ERASysAPP

IMOMESIC - Integrating Modelling of Metabolism and Signalling towards an Application in Liver Cancer

One of the most challenging questions in cancer research is currently the interconnection of metabolism and signalling. An understanding of mechanisms that facilitate the physiological shift towards a proliferative metabolism in cancer cells is considered a major upcoming topic in oncology and is a key activity for future drug development. Due to the complexity of interrelations, a systems biology ...

Biomining is a biotechnological process carried out in many parts of the world that exploits acid loving microorganisms to extract metals from sulphide minerals. One industrial biomining method is called ‘heap bioleaching’ where typically copper containing minerals are piled into very large heaps, acid and microorganisms are added to the top and the soluble metal is collected at the heap base.

The role of the different types of microbes in the process is to speed up metal solubilisation by oxidising ...

Microbial strains used in biotechnological industry need to produce their biotechnological products at high yield and at the same time they are desired to be robust to the intrinsic nutrient dynamics of large-scale bioreactors, most noticeably to transient limitations of carbon sources and oxygen. The engineering principles for robustness of metabolism to nutrient dynamics are however not yet well understood. The ROBUSTYEAST project aims to reveal these principles for microbial strain improvement ...

D-xylose is a major component of lignocellulose and is after D-glucose the most abundant monosaccharide on earth. However, D-xylose cannot be naturally utilised by several industrially relevant microorganisms. On the way to a strong bio-based economy in Europe, this widely available feedstock has to be made accessible for the sustainable microbial synthesis of value-added chemical building blocks to be used in a broad range of applications. The project aims at engineering Corynebacterium glutamicum ...

There is an urgent need for novel antibiotics to fight life-threatening infections and to counteract the increasing problem of propagating antibiotic resistance. Recently, new molecular genetic and biochemical tools have provided insight into the enormous unexploited genetic pool of environmental microbial biodiversity for new antibiotic compounds. New tools for more efficiently lifting this hidden treasure are needed to strengthen competitiveness of European industry, as well as for a cost-saving ...

Translating Systems Virology data into broad-spectrum antiviral Drugs

Escherichia coli is a well-established and the most widely used organism for the production of recombinant proteins (used in medical and industrial applications, as molecular biology reagents, etc.). Production of proteins is the most resource exhaustive process for the cells and therefore needs to be optimized to achieve maximal productivities. Natural environment of E. coli is much harsher compared to the near optimal growth conditions used in production processes. In order to survive cells ...

CropClock - Increasing Crops Biomass by Uncovering the Circadian Clock Network Using Dynamical Models

The circadian clock is an internal timing system that allows plants to predict daily and seasonal changes in light and temperature and thus to adapt photosynthesis, growth, and development to external conditions. The core oscillator is well understood in the model plant Arabidopsis, however, relatively little is known about the dynamic effects of the clock on agronomic behaviour of crop plants. ...

GMO free systems optimization of wine yeast for wine production by massive scale directed evolution

Designer microbial communities for fermented milk products: A Systems Biology Approach

ErasysApp Funders

Systems biology of bacterial methylotrophy for biotechnological

The raw data generated in the scope of the SysMetEx project for RNAseq, proteomics, and imaging analysis. The data was generated on single and mixed species cultures of A. Caldus, L.ferriphilum, and/or S.thermosulfidooxidans. Raw RNA data is combined in an ENA umbrella study summarising all short read data generated in the project. Raw proteomics data is provided for distinct conditions at the pride repository. Imaging data is provided for distinct conditions at a zenodo repository.

Supplementary files for the submission: Reverse Engineering Directed Gene Regulatory Networks from Transcriptomics and Proteomics Data of Biomining Bacterial Communities with Approximate Bayesian Computation and Steady-State Signalling Simulations

Supplementary files for the publication: Deep Neural Networks Outperform Human Expert’s Capacity in Characterizing Bioleaching Bacterial Biofilm Composition

Publication data made available for Biotechnology Reports, supplementary data

Experimental data and all related material for the publication "Multi -omics reveal lifestyle of acidophile, mineral-oxidizing model species Leptospirillum ferriphilumT". changed ID

Experimental data and all related material for the publication "Multi -omics reveal lifestyle of acidophile, mineral-oxidizing model species Leptospirillum ferriphilumT".

Gene co-epxression network analyses are common in the study of large scale biological data sets. In this study, we have developed a methodology for the comparison of pairs of co-expression networks using the s-core network peeling approach. We apply the methodology to gene-expression data for human and mouse.

Antibiotics are made during the second phase of growth when there is a transition in metabolism from primary to secondary metabolism. Primary metabolism is growth related and involves all the normal cellular activities associated with cell growth and division. Whereas secondary metabolism is non-growth linked and is non-essential but many important activities occur during this phase which help the bacterium survive.

One of these activities is antibiotic production and is widespread in streptomycetes ...

Supplemental files for the publication of the dataset collection.

Proteomics and transcriptomics data tables, sample IDs and description, source code

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Publication data made available for Biotechnology Reports, supplementary data

This stores: Processed data files Links to raw data files Links to repositories containing applied workflows

s-core/ s-core+ network peeling is a methodology to identify cores of weighted complex networks.

Genotype: Wildtype (M145E) Medium: Phosphate-limited (F134)

RNAseq data for L.ferriphilum samples

Proteomics data for L.ferriphilum samples (continuous and with mineral)

Genomics data, for L.ferriphilum Sequencing of the genome and functional annotation

Collection for experimental SOPs

We have developed a method for comparative analysis of pairs of complex networks based on gene co-expression analysis. We apply this modeling analysis to data set for gene expressions in multiple tissues of mus musculus and homo sapiens.

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Data derived from RNAseq

Data derived from protein samples

Scripts for running network simulations

Rawdata for RNAseq derived from biofilms on chalcopyrite grains

Raw proteomics data derived from planktonic cells in chalcopyrite leaching experiments

Proteomics data for samples derived from continuous cultures

Umbrella study for all RNAseq rawdata Separate projects can be accessed under "component projects"

Overview of the quality control for the RNAseq short read data after quality filtering of the reads

Overview for quality control of mapping statistics, after mapping processed reads to reference genomes

Repository for code used in data analysis (mainly for RNAseq) and for generating summary tables and overviews.

Files for the reference genomes utilized in the analysis are summarised in this repository.

Overivew of RNAseq and proteomics samples with respective accessions to access the raw data on ENA or PRIDE respectively.

Pride project for raw and search data for AXX-Cn samples: LNU-AXX-Si00-CnB-P-B6ST-Pr_180min LNU-AXX-Si00-CnB-P-B7ST-Pr_180min LNU-AXX-Si00-CnB-P-B8ST-Pr_180min

Raw proteomics data for L. ferriphilum planktonic samples

Raw proteomics data for S. thermosulfidooxidans samples

Raw data for tertiary mixture, planktonic cells, proteomics

Raw data for , Ac + St or Lf + St planktonic cells from leaching experiments

Raw proteomics data for St + Ac planktonic cells

Raw data for Ac + St leaching experiments proteomics data, with Ac 10x inoculum sizing

Raw data for Ac+St planktonic cells, leaching experiments with St 10x incoulum sizing

Parameters used in maxQuant analysis

Overview of the quality control for the RNAseq short read data before quality filtering of the reads

Raw data for mixed culture biofilm proteomics

Samples: UDE-SAL8-7O-M-B-Pr-2 UDE-SAL8-7O-M-B-Pr UDE-SAL7-Pc20-7M-P-B-Pr_2uL UDE-SAL7-A7-M-B-Pr UDE-SAL7-7C-M-B-Pr UDE-SAL7-7A-M-B-Pr

A small model representing the core carbon network in each cell. For more detail on the model creation see [1]. The model is written in SBML using the RAM extension for use in deFBA. Compatible python software for simulation can be found at https://tinyurl.com/yy8xu4v7

[1] S. Waldherr, D. A. Oyarzún, A. Bockmayr. Dynamic optimization of metabolic networks coupled with gene expression. In: Journal of Theoretical Biology, 365(0): 469 - 485.

This model was created to showcase all functions of the SBML extension RAM. The model can be unr in deFBA with the python software deFBA-Python. The software is freely available at at https://tinyurl.com/yy8xu4v7

A minimal model showing the core of resource allocation models as it can either be invested in enzymatic machinery or single biomass components with the best yield. The model is written in SBML using the RAM extension for use in deFBA. Compatible python software for simulation can be found at https://tinyurl.com/yy8xu4v7

This SBML file uses the RAM extension and contains a minimal genome scaled model for Saccharomyces cerevisiae. The model is based of Yeast 6.06 and was published first in A.-M. Reimers Thesis "Understanding metabolic regulation and cellular resource allocation through optimization".

Aceton precipitation and protein digestion for proteomic workflow

SOP for bioleaching experiments carried out for microscopy analysis at UDE

steps conducted to pellet cells for RNA and protein extraction

This file describes the naming system used to uniquely identify samples

SOP for extracting DNA, RNA and Proteins from the mineral pellet of a bioleaching culture using hot acidic phenol. Current draft.

Protocol for the extraction of biomolecules (DNA,RNA,protein,metabolties) from the same sample. Originally intended for lipid accumulating organisms (LAO) from wastewater treatment plant samples. So far succesfully used for pelleted cells from planktonic cultures.

sampling procedure for mineral containing leaching experiments

Initial configuration and parameters of the Bioleaching

This README file describes how the s-core / s-core+ analysis perl script is to be executed together with data files.

Metabolic networks with gene expression are researched under very different banners with different techniques. For example, there are the dynamic enzyme-cost Flux Balance Analysis (deFBA) [1], conditional Flux Balance Analysis [2], Metabolism and Expression models (ME models) [3], Resource Balance Analysis [4], etc. At their core, these methods can all understood as Resource Allocation Models (RAM) and while investigating their potential and their results, we encountered the problem of sharing ...

TATAA biocenter workshop lecture

about Mark

Workshop by the fairdomhub team on reproducible and citable data and models

Stephan's poster for presentation at IBS 2015

The second project review meeting took place on May 24-25, 2016, at the University of Luxembourg, Luxembourg. This was a theory meeting between the team members to discuss research progress and plans for the project.

The kickoff meeting of CropClock team took place in November 12, 2014 at the Max Plank Institute (MPIPZ, http://www.mpipz.mpg.de/en), Kologne, Germany. The goal for this meeting was for the members of the various teams to get acquainted with each other’s expertise and to discuss the following items: 1) Logistics about the project start, 2) Data that need to be shared, 3) Matlab code that needs to shared among the teams. The initial plan of action for data sharing was also laid out.