Bioinformatics Medical Research System Biology

Computational Biomodeling

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Studies that entirely focus on the discipline and techniques of computer science and mathematics as they relate to biological systems. This includes the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavior, and social systems.

The Computational Bio-Modeling Lab’s research includes computational geometry (especially mesh generation), computer graphics, visualization, finite element method, iso-geometric analysis and their applications in computational biomedicine, computational biology, and engineering. Computational biomodeling refers to a type of artificial life research concerned with building computer simulations of biological systems (biomodeling). It combines research from the fields of biology, computer science, applied mathematics, chemistry, and physics. The immediate goal is to understand how biological entities such as cells or whole organisms, develop, work collectively, and survive in changing environments using a purely computational model. 

Computational modelling has become increasingly common in life science research. To provide a platform to support universal sharing, easy accessibility and model reproducibility, BioModels, a repository for mathematical models, was established in 2005. The current BioModels platform allows submission of models encoded in diverse modeling formats, including SBML, CellML, PharmML, COMBINE archive, MATLAB, Mathematica, R, Python or C++. The models submitted to BioModels are curated to verify the computational representation of the biological process and the reproducibility of the simulation results in the reference publication. The curation also involves encoding models in standard formats and annotation with controlled vocabularies following MIRIAM (minimal information required in the annotation of biochemical models) guidelines. BioModels now accepts large-scale submission of auto-generated computational models. With gradual growth in content over 15 years, BioModels currently hosts about 2000 models from the published literature. With about 800 curated models, BioModels has become the world’s largest repository of curated models and emerged as the third most used data resource after PubMed and Google Scholar among the scientists who use modeling in their research. Thus, BioModels benefits modelers by providing access to reliable and semantically enriched curated models in standard formats that are easy to share, reproduce and reuse.

Uses

  • Weather forecasting
  • Flight simulators
  • Earthquake
  • Tracking infectious diseases
  • Clinical decision support
  • Predicting drug side effects
  • Modeling infectious disease spread to identify effective interventions
  • Tracking viral evolution during spread of infectious disease
  • Transforming wireless health data into improved health and healthcare
  • Human and machine learning for customized control of assistive robots

Subcategories 

Computational Biomodeling (MOD): Studies that involve computer simulations of biological systems most commonly with a goal of understanding how cells or organisms develop, work collectively and survive.

Computational Epidemiology (EPD): The study of disease frequency and distribution, and risk factors and socioeconomic determinants of health within populations. Such studies may include gathering information to confirm the existence of disease outbreaks, developing case definitions and analyzing epidemic data, establishing disease surveillance, and implementing methods of disease prevention and control.

Computational Evolutionary Biology (EVO): A study that applies the discipline and techniques of computer science and mathematics to explore the processes of change in populations of organisms, especially taxonomy, paleontology, ethology, population genetics and ecology.

Computational Neuroscience (NEU): A study that applies the discipline and techniques of computer science and mathematics to understand brain function in terms of the information processing properties of the structures that makes up the nervous system.

Computational Pharmacology (PHA): A study that applies the discipline and techniques of computer science and mathematics to predict and analyze the responses to drugs.

Genomics (GEN): The study of the function and structure of genomes using recombinant DNA, sequencing, and bioinformatics.

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