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In addition to capturing the core data mandatory for each UniProtKB entry mainly, the amino acid sequence, protein name or description, taxonomic data and citation information , as much annotation information as possible is added. The table below directly taken from Wikipedia shows some of the incredible information available the proteome and genome of each human chromosome. Table: Human proteome and genome from Wikipedia Data source: Ensembl genome browser release 68, July This chapter will describe programs that allow predictions of secondary and tertiary structures of proteins.

Specific exercises using web-based bioinformatics programs can be found at the end. John's University. It consists of biologically significant sites, patterns and profiles that help to reliably identify to which known protein family if any a new sequence belongs. Formal systems. Mathematical deduction. Logical concepts.

Computational biology and bioinformatics

Theorem proving. Sets, relations on sets, operations on sets. Functions, graphs, mathematical structures, morphisms, algebraic structures, semigroups, quotient groups, finite-state machines, their homomorphism, and simulation. Machines as recognizers, regular sets. Kleene theorem. This course will teach students to apply or develop new computational network, AI and machine learning concepts to probe into the systems biology of disease and personalized medicine. The emphasis this semester would be on cancer, aging and diabetes. Description: Describes relational data models and database management systems.

Teaches the theories and techniques of constructing relational databases with emphasis on those aspects needed for various biological data, including sequences, structures, genetic linkages and maps, and signal pathways. Introduces relational database query language SQL. Summarizes currently existing biological databases and the Web-based programming tools for their access. Object-oriented modeling is introduced primarily as a design aid for dealing with the particular complexities of biological information in standard RDB design. Emphasis will be on those problems associated with dealing with data whose nomenclature and interrelationships are undergoing rapid change.

Description: Project course for first year Bioinformatics graduate students. Description: The course focuses on mathematical models for exploring the organization, dynamics, and evolution of biochemical and genetic networks. Topics include: introductions to metabolic and genetic networks, deterministic and stochastic kinetics of biochemical pathways; genome-scale models of metabolic reaction fluxes; models of regulatory networks; modular architecture of biological networks.

Familiarity with differential equations and linear algebra at equivalent levels and the consent of instructor can be used in lieu of both pre-reqs. This course introduces graduate and upper—level undergraduate students to the principles of bioinformatic analysis applied to translational studies.

Bioinformatics methods including microarray analysis, short read sequence analysis, biological pathways and geneset enrichment analysis, and Quantitative Trait Loci QTL will be covered. Lectures and assignments will be designed around reproducing the results of preselected studies from the literature that exemplify the topics. The primary focus will be using existing software tools and published data to perform analyses, but most tasks will require some programming. Students must bring laptop to each class. Description: This course will address the ethical, legal and scientific aspects of the new genetics.

Students in bioinformatics will discuss the questions raised from another view that they normally would not see. As part of the new technologies, individuals, families and society as a whole will have to make decisions that will affect everyone. Gene therapy, DNA forensics, new reproductive techniques and cloning are only a few of the topics that will be addressed. Description: Three laboratory rotations are required during a Bioinformatics Ph. Rotations typically last for a minimum of nine weeks.

It is expected that the student will participate in the lab full time except for time spent on courses. One rotation must be experimental, one computational, and the third can be either. Stduents who participate in the Summer Wet-Lab Experience prior entering the program receive credit toward one of the required rotations. Description: BF is a graduate seminar covering current topics in bioinformatics.

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This is achieved through the critical reading, presentation, and discussion of recent literature. Additionally, the course is intended to give students the opportunity to practice and improve their scientific presentation abilities. As such, peer feedback on presentations is an integral aspect of the course. Students will present twice during the semester so that they may improve upon their presentation skills based on peer comments. Instructor: Dukovski; Credits: 2; Fri am — am Electives. Computer labs emphasize the acquisition of practical bioinformatics skills for use in students research.

Prereq: CAS CS ; or equivalent programming experience, and familiarity with linear algebra, probability, and statistics. In this course, students will be presented with the methods for the analysis of gene expression data measured through microarrays. The course will start with a review of the basic biology of gene expression and an overview of microarray technology. The course will then describe the statistical techniques used to compare gene expression across different conditions and it will progress to describe the analysis of more complex experiments designed to identify genes with similar functions and to build models for molecular classification.

The statistical techniques described in this course will include general methods for comparing population means, clustering, classification, simple graphical models and Bayesian networks.

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Methods for computational and biological validation will be discussed. Bayesian methods have enjoyed a growing popularity in science and technology and have become the methods of analysis in many areas of public health and biomedical research including genetics and genomics, disease surveillance, disease mapping. Competent biostatisticians nowadays are expected to have knowledge in Bayesian modeling and Markov Chain Monte Carlo methods to be effective collaborators in interdisciplinary research groups.

This course will introduce Bayesian statistical reasoning through graphical modeling and describe Markov Chain Monte Carlo methods for Bayesian inference. The course will cover Bayesian methods for estimation of odds and risk in observational studies; methods for multivariable linear, loglinear and logistic regression; hierarchical models; latent class modeling including hidden Markov models and model-based clustering.

These topics will be taught using real examples from genetics, genomics, and observational studies, class discussion and critical reading. Students will be asked to analyze real data sets in their homeworks and a final project. This paper- and problem-based course focuses on functional genomics topics such as genetic variation, genome organization, and mechanisms of transcriptional and post-transcriptional gene regulation. Up-to-date methods include NGS, genome editing, ChIP-seq, chromatin accessibility assays, transcriptomics, and proteomics.

This course will train students to apply or develop computational network, modeling, and machine learning concepts to probe into the systems biology of disease. The aim of this course is to cover general concepts in biological computing that provide the foundation of thinking computationally about anomalous behavior in biological systems that cause diseases. The course also aims to teach students to work in teams and develop the skills to plan and coordinate a scientific project.

The course will cover computational frameworks, such as biological networks including metabolic, regulatory, and signal transduction networks , micro array analysis, proteomic analysis, next generation sequencing, imaging, machine learning, probabilistic inference, genetics, pathway analysis, network and graph theory, and other technologies to medical diseases initially focusing on clincal problems such as cancer, diabetes, inflammation, and aging.

The course is aimed at seniors and graduate students in biomedical engineering or bioinformatics; however, students from other disciplines ranging from medicine to physics or computer science can attend the class with some prerequisites. Modern concepts, controversies, and analytical approaches in evolutionary biology.


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Topics include adaptation, natural and sexual selection, species and species formation, phylogenetics, origin of evolutionary novelty, adaptive radiation, basic population and quantitative genetics, development and evolution. Emphasizes protein, carbohydrate, nucleic acid, and lipid chemistry. Development and use of modern instrumentation and techniques. Four hours lab, one hour discussion.

General areas of focus include genome organization, mechanisms of gene regulation, and cell signaling. Topics including genomics, mouse transgenics systems, signal transduction, chromatin structure, and cell cycle. Topics include 1 the human microbiome; and 2 fundamental aspects of the interactions between animals and the microorganisms that reside with them. Bioinformatics began to develop in the early s. It was considered the science of analyzing informatics processes of various biological systems. At this time, research in artificial intelligence was using network models of the human brain in order to generate new algorithms.

This use of biological data to develop other fields pushed biological researchers to revisit the idea of using computers to evaluate and compare large data sets. By , information was being shared among researchers through the use of punch cards. The amount of data being shared began to grow exponentially by the end of the s. This required the development of new computational methods in order to quickly analyze and interpret relevant information. Since the late s, computational biology has become an important part of developing emerging technologies for the field of biology.

Bioinformatics and Functional Genomics

Unlike computational biology, evolutionary computation is not concerned with modeling and analyzing biological data. It instead creates algorithms based on the ideas of evolution across species. Sometimes referred to as genetic algorithms , the research of this field can be applied to computational biology. While evolutionary computation is not inherently a part of computational biology, computational evolutionary biology is a subfield of it. Computational biology has been used to help sequence the human genome, create accurate models of the human brain, and assist in modeling biological systems.

It involves the development and application of computational, mathematical and data-analytical methods for modeling and simulation of biological structures. It focuses on the anatomical structures being imaged, rather than the medical imaging devices. Due to the availability of dense 3D measurements via technologies such as magnetic resonance imaging MRI , computational anatomy has emerged as a subfield of medical imaging and bioengineering for extracting anatomical coordinate systems at the morphome scale in 3D. The original formulation of computational anatomy is as a generative model of shape and form from exemplars acted upon via transformations.

It relates with shape statistics and morphometrics , with the distinction that diffeomorphisms are used to map coordinate systems, whose study is known as diffeomorphometry. Computational biomodeling is a field concerned with building computer models of biological systems. 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.