In this paper, we present several methods for computing a solution to the protein side chain packing problem, with all methods having a common solution approach of breaking the polymer into subpolymers and using maximum edge weight cliques to prune the search space for the optimal side chain packing. We characterize the graph sizes generated for each method and compare their prediction accuracies. These methods are demonstrated for computing proteins up to approximately 8000 residues. In addition, we update a result published previously.

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The homozygous deletion of Serine 171 results in the catalytic inactivation of the human transaldolase. Since Ser171 is in an outside loop, whereas the catalytic site is inside of the α/β-barrel of the protein at least 15 Å away, the loss of activity is difficult to explain. Two distinct computational methods are used to elucidate the potential origin of inactivation. Computational solvent mapping, which moves small organic molecules as probes around a protein surface and finds favorable binding positions, identifies the region around Ser171 as an important binding site. Three-dimensional cluster analysis, based both on a reference structure and multiple sequence alignment, shows that a patch of functionally important residues extends from Ser171 toward the catalytic site. Based on the findings of these two methods, we propose a novel ligand access path connecting these specific sites to the enzyme's active site. We also suggest that this mechanism may be aided by a significant conformational change involving the separation of two helices, αD and αG, in order to create an easy-access channel between the Ser171-related site and the active site. Further experimental procedures will be necessary to examine the biological feasibility of this proposed ligand shuttling path.

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We introduce a new and potentially valuable method for delineating the repertoire of protein complexes in highly mutable organisms and, in conjunction with other methods, for specifying the structural details of complexes. In the first instance the method provides a guide to selecting proteins for co-crystallization; in the second it augments the collection of structures determined by crystallography and other methods, including the discovery of possible alternative binding sites of known complexes.
The key to the method is the availability of multiple sequence variants of an organism-arrived at either naturally or by directed mutagenesis in appropriate laboratory facilities. Amino acids that are important for the structural stability of a protein or complex tend to be conserved, generally mutating only when compensatory changes occur. Consequently significant correlations in variation of two conserved amino acids in the same protein suggest that they interact with one another, either directly or indirectly. Similarly, correlated mutations between conserved amino acids in different proteins suggest that they may be at a site of physical interaction. We have identified all highlyconserved 9-11 amino acid long segments from HIV proteins and then identified pairs from different proteins with highly significant co-variation. Using the HIV reverse transcriptase and integrase proteins as an example, we demonstrate how the interface and combining sites can be inferred by co-variation analysis and rigid body docking. The potential significance for antiviral drug and vaccine design is briefly discussed.

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Protein-protein interaction maps can contribute substantially to the discovery of protein cooperation patterns in the cell. Recently, several large-scale human protein-protein interaction maps have been generated using experimental or computational approaches. Evaluation of these maps is likely to provide a better understanding of human biology. However, careful analysis is needed, as the comparison of interaction maps of lower eukaryotes showed a surprising divergence between different maps. Here, we present a first systematic functional assessment of eight currently available large-scale human protein-protein interaction maps. The analysis shows that these maps include a large number of common proteins, but only a small number of common interactions. We detected several types of biases that need to be considered in the future utilization of these maps.

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We propose an automatic construction method of the hybrid functional Petri net as a simulation model of biological pathways. The problems we consider are how we choose the values of parameters and how we set the network structure. Usually, we tune these unknown factors empirically so that the simulation results are consistent with biological knowledge. Obviously, this approach has the limitation in the size of network of interest. To extend the capability of the simulation model, we propose the use of data assimilation approach that was originally established in the field of geophysical simulation science. We provide genomic data assimilation framework that establishes a link between our simulation model and observed data like microarray gene expression data by using a nonlinear state space model. A key idea of our genomic data assimilation is that the unknown parameters in simulation model are converted as the parameter of the state space model and the estimates are obtained as the maximum a posteriori estimators. In the parameter estimation process, the simulation model is used to generate the system model in the state space model. Such a formulation enables us to handle both the model construction and the parameter tuning within a framework of the Bayesian statistical inferences. In particular, the Bayesian approach provides us a way of controlling overfitting during the parameter estimations that is essential for constructing a reliable biological pathway. We demonstrate the effectiveness of our approach using synthetic data. As a result, parameter estimation using genomic data assimilation works very well and the network structure is suitably selected.

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The Systems Biology Markup Language (SBML) is an XML-based format for representing mathematical models of biochemical reaction networks, and it is likely to become a main standard in the systems biology community. As published mathematical models in cell biology are growing in number and size, modular modelling approaches will gain additional importance. The main issue to be addressed in computer-assisted model combination is the specification and handling of model semantics.
The software SBMLmerge assists the user in combining models of biological subsystems to larger biochemical networks. First, the program helps the user in annotating all model elements with unique identifiers pointing to databases such as KEGG or Gene Ontology. Second, during merging, SBMLmerge detects and resolves various syntactic and semantic problems. Typical problems are conflicting variable names, elements which appear in more than one input model, and mathematical problems arising from the combination of equations. If the input models make contradicting statements about a biochemical quantity, the user is asked to choose between them. In the end the merging process results in a new, valid SBML model.

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A typical proliferating human cell divides on average every 24 h. This division timing allows cells to synchronize with other physiological processes and with the environment. The circadian clock, which orchestrates daily rhythms, directly regulates the cell division cycle and is a major synchronizing factor. There is, however, no evidence that the circadian clock is able to entrain the cell cycle to a 24 h period. We show here, using a computational model for the cell cycle, that cells under circadian control that have an interdivision time close to multiples of 24 h proliferate faster. Moreover, growth of cell populations with a markedly different cell cycle time is impaired. We propose that this resonance effect in cell proliferation has a role to play in efficient normal cell proliferation and suppression of tumor growth.

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Values of enzyme kinetic parameters are a key requisite for the kinetic modelling of biochemical systems. For most kinetic parameters, however, not even an order of magnitude is known, so the estimation of model parameters from experimental data remains a major task in systems biology. We propose a statistical approach to infer values for kinetic parameters across species and enzymes making use of parameter values that have been measured under various conditions and that are nowadays stored in databases. We fit the data by a statistical regression model in which the substrate, the combination enzyme-substrate and the combination organism-substrate have a linear effect on the logarithmic parameter value. As a result, we obtain predictions and error ranges for unknown enzyme parameters. We apply our method to decadic logarithmic Michaelis-Menten constants from the BRENDA database and confirm the results with leave-one-out crossvalidation, in which we mask one value at a time and predict it from the remaining data. For a set of 8 metabolites we obtain a standard prediction error of 1.01 for the deviation of the predicted values from the true values, while the standard deviation of the experimental values is 1.16. The method is applicable to other types of kinetic parameters for which many experimental data are available.

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Alternative splicing is an important regulatory mechanism that generates multiple mRNA transcripts which are transcribed into functionally diverse proteins. According to the current studies, aberrant transcripts due to splicing mutations are known to cause for 15% of genetic diseases. Therefore understanding regulatory mechanism of alternative splicing is essential for identifying potential biomarkers for several types of human diseases. Most recently, advent of GeneChip^®Human Exon 1.0 ST Array enables us to measure genome-wide expression profiles of over one million exons. With this new microarray platform, analysis of functional gene expressions could be extended to detect not only differentially expressed genes, but also a set of specific-splicing events that are differentially observed between one or more experimental conditions, e.g. tumor or normal control cells. In this study, we address the statistical problems to identify differentially observed splicing variations from exon expression profiles. The proposed method is organized according to the following process:(1) Data preprocessing for removing systematic biases from the probe intensities.(2) Whole transcript analysis with the analysis of variance (ANOVA) to identify a set of loci that cause the alternative splicing-related to a certain disease. We test the proposed statistical approach on exon expression profiles of colorectal carcinoma. The applicability is verified and discussed in relation to the existing biological knowledge. This paper intends to highlight the potential role of statistical analysis of all exon microarray data. Our work is an important first step toward development of more advanced statistical technology. Supplementary information and materials are available from http://bonsai.ims.u-tokyo.ac.jp/-yoshidar/IBSB2006_ExonArray.htm.

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Biological regulatory networks have been extensively researched. Recently, the microRNA regulation has been analyzed and its importance has increasingly emerged. We have applied the Hybrid Functional Petri net with extension (HFPNe) model and succeeded in creating model biological pathways, e.g. metabolic pathways, gene regulatory networks, cell signaling networks, and cell-cell interaction models with one of the HFPNe implementations Cell Illustrator. Thus, we have applied HFPNe to model regulatory networks that involve a new key regulator microRNA. As a test case, we selected the cell fate determination model of two gustatory neurons of Caenorhabditis elegans-ASE left (ASEL) and ASE right (ASER). These neurons are morphologically bilaterally symmetric but physically asymmetric in function. Johnston et al. have suggested that their cell fate is determined by the double-negative feedback loop involving the lsy-6 and mir-273 microRNAs. Our simulation model confirms their hypothesis. In addition, other well-known mutants that are related with the double-negative feedback loop are also well-modeled. The new upstream regulator of lsy-6 (lsy-2) that is mentioned in another paper is also integrated into this model for the mechanism of switching between ASEL and ASER without any contradictions. Therefore, the HFPNe-based modeling will be one of the promising modeling methods and simulation architectures that illustrate microRNA regulatory networks.

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The protein p53 is phosphorylated by a member of protein kinases such as CDK7, and stabilized by the protein ARF. The phosphorylation and stabilization of p53 is believed to enhance its transcriptional activity and act simultaneously. Biological pathways composed of experts knowledge obtained from the literature are including these activation mechanisms. However, the map of biological pathways does not reflect the combination effect of phosphorylation and stabilization.
We have conducted some simulations of biological pathways with hybrid functional Petri net (HFPN) after careful reading of papers. In this paper, we constructed the HFPN based biological pathway of CDK-dependent phosphorylation pathway and combine with ARF-dependent pathway described previously, to observe the effect of the phosphorylation on the stabilization with simulation-based validation.

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DNA motifs, or cis-elements, are short nucleotide sequence patterns recognized by various transcription factors (TFs). In promoters, these TFs bind in a complex combinatorial manner in order to regulate the expression of a downstream gene. The combinatorial space is frequently large and difficult to manage since vertebrates have thousands of transcription factors and more than 20, 000 genes. We introduce a computer program called CAYCE (Combinatorial AnalYsis of Cis-Elements) that systematically detects statistically overrepresented DNA motif association rules independent of Microarray information. CAYCE is an adaptation of the apriori algorithm traditionally used for association rule mining, but offers three significant advancements.(1) It analyzes multiple occurrences of an item, corresponding to multiple TF binding sites, (2) It compares results with a biologically relevant background, and (3), it provides p-values for straightforward statistical interpretation. CAYCE can be easily applied to any item-set data where the investigator is also interested in multiple occurrences of a single item, and/or overrepresentation of association rules compared with a background. Applying CAYCE to human promoters in 1% of the human genome, we discover that motif clusters containing five repetitions of SP1 are the most statistically significant.

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Transcription factors (TFs) play a key role in gene regulation by binding to target sequences. In silico prediction of potential binding to a sequence is a main task in computational biology. Although many methods have been proposed to tackle this problem, the statistical significance of the prediction is still not solved. We propose an approach to give a good approximation for the potential of a sequence to be bound by a TF. Instead of assessing distinct binding sites, we motivate to focus on the number of binding sites. Based on a suitable statistical model, probabilities for scoring are approximated for a TF to bind to a sequence. Two examples show the necessity of such a model as well as the superiority of the proposed method compared to standard approaches.

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Photo-switchable compounds are becoming increasingly popular for a series of biological applications based on the reversible photo-control of structure and function of biomolecules. Three applications for the usage of BODTCM and hemithioindigo as photo-reactive compounds are described here. The structure of the villin headpiece was modified by replacing a part of the backbone with hemithioindigo, aiming at induction of the folding process by irradiation with a defined wavelength. The E-isomer of BODTCM was applied as potential inhibitor of the 12/15-lipoxygenase (12/15-LOX), which is implicated in the pathogenesis of inflammatory diseases.
A required death domain for the binding of proapoptotic proteins (e.g. Bak) to the hydrophobic groove of antiapoptotic proteins is the BH3 helix. Inserting hemithioindigo into this short peptide, stabilization towards proteolytic degradation is achieved.
Such photo-reactive compounds might be developed as potential drugs for a great variety of diseases.

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Signal transduction involves the transitions of proteins between inactive and active states thatcan be achieved by reversible phosphorylation, nucleo-cytoplasmic transport, and other processes.We consider a network of such state transitions governed by first-order kinetics and analyse howthe reactions control the occupancy of the network states. First, a theorem is derived that relatesconcentration control coefficients and occupancy of the network states. Second, it is shown thatthe absolute value of each control coefficient is bounded by unity, so that the network does notexhibit ultrasensitive responses. Third, the signs of certain control coefficients are derived from thenetwork topology. These results are applied to a mathematical model of the Jak/Statl signaling.This pathway has been thought to function as a continuous cycle of cytoplasmic activation, nuclearimport, inactivation and re-export of Statl transcription factors, but the recent discovery of anapparently futile nucleo-cytoplasmic cycle of inactive Stat 1 has yielded a more complex picture.We demonstrate here two consequences of shuttling:(1) homeostasis of unphosphorylated Statl inthe cell nucleus and (2) enhanced stimulus sensitivity of the pathway, and discuss their functional implications.

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Expression of metabolic enzymes increases the metabolic capabilities of the cell, but it alsoconsumes resources, and the gene regulatory systems of cells have to handle this tradeoff. To studywhether gene expression patterns reflect the varying metabolic needs of the cell, we translatedgene expression profiles into sets of active biochemical reactions, which constitute the expressedmetabolic subnetworks. The metabolic capacity of a carbon source denotes the number of metabolitesthat can be produced from this carbon source and some inorganic nutrients. We studied themetabolic subnetworks that are expressed during diauxic shift in yeast and found that the capacitiesof different carbon sources tend to decrease during the diauxic shift. The subnetwork expressedin the initial glucose environment shows a high capacity of glucose, much higher than networks ofthe same size arising in later stages of the diauxic shift or from a random selection of reactions.The same holds for the essential capacity, the number of important, constitutive compounds thatcan be produced from glucose. These findings indicate that gene regulation increases the rangeof essential compounds that can be obtained from the available nutrients, while minimising the number of expressed enzymes and therefore the burden of protein synthesis.

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Fatty acids are the major components of membrane molecules. The fact that unsaturated fattyacids play multiple important roles, physically and biologically, means that the ratio of unsaturatedto saturated fatty acids in the membrane needs to be strictly regulated to maintain cellular homeostasis.The most ubiquitous and widespread modification to fatty acids, which results in a greatdiversity of different structures, is the insertion of double bonds. Fatty acid desaturases directlyintroduce regioselective double bonds into fatty acids. A phylogenetic analysis of desaturases suggeststhat the sequences of these proteins include a highly conserved domain, which determines thedifferences in specificity and regioselectivity found in these enzymes. In this study, we performeda systematic analysis of fatty acid desaturases found in the genomic data of 397 organisms. Weobtained a set of desaturases clustered by regioselectivity using a hierarchal clustering analysis.

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Vγ9Vδ2 cells, which constitute a small portion of peripheral blood T-cells (-5%), are knownto be the biggest subset of human γδ T-cells (-70%) in circulating blood. The Vγ9Vδ2 T-cellsexpressing Vγ9Vδ2 T-cell receptors have an ability to recognize non-peptide antigens directly orindirectly, for example, phosphorylated metabolites referred to as phosphoantigens, synthesizedaminobisphosphonates used for therapeutic purpose such as pamidronate, and alkylamines. Thesechemical compounds recognized by Vγ9Vδ2 T-cells are produced by many prokaryoticand eukaryoticorganisms. Previous works show that the phosphoantigens recognized by Vγ9Vδ2 T-cells arefound as the intermediates in the pathogens' pathway producing IPP. In this paper, we show thatthe other compounds recognized by Vγ9Vδ2 T-cells are found on the pathogens' biosynthetic pathwaysleading to production of shared compounds with human. In addition, many compounds havinghigh structural similarity with alkylamine antigens are also found only in pathogen's biosynthetic pathways or produced only by non-human enzymes.

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All cellular functions are ultimately linked to the metabolism which constitutes a highly branchednetwork of thousands of enzyme-catalyzed chemical reactions and carrier-mediated transport processes.Depending on the prevailing functions (e.g. detoxification of a toxin or accumulation ofbiomass) the distribution of fluxes in the metabolic network may vary considerably. To better revealand quantify this flux-function relationship we propose a novel computational approach which identifiesdistinct contributions -so called minimal flux modes (short: MinModes)-to a stationaryflux distribution in the network. Each of these contributions is characterized by a single metabolicoutput. A MinMode is a minimal (according to a defined cost function) steady state flux distributionthat enables the production of a single metabolite. We apply this concept to a metabolicnetwork of Methylobacterium extorquens AM1 comprising of 95 reactions and 74 metabolites, 17 ofthese metabolites entering the biomass of the bacterium and are thus considered as the metabolicoutput of the network. MinModes represent a manageable set of fundamental flux modes in thenetwork having a clear physiological meaning and-although not representing a basis in strictmathematical sense -provide a satisfactory approximation of the overall flux distribution in cases tested so far.

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A large scale structural analysis of metabolic networks is presented focusing on neighbourhood relationships between individual reactions. We define two reactions to be neighbored if one of them provides the necessary set of substances for the other to proceed. A method is developed which allows determining all possible neighborhood relationships categorized as interaction patterns. These patterns differ in the types of participating reactions and in the way they share their reactants. The method is applied to a set of 4795 metabolic reactions contained in the KEGG database. We show that from the 1547 theoretically possible types of interactions 282 patterns are found in metabolism. More than 55% of all interactions occur between reactions with at most two reactants on one side. In these interactions only 25 different patterns play a role. We propose to use these neighborhood relationships as a concept of adjacency in large scale graph theoretical analyses of metabolism.

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Intronic sequences represent a large fraction of most eukaryotic genomes, and they are knownto play a critical role in genome evolution. Based on the conserved location of introns, conservedsequence within introns, and direct experimental evidence, it is becoming increasingly clear thatintrons perform important functions such as modulating gene expression. Here, we demonstratethat the positions of 69% (862/1246) of human introns in 343 orthologous genes are conserved in thestarlet sea anemone Nematostella vectensis, a phylogenetically basal animal (phylum Cnidaria; classAnthozoa). This degree of intron concordance greatly exceeds that between humans and three moreclosely related animals: fruitfly (14%), mosquito (13%) and nematode worm (19%). Surprisingly, the fruitfly and mosquito, two members of the order Diptera, share only 43% of intron locations, fewer than the percentage of cumulative introns shared between human and sea anemone (47%), despite sharing a much more recent common ancestor. Our analysis indicates (1) that early animalgenomes were intron-rich, (2) that a large fraction of introns present within the human genomelikely originated early in evolution, before the cnidarian-bilaterian split, at least 600 million yearsago, and (3) that there has been a high degree of intron loss during the evolution of the protostomelineage leading to the fruitfly, mosquito, and nematode. These data also reinforce the conclusionthat there are functional constraints on the placement of introns in eukaryotic genes.

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Recent evidence points to the existence of scale-free properties in many biological networks.By topological analysis, several models including preferential attachment and hierarchical moduleshave been proposed to explain how these networks are organized. On the other hand, analyses usingdynamics have suggested that gene expression and metabolic networks have been organized withthe scale-free property by the other models such as “rich-travel-more” and “log-normal dynamics.” Because most of these approaches are based on comparative genomics of extant species, and didnot consider evolutionary events such as horizontal gene transfer, gene loss and gene gain, wehave analyzed transition of metabolic networks from the vertical point of view of evolution. First, to identify metabolic networks of common ancestors, we applied a parsimony algorithm for theenzymatic reaction set. Then by comparing the estimated metabolic networks among commonancestors, we investigated the transition of metabolic networks along the evolutionary process.As a result, we estimated enzymatic reaction contents of 227 common ancestors from 228 extantspecies, and found that links of several specific metabolites have frequently changed during the course of evolution.

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Evolutionary rate and gene age are interrelated when the age of a gene is assessed by the taxonomicdistribution in the gene family. This is because homology detection by sequence comparisonis depending on sequence similarity. We estimate family specific rates of protein evolution for orthologousfamilies with representatives from man, fugu, fly, and worm. In fact, we observe thatyounger proteins tend to evolve faster than older ones. We estimate time points of duplicationevents that gave rise to novel protein functions and show that younger proteins were duplicated more recently than older ones.

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