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Plant Computational Biology concentration

See also: Course requirements for the Big Data concentration

Summary:

Plant computational biologists analyze large datasets and devise computer modeling simulations for practical and research applications in academia, in biotechnology and pharmaceutical companies, in health science-related fields, and in governmental research institutions. Computational Biology, sometimes referred to as bioinformatics, is the science of using biological data to develop algorithms and relations among various biological systems. Prior to the advent of computational biology, biologists were unable to have access to large amounts of data. Researchers were able to develop analytical methods for interpreting biological information, but were unable to share them quickly among colleagues.

Subfields:

  • Computational biomodeling: Computational biomodeling aims to develop and use visual simulations in order to assess the complexity of biological systems. This is accomplished through the use of specialized algorithms, and visualization software. These models allow for prediction of how systems will react under different environments.
  • Computational genomics & genetics: Computational genomics is a field within genomics which studies the genomes of cells and organisms. It is often referred to as Computational and Statistical Genetics. The Human Genome Project is one example of computational genomics. This project looks to sequence the entire human genome into a set of data. Once fully implemented, this could allow for doctors to analyze the genome of an individual patient.
  • Computational neuroscience: This is the study of brain function in terms of the information processing properties of the structures that make up the nervous system. It is a subset of the field of neuroscience, and looks to analyze brain data to create practical applications. It looks to model the brain in order to examine specific types aspects of the neurological system.
  • Computational pharmacology: The study of the effects of genomic data to find links between specific genotypes and diseases and then screening drug data.
  • Computational evolutionary biology:
    • Using DNA data to reconstruct the phylogenetic tree of life
    • Fitting population genetics models (either forward time or backward time) to DNA data to make inferences about demographic or selective history
    • Building population genetics models of evolutionary systems from first principles in order to predict what is likely to evolve
  • Cancer computational biology: This field aims to determine future mutations in cancer through an algorithmic approach to analyzing data.

Career Options:

  • Academia (Universities)
  • Government
    • NIH (National Institutes of Health): As the field of biology has become more diverse and complex, so the field of computational biology has grown to support it. At the same time, as computational power and programming have become more sophisticated, computational biologists have stepped in as motivated and capable partners in the quest to understand disease. Today, computational biologists in the Intramural Research Program (IRP) take many different approaches to answer theoretical and experimental biological questions across a range of disciplines, including:
      • Image Analysis: High-resolution optical imaging is a key to much of our biomedical research. Computers supply the advanced imaging methods and algorithms that allow us to view the human body from macro to nano.
      • Biomodelling or Systems Biology: Computational biomodelling, or systems biology, is a computer-based simulation of a biological system used to understand and predict interactions within that system. Computers can model systems at any level, from populations to cellular networks and the sub-cellular worlds of signal transduction pathways and gene regulatory networks.
      • Neuroscience: Computers are often compared to the brain, in terms of their ability to process information. So it’s no surprise that scientists use computers to further understand how this processing occurs.
      • Bioinformatics: Biomedical science has experienced a recent increase in “-omics” research—genomics, proteomics, metabolomics, etc.—and as a result has embraced computational methods designed to simplify the analysis of the enormous amounts of data associated with this type of research.
    • USDA – ARS (Agricultural Research Service): There are a number of computational biology projects at USDA-ARS Plant Genetic Resources Unit (PGRU) and Grape Genetics Research Unit (GGRU) in Geneva, NY, working on problems of germplasm conservation, characterization, and improvement. Typical applications of bioinformatics include molecular marker discovery/prediction, sequence clustering and annotation, genetic data analyses, molecular evolution, data mining for genes of interest to our rootstock and scion breeders, virtual differential display analyses, population genetics, and trait mapping.
  • Private industry
  • Non-profit or non-government organizations

Education requirement (skills):

  • Undergraduate research
  • Programming language: Java Python C
  • Software: R SAS JAMP

Job prospects:

If you have a strong background in biology, statistics and programming, it’s very easy to find a job.

Internship or Research? Yes!

Students in this concentration are encouraged to perform an internship in a Bioinformatic Applications Workshop. The Cornell Core Laboratories Center runs a series of bioinformatics workshops to teach users how to do data analysis. The workshops cover a broad range of topics, from introduction to the Linux computing environment to next generation sequencing data analysis. Each workshop includes both lectures and hands-on sessions.

Supplementary Courses:

BTRY 4810, Population Genetics
BTRY 4840, Computational Genetics and Genomics
BTRY 4830, Quantitative Genomics and Genetics
BTRY 4381, Bioinformatics Programming