All Stories

  1. SynCoKnock: a computational workflow for,knockout-based design of two-strain microbial,co-cultures to optimize target metabolite production and engineer cross-feeding
  2. Calibrating a parameterized stochastic Boolean network model of gene regulation using a single steady-state gene expression profile
  3. Genome-Scale Metabolic Modeling Identifies Synergistic Metabolites that Enhance 5-Fluorouracil Efficacy in Colon Cancer.
  4. pastboon: an R package to simulate parameterized stochastic Boolean networks
  5. AmiR-P3: An AI-based microRNA prediction pipeline in plants
  6. RT-DOb, a switch gene for the gene pair {Csf1r, Milr1}, can influence the onset of Alzheimer’s disease by regulating communication between mast cell and microglia
  7. Genome-scale metabolic model-based engineering of Escherichia coli enhances recombinant single-chain antibody fragment production
  8. On the role of oxygen and glucose in bone marrow-derived mesenchymal stem cell proliferation
  9. Computational modeling of the evolutionary transition from C3 to C4 photosynthesis
  10. Critical assessment of genome-scale metabolic models of Arabidopsis thaliana
  11. Structural systems pharmacology: A framework for integrating metabolic network and structure-based virtual screening for drug discovery against bacteria
  12. Transcriptome Meta-Analysis Suggests the Existence of Two Molecular Subtypes in Alzheimer’s Disease
  13. A constraint-based modeling approach to reach an improved chemically defined minimal medium for recombinant antiEpEX-scFv production by Escherichia coli
  14. CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data
  15. Systematically gap-filling the genome-scale metabolic model of CHO cells
  16. Rps27a might act as a controller of microglia activation in triggering neurodegenerative diseases
  17. In silico prediction of enzymatic reactions catalyzed by acid phosphatases
  18. A meta-analysis of gene expression data highlights synaptic dysfunction in the hippocampus of brains with Alzheimer’s disease
  19. A reconciliation of genome-scale metabolic network model of Zymomonas mobilis ZM4
  20. Systematically gap-filling the genome-scale model of CHO cells
  21. Manually curated genome-scale reconstruction of the metabolic network of Bacillus megaterium DSM319
  22. A metabolic network-based approach for developing feeding strategies for CHO cells to increase monoclonal antibody production
  23. The metabolic network model of primed/naive human embryonic stem cells underlines the importance of oxidation-reduction potential and tryptophan metabolism in primed pluripotency
  24. SAFEPPP: a Simple And Fast method to Find and analyze Extreme Points of a metabolic Phenotypic Phase Plane
  25. A genome-scale metabolic network reconstruction of extremely halophilic bacterium Salinibacter ruber
  26. Accounting for robustness in modeling signal transduction responses
  27. Three-way interaction model with switching mechanism as an effective strategy for tracing functionally-related genes
  28. Genome-Scale Metabolic Network Models of Bacillus Species Suggest that Model Improvement is Necessary for Biotechnological Applications
  29. A graph-based approach to analyze flux-balanced pathways in metabolic networks
  30. Applications of a metabolic network model of mesenchymal stem cells for controlling cell proliferation and differentiation
  31. Three-way interaction model to trace the mechanisms involved in Alzheimer’s disease transgenic mice
  32. Biomedical applications of cell- and tissue-specific metabolic network models
  33. Discovering missing reactions of metabolic networks by using gene co-expression data
  34. A sperm-specific proteome-scale metabolic network model identifies non-glycolytic genes for energy deficiency in asthenozoospermia
  35. A multiscale agent-based framework integrated with a constraint-based metabolic network model of cancer for simulating avascular tumor growth
  36. In silico prediction of specific pathways that regulate mesangial cell proliferation in IgA nephropathy
  37. A network biology approach to understanding the importance of chameleon proteins in human physiology and pathology
  38. A kidney-specific genome-scale metabolic network model for analyzing focal segmental glomerulosclerosis
  39. CAMWI: Detecting protein complexes using weighted clustering coefficient and weighted density
  40. PCD-GED: Protein complex detection considering PPI dynamics based on time series gene expression data
  41. On correlated reaction sets and coupled reaction sets in metabolic networks
  42. Evidence for the relationship between the regulatory effects of microRNAs and attack robustness of biological networks
  43. Reconstruction and validation of a constraint-based metabolic network model for bone marrow-derived mesenchymal stem cells
  44. Hierarchical organization of fluxes in Escherichia coli metabolic network: Using flux coupling analysis for understanding the physiological properties of metabolic genes
  45. Can scientific journals be classified based on their ‘citation profiles’?
  46. Genome-scale reconstruction of the metabolic network in Pseudomonas stutzeri A1501
  47. Reconstruction of phylogenetic trees of prokaryotes using maximal common intervals
  48. FCDECOMP: Decomposition of metabolic networks based on flux coupling relations
  49. A mathematical approach to emergent properties of metabolic networks: Partial coupling relations, hyperarcs and flux ratios
  50. Reconstruction of a generic metabolic network model of cancer cells
  51. Modeling the Differences in Biochemical Capabilities ofPseudomonasSpecies by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences?
  52. Evolutionarily conserved motifs and modules in mitochondrial protein–protein interaction networks
  53. Finding elementary flux modes in metabolic networks based on flux balance analysis and flux coupling analysis: application to the analysis of Escherichia coli metabolism
  54. Exploring biological processes involved in embryonic stem cell differentiation by analyzing proteomic data
  55. Studying the Relationship between Robustness against Mutations in Metabolic Networks and Lifestyle of Organisms
  56. On flux coupling analysis of metabolic subsystems
  57. Analysis of Metabolic Subnetworks by Flux Cone Projection
  58. Flux coupling analysis of metabolic networks is sensitive to missing reactions
  59. FFCA: a feasibility-based method for flux coupling analysis of metabolic networks
  60. Binding of Tris to Bacillus licheniformis α-Amylase Can Affect Its Starch Hydrolysis Activity
  61. Impact of residue accessible surface area on the prediction of protein secondary structures
  62. β-Sheet capping: Signals that initiate and terminate β-sheet formation
  63. A tale of two symmetrical tails: Structural and functional characteristics of palindromes in proteins
  64. Evolution of ‘Ligand-Diffusion Chreodes’ on Protein-Surface Models: A Genetic-Algorithm Study
  65. Adaptation of proteins to different environments: A comparison of proteome structural properties in Bacillus subtilis and Escherichia coli
  66. On the mechanism of apoptosis-inducing activity of human calprotectin: Zinc sequestration, induction of a signaling pathway, or something else?
  67. Conformational Study of Human Serum Albumin in Pre-denaturation Temperatures by Differential Scanning Calorimetry, Circular Dichroism and UV Spectroscopy
  68. Importance of RNA secondary structure information for yeast donor and acceptor splice site predictions by neural networks
  69. Application of β-lactamase-dependent prodrugs in clostridial-directed enzyme therapy (CDEPT): A proposal
  70. Modeling directed ligand passage toward enzyme active site by a ‘double cellular automata’ model
  71. On the identity of “citers”: Are papers promptly recognized by other investigators?
  72. Nanotechnology helps medicine: Nanoscale swimmers and their future applications
  73. Why major nonenzymatic glycation sites of human serum albumin are preferred to other residues?
  74. Correlations between genomic GC levels and optimal growth temperatures are not ‘robust’
  75. How reliable re-adjustment is: correspondence regarding A. Fuglsang, “The ‘effective number of codons’ revisited”