The MeDaX Knowledge Graph Prototype.

Data sharing is sustainable for several reasons, including minimising economical and human costs or maximising knowledge gain. Still, reuse of biomedical (research) data is often hampered by the diverse technical, juridical, and scientific requirements for biomedical data handling and specifically sharing. We are building a toolbox for automated generation of knowledge graphs (KGs) from diverse sources, for data enrichment, and for data analysis. Into the MeDaX KG prototype, we integrated data from the core data set of the German Medical Informatics Initiative (MII) with ontological and provenance information. This prototype is currently used for internal concept and method testing only. In subsequent versions it will be expanded by including more meta-data and relevant data sources as well as further tools, including a user interface.

Control of COVID-19 Outbreaks under Stochastic Community Dynamics, Bimodality, or Limited Vaccination.

Reaching population immunity against COVID-19 is proving difficult even in countries with high vaccination levels. Thus, it is critical to identify limits of control and effective measures against future outbreaks. The effects of nonpharmaceutical interventions (NPIs) and vaccination strategies are analyzed with a detailed community-specific agent-based model (ABM). The authors demonstrate that the threshold for population immunity is not a unique number, but depends on the vaccination strategy. Prioritizing highly interactive people diminishes the risk for an infection wave, while prioritizing the elderly minimizes fatalities when vaccinations are low. Control over COVID-19 outbreaks requires adaptive combination of NPIs and targeted vaccination, exemplified for Germany for January-September 2021. Bimodality emerges from the heterogeneity and stochasticity of community-specific human-human interactions and infection networks, which can render the effects of limited NPIs uncertain. The authors' simulation platform can process and analyze dynamic COVID-19 epidemiological situations in diverse communities worldwide to predict pathways to population immunity even with limited vaccination.

Integration of multi-omics data of a genome-reduced bacterium: Prevalence of post-transcriptional regulation and its correlation with protein abundances.

We developed a comprehensive resource for the genome-reduced bacterium Mycoplasma pneumoniae comprising 1748 consistently generated '-omics' data sets, and used it to quantify the power of antisense non-coding RNAs (ncRNAs), lysine acetylation, and protein phosphorylation in predicting protein abundance (11%, 24% and 8%, respectively). These factors taken together are four times more predictive of the proteome abundance than of mRNA abundance. In bacteria, post-translational modifications (PTMs) and ncRNA transcription were both found to increase with decreasing genomic GC-content and genome size. Thus, the evolutionary forces constraining genome size and GC-content modify the relative contributions of the different regulatory layers to proteome homeostasis, and impact more genomic and genetic features than previously appreciated. Indeed, these scaling principles will enable us to develop more informed approaches when engineering minimal synthetic genomes.

MyMpn: a database for the systems biology model organism Mycoplasma pneumoniae.

MyMpn (http://mympn.crg.eu) is an online resource devoted to studying the human pathogen Mycoplasma pneumoniae, a minimal bacterium causing lower respiratory tract infections. Due to its small size, its ability to grow in vitro, and the amount of data produced over the past decades, M. pneumoniae is an interesting model organisms for the development of systems biology approaches for unicellular organisms. Our database hosts a wealth of omics-scale datasets generated by hundreds of experimental and computational analyses. These include data obtained from gene expression profiling experiments, gene essentiality studies, protein abundance profiling, protein complex analysis, metabolic reactions and network modeling, cell growth experiments, comparative genomics and 3D tomography. In addition, the intuitive web interface provides access to several visualization and analysis tools as well as to different data search options. The availability and--even more relevant--the accessibility of properly structured and organized data are of up-most importance when aiming to understand the biology of an organism on a global scale. Therefore, MyMpn constitutes a unique and valuable new resource for the large systems biology and microbiology community.

Bridging the gaps in systems biology.

Systems biology aims at creating mathematical models, i.e., computational reconstructions of biological systems and processes that will result in a new level of understanding-the elucidation of the basic and presumably conserved "design" and "engineering" principles of biomolecular systems. Thus, systems biology will move biology from a phenomenological to a predictive science. Mathematical modeling of biological networks and processes has already greatly improved our understanding of many cellular processes. However, given the massive amount of qualitative and quantitative data currently produced and number of burning questions in health care and biotechnology needed to be solved is still in its early phases. The field requires novel approaches for abstraction, for modeling bioprocesses that follow different biochemical and biophysical rules, and for combining different modules into larger models that still allow realistic simulation with the computational power available today. We have identified and discussed currently most prominent problems in systems biology: (1) how to bridge different scales of modeling abstraction, (2) how to bridge the gap between topological and mechanistic modeling, and (3) how to bridge the wet and dry laboratory gap. The future success of systems biology largely depends on bridging the recognized gaps.

Large-scale metabolome analysis and quantitative integration with genomics and proteomics data in Mycoplasma pneumoniae.

Systems metabolomics, the identification and quantification of cellular metabolites and their integration with genomics and proteomics data, promises valuable functional insights into cellular biology. However, technical constraints, sample complexity issues and the lack of suitable complementary quantitative data sets prevented accomplishing such studies in the past. Here, we present an integrative metabolomics study of the genome-reduced bacterium Mycoplasma pneumoniae. We experimentally analysed its metabolome using a cross-platform approach. We explain intracellular metabolite homeostasis by quantitatively integrating our results with the cellular inventory of proteins, DNA and other macromolecules, as well as with available building blocks from the growth medium. We calculated in vivo catalytic parameters of glycolytic enzymes, making use of measured reaction velocities, as well as enzyme and metabolite pool sizes. A quantitative, inter-species comparison of absolute and relative metabolite abundances indicated that metabolic pathways are regulated as functional units, thereby simplifying adaptive responses. Our analysis demonstrates the potential for new scientific insight by integrating different types of large-scale experimental data from a single biological source.

Dissecting the energy metabolism in Mycoplasma pneumoniae through genome-scale metabolic modeling.

Mycoplasma pneumoniae, a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology for which substantial experimental information is available. With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized the metabolic network of M. pneumoniae in great detail, integrating data from different omics analyses under a range of conditions into a constraint-based model backbone. Iterating model predictions, hypothesis generation, experimental testing, and model refinement, we accurately curated the network and quantitatively explored the energy metabolism. In contrast to other bacteria, M. pneumoniae uses most of its energy for maintenance tasks instead of growth. We show that in highly linear networks the prediction of flux distributions for different growth times allows analysis of time-dependent changes, albeit using a static model. By performing an in silico knock-out study as well as analyzing flux distributions in single and double mutant phenotypes, we demonstrated that the model accurately represents the metabolism of M. pneumoniae. The experimentally validated model provides a solid basis for understanding its metabolic regulatory mechanisms.

Impact of genome reduction on bacterial metabolism and its regulation.

To understand basic principles of bacterial metabolism organization and regulation, but also the impact of genome size, we systematically studied one of the smallest bacteria, Mycoplasma pneumoniae. A manually curated metabolic network of 189 reactions catalyzed by 129 enzymes allowed the design of a defined, minimal medium with 19 essential nutrients. More than 1300 growth curves were recorded in the presence of various nutrient concentrations. Measurements of biomass indicators, metabolites, and 13C-glucose experiments provided information on directionality, fluxes, and energetics; integration with transcription profiling enabled the global analysis of metabolic regulation. Compared with more complex bacteria, the M. pneumoniae metabolic network has a more linear topology and contains a higher fraction of multifunctional enzymes; general features such as metabolite concentrations, cellular energetics, adaptability, and global gene expression responses are similar, however.