Introduction

Bioinformatics is an interdisciplinary science at the interfaces of the biological, informational and computational sciences, uses computation to better understand biology. Bioinformatics involves the analysis of biological data, particularly DNA, RNA, and protein sequences. The field of bioinformatics experienced explosive growth starting in the mid-1990s, driven largely by the Human Genome Project and by rapid advances in DNA sequencing technology. Recent and novel technologies produce biological datasets of ever-increasing resolution that reveal not only genomic sequences but also RNA and protein abundances, their interactions with one another, their subcellular localization, and the identity and abundance of other biological molecules. This requires the development and application of sophisticated computational methods. Bioinformatics utilizes computational approaches to analyze patterns in biological data and to create complex models of biological activity, including attempts to elucidate the functions of genes and their interactions in genetic pathways. Widespread social benefits are expected from the exploitation of the wealth of new knowledge concerning the genetic mechanisms of life and related processes.

Analyses in bioinformatics predominantly focus on three types of large datasets available in molecular biology: macromolecular structures, genome sequences, and the results of functional genomics experiments (e.g. expression data). Additional information includes the text of scientific papers and "relationship data" from metabolic pathways, taxonomy trees, and protein-protein interaction networks. Bioinformatics employs a wide range of computational techniques including sequence and structural alignment, database design and data mining, macromolecular geometry, phylogenetic tree construction, prediction of protein structure and function, gene finding, and expression data clustering. The emphasis is on approaches integrating a variety of computational methods and heterogeneous data sources.

The main objective of the Ph.D. program in Bioinformatics at Kish international campus is to train the next generation of computational biologists for careers in academia, industry, and government.

Ph.D. Curriculum

The Ph.D. of Bioinformatics requires completion of 32 credits, a set of core courses (9 credits), a seminar (1 credit) and 8 credits of elective courses and a Ph.D. thesis (18 credits). The main emphasis of the program is on the successful completion of an original and independent research project written and defended as a dissertation.

Comprehensive Exam

Comprehensive Exam should be taken at most at the end of the 4th semester and is required before a student could defend the Ph.D. proposal. Students will have two chances to pass the Ph.D. Comprehensive Exam. If students receive an evaluation of “unsatisfactory” on their first Comprehensive Exam attempt, the student may retake the qualifier once. A second failure will result in termination from the program. The Comprehensive Exam is designed to ensure that the student starts early in gaining research experience; it also ensures that the student has the potential to conduct doctoral-level research.

Ph.D. PROPOSAL

The Ph.D. proposal must contain Specific Aims, Research Design and Methods, and Proposed Work and Timeline. In addition, the proposal must also contain a bibliography and, as attachments, any publications/supplementary materials. The student must defend their thesis proposal to their committee in an oral exam.

THESIS

A student should choose a thesis advisor (and one or two co-advisors if required) within the first year of being in the Ph.D. program, approved by the Faculty committee. In the second year, a thesis committee suggested by the advisor alongside by the Ph.D. proposal should be handed over for approval. The thesis committee should consist of a minimum of five faculty members. Two members of thesis committee should be from the other Universities at the Associate Professor level. Not later than the end of the 5th semester, a student has to present and defend a written Ph.D. proposal.

RESEARCH PROGRESS

A student is expected to meet with his/her thesis committee at least once a year to review the research progress. At the beginning of each university calendar year, each student and the student’s advisor are required to submit an evaluation assessment of the student’s progress, outlining past year accomplishments and plans for the current year. The thesis committee reviews these summaries and sends the student a letter summarizing their status in the program. Students who are failing to make satisfactory progress are expected to correct any deficiencies and move to the next milestone within one year. Failure to do so will result in dismissal from the program.

Ph.D. DISSERTATION

Within 4 years after entering the Ph.D. program, the student is expected to complete the thesis research; the student must have the results of the research accepted or published in peer-reviewed journals. Upon submitting a written thesis and public defense and approval by the committee, the student is awarded the Ph.D. degree. The defense will consist of (1) a presentation of the dissertation by the graduate student, (2) questioning by the general audience, and (3) closed-door questioning by the dissertation committee. The student will be informed of the exam result at the completion of all three parts of the dissertation defense. All members of the committee must sign the final report of the doctoral committee and the final version of the dissertation.

A minimum GPA of 16 over 20 must be maintained for graduation.

Leveling Courses (not applicable to the degree)

The Ph.D. in Bioinformatics assumes a Master degree in related fields. However, students holding any other master degree besides will be required to complete a few of the following leveling courses that are designed to provide a background for the Ph.D. courses. These leveling courses are not counted for graduate credit towards the Ph.D. in Bioinformatics.

Leveling courses: At most 3 courses required; 6 credits

Core courses: 4 courses required; 10 credits

Elective Courses: 4 courses required, 8 credits

Course Descriptions

Advanced bioinformatics

Course Content:
Introduction to Bioinformatics, Introduction to Molecular Biology, Biological Databases, Processing Biological Sequences with MATLAB, Sequence Homology, Protein Alignments, Multiple Sequence Alignment, Alignment Tools, Biolinguistic Methods, Sequence Models, Subsequence Pattern Models, Gene Models, Introduction to Phylogenetic Reconstruction, Distance-Based Methods, Character-Based Methods: Parsimony, Probabilistic Methods: Maximum Likelihood, Microarrays, Matlab

Algorithms in bioinformatics

Course Content:
Introduction to Molecular Biology, Sequence Similarity, Suffix Tree, Genome Alignment, Database Search, Multiple Sequence Alignment, Phylogeny Reconstruction, Phylogeny Comparison, Genome Rearrangement, Motif Finding, RNA Secondary Structure Prediction, Peptide Sequencing, Population Genetics

Structural bioinformatics

Course Content:
Constrain molecular modeling, Defining bioinformatics and structural, Fundamentals of protein structure, Search and sampling in structural, Search methods, Data analysis, and reduction, Molecular visualization

Computational genomics

Course Content:
Introduction , Concepts of Genetic Epidemiology, Integration of Linkage Analysis and Next Generation Sequencing Data , QTL Mapping of Molecular Traits for Studies of Human Complex Diseases , Renewed Interest in Haplotype From Genetic Marker to Gene Prediction , Analytical Approaches for Exome Sequence Data , Rare Variants Analysis in Unrelated Individuals , Gene Duplication and Functional Consequences, From GWAS to Next Generation Sequencing on Human Complex Diseases The Implications for Translational Medicine and Therapeutics

Metabolic modeling

Course Content:
Engineering Synthetic Metabolons from Metabolic Modeling to Rational Design of Biosynthetic Devices, Building synthetic sterols computationally unlocking the secrets of evolution? ,Characteristics of Sucrose Transport through the Sucrose Specific Porin ScrY Studied by Molecular Dynamics Simulations ,Fast Solver for Implicit Electrostatics of Biomolecules, Model-Based Design of Biochemical Microreactors ,Underpinning starch biology with in vitro studies on carbohydrate-active enzymes and biosynthetic glycomaterials, Compartmentalization and Transport in Synthetic Vesicles, Metabolomics standards and metabolic modeling for synthetic biology in plants ,Are Predictions Consistent with Experimental Evidence? , Optimization of Engineered Production of the Glucoraphanin Precursor Dihomomethionine in Nicotiana benthamiana, Synthetic Peptides as Protein Mimics, Synthetic Protein Scaffolds Based on Peptide Motifs and Cognate Adaptor Domains for Improving Metabolic Productivity, Engineering of metabolic pathways by artificial enzyme channels

Modeling in system biology

Course Content:
Biological Basics, Fundamentals of Mathematical Modeling, Model Calibration and Experimental Design, Modeling of Cellular Processes, Enzymatic Conversion, Polymerization Processes, Signal Transduction and Genetically Regulated Systems, Analysis of Modules and Motifs, General Methods of Model Analysis, Aspects of Control Theory, Motifs in Cellular Networks, Analysis of Cellular Networks, Metabolic Engineering, Topological Characteristics

Advanced data mining

Course Content:
Introduction to Data Mining in Bioinformatics, Hierarchical Profiling Scoring and Applications in Bioinformatics Methods and Practices of Combining Multiple Scoring Systems, DNA Sequence Visualization, Proteomics with Mass Spectrometry, Efficient and Robust Analysis of Large Phylogenetic Datasets, Algorithmic Aspects of Protein Threading, Pattern Differentiations and Formulations for Heterogeneous Genomic Data, Parameterless Clustering Techniques for Gene Expression Analysis, Joint Discriminatory Gene Selection for Molecular Classification of Cancer, A Haplotype Analysis System for Genes Discovery of Common Diseases, A Bayesian Framework for Improving Clustering Accuracy of Protein Sequences

Machine learning

Course Content:
Why we are interested in machine learning, Machine learning statistics and data analytics, Pattern recognition, Neural networks and deep learning, Learning clusters and recommendations, Learning to take actions, Where do we go from here?

Computer-aided Drug design

Course Content:
Quantum Mechanical and Molecular Mechanical Approaches, Transition Metal Systems, Modeling Protein-Protein Interactions by Rigid body, QM Based Modelling, Current Status and Future
Program taught in:
  • English

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