mathematical modeling & informatics

course description & objective

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Introduction to the strategies, approaches, and computer applications used in data mining, database design, phylogenomics, biostatistics, and drug discovery. Case studies illustrate specific applications of the methods for measuring, visualizing, representing, inferring, clustering, classifying and modeling biotechnological data.

This course surveys the various computational methods and tools commonly used in molecular sequence analysis, characteristics, forms, and querying strategies used for database-information gathering, and basics in the drug development and validation processes. Students also survey the statistical methods and mathematical modeling used in various stages of sequence analysis, and drug discovery.

student learning goals

retrieve biological relevant information from literature (sequence and small molecule databases)

use computational tools to analyze molecular information

formulate research hypotheses and design computational experiments to test them

use statistical software and methods to mine, transform, and analyze experimental data, and to test research hypotheses

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description of student learning goals

identify several appropriate databases for a particular question related to the drug development process

differentiate the content of literature (genomic,  proteomic, or small molecule) and their appropriate usage

describe considerations in the processes of developing a database, entering data to a database, querying a database, and summarizing and interpreting information retrieved from databases

retrieve relevant data efficiently from a database using appropriate text-mining and other search strategies

appreciate that the order and content of word clusters for text-mining is critical

examine and evaluate data retrieved by text searching and other search strategies

use BLAST to obtain sequence data and information related to sequence information

interpret outputs from databases, including those from BLAST (E-values and scores)

build multiple sequence  alignments, draw phylogenetic trees, infer relationships, predict functionally important protein residues

describe levels of protein structure, use software to view and model protein dynamics

describe chemical databases and their usage

identify the importance of next generation sequencing projects in drug discovery and development

identify the use of various statistical methodologies in drug development and approval process

categorize clinical trials

describe the populations of clinical trials and the basis for defining demographic sub-groups representation

define the various types of experimental designs.

describe control and treatment groups in clinical trials

decide whether single or double blinding should be used

appreciate the importance of randomization and the assignment of patients to treatment groups

explain the considerations for determining sample size in clinical trials

discuss ethical issues in allocating treatments to patient populations

identify the methods to explore data

characterize the types of data (quantitative and qualitative)

visualize the data

summarize basic statistics

identify appropriate statistical tests for analyzing data

describe basic methodologies for statistical inference