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Record Nr. |
UNISA996466313003316 |
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Titolo |
Computational Methods in Systems Biology [[electronic resource] ] : 17th International Conference, CMSB 2019, Trieste, Italy, September 18–20, 2019, Proceedings / / edited by Luca Bortolussi, Guido Sanguinetti |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (XI, 388 p. 487 illus., 87 illus. in color.) |
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Collana |
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Lecture Notes in Bioinformatics ; ; 11773 |
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Disciplina |
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Soggetti |
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Bioinformatics |
Artificial intelligence |
Algorithms |
Mathematical logic |
Computer science—Mathematics |
Software engineering |
Computational Biology/Bioinformatics |
Artificial Intelligence |
Algorithm Analysis and Problem Complexity |
Mathematical Logic and Formal Languages |
Mathematics of Computing |
Software Engineering/Programming and Operating Systems |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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Regular Papers -- Sequential Reprogramming of Boolean Networks Made Practical -- Sequential Reprogramming of Biological Network Fate -- Control Variates for Stochastic Simulation of Chemical Reaction Networks -- Effective computational methods for hybrid stochastic gene networks -- On Chemical Reaction Network Design by a Nested Evolution Algorithm -- Designing Distributed Cell Classifier Circuits using a Genetic Algorithm -- Investigating a Hodgkin-Huxley type model for Drosophila larval neuromuscular junctions via particle swarm |
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fitting -- Cell volume distributions in exponentially growing populations -- Transient Memory in Gene Regulation -- A Logic-Based Learning Approach to Explore Diabetes Patient Behaviors -- Reachability design through Approximate Bayesian Computation -- Fast enumeration of non-isomorphic chemical reaction networks -- A large-scale assessment of exact model reduction in the BioModels repository -- Computing Difference Abstractions of Metabolic Networks Under Kinetic Constraints -- Tool Papers -- BRE:IN - A Backend for Reasoning about Interaction Networks with Temporal Logic -- The Kappa simulator made interactive -- Biochemical reaction networks with fuzzy kinetic parameters in Snoopy -- Compartmental Modeling Software: a fast, discrete stochastic framework for biochemical and epidemiological simulation -- Spike - reproducible simulation experiments with configuration file branching -- KAMIStudio: an environment for biocuration of cellular signalling knowledge -- A new version of DAISY to test structural identifiability of biological models -- Extended Abstracts (Posters and Highlight Talks) -- Semi-Quantitative Abstraction and Analysis of Chemical Reaction Networks -- Bayesian parameter estimation for stochastic reaction networks from steady-state observations -- Wasserstein Distances for Estimating Parameters in Stochastic Reaction Networks -- On Inferring Reactions from Data Time Series by a Statistical Learning Greedy Heuristics -- Barbaric Robustness Monitoring Revisited for STL* in Parasim -- Symmetry breaking for GATA-1/PU.1 model -- Scalable Control of Asynchronous Boolean Networks -- Transcriptional response of SK-N-AS cells to methamidophos (Extended Abstract) -- Separators for polynomial dynamic systems with linear complexity -- Bounding First Passage Times in Chemical Reaction Networks -- Data-informed parameter synthesis for population Markov chains. |
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Sommario/riassunto |
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This book constitutes the refereed proceedings of the 17th International Conference on Computational Methods in Systems Biology, CMSB 2019, held in Trieste, Italy, in September 2019. The 14 full papers, 7 tool papers and 11 posters were carefully reviewed and selected from 53 submissions. Topics of interest include formalisms for modeling biological processes; models and their biological applications; frameworks for model verification, validation, analysis, and simulation of biological systems; high-performance computational systems biology and parallel implementations; model inference from experimental data; model integration from biological databases; multi-scale modeling and analysis methods; computational approaches for synthetic biology; and case studies in systems and synthetic biology. |
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