Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations

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Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations

Buetti-Dinh, Antoine, 1984- (författare): Linnéuniversitetet,Institutionen för kemi och biomedicin (KOB),Università della Svizzera italiana, Switzerland;Swiss Institute of Bioinformatics, Switzerland,Linnaeus Ctr Biomat Chem, BMC;Ctr Ecol & Evolut Microbial Model Syst EEMiS

Herold, Malte (författare): University of Luxembourg, Luxembourg

Christel, Stephan (författare): Linnéuniversitetet,Institutionen för biologi och miljö (BOM),Ctr Ecol & Evolut Microbial Model Syst EEMiS

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El Hajjami, Mohamed (författare): QNLM, China

Delogu, Francesco (författare): Norwegian University of Life Sciences, Norway

Ilie, Olga (författare): Università della Svizzera italiana, Switzerland;Swiss Institute of Bioinformatics, Switzerland

Bellenberg, Sören (författare): Linnéuniversitetet,Institutionen för biologi och miljö (BOM),Ctr Ecol & Evolut Microbial Model Syst EEMiS

Wilmes, Paul (författare): University of Luxembourg, Luxembourg

Poetsch, Ansgar (författare): Ruhr University Bochum, Germany;QNLM, China;Ocean University of China, China

Sand, Wolfgang (författare): University Duisburg-Essen, Germany;Donghua University, China;Mining Academy and Technical University Freiberg, Germany

Vera, Mario (författare): Pontificia Universidad Católica de Chile, Chile

Pivkin, Igor V. (författare): Università della Svizzera italiana, Switzerland;Swiss Institute of Bioinformatics, Switzerland

Friedman, Ran (författare): Linnéuniversitetet,Institutionen för kemi och biomedicin (KOB),Vatten,CCBG;Linnaeus Ctr Biomat Chem, BMC

Dopson, Mark, 1970- (författare): Linnéuniversitetet,Institutionen för biologi och miljö (BOM),Ctr Ecol & Evolut Microbial Model Syst EEMiS

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2020-01-21
2020
Engelska.
Ingår i: BMC Bioinformatics. - : BioMed Central (BMC). - 1471-2105. ; 21:1, s. 1-15

Relaterad länk:: https://doi.org/10.1...; visa fler...; https://doi.org/10.1...; https://urn.kb.se/re...; https://doi.org/10.1...; visa färre...

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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.

Ämnesord

NATURVETENSKAP -- Biologi -- Mikrobiologi (hsv//swe)
NATURAL SCIENCES -- Biological Sciences -- Microbiology (hsv//eng)
NATURVETENSKAP -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
NATURAL SCIENCES -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)

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Mikrobiologi
Microbiology

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Av författaren/redakt...: Buetti-Dinh, Ant ...; Herold, Malte; Christel, Stepha ...; El Hajjami, Moha ...; Delogu, Francesc ...; Ilie, Olga; visa fler...; Bellenberg, Söre ...; Wilmes, Paul; Poetsch, Ansgar; Sand, Wolfgang; Vera, Mario; Pivkin, Igor V.; Friedman, Ran; Dopson, Mark, 19 ...; visa färre...

Om ämnet

NATURVETENSKAP: NATURVETENSKAP; och Biologi; och Mikrobiologi

NATURVETENSKAP: NATURVETENSKAP; och Biologi; och Bioinformatik oc ...

Artiklar i publikationen: BMC Bioinformati ...

Av lärosätet: Linnéuniversitetet

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