What is he best known for?
The fields of study he is best known for:
His primary areas of study are Biochemistry, Biological system, Flux, Metabolic pathway and Computational biology.
His study on Calcium signaling is often connected to Inositol trisphosphate receptor as part of broader study in Biochemistry.
His Flux research is multidisciplinary, incorporating elements of Metabolic network, Nucleotide Metabolism, Steady state, Algebraic properties and Algorithm.
His work on Metabolic network modelling as part of general Metabolic pathway research is frequently linked to Nicotinamide N-methyltransferase, bridging the gap between disciplines.
His Computational biology research incorporates elements of Genetics, Human erythrocytes, Metabolic pathway analysis and Pathway analysis.
He works mostly in the field of Metabolic engineering, limiting it down to topics relating to Product and, in certain cases, Yield, as a part of the same area of interest.
His most cited work include:
- The Regulation of Cellular Systems (1021 citations)
- Cooperation and Competition in the Evolution of ATP-Producing Pathways (907 citations)
- A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. (863 citations)
What are the main themes of his work throughout his whole career to date?
Stefan Schuster focuses on Biochemistry, Flux, Computational biology, Biological system and Metabolic pathway.
His research on Biochemistry frequently links to adjacent areas such as Biophysics.
His research in Flux intersects with topics in Yield and Metabolic network.
Stefan Schuster interconnects Genetics, Metabolic pathway analysis and Pathway analysis in the investigation of issues within Computational biology.
Stefan Schuster integrates Biological system with Metabolic control analysis in his study.
His study in Metabolic pathway focuses on Metabolic network modelling in particular.
He most often published in these fields:
- Biochemistry (26.53%)
- Flux (16.73%)
- Computational biology (14.29%)
What were the highlights of his more recent work (between 2013-2021)?
- Biochemistry (26.53%)
- Immune system (3.67%)
- Computational biology (14.29%)
In recent papers he was focusing on the following fields of study:
Stefan Schuster spends much of his time researching Biochemistry, Immune system, Computational biology, Amino acid and Warburg effect.
The Immune system study which covers Pathogen that intersects with Systems biology and Virulence.
His Computational biology research integrates issues from Gene expression, Identification, Clique, Heuristic and Community structure.
His research integrates issues of Flux and Constraint in his study of Topology.
The study incorporates disciplines such as Yield, Penicillin synthesis, Equilibrium constant, Computational chemistry and Chemical engineering in addition to Flux.
Stefan Schuster specializes in Metabolism, namely Metabolic pathway.
Between 2013 and 2021, his most popular works were:
- Challenges in microbial ecology: building predictive understanding of community function and dynamics (390 citations)
- Fitness and stability of obligate cross-feeding interactions that emerge upon gene loss in bacteria (173 citations)
- Evolutionary game theory: cells as players (83 citations)
In his most recent research, the most cited papers focused on:
His main research concerns Biochemistry, Gene, Genetics, Computational biology and Flux.
All of his Biochemistry and Lipid metabolism, Fatty acid, Beta oxidation, Urea and Glutaminase investigations are sub-components of the entire Biochemistry study.
Genome is closely connected to Metabolism in his research, which is encompassed under the umbrella topic of Fatty acid.
In the subject of general Gene, his work in Gene expression and Gene regulatory network is often linked to Genetic association, thereby combining diverse domains of study.
His Computational biology study combines topics from a wide range of disciplines, such as Open reading frame, Bounded function, Stop codon and Gene prediction.
His Flux research is mostly focused on the topic Metabolic flux analysis.
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