Browsing by Author "Vera Véliz, Mario Andrés"
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- ItemActive microbial biofilms in deep poor porous continental subsurface rocks(2018) Escudero, Cristina; Vera Véliz, Mario Andrés; Oggerin, Monike; Amils, Ricardo
- ItemAutomated Microscopic Analysis of Metal Sulfide Colonization by Acidophilic Microorganisms(2018) Bellenberg, Soren; Buetti-Dinh, Antoine; Galli, Vanni; Ilie, Olga; Herold, Malte; Christel, Stephan; Boretska, Mariia; Pivkin, Igor V.; Wilmes, Paul; Sand, Wolfgang; Vera Véliz, Mario Andrés; Dopson, Mark
- ItemBiofilm dynamics and EPS production of a thermoacidophilic bioleaching archaeon(2019) Zhang, Ruiyong Y.; Neu, Thomas R.; Blanchard, Véronique; Vera Véliz, Mario Andrés; Sand, Wolfgang
- ItemHigh copper concentration reduces biofilm formation in Acidithiobacillus ferrooxidans by decreasing production of extracellular polymeric substances and its adherence to elemental sulfur(2020) Vargas Straube, M. J.; Beard, S.; Norambuena, R.; Paradela, A.; Vera Véliz, Mario Andrés; Jerez, C. A.
- ItemInsights into the biology of acidophilic members of the Acidiferrobacteraceae family derived from comparative genomic analyses(2018) Issotta, Francisco; Moya-Beltran, Ana; Mena, Cristobal; Covarrubias, Paulo C.; Thyssen, Christian; Bellenberg, Sören; Sand, Wolfgang; Quatrini, Raquel; Vera Véliz, Mario Andrés
- ItemInteractions of the extremely acidophilic archaeon Ferroplasma acidiphilum with acidophilic bacteria during pyrite bioleaching(2016) Maulani, N.; Li, Q.; Sand, W.; Vera Véliz, Mario Andrés; Zhang, R.
- ItemPotential use of sulfite as a supplemental electron donor for wastewater denitrification(2016) Sabba, Fabrizio; DeVries, Andrew.; Vera Véliz, Mario Andrés; Druschel, Gregory.; Bott, Charles.; Nerenberg, Robert
- ItemProteomics Reveal Enhanced Oxidative Stress Responses and Metabolic Adaptation in Acidithiobacillus ferrooxidans Biofilm Cells on Pyrite(2019) Bellenberg, S.; Huynh, D.; Poetsch, A.; Sand, W.; Vera Véliz, Mario Andrés
- ItemReverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations(2020) Buetti-Dinh, Antoine.; Vera Véliz, Mario Andrés; Herold, Malte.; Christel, Stephan.; El Hajjami, Mohamed.; Delogu, Francesco.; Ilie, Olga.; Bellenberg, Sören.; Wilmes, Paul.; Poetsch, Ansgar.Abstract Background Network inference is an important aim of systems biology. It enables the transformation of OMICs datasets into biological knowledge. It consists of reverse engineering gene regulatory networks from OMICs data, such as RNAseq or mass spectrometry-based proteomics data, through computational methods. This approach allows to identify signalling pathways involved in specific biological functions. The ability to infer causality in gene regulatory networks, in addition to correlation, is crucial for several modelling approaches and allows targeted control in biotechnology applications. Methods We performed simulations according to the approximate Bayesian computation method, where the core model consisted of a steady-state simulation algorithm used to study gene regulatory networks in systems for which a limited level of details is available. The simulations outcome was compared to experimentally measured transcriptomics and proteomics data through approximate Bayesian computation. Results The structure of small gene regulatory networks responsible for the regulation of biological functions involved in biomining were inferred from multi OMICs data of mixed bacterial cultures. Several causal inter- and intraspecies interactions were inferred between genes coding for proteins involved in the biomining process, such as heavy metal transport, DNA damage, replication and repair, and membrane biogenesis. The method also provided indications for the role of several uncharacterized proteins by the inferred connection in their network context. Conclusions The combination of fast algorithms with high-performance computing allowed the simulation of a multitude of gene regulatory networks and their comparison to experimentally measured OMICs data through approximate Bayesian computation, enabling the probabilistic inference of causality in gene regulatory networks of a multispecies bacterial system involved in biomining without need of single-cell or multiple perturbation experiments. This information can be used to influence biological functions and control specific processes in biotechnology applications.
- ItemThe Electrochemically Active Arsenic Oxidising Bacterium Ancylobacter sp TS-1(2018) Anguita, Javiera M.; Vera Véliz, Mario Andrés; Vargas Cucurella, Ignacio Tomás; CEDEUS (Chile)
- ItemWeak Iron Oxidation by Sulfobacillus thermosulfidooxidans Maintains a Favorable Redox Potential for Chalcopyrite Bioleaching(2018) Christel, Stephan; Herold, Malte; Bellenberg, Soeren; Buetti-Dinh, Antoine; El Hajjami, Mohamed; Pivkin, Igor, V; Sand, Wolfgang; Wilmes, Paul; Poetsch, Ansgar; Vera Véliz, Mario Andrés; Dopson, Mark