally, we use the optimized parameter values to feed different machine learning algorithms. The methodology was applied to an existing database of 48 Spanish-speaking children (24 normal and 24 SLI children) using clustering techniques for the characterization, and different classifier techniques for the diagnosis. Results: The characterization results show three well-differentiated groups that can be associated with the three main theories on SLI. Using a leave-one-subject-out testing methodology, all the classifiers except the DT produced sensitivity, specificity and area under curve values above 90%, reaching 100% in some cases. Conclusions: The results show that our methodology is able to find relevant information on the underlying cognitive mechanisms and to use it appropriately to provide better diagnosis than existing techniques. It is also worth noting that the individualized characterization obtained using our methodology could be extremely helpful in designing individualized therapies. Moreover, the proposed methodology could be easily extended to other languages and even to other cognitive impairments not necessarily related to language. [Copyright &y& Elsevier]

Abstract: Experimental folding studies of membrane proteins are more challenging than water-soluble proteins because of the higher hydrophobicity content of membrane embedded sequences and the need to provide a hydrophobic milieu for the transmembrane regions. The first challenge is their denaturation: due to the thermodynamic instability of polar groups in the membrane, secondary structures in membrane proteins are more difficult to disrupt than in soluble proteins. The second challenge is to refold from the denatured states. Successful refolding of membrane proteins has almost always been from very subtly denatured states. Therefore, it can be useful to analyze membrane protein folding using computational methods, and we will provide results obtained with simulated unfolding of membrane protein structures using the Floppy Inclusions and Rigid Substructure Topography (FIRST) method. Computational methods have the advantage that they allow a direct comparison between diverse membrane proteins. We will review here both, experimental and FIRST studies of the retinal binding proteins bacteriorhodopsin and mammalian rhodopsin, and discuss the extension of the findings to deriving hypotheses on the mechanisms of folding of membrane proteins in general. This article is part of a Special Issue entitled: Retinal Proteins—You can teach an old dog new tricks. [Copyright &y& Elsevier]

Abstract: Simulations of delivery of IgG1 Fab, an anti-vascular epithelial growth factor (VEGF) macromolecular drug for the treatment of age-related macular degeneration (AMD), from an episcleral thermally responsive-poly(N-isopolyacrylamide) (NIPAM)-gel implant, are made to evaluate the effectiveness of sustained delivery. The model of the human eye used in the above simulations is validated as far as its anatomical and physiological features by the agreement observed in comparing results of simulations of intravitreal fluorescein delivery with relevant experimental data. Simulations of IgG1 Fab delivery from an episcleral NIPAM-gel implant to the posterior eye, with the previously validated anatomically and physiologically correct model of the human eye, show that drug therapeutic levels in the posterior eye are sustained for 8 weeks similarly to those associated with intravitreal injection of IgG1 Fab. Thus, delivery of the macromolecular anti-VEGF drug IgG1 Fab from an episcleral NIPAM-gel implant seems to be a viable alternative to more invasive, risk-related delivery by intravitreal injection, as effective and sustainable as the latter. [Copyright &y& Elsevier]

Fermentation reactor systems are a key platform in studying intestinal microflora, specifically with respect to questions surrounding the effects of diet. In this study, we develop computational representations of colon fermentation reactor systems as a way to assess the influence of three design elements (number of reactors, emptying mechanism, and inclusion of microbial immobilization) on three performance measures (total biomass density, biomass composition, and fibre digestion efficiency) using a fractional-factorial experimental design. It was determined that the choice of emptying mechanism showed no effect on any of the performance measures. Additionally, it was determined that none of the design criteria had any measurable effect on reactor performance with respect to biomass composition. It is recommended that model fermentation systems used in the experimenting of dietary effects on intestinal biomass composition be streamlined to only include necessary system design complexities, as the measured performance is not benefited by the addition of microbial immobilization mechanisms or semi-continuous emptying scheme. Additionally, the added complexities significantly increase computational time during simulation experiments. It was also noted that the same factorial experiment could be directly adapted using in vitro colon fermentation systems. [ABSTRACT FROM AUTHOR]

Highlights: [•] The capacity optimization reduces the optimum annual energy cost of combined cooling, heating and power systems. [•] The deterministic solution to the optimum operation strategy reduces the inaccuracies and the computation down time. [•] The graphical user interface used in system operation increases the practical applicability of the methodology. [•] The proposed methodology has reduced the total annual cost over 13% compared to the thermal load following mode. [ABSTRACT FROM AUTHOR]

Abstract: We introduce a new measure for assessing similarity among chemical structures, based on well-established computational-topology algorithms. We argue that although the method considers geometry, it is more than a mere geometric similarity measure, as it takes into account, on different geometric scales, the important topological features of the compared structures. We prove that our measure is rigorous and complies with the proper mathematical requirements. We validate the method through comparing different configurations of simple zinc finger proteins and present an application on ligands binding to membrane-proteINS extracted from the Directory of Useful Decoys: Enhanced database and corresponding decoys. This article is part of a Special Issue entitled: Viral membrane proteins — Channels for cellular networking. [Copyright &y& Elsevier]

Abstract: Vpu is an 81 amino acid type I integral membrane protein encoded by human immunodeficiency virus type 1 (HIV-1). It is identified to support viral release by potentially forming ion and substrate conducting channels and by modulating the function of host factors. The focus is on the interaction of the transmembrane domains of Vpu with those of host factors using a combination of molecular dynamics simulations and docking approach. Binding poses and adopted tilt angles of the dimers are analyzed and correlated with experimentally derived activity data from literature. Vpu activity is driven by dimerization with the host protein via its alanine rim Ala-8/11/15/19. Tight binding is shown by an almost parallel alignment of the helices in the dimers. Less parallel alignment is proposed to correlate with lower activity. This article is part of a Special Issue entitled: Viral Membrane Proteins — Channels for Cellular Networking. [Copyright &y& Elsevier]

Abstract: The Influenza Matrix 2 (M2) protein is the target of Amantadine and Rimantadine which block its H+ channel activity. However, the potential of these aminoadamantyls to serve as anti-flu agents is marred by the rapid resistance that the virus develops against them. Herein, using a cell based assay that we developed, we identify two new aminoadamantyl derivatives that show increased activity against otherwise resistant M2 variants. In order to understand the distinguishing binding patterns of the different blockers, we computed the potential of mean force of the drug binding process. The results reveal that the new derivatives are less mobile and bind to a larger pocket in the channel. Finally, such analyses may prove useful in designing new, more effective M2 blockers as a means of curbing influenza. This article is part of a Special Issue entitled: Viral Membrane Proteins — Channels for Cellular Networking. [Copyright &y& Elsevier]

Abstract: In phylogenetics, computing the likelihood that a given tree generated the observed sequence data requires calculating the probability of the available data for a given tree (topology and branch lengths) under a statistical model of sequence evolution. Here, we focus on selecting an appropriate model for the data, which represents a generally non-trivial task. The data is represented as a so-called multiple sequence alignment. That is, each individual sequence of any one species (taxa) is arranged (aligned) in such a way, that the characters of all species at a given position (site) are assumed to share a common evolutionary history. It is well known, that an inappropriate model, which does not fit the data, can generate misleading tree topologies [3,4,26]. More specifically, we consider the case of partitioned protein sequence alignments. This means that the sites of the alignment may be clustered together into different partitions. Each partition may have an individual model of evolution. Our objective is to maximize the likelihood of the per-partition protein model assignments (e.g., JTT, WAG, etc.) when branches are linked across partitions on a given, fixed tree topology. That is, branch lengths are not estimated individually for each partition. Linked branch lengths across partitions substantially reduce the number of free parameters. For p partitions and possible substitution models, there are possible model assignments. Since the number of combinations grows exponentially with p, an exhaustive search for the highest scoring assignment is computationally prohibitive for . We show that the problem of finding the optimal protein substitution model assignment under linked branch lengths on a given, tree topology, is NP-hard. Our results imply that one should employ heuristics to approximate the solution, instead of striving for the exact solution. Alternatively, the problem can be simplified by relaxing the assumptions. [Copyright &y& Elsevier]

Highlights: [•] We developed hybrid elastic network (HENM) model based on rigid cluster model. [•] HENM has been applied to both normal mode analysis and pathway generation. [•] The simulation results of selected proteins and ribosome show the robustness and reliability of the model. [•] HENM is expected to improve the computational efficiency of elastic network model. [Copyright &y& Elsevier]

Abstract: Computational analysis of sequence homology of the chiSRC gene cluster, encoding a chitinase in Streptomyces peucetius, showed that the gene cluster could be a two-component regulon comprising a sensor kinase (chiS) and a response regulator (chiR). To prove that the ChiSRC is an authentic two-component system, the chiS gene was cloned and expressed in E.coli and the purified protein was used for biochemical analysis. In this report, we provide biochemical evidence to show that the sensor kinase encoded by chiS gene indeed is a histidine kinase capable of autophosphorylation and the histidine 144 residue of the ChiS protein is the phosphate acceptor. An insertion mutation at the chiS locus led to overproduction chitinase protein in S. peucetius implying that the chiC gene is negatively regulated by the two-component system. [Copyright &y& Elsevier]

Highlights: [•] Consumer electronics creates new opportunities for imaging and sensing technologies. [•] We review emerging computational imaging and sensing platforms for global health. [•] Computational imagers and sensors are in general field-portable and cost-effective. [Copyright &y& Elsevier]

Salmonellosis outbreaks in Europe, the United States, and Latin America have been associated with contaminated food derivatives including meat from the poultry industry. Salmonella grown under iron-limiting conditions has the capability to increase concentration of several iron-regulated outer-membrane proteins to augment the acquisition of the metal. These proteins have been proved to have immunogenic properties. Our aim was to increase the relative expression of iroN, fepA, and cirA in Salmonella Enteritidis domestic strain. Furthermore, we proposed a 3-dimensional structure model for each protein to predict and locate antigenic peptides. Our eventual objective is to produce an effective vaccine against regional avian salmonellosis. Two simple factorial designs were carried out to discriminate between 2 nitrogen sources and determine chelating-agent addition timing to augment relative gene expression. Two antigenic peptides located at the external face of each protein and 2 typical domains of iron-regulated outer-membrane proteins, plug and TonB-dep-Rec, were identified from the 3-dimensional models. Tryptone was selected as the best nitrogen source based on growth rate (μx = 0.36 h−1) and biomass productivity (Px = 0.9 g∙h−1∙L−1) as determined by a general factorial design. Optimum timing for chelating agent addition was in the middle of the log phase, which allowed relative expressions at 4 h of culture. Increase in iroN, fepA, and cirA relative expression was favored by the length of log phase and the addition of chelating agent, which decreased chelating toxicity and enhanced cell growth rate. [ABSTRACT FROM AUTHOR]

Determining how genetic variation contributes to human health and disease is a critical challenge. As one of the most genetically tractable model organisms, yeast has played a central role in meeting this challenge. The advent of new technologies, including high-throughput DNA sequencing and synthesis, proteomics, and computational methods, has vastly increased the power of yeast-based approaches to determine the consequences of human genetic variation. Recent successes include systematic exploration of the effects of gene dosage, large-scale analysis of the effect of coding variation on gene function, and the use of humanized yeast to model disease. By virtue of its manipulability, small genome size, and genetic tractability, yeast is poised to help us understand human genetic variation. [ABSTRACT FROM AUTHOR]

A holy grail of genetics is to decipher the mapping from genotype to phenotype. Recent advances in sequencing technologies allow the efficient genotyping of thousands of individuals carrying a particular phenotype in an effort to reveal its genetic determinants. However, the interpretation of these data entails tackling significant statistical and computational problems that stem from the complexity of human phenotypes and the huge genotypic search space. Recently, an alternative pathway-level analysis has been employed to combat these problems. In this review we discuss these developments, describe the challenges involved and outline possible solutions and future directions for improvement. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposed a theoretical model to determine the thermal contact resistance (TCR) on the surfaces of silicon and copper during electron beam melting. The effect of temperature and pressure on TCR, based on specific melting process conditions, was discussed. Hertz's theory was used to analyze the characteristics of material surfaces and to calculate the relationship between the pressure and distance of contact surfaces, the real contact area and the number of contact asperities combined with the physical characteristics of the material. The geometric parameter of the theoretical model was obtained based on theoretical calculations. The TCR of the entire surface was obtained by analyzing the temperature field of silicon and copper using Ansys and TCR equations. The relationship among TCR, pressure, and temperature were found. The computational results agreed with existing experimental results. [Copyright &y& Elsevier]

Abstract: This paper explores the use of a micro genetic algorithm that uses variable-length chromosomes and a seeding scheme based on tabu search. The problem is to find the sequence of actions that have to be executed in the shortest time possible, but also in a way that minimizes the possibility of situations that may endanger the plant personnel and plant facilities. The proposed approach was tested on the generation of the optimum sequences for startup and shutdown of a mixing vessel similar to the equipment used in the synthesis of acrylic acid. The results show that the proposed method outperforms the traditional GA algorithm both in terms of the quality of the solution and computational effort. [Copyright &y& Elsevier]

Abstract: The design of reliable DNA sequences is crucial in many engineering applications which depend on DNA-based technologies, such as nanotechnology or DNA computing. In these cases, two of the most important properties that must be controlled to obtain reliable sequences are self-assembly and self-complementary hybridization. These processes have to be restricted to avoid undesirable reactions, because in the specific case of DNA computing, undesirable reactions usually lead to incorrect computations. Therefore, it is important to design robust sets of sequences which provide efficient and reliable computations. The design of reliable DNA sequences involves heterogeneous and conflicting design criteria that do not fit traditional optimization methods. In this paper, DNA sequence design has been formulated as a multiobjective optimization problem and a novel multiobjective approach based on swarm intelligence has been proposed to solve it. Specifically, a multiobjective version of the Artificial Bee Colony metaheuristics (MO-ABC) is developed to tackle the problem. MO-ABC takes in consideration six different conflicting design criteria to generate reliable DNA sequences that can be used for bio-molecular computing. Moreover, in order to verify the effectiveness of the novel multiobjective proposal, formal comparisons with the well-known multiobjective standard NSGA-II (fast non-dominated sorting genetic algorithm) were performed. After a detailed study, results indicate that our artificial swarm intelligence approach obtains satisfactory reliable DNA sequences. Two multiobjective indicators were used in order to compare the developed algorithms: hypervolume and set coverage. Finally, other relevant works published in the literature were also studied to validate our results. To this respect the conclusion that can be drawn is that the novel approach proposed in this paper obtains very promising DNA sequences that significantly surpass other results previously published. [Copyright &y& Elsevier]