<![CDATA[ IEEE/ACM Transactions on Computational Biology and Bioinformatics - new TOC ]]>
http://ieeexplore.ieee.org
TOC Alert for Publication# 8857 2019April 18<![CDATA[Editorial]]>162350351142<![CDATA[Subspace Weighting Co-Clustering of Gene Expression Data]]>1623523643185<![CDATA[Reinforce: An Ensemble Approach for Inferring PPI Network from AP-MS Data]]>1623653762200<![CDATA[Essential Protein Detection by Random Walk on Weighted Protein-Protein Interaction Networks]]>162377387720<![CDATA[An Improved Approach for N-Linked Glycan Structure Identification from HCD MS/MS Spectra]]>162388395433<![CDATA[Integrating Multiple Heterogeneous Networks for Novel LncRNA-Disease Association Inference]]>162396406960<![CDATA[KATZLGO: Large-Scale Prediction of LncRNA Functions by Using the KATZ Measure Based on Multiple Networks]]>1624074161329<![CDATA[MGT-SM: A Method for Constructing Cellular Signal Transduction Networks]]>1624174241438<![CDATA[A CPU/MIC Collaborated Parallel Framework for GROMACS on Tianhe-2 Supercomputer]]>162425433717<![CDATA[A Feature Sampling Strategy for Analysis of High Dimensional Genomic Data]]>162434441436<![CDATA[A Unified Model for Joint Normalization and Differential Gene Expression Detection in RNA-Seq Data]]>162442454899<![CDATA[An Integrated Framework for Identifying Mutated Driver Pathway and Cancer Progression]]>1624554641826<![CDATA[An RJMCMC-Based Method for Tracking and Resolving Collisions of Drosophila Larvae]]>162465474818<![CDATA[Bayesian Network Construction and Genotype-Phenotype Inference Using GWAS Statistics]]>162475489824<![CDATA[Biomarker Identification for Cancer Disease Using Biclustering Approach: An Empirical Study]]>162490509693<![CDATA[Chemical Transformation Motifs—Modelling Pathways as Integer Hyperflows]]>162510523894<![CDATA[Constructing Pathway-Based Priors within a Gaussian Mixture Model for Bayesian Regression and Classification]]>1625245371500<![CDATA[Detecting Population-Differentiation Copy Number Variants in Human Population Tree by Sparse Group Selection]]>1625385491483<![CDATA[Estimation of the Spatial Chromatin Structure Based on a Multiresolution Bead-Chain Model]]>162550559887<![CDATA[Estrogenic Active Stilbene Derivatives as Anti-Cancer Agents: A DFT and QSAR Study]]>162560568489<![CDATA[Fast Algorithms for Computing Path-Difference Distances]]>162569582980<![CDATA[Modelling and Control of Gene Regulatory Networks for Perturbation Mitigation]]>1625835951005<![CDATA[New Deep Learning Methods for Protein Loop Modeling]]>162596606791<![CDATA[Protein-Protein Interaction Identification Using a Similarity-Constrained Graph Model]]>162607616894<![CDATA[Safely Filling Gaps with Partial Solutions Common to All Solutions]]>162617626477<![CDATA[Stable IL-<inline-formula><tex-math notation="LaTeX">$1beta$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mi>β</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="lopezcaamal-ieq1-2794971.gif"/></alternatives></inline-formula>-Activation in an Inflammasome Signalling Model Depends on Positive and Negative Feedbacks and Tight Regulation of Protein Production]]>$kappa$B-pathway signalling, subsequently proceeds via lower transcription/translation process for producing pro-enzymes, and finally leads to the medium-speed enzymatic activation of the central inflammatory ediator IL-1$beta$ [1]. We here were interested how the timing of the rate-limiting step of transcription/translation and the presence of a positive and negative auto-regulation would pose conditions for meaningful and stable IL-1$beta$-activation. Methods: We extracted the essential topology of the inflammasome pathway network using a linear chain of first order reaction and a second order reaction for inhibitory feedback. We then performed an analytical treatment of the resulting ODE set to obtain closed-form formulae. We therefore looked for the steady states and characterised their stability by using a Jacobian-based, local analysis. We employed the Small Gain Theorem from Control Theory as recently applied by us [2] and the Gershgorin Circle Theorem to obtain mathematically exact conditions for a positive on state and stabilities for on and off steady states. Results: We identified an on- and one off- steady state whose properties we characterised in terms of the kinetic parameters by closed-form formulae. We found that under the assumption of a first-order information flow through the network the existence of a biologically reasonable ON steady state required the simultaneous presence of the positive and the negative feedback. Assuming non-competitivity between IL-1$beta$ entities binding to different receptors, we found that a minimum kinetics for protein production is required to sustain a steady-
state with IL-1$beta$ activation. Assuming competitivity between IL-1$beta$ entities introduced additional restrictions on the maximum protein production speed to guarantee a biologically reasonable ON steady state. Finally, for both models we ruled out bistability, suggesting that IL- 1$beta$ activation would undergo a smooth change upon alterations of its parameters. Conclusion: Exemplified by the core pathway of NLRP3-inflammasome signalling, we here demonstrate that a mostly linear activation cascade containing an intermediate rate limiting step poses kinetic restrictions on this step and requires positive and negative autoregulation for obtaining a meaningful ON steady state. Due to the generality of our framework, our results are important for a wide class of receptor mediated-pathways, where a fast initial phosphorylation cascade is followed by a (slower) transcriptional response and subsequent autoregulation. Our results may further provide important design principles for synthetic biological networks involving biochemical activation and transcription/translation, by relating timing considerations and autoregulation to stable pathway activation.]]>162627637567<![CDATA[Statistical Association Mapping of Population-Structured Genetic Data]]>162638649720<![CDATA[The Robust Classification Model Based on Combinatorial Features]]>162650657631<![CDATA[Theory and A Heuristic for the Minimum Path Flow Decomposition Problem]]>1626586701109<![CDATA[Unsupervised Discovery of Geometrically Common Structural Motifs and Long-Range Contacts in Protein 3D Structures]]>162671680931<![CDATA[A Novel Cluster-Based Computational Method to Identify miRNA Regulatory Modules]]>162681687650<![CDATA[A Novel Method for LncRNA-Disease Association Prediction Based on an lncRNA-Disease Association Network]]>162688693355<![CDATA[Phylogenetic Reconstruction for Copy-Number Evolution Problems]]>1626946991573<![CDATA[Corrections to “A Novel Computational Approach for Global Alignment for Multiple Biological Networks”]]>162700700642