Program
Monday, July 24 |
|
9:00 a.m. |
Opening Remarks Prof. Charles DeLisi, Prof. Reinhart Heinrich, Prof. Minoru Kanehisa |
9:30 |
Daniel Segrè (Boston University) |
10:00 |
J.B. Brown (Kyoto University) |
10:30 |
Melissa Landon (Boston University) |
11:00 |
Break |
11:30 |
Yaoyu Wang (Boston University) |
12:00 Noon |
Gautam Chaurasia (Humboldt University) |
12:30 |
Lunch |
2:00 |
Rui Yamaguchi (University of Tokyo) |
2:30 |
Edda Klipp (Max Planck Institute for Molecular Genetics) |
3:00 |
Samuel Bernard (Institute Of Applied Computational Mathematics) |
3:30 |
Simon Borger (Max Planck Institute for Molecular Genetics) |
4:00 |
Opening Reception, LSEB Lobby |
Tuesday, July 25 |
|
9:00 |
Ryo Yoshida (University of Tokyo) |
9:30 |
Ayumu Saito (University of Tokyo) |
10:00 |
Atsushi Doi (University of Tokyo) |
10:30 |
Break |
10:50 |
Jane Lin (Boston University) |
11:20 |
Utz J. Pape (Max Planck Inst. for Mol. Genetics, Free Univ. of Berlin) |
11:50 |
Melanie Füllbeck (Charité-University Medicine) |
12:20 |
Stephan Beirer (Humboldt University) |
12:50 |
Lunch |
2:30 |
Oliver Ebenhöh (Humboldt University) |
3:00 |
Kosuke Hashimoto (Kyoto University) |
3:30 |
|
6:00 |
Freedom Trail Tour (registration required) |
Wednesday, July 26 |
|
9:00 |
|
11:00 |
Wataru Honda (Kyoto University) |
11:30 |
Sabrina Hoffmann (Humboldt University) |
12:00 Noon |
József Bruck (Humboldt University) |
12:30 |
Lunch |
2:00 |
James Sullivan (Boston University) |
2:30 |
Michihiro Tanaka (Kyoto University) |
3:00 |
Hannes Luz (Max Planck Institute for Molecular Genetics) |
3:30 |
Closing Remarks |
6:00 |
Banquet (registration required) |
Paper abstracts
Searching for evolutionary paths and organization principles in metabolic networks
Daniel Segrè
Boston University
We are interested in the evolutionary dynamics of metabolic networks, in particular in the interplay between genetic and environmental perturbations, genome-level functional organization, and optimal adaptation. By implementing steady state models of biochemical networks, and computing gene-gene interactions through the study of double gene deletions, one can make new hypothesis about the organization of metabolic genes and pathways into hierarchical modules. Beyond single organisms, reaction topology and a network expansion algorithm can be used to study the effect of key molecules (such as oxygen) on the evolution of life.
Monday, July 24 at 10:00 AM
Multiple Methods for Protein Side Chain Packing using Maximum Weight Cliques
J.B. Brown¹, Dukka Bahadur K.C.², Etsuji Tomita³, Tatsuya Akutsu¹
¹Kyoto University
²Georgia Institute of Technology
³The University of Electro-communications
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 of up to approximately 1000 residues.
Computational Methods for Functional Site Identification Suggest a Substrate Access Channel in Transaldolase
Michael Silberstein¹, Melissa Landon¹, Yaoyu Wang¹, Andras Perl², Sandor Vajda³
¹Program in Bioinformatics, Boston University
²Department of Medicine and Microbiology and Immunology, College of Medicine, State University of New York
³Department of Biomedical Engineering, Boston University
The homozygous deletion of Serine 171 results in the catalytic inactivation of the human transaldolase. Since Ser 171 is in an outside loop, whereas the catalytic site is inside of the a/b-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.
Inferring Protein-Protein Interactions in Viral Proteins by Co-evolution of Conserved Side Chains
Yaoyu Wang, Charles DeLisi
Boston University
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 highly conserved 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.
Systematic Functional Assessment of Human Proteins-Protein Interaction Maps
Gautam Chaurasia¹², Hanspeter Herzel¹, Erich E. Wanker², Matthias E. Futschik¹
¹Institute for Theoretical Biology, Humboldt-Universität, Berlin, Germany
²Max-Delbrück-Centrum for Molecular Medicin, Berlin-Buch, Berlin, Germany
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.
Genomic Data Assimilation for Estimating Hybrid Functional Petri Net from Time-course Gene Expression Data
Masao Nagasaki¹, Rui Yamaguchi¹, Ryo Yoshida¹, Seiya Imoto¹, Atsushi Doi¹, Yoshinori Tamada², Hiroshi Matsuno³, Satoru Miyano¹, Tomoyuki Higuchi
¹Human Genome Center, Institute of Medical Science, University of Tokyo
²Institute of Statistical Mathematics
³Faculty of Science, Yamaguchi University
Institute of Statistical Mathematics
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 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 enable
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.
SBMLmerge, A System for Combining Biochemical Network Models
Marvin Schulz, Jannis Uhlendorf, Edda Klipp, Wolfram Liebermeister
Max Planck Institute for Molecular Genetics, Berlin
The Systems Biology Markup Language is an XML-based format for representing mathematical models of biochemical reaction networks, and it is likely to become a main standard in the systemsbiology community. As published mathematical models in cell biology are growing in number and size, modular modeling approaches will gain additional importance.The program SBMLmerge assists the user in combining different models to larger biochemical networks. First, the user is supported 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, but also mathematical problems arising from the combination of equations. If the input models make different statements about a biochemical quantity, the user is asked to choose between them. At the end, merging results in a new, valid SBML model.
Why Do Cells Cycle With a 24 Hour Period?
Samuel Bernard¹, Hanspeter Herzel²
¹Institute of Applied and Computational Mathematics, Foundation for Research and
Technology – Hellas
²Institute for Theoretical Biology, Humboldt University
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.
Prediction of Enzyme Kinetic Parameters Based On Statistical Learning
Simon Borger, Wolfram Liebermeister, Edda Klipp
Max Planck Institute for Molecular Genetics
Values of enzyme kinetic parameters are a key requisite for the kinetic modelling of biochemical systems. For most kinetic parameters, however, not even the 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. We make use of the parameter values that have been measured under various conditions and that are nowadays stored in databases. We assume a regression model in which the substrate, the combination enzyme-substrate and the combination organism-substrate have linear effects on the logarithmic parameter value. As a result, we obtain predictions and error ranges for enzyme parameters. We apply our method to 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. With this data set, we obtain a correlation of r = 0.267 between the true and predicted values. The method is also applicable to other kinetic parameters that are stored in databases in large quantities.
A Statistical Framework to Genome-Wide Discovery of Biomarker Splice Variations with GeneChip Human Exon 1.0 ST Arrays
Ryo Yoshida, Kazuyuki Numata, Seiya Imoto, Masao Nagasaki, Atsushi Doi, Kazuko Ueno, Satoru Miyano
Human Genome Center, Institute of Medical Science, University of Tokyo
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 biomarker for several types of human diseases. Most recently, advent of GeneChip$^{\tiny \textregistered}$ Human Exon 1.0 ST Array enables us to measure genome-wide expression profiles of over one million exons. With this 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. cancer or normal control cells.
%In the near future, this technology will open up a way to genome-wide discovery of splicing factors associated with several types of human diseases. In this study, we address the statistical problems to identify differentially observed splicing events from the exon expression profiles. The proposed method is a series of the following process: (1) Data preprocessing for removing systematic biases from the observed 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 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 \url{http://bonsai.ims.u-tokyo.ac.jp/~yoshidar/IBSB2006_ExonArray.htm}.
Cell Fate Simulation Model of Gustatory Neurons with MicroRNAs Double-Negative Feedback Loop by Hybrid Function Petri Net with Extension
Ayumu Saito, Masao Nagaskai, Atushi Doi, Kazuko Ueno, Satoru Miyano
Human Genome Center, Institute of Medical Science, University of Tokyo, Japan
Biological regulatory networks are extensively researched. Recently, the microRNA regulation is analyzed and its importance is increasingly emerged. We have applied the Hybrid Functional Petri net with extension (HFPNe) model and succeeded to model biological pathways, e.g. metabolic pathway, gene regulatory network, cell signaling network, and cell-cell interaction model. Thus, the new key regulator microRNA included regulatory networks is modeled on its application Cell Illustrator. As the test case, the cell fate determination model of two Caenorhabditis elegans gustatory neurons, ASE left (ASEL) and ASE right (ASER) is selected. They are morphologically bilaterally symmetric but physically asymmetric in function. Their cell fate is suggested to be determined with the double-negative feedback loop with microRNAs lsy-6 and mir-273 in Robert et al. With our simulation model, their hypothesis is confirmed. In addition, other well-known mutants that related with the double-negative feedback loop are also well-modeled. The new upstream regulator lsy-2 of the lsy-6 in other paper is also integrated into the model for switching mechanism of ASEL and ASER without any contradictions. Accordingly, HFPNe based model will be one of the promising modeling and simulation architecture that work on microRNA regulatory networks.
A Combined Pathway to Simulate CDK-dependent phosphorylation and ARF-dependent Stabilization for p53 Transcriptional Activity
Atsushi Doi¹, Masao Nagasaki¹, Kazuko Ueno¹ , Hiroshi Matsuno², Satoru Miyano¹
¹Human Genome Center, Institute of Medical Science, University of Tokyo
²Graduate School of Science and Engineering, Yamaguchi University
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.
Systematic Detection of Statistically Over-Represented DNA Motif Association Rules
Jane Lin, Zhiping Weng
Boston University
DNA motifs, or cis-elements, are short nucleotide sequence patterns recognized by various transcription factors (TFs). These TFs bind to promoters 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 over-represented DNA motif association rules independent of Microarray information. CAYCE is an adaptation of the a priori 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 over-representation 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.
A New Statistical Model To Select Target Sequences Bound By Transcription Factors
Utz J. Pape¹², Steffen Grossmann¹, Stefanie Hammer³, Silke Sperling³, Martin Vingron¹
¹Computational Biology, Max Planck Institute for Molecular Genetics, Berlin
²Mathematics and Computer Science, Free University of Berlin, Berlin
³Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin
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, p-values 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.
Design and Biological Evaluation of Photo-Switchable Inhibitors
Melanie Füllbeck¹, Elke Michalsky¹, Ines Jäger¹, Peter Henklein¹, Hartmut Kühn¹, Karola Rück-Braun², Robert Preissner¹
¹Institute of Biochemistry, Charité-University Medicine Berlin
²Institut für Chemie, Technische Universität Berlin
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.
Control of Signal Transduction Cycles
Stephan Beirer, Thomas Höfer
Humboldt University Berlin
Signal transduction involves the transitions of proteins between inactive and active states that can 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 how the reactions control the occupancy of the network states. First, a theorem is derived that relates concentration control coefficients and occupancy of the network states. Second, it is shown that the absolute value of each control coefficient is bounded by unity, so that the network does not exhibit ultrasensitive responses. Third, the signs of certain control coefficients are derived from the network topology. These results are applied to a mathematical model of the Jak/Stat1 signaling. This pathway has been thought to function as a continuous cycle of cytoplasmic activation, nuclear import, inactivation and re-export of Stat1 transcription factors, but the recent discovery of an apparently futile nucleo-cytoplasmic cycle of inactive Stat1 has questioned this picture. We demonstrate here two consequences of shuttling: (1) homeostasis of unphosphorylated Stat1 in the cell nucleus and (2) enhanced stimulus sensitivity of the pathway, and discuss their functional implications.
Structural Analysis of Active Metabolic Subnetworks
Oliver Ebenhöh¹, Wolfram Liebermeister²
¹Humboldt University Berlin
²MPI for Molecular Genetics
Biochemical reactions in the cell can be controlled by transcriptional regulation of the corresponding genes. We translated gene expression profiles during the diauxic shift in
yeast into metabolic networks comprising the corresponding active reactions. In the framework of network expansion, the metabolic capacity of a carbon source denotes the number of metabolites that can be produced from this carbon source together with a number of additional nutrients. We find that during diauxic shift, the metabolic capacities of different nutrients go down. Glucose as a carbon source has a high capacity in the active network in a glucose environment. In fact, the capacity is much higher than in networks of the same size arising from later stages of the diauxic shift or from a random selection of reactions. This indicates that under glucose-rich conditions gene regulation maximises the range of products obtainable from glucose, while minimising the number of active reactions and therefore the burden of protein production.
The Repertoire of Desaturases for Unsaturated Fatty Acid Synthesis in 397 Genomes
Kosuke Hashimoto, Akiyasu Yoshizawa, Koichi Saito, Takuji Yamada, Minoru Kanehisa
Bioinformatics Center, Institute for Chemical Research, Kyoto University
Fatty acids are major components of membrane molecules, and have a great diversity. Most ubiquitous and widespread modification to fatty acids is the insertion of double bonds. The fact that unsaturated fatty acids play multiple important roles physically and biologically, means that the ratio of unsaturated to saturated fatty acids in the membrane needs to be strictly regulated to maintain cellular homeostasis. Fatty acid desaturases directly introduce regioselective double bonds into fatty acids. A phylogenetic analysis of desaturases suggests that the sequences of these proteins include highly conserved domain, and contain the differences in specificity and regioselectivity. In this study, we performed a systematic analysis of fatty acid desaturases found in the genomic data of 397 organisms. We obtained a set of desaturases separated by regioselectivity using a hierarchal clustering analysis.
Wednesday, July 26 at 11:00 AM
Metabolite Antigens and Pathway Incompatibility
Wataru Honda¹, Shuichi Kawashima², Minoru Kanehisa¹
¹Bioinformatics Center, Institute for Chemical Research, Kyoto University
²Human Genome Center, Institute of Medical Science, University of Tokyo
Vgamma9Vdelta2 cells, which constitute a small portion of peripheral blood T-cells (~5%), are known to be the T-cells (~70%) in circulating blood. The dgbiggest subset of human 2 T-cell receptors have and9VgVgamma9Vdelta2 T-cells expressing V ability to recognize non-peptide antigens directly or indirectly, for example, phosphorylated metabolites referred to as phosphoantigens, synthesized aminobisphosphonates used for therapeutic purpose such as pamidronate, and alkylamines. These chemical compounds recognized by 2 T-cells are produced by many prokaryotic and eukaryoticd9VgV organisms. Previous works show that the phosphoantigens recognized by Vgamma9Vdelta2 T-cells are found as the intermediates in the pathogens’ pathway producing IPP. In this paper, we show that the other compounds recognized by Vgamma9Vdelta2 T-cells are found on the pathogens’ biosynthetic pathways leading to production of shared compounds with human. In addition, many compounds having high structural similarity with alkylamine antigens are also found only in pathogen’s biosynthetic pathways or produced only by non-human enzymes.
Wednesday, July 26 at 11:30 AM
Composition of Metabolic Flux Distributions By Functionally Interpretable Minimal Flux Modes (MinModes)
Sabrina Hoffmann, Andreas Hoppe, Hermann-Georg Holzhütter
Medical Faculty of the Humboldt University (Charite), Institute of Biochemistry
All cellular functions are ultimately linked to the metabolism which constitutes a highly branched network 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 of biomass) the distribution of fluxes in the metabolic network may vary considerably. To better reveal and quantify this flux-function relationship we propose a novel computational approach which identifies distinct contributions - so called minimal flux modes (short: MinModes) - to a stationary flux distribution in the network. Each of these contributions is characterized by a single metabolic output. A MinMode is a minimal (according to a defined cost function) steady state flux distribution that enables the production of a single metabolite. We apply this concept to a metabolic network of Methylobacterium extorquens AM1 comprising of 95 reactions and 74 metabolites, 17 of these metabolites entering the biomass of the bacterium and are thus considered as the metabolic output of the network. MinModes represent a manageable set of fundamental flux modes in the network having a clear physiological meaning and - although not representing a basis in strict mathematical sense - provide a satisfactory approximation of the overall flux distribution in cases tested so far.
Wednesday, July 26 at 12:00 PM
Patterns of Interactions of Reaction Pairs in Metabolic Networks
József Bruck, Oliver Ebenhöh, Reinhart Heinrich
Humboldt University, Institute of Biology, Dept. of Theoretical Biophysics
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.
A High Percentage of Introns in Human Genes Were Present Early in Animal Evolution: Evidence from the Basal Metazoan Nematostella vectensis
James Sullivan, Adam Reitzel, John Finnerty
Boston University
Intronic sequences represent a large fraction of most eukaryotic genomes, and they are known to play a critical role in genome evolution. Based on the conserved location of introns, conserved sequences within introns, and direct experimental evidence, it is becoming increasingly clear that introns perform important functions such as modulating gene expression. Here, we demonstrate concordance in the location of 44% of introns (234/526) from conserved portions of 100 orthologous genes between human and the starlet sea anemone Nematostella vectensis, a basal animal (phylum Cnidaria; class Anthozoa). This degree of intron concordance greatly exceeds that of human and fruitfly (13%), human and mosquito (13%) and human and nematode worm (14%). Surprisingly, given the high degree of conservation between human and sea anemone, the fruitfly and mosquito, two members of the order Diptera, share only 39% of intron locations. Our analysis indicates (1) that early animal genomes were intron-rich, (2) that a large fraction of introns present within the human genome likely originated early in evolution, before the cnidarian-bilaterian split, some 600 million years ago, and (3) that there has been a high degree of intron loss during the evolution of the protostome lineage leading to the fruitfly, mosquito, and nematode. These data also reinforce the conclusion that there are functional constraints on the placement of introns in eukaryotic genes.
Analysis of the Differences in Metabolic Network Expansion Between Prokaryotes and Eukaryotes
Michihiro Tanaka, Takuji Yamada, Masumi Itoh, Shujiro Okuda, Susumu Goto, Minoru Kanehisa
Bioinformatics Center, Institute for Chemical Research, Kyoto University
Recent evidences indicate scale-free properties in many biological networks. By the topological analyses, several models including preferential attachment and hierarchical modules have been proposed to explain how these networks are organized. On the other hand, dynamics analyses have suggested that gene expression and metabolic networks have been organized with the scale-free property by the other models such as hrich-travel-moreh and glog-normal dynamics.h Because most of these approaches are based on comparative genomics of extant species, and did not consider evolutionary events such as horizontal gene transfer, gene loss and gene gain, we have analyzed transition of metabolic networks from the vertical point view of evolution. First, to identify metabolic networks of ancestral species, we applied a parsimony algorithm for the enzymatic reaction set. Then by comparing the estimated metabolic networks among ancestral species, we investigated the transition of metabolic networks along the evolutionary process. As a result, we estimated enzymatic reaction contents of 227 ancestral species from 228 extant species, and found that links of several specific metabolites have frequently changed along the evolution.
About the Interrelation of Evolutionary Rate and Protein Age
Hannes Luz, Eike Staub, Martin Vingron
Max Planck Institute for Molecular Genetics
Evolutionary rate and gene age are interrelated when the age of a gene is assessed by the taxonomic distribution in the gene family. This is because homology detection by sequence comparison is depending on sequence similarity. We estimate family specific rates of protein evolution for orthologous families with representatives from man, fugu, fly, and worm. In fact, we observe that younger proteins tend to evolve faster than older ones. We estimate time points of duplication events that gave rise to novel protein functions and show that younger proteins were duplicated more recently than older ones.
