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Author Travisany, D.; Goles, E.; Latorre, M.; Cort?s, M.P.; Maass, A.
Title Generation and robustness of Boolean networks to model Clostridium difficile infection Type
Year 2020 Publication Natural Computing Abbreviated Journal Nat. Comput.
Volume 19 Issue 1 Pages (down) 111-134
Keywords Threshold network; Neutral space; Evolutionary computation; Microbiome; Clostridium difficile infection
Abstract One of the more common healthcare associated infection is Chronic diarrhea. This disease is caused by the bacterium Clostridium difficile which alters the normal composition of the human gut flora. The most successful therapy against this infection is the fecal microbial transplant (FMT). They displace C. difficile and contribute to gut microbiome resilience, stability and prevent further episodes of diarrhea. The microorganisms in the FMT their interactions and inner dynamics reshape the gut microbiome to a healthy state. Even though microbial interactions play a key role in the development of the disease, currently, little is known about their dynamics and properties. In this context, a Boolean network model for C. difficile infection (CDI) describing one set of possible interactions was recently presented. To further explore the space of possible microbial interactions, we propose the construction of a neutral space conformed by a set of models that differ in their interactions, but share the final community states of the gut microbiome under antibiotic perturbation and CDI. To begin with the analysis, we use the previously described Boolean network model and we demonstrate that this model is in fact a threshold Boolean network (TBN). Once the TBN model is set, we generate and use an evolutionary algorithm to explore to identify alternative TBNs. We organize the resulting TBNs into clusters that share similar dynamic behaviors. For each cluster, the associated neutral graph is constructed and the most relevant interactions are identified. Finally, we discuss how these interactions can either affect or prevent CDI.
Address [Travisany, Dante; Goles, Eric] Univers Adolfo Ibanez, Facultad Ingn Ciencias, Santiago, Chile, Email: dtravisany@dim.uchile.cl
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1567-7818 ISBN Medium
Area Expedition Conference
Notes WOS:000517129300008 Approved
Call Number UAI @ eduardo.moreno @ Serial 1167
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Author Loira, N.; Mendoza, S.; Cortes, M.P.; Rojas, N.; Travisany, D.; Di Genova, A.; Gajardo, N.; Ehrenfeld, N.; Maass, A.
Title Reconstruction of the microalga Nannochloropsis salina genome-scale metabolic model with applications to lipid production Type
Year 2017 Publication Bmc Systems Biology Abbreviated Journal BMC Syst. Biol.
Volume 11 Issue Pages (down) 17 pp
Keywords Genome-scale Metabolic model; Nannochloropsis salina; TAG; Microalg ae
Abstract Background: Nannochloropsis salina (= Eustigmatophyceae) is a marine microalga which has become a biotechnological target because of its high capacity to produce polyunsaturated fatty acids and triacylglycerols. It has been used as a source of biofuel, pigments and food supplements, like Omega 3. Only some Nannochloropsis species have been sequenced, but none of them benefit from a genome-scale metabolic model (GSMM), able to predict its metabolic capabilities. Results: We present iNS934, the first GSMM for N. salina, including 2345 reactions, 934 genes and an exhaustive description of lipid and nitrogen metabolism. iNS934 has a 90% of accuracy when making simple growth/no-growth predictions and has a 15% error rate in predicting growth rates in different experimental conditions. Moreover, iNS934 allowed us to propose 82 different knockout strategies for strain optimization of triacylglycerols. Conclusions: iNS934 provides a powerful tool for metabolic improvement, allowing predictions and simulations of N. salina metabolism under different media and genetic conditions. It also provides a systemic view of N. salina metabolism, potentially guiding research and providing context to -omics data.
Address [Loira, Nicolas; Mendoza, Sebastian; Paz Cortes, Maria; Travisany, Dante; Di Genova, Alex; Maass, Alejandro] Univ Chile, Ctr Math Modeling, Math, Beauchef 851,7th Floor, Santiago, Chile, Email: nloira@dim.uchile.cl
Corporate Author Thesis
Publisher Biomed Central Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1752-0509 ISBN Medium
Area Expedition Conference
Notes WOS:000404918700001 Approved
Call Number UAI @ eduardo.moreno @ Serial 744
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Author Cortes, M.P.; Mendoza, S.N.; Travisany, D.; Gaete, A.; Siegel, A.; Cambiazo, V.; Maass, A.
Title Analysis of Piscirickettsia salmonis Metabolism Using Genome-Scale Reconstruction, Modeling, and Testing Type
Year 2017 Publication Frontiers In Microbiology Abbreviated Journal Front. Microbiol.
Volume 8 Issue Pages (down) 15 pp
Keywords pathogen; genome-scale; metabolism; constraint-based; Piscirickettsia; salmonis
Abstract Piscirickettsia salmonis is an intracellular bacterial fish pathogen that causes piscirickettsiosis, a disease with highly adverse impact in the Chilean salmon farming industry. The development of effective treatment and control methods for piscireckttsiosis is still a challenge. To meet it the number of studies on P. salmonis has grown in the last couple of years but many aspects of the pathogen's biology are still poorly understood. Studies on its metabolism are scarce and only recently a metabolic model for reference strain LF-89 was developed. We present a new genomescale model for P. salmonis LF-89 with more than twice as many genes as in the previous model and incorporating specific elements of the fish pathogen metabolism. Comparative analysis with models of different bacterial pathogens revealed a lower flexibility in P. salmonis metabolic network. Through constraint-based analysis, we determined essential metabolites required for its growth and showed that it can benefit from different carbon sources tested experimentally in new defined media. We also built an additional model for strain A1-15972, and together with an analysis of P. salmonis pangenome, we identified metabolic features that differentiate two main species clades. Both models constitute a knowledge-base for P. salmonis metabolism and can be used to guide the efficient culture of the pathogen and the identification of specific drug targets.
Address [Cortes, Maria P.; Mendoza, Sebastian N.; Travisany, Dante; Maass, Alejandro] Univ Chile, Ctr Math Modeling, Math, Santiago, Chile, Email: mpcortes@dim.uchile.cl
Corporate Author Thesis
Publisher Frontiers Media Sa Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1664-302x ISBN Medium
Area Expedition Conference
Notes WOS:000417578100001 Approved
Call Number UAI @ eduardo.moreno @ Serial 787
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