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The discipline of Synthetic Biology has recently emerged at the interface of biology and engineering. The definition of Synthetic Biology has been dynamic over time ever since, which exemplifies that the field is rapidly moving and comprises a broad range of research areas. In the frame of this Research Topic, we focus on Synthetic Biology approaches that aim at rearranging biological parts/ entities in order to generate novel biochemical functions with inherent metabolic activity. This Research Topic encompasses Pathway Engineering in living systems as well as the in vitro assembly of biomolecules into nano- and microscale bioreactors. Both, the engineering of metabolic pathways in vivo, as well as the conceptualization of bioreactors in vitro, require rational design of assembled synthetic pathways and depend on careful selection of individual biological functions and their optimization. Mathematical modelling has proven to be a powerful tool in predicting metabolic flux in living and artificial systems, although modelling approaches have to cope with a limitation in experimentally verified, reliable input variables. This Research Topic puts special emphasis on the vital role of modelling approaches for Synthetic Biology, i.e. the predictive power of mathematical simulations for (i) the manipulation of existing pathways and (ii) the establishment of novel pathways in vivo as well as (iii) the translation of model predictions into the design of synthetic assemblies.

Metabolic Engineering --- Metabolic Modelling --- metabolite profiling --- Protein scaffolds --- Protein Engineering --- Interaction domains --- Starch biosynthesis --- Membrane Transport Proteins --- reconstitution --- molecular dynamics simulations

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Modern biology is rapidly becoming a study of large sets of data. Understanding these data sets is a major challenge for most life sciences, including the medical, environmental, and bioprocess fields. Computational biology approaches are essential for leveraging this ongoing revolution in omics data. A primary goal of this Special Issue, entitled “Methods in Computational Biology”, is the communication of computational biology methods, which can extract biological design principles from complex data sets, described in enough detail to permit the reproduction of the results. This issue integrates interdisciplinary researchers such as biologists, computer scientists, engineers, and mathematicians to advance biological systems analysis. The Special Issue contains the following sections:•Reviews of Computational Methods•Computational Analysis of Biological Dynamics: From Molecular to Cellular to Tissue/Consortia Levels•The Interface of Biotic and Abiotic Processes•Processing of Large Data Sets for Enhanced Analysis•Parameter Optimization and Measurement

biomass reaction --- computational biology --- macromolecular composition --- metabolic model --- methods --- metabolic network visualization --- metabolic modelling --- elementary flux modes visualization --- flux balance analysis --- ADAR --- breast --- cancer --- inosine --- microRNA --- microRNA targeting --- RNA editing --- computational model --- explanatory model --- hybrid model --- mechanism --- mechanistic model --- modeling methods --- provenance --- workflow --- systems modeling --- simulation --- bioreactor integrated modeling --- CFD simulation --- compartmental modeling --- reduced-order model --- bioreactor operation optimization --- ordinary differential equation --- SREBP-2 --- nonlinear dynamics --- multiple time scales --- geometric singular perturbation theory --- bifurcation analysis --- canard-induced EADs --- calcium current --- multiscale systems biology --- computational biology --- quantitative systems pharmacology (QSP) --- immuno-oncology --- immunotherapy --- immune checkpoint inhibitor --- mathematical modeling --- gut microbiota dysbiosis --- Clostridium difficile infection --- bacterial biofilms --- metabolic modeling --- parameter optimization --- differential evolution --- evolutionary algorithm --- bistable switch --- oscillator --- turning point bifurcation --- Hopf bifurcation --- biological networks --- mass-action networks --- BioModels Database --- n/a