Bioinformatics and Computational Biology BS - Curriculum

This course serves as an introduction to biology for majors, focusing on the organismal, population, and ecosystem levels. Major themes include: evolution, structure and function, information flow and storage, pathways and transformations of energy and matter, and systems. The course also focuses on developing core competencies, such as applying the process of science, using quantitative reasoning, communicating, and collaborating. (This course is restricted to BIOL-BS, BIOTECH-BS, ENVS-BS, BIOINFO-BS, BIOMED-BS, BIOCHEM-BS, or NEURO-BS students.) Lecture 3 (Fall).

This course serves as an introduction to biology for majors, focusing on the molecular and cellular level. Major themes include: evolution, structure and function, information flow and storage, pathways and transformations of energy and matter, and systems. The course also focuses on developing core competencies, such as applying the process of science, using quantitative reasoning, communicating, and collaborating. (This course is restricted to BIOL-BS, BIOTECH-BS, ENVS-BS, BIOINFO-BS, BIOMED-BS, BIOCHEM-BS, or NEURO-BS students.) Lecture 3 (Spring).

This course is an introduction to laboratory work in life sciences. The laboratory work is project-based, and may involve field work as well as laboratory experiments. The course is designed to show the huge scope of biology and will encompass how some molecular biology and bioinformatics techniques connect with organismal and ecological biology. (This course is restricted to BIOL-BS, BIOTECH-BS, ENVS-BS, BIOINFO-BS, BIOMED-BS, BIOCHEM-BS, or NEURO-BS students. Co-requisites: BIOL-123 or equivalent course.) Lab 3 (Fall).

This course is an introduction to laboratory work in life sciences. The laboratory work is project based, and the subject matter of the project(s) may vary. The course is designed to show the huge scope of biology and will encompass some molecular biology and bioinformatics techniques connect with organismal and ecological biology. (This course is restricted to BIOL-BS, BIOTECH-BS, ENVS-BS, BIOINFO-BS, BIOMED-BS, BIOCHEM-BS, or NEURO-BS students. Co-requisites: BIOL-124 or equivalent course.) Lab 3 (Spring).

This course will explore topics in the field of bioinformatics including tools and resources used by the discipline, including direct experience with the common user environment. Lecture 3 (Fall).

This is a general chemistry course for students in the life and physical sciences. College chemistry is presented as a science based on empirical evidence that is placed into the context of conceptual, visual, and mathematical models. Students will learn the concepts, symbolism, and fundamental tools of chemistry necessary to carry on a discourse in the language of chemistry. Emphasis will be placed on the relationship between atomic structure, chemical bonds, and the transformation of these bonds through chemical reactions. The fundamentals of organic chemistry are introduced throughout the course to emphasize the connection between chemistry and the other sciences. Lecture 3 (Fall, Spring, Summer).

The course covers the thermodynamics and kinetics of chemical reactions. The relationship between energy and entropy change as the driving force of chemical processes is emphasized through the study of aqueous solutions. Specifically, the course takes a quantitative look at: 1) solubility equilibrium, 2) acid-base equilibrium, 3) oxidation-reduction reactions and 4) chemical kinetics. (Prerequisites: CHMG-141 or CHMG-131 or equivalent course.) Lecture 3 (Fall, Spring, Summer).

The course combines hands-on laboratory exercises with workshop-style problem sessions to complement the CHMG-141 lecture material. The course emphasizes laboratory techniques and data analysis skills. Topics include: gravimetric, volumetric, thermal, titration and spectrophotometric analyses, and the use of these techniques to analyze chemical reactions. (Corequisite: CHMG-141 or CHMG-131 or equivalent course.) Lab 3 (Fall, Spring, Summer).

The course combines hands-on laboratory exercises with workshop-style problem sessions to complement the CHMG-142 lecture material. The course emphasizes the use of experiments as a tool for chemical analysis and the reporting of results in formal lab reports. Topics include the quantitative analysis of a multicomponent mixture using complexation and double endpoint titration, pH measurement, buffers and pH indicators, the kinetic study of a redox reaction, and the electrochemical analysis of oxidation reduction reactions. (Prerequisites: CHMG-131 or CHMG-141 or equivalent course. Corequisites: CHMG-142 or equivalent course.) Lab 3 (Fall, Spring, Summer).

This is the first in a two-course sequence intended for students majoring in mathematics, science, or engineering. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers functions, limits, continuity, the derivative, rules of differentiation, applications of the derivative, Riemann sums, definite integrals, and indefinite integrals. (Prerequisites: MATH-111 or (NMTH-220 and NMTH-260 or NMTH-272 or NMTH-275) or equivalent courses with a minimum grade of B-, or a score of at least 60% on the RIT Mathematics Placement Exam. Co-requisites: MATH-181R or equivalent course.) Lecture 6 (Fall, Spring).

This is the second in a two-course sequence. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers techniques of integration including integration by parts, partial fractions, improper integrals, applications of integration, representing functions by infinite series, convergence and divergence of series, parametric curves, and polar coordinates. (Prerequisites: C- or better in MATH-181 or MATH-181A or equivalent course. Co-requisites: MATH-182R or equivalent course.) Lecture 6 (Fall, Spring).

RIT 365 students participate in experiential learning opportunities designed to launch them into their career at RIT, support them in making multiple and varied connections across the university, and immerse them in processes of competency development. Students will plan for and reflect on their first-year experiences, receive feedback, and develop a personal plan for future action in order to develop foundational self-awareness and recognize broad-based professional competencies. (This class is restricted to incoming 1st year or global campus students.) Lecture 1 (Fall, Spring).

Computer programming in the life sciences is used for modeling and data analysis across all fields. In this course, students will learn the fundamentals of computer programming and apply it to solve real problems in the life sciences. Breaking down problems, common syntax, and thoughtful decisions on proper use of data structures will be emphasized. (UGRD-COS) Lab 2, Lecture 2 (Fall).

This course will address the fundamental concepts of Molecular Biology. Class discussions, assignments, and projects will explore the structure and function of biologically important molecules (DNA, RNA and proteins) in a variety of cellular and molecular processes. Students in this course will explore the molecular interactions that facilitate the storage, maintenance and repair of DNA and processes that drive the flow of genetic information and evolution. Students in this course will gain an understanding of various molecular mechanisms, structure/function relationships, and processes as they relate to molecular biology. The foundational molecular concepts in this course will be built upon in a variety of upper-level biology courses. (Prerequisite:(BIOL-101,BIOL-102,BIOL-103&BIOL-104) or (BIOL-121&BIOL-122) or (BIOL-123,BIOL-124,BIOL-125&BIOL-126)or equivalent courses with a grade of C- or higher. Co-requisite:(CHMG-141&CHMG-145)or(CHEM-151&CHEM-155) or CHMG-131 or equivalent courses.) Lecture 3 (Fall, Spring).

This laboratory course will address the fundamental concepts of Molecular Biology. Students in this laboratory will complement their understanding of core concepts in Molecular Biology through the implementation and practice of laboratory techniques used by Molecular Biologists. Laboratory techniques and projects will focus on recombinant DNA technology and the detection and tracking of biomolecules such as DNA, RNA and proteins. (Prerequisite:(BIOL-101&BIOL-102&BIOL-103&BIOL-104)or(BIOL-121&BIOL-122)or(BIOL-123&BIOL-124&BIOL-125&BIOL-126)or equivalent courses w/ grade of C- or higher. Co-requisite:BIOL-206&((CHMG-141&CHMG-145)or(CHEM-151&CHEM-155)orCHMG-131)or equivalent courses.) Lab 3 (Fall, Spring).

This is an introductory course in languages commonly used in bioinformatics and their application to biological data. We will investigate the use of multiple languages for processing sequence and "-omics" data, building analysis pipelines, integrating languages, managing a variety of biological data types, and providing effective interfaces to existing tools for analysis of these data. The course is largely based around live-code demonstration, in-class assisted coding assignments, and a student-designed final class project. (Prerequisites: BIOL-135 or equivalent course.) Lecture 2, Studio 2 (Spring).

Introduction to the principles of inheritance; the study of genes and chromosomes at molecular, cellular, organismal, and population levels. (Prerequisites: (BIOL-206 and BIOL-216) or BIOL-201 or BIOL-202 or BIOG-240 or equivalent courses.) Lecture 3, Recitation 1 (Fall, Spring, Summer).

This course is a study of the structure, nomenclature, reactions and synthesis of the following functional groups: alkanes, alkenes, alkynes. This course also introduces chemical bonding, IR and NMR spectroscopy, acid and base reactions, stereochemistry, nucleophilic substitution reactions, and alkene and alkyne reactions. In addition, the course provides an introduction to the use of mechanisms in describing and predicting organic reactions. (Prerequisites: CHMG-142 or CHMG-131 or equivalent course. Corequisites: CHMO-235 or equivalent course.) Lecture 3 (Fall, Spring, Summer).

This course trains students to perform techniques important in an organic chemistry lab. The course also covers reactions from the accompanying lecture CHMO-231. (Corequisite: CHMO-231 or equivalent course.) Lab 3 (Fall, Spring, Summer).

This course introduces students to ideas and techniques from discrete mathematics that are widely used in Computer Science. Students will learn about the fundamentals of propositional and predicate calculus, set theory, relations, recursive structures and counting. This course will help increase students’ mathematical sophistication and their ability to handle abstract problems. (Co-requisites: MATH-182 or MATH-182A or MATH-172 or equivalent courses.) Lecture 3, Recitation 1 (Fall, Spring).

This course introduces statistical methods of extracting meaning from data, and basic inferential statistics. Topics covered include data and data integrity, exploratory data analysis, data visualization, numeric summary measures, the normal distribution, sampling distributions, confidence intervals, and hypothesis testing. The emphasis of the course is on statistical thinking rather than computation. Statistical software is used. (Prerequisites: Any 100 level MATH course, or NMTH-260 or NMTH-272 or NMTH-275 or (NMTH-250 with a C- or better) or a Math Placement Exam score of at least 35.) Lecture 3 (Fall, Spring, Summer).

Computer programming in the life sciences is used for modeling and data analysis across all fields. In this course, students will learn more advanced techniques to solve life sciences modeling problems efficiently using parallelization and distributed computing. Common methods and thoughtful decisions on proper use of tools will be emphasized. (Prerequisites: BIOL-230 or equivalent course and students in COS Majors.) Lab 2, Lecture 2 (Fall).

This course explores major ethical issues in medicine and biology via lecture, readings, films, and presentation and discussion of cases. Students report on current events in ethics as researched on the internet or other news media. The first portion of the course is in a lecture format. Students learn about various theories of ethical analysis that are in current use. Subsequent classes are devoted to particular ethical areas. Relevant cases are given to the students for presentation in both written and oral formats. Any additional background material that may be required to discuss the cases is presented by the instructor and the remainder of the period is discussion based on the philosophical foundation provided at the beginning of the course. (Prerequisites: (BIOL-101 and BIOL-102 and BIOL-103 and BIOL-104) or (BIOL-121 and BIOL-122) or (BIOL-123 and BIOL-124 and BIOL-125 and BIOL-126) or equivalent courses.) Lecture 3 (Spring).

This course addresses the fundamental concepts of bioinformatics, focusing on computational analysis of nucleic acids and proteins. Utilization of computational programs for analysis of individual and multiple sequences for functional and evolutionary information will be discussed. The computational laboratory will highlight the applications available for analysis of molecular sequences. (Prerequisite: BIOL-201 or BIOL-202 or BIOL-206 or BIOG-240 or equivalent course.) Lecture 2, Studio 2 (Fall).

Cooperative education experience for undergraduate biological sciences students. CO OP (Fall, Spring, Summer).

Students will utilize commonly used bioinformatics tools to analyze a real High Throughput Sequencing data set starting with raw data, proceeding with quality control, either aligning to a reference genome or performing de novo assembly, assessing differential gene expression determination, and finally annotating their results. Weekly lab reports will be required, and a group manuscript is expected at the end of the semester. (Prerequisite: BIOL-201 or BIOL-202 or BIOL-206 or BIOG-240 or equivalent course.) Lab 2, Lecture 2 (Spring).

This course introduces the structure and function of biological macromolecules and their metabolic pathways. The relationship between the three-dimensional structure of proteins and their function in enzymatic catalysis will be examined. Membrane structure and the physical laws that apply to metabolic processes will also be discussed. (Prerequisite: CHMO-231 or CHMO-331 or equivalent course.) Lecture 3 (Fall, Spring, Summer).

A presentation of the fundamental concepts and theories used in organizing and structuring data. Coverage includes the data modeling process, basic relational model, normalization theory, relational algebra, and mapping a data model into a database schema. Structured Query Language is used to illustrate the translation of a data model to physical data organization. Modeling and programming assignments will be required. Note: students should have one course in object-oriented programming. (Prerequisites: ISTE-120 or ISTE-200 or IGME-101 or IGME-105 or CSCI-140 or CSCI-142 or NACA-161 or NMAD-180 or BIOL-135 or GCIS-123 or GCIS-127 or equivalent course.) Lec/Lab 3 (Fall, Spring).

The overall goal of this course is to familiarize students with the theory and analysis of genomics data. Students will survey topics including the structure, organization, and expression of the genome in a diverse array of organisms ranging from microbes to humans. Students will also become familiar with the analysis of next generation ‘omics-type data through a series of computational activities and problem sets. A hands-on laboratory component will guide students through a rigorous investigation of genomes. (Prerequisites: BIOL-321 or equivalent course.) Lab 3, Lecture 3 (Fall).

This course is an introduction to the probabilistic models and statistical techniques used in computational molecular biology. Examples include Markov models, such as the Jukes-Cantor and Kimura evolutionary models and hidden Markov models, and multivariate models use for discrimination and classification. (Prerequisites: (MATH-161 or MATH-173 or MATH-182) and (STAT-145 or MATH-251) or equivalent courses.) Lecture 3 (Spring).

The experiential learning (EL) requirement may be fulfilled through a variety of methods including co-op, undergraduate research, summer research experiences, study abroad relevant to the major, designated EL courses, etc. All experiences must be approved by the GSOLS EL Committee. Lecture (Fall, Spring, Summer).

Bioinformatics Algorithms will focus on the types of analyses, tools, and databases that are available and commonly used in Bioinformatics. The labs will apply the lecture material in the analysis of real data through computer programming. (Prerequisites: BIOL-230 and BIOL-327 or equivalent courses.) Lab 2, Lecture 2 (Fall).

This course will explore two facets of protein molecules: separation and structure. The separation component will address common protein separation techniques such as 2D gel electrophoresis and chromatography. The structure component will follow the levels of protein structures, focusing on both experimental and computational methods to determine protein structures. Methods for determining primary structures such as Edman degradation method, Sanger method and mass spectrometry will be taught in lectures. Algorithms of predicting secondary structures will be introduced and implemented. Tertiary structure determination techniques such as NMR will be covered, with an emphasis on proton NMR, 13C NMR and multi-dimensional NMR. Homology modeling will be used to predict protein tertiary structures. (Prerequisite: BIOL-327 or equivalent course.) Lab 2, Lecture 2 (Spring).

This course serves as an introduction to biology for majors, focusing on the organismal, population, and ecosystem levels. Major themes include: evolution, structure and function, information flow and storage, pathways and transformations of energy and matter, and systems. The course also focuses on developing core competencies, such as applying the process of science, using quantitative reasoning, communicating, and collaborating. (This course is restricted to BIOL-BS, BIOTECH-BS, ENVS-BS, BIOINFO-BS, BIOMED-BS, BIOCHEM-BS, or NEURO-BS students.) Lecture 3 (Fall).

This course serves as an introduction to biology for majors, focusing on the molecular and cellular level. Major themes include: evolution, structure and function, information flow and storage, pathways and transformations of energy and matter, and systems. The course also focuses on developing core competencies, such as applying the process of science, using quantitative reasoning, communicating, and collaborating. (This course is restricted to BIOL-BS, BIOTECH-BS, ENVS-BS, BIOINFO-BS, BIOMED-BS, BIOCHEM-BS, or NEURO-BS students.) Lecture 3 (Spring).

This course is an introduction to laboratory work in life sciences. The laboratory work is project-based, and may involve field work as well as laboratory experiments. The course is designed to show the huge scope of biology and will encompass how some molecular biology and bioinformatics techniques connect with organismal and ecological biology. (This course is restricted to BIOL-BS, BIOTECH-BS, ENVS-BS, BIOINFO-BS, BIOMED-BS, BIOCHEM-BS, or NEURO-BS students. Co-requisites: BIOL-123 or equivalent course.) Lab 3 (Fall).

This course is an introduction to laboratory work in life sciences. The laboratory work is project based, and the subject matter of the project(s) may vary. The course is designed to show the huge scope of biology and will encompass some molecular biology and bioinformatics techniques connect with organismal and ecological biology. (This course is restricted to BIOL-BS, BIOTECH-BS, ENVS-BS, BIOINFO-BS, BIOMED-BS, BIOCHEM-BS, or NEURO-BS students. Co-requisites: BIOL-124 or equivalent course.) Lab 3 (Spring).

This course will explore topics in the field of bioinformatics including tools and resources used by the discipline, including direct experience with the common user environment. Lecture 3 (Fall).

This is a general chemistry course for students in the life and physical sciences. College chemistry is presented as a science based on empirical evidence that is placed into the context of conceptual, visual, and mathematical models. Students will learn the concepts, symbolism, and fundamental tools of chemistry necessary to carry on a discourse in the language of chemistry. Emphasis will be placed on the relationship between atomic structure, chemical bonds, and the transformation of these bonds through chemical reactions. The fundamentals of organic chemistry are introduced throughout the course to emphasize the connection between chemistry and the other sciences. Lecture 3 (Fall, Spring, Summer).

The course covers the thermodynamics and kinetics of chemical reactions. The relationship between energy and entropy change as the driving force of chemical processes is emphasized through the study of aqueous solutions. Specifically, the course takes a quantitative look at: 1) solubility equilibrium, 2) acid-base equilibrium, 3) oxidation-reduction reactions and 4) chemical kinetics. (Prerequisites: CHMG-141 or CHMG-131 or equivalent course.) Lecture 3 (Fall, Spring, Summer).

The course combines hands-on laboratory exercises with workshop-style problem sessions to complement the CHMG-141 lecture material. The course emphasizes laboratory techniques and data analysis skills. Topics include: gravimetric, volumetric, thermal, titration and spectrophotometric analyses, and the use of these techniques to analyze chemical reactions. (Corequisite: CHMG-141 or CHMG-131 or equivalent course.) Lab 3 (Fall, Spring, Summer).

The course combines hands-on laboratory exercises with workshop-style problem sessions to complement the CHMG-142 lecture material. The course emphasizes the use of experiments as a tool for chemical analysis and the reporting of results in formal lab reports. Topics include the quantitative analysis of a multicomponent mixture using complexation and double endpoint titration, pH measurement, buffers and pH indicators, the kinetic study of a redox reaction, and the electrochemical analysis of oxidation reduction reactions. (Prerequisites: CHMG-131 or CHMG-141 or equivalent course. Corequisites: CHMG-142 or equivalent course.) Lab 3 (Fall, Spring, Summer).

This is the first in a two-course sequence intended for students majoring in mathematics, science, or engineering. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers functions, limits, continuity, the derivative, rules of differentiation, applications of the derivative, Riemann sums, definite integrals, and indefinite integrals. (Prerequisites: MATH-111 or (NMTH-220 and NMTH-260 or NMTH-272 or NMTH-275) or equivalent courses with a minimum grade of B-, or a score of at least 60% on the RIT Mathematics Placement Exam. Co-requisites: MATH-181R or equivalent course.) Lecture 6 (Fall, Spring).

This is the second in a two-course sequence. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers techniques of integration including integration by parts, partial fractions, improper integrals, applications of integration, representing functions by infinite series, convergence and divergence of series, parametric curves, and polar coordinates. (Prerequisites: C- or better in MATH-181 or MATH-181A or equivalent course. Co-requisites: MATH-182R or equivalent course.) Lecture 6 (Fall, Spring).

RIT 365 students participate in experiential learning opportunities designed to launch them into their career at RIT, support them in making multiple and varied connections across the university, and immerse them in processes of competency development. Students will plan for and reflect on their first-year experiences, receive feedback, and develop a personal plan for future action in order to develop foundational self-awareness and recognize broad-based professional competencies. (This class is restricted to incoming 1st year or global campus students.) Lecture 1 (Fall, Spring).

Computer programming in the life sciences is used for modeling and data analysis across all fields. In this course, students will learn the fundamentals of computer programming and apply it to solve real problems in the life sciences. Breaking down problems, common syntax, and thoughtful decisions on proper use of data structures will be emphasized. (UGRD-COS) Lab 2, Lecture 2 (Fall).

This course will address the fundamental concepts of Molecular Biology. Class discussions, assignments, and projects will explore the structure and function of biologically important molecules (DNA, RNA and proteins) in a variety of cellular and molecular processes. Students in this course will explore the molecular interactions that facilitate the storage, maintenance and repair of DNA and processes that drive the flow of genetic information and evolution. Students in this course will gain an understanding of various molecular mechanisms, structure/function relationships, and processes as they relate to molecular biology. The foundational molecular concepts in this course will be built upon in a variety of upper-level biology courses. (Prerequisite:(BIOL-101,BIOL-102,BIOL-103&BIOL-104) or (BIOL-121&BIOL-122) or (BIOL-123,BIOL-124,BIOL-125&BIOL-126)or equivalent courses with a grade of C- or higher. Co-requisite:(CHMG-141&CHMG-145)or(CHEM-151&CHEM-155) or CHMG-131 or equivalent courses.) Lecture 3 (Fall, Spring).

This laboratory course will address the fundamental concepts of Molecular Biology. Students in this laboratory will complement their understanding of core concepts in Molecular Biology through the implementation and practice of laboratory techniques used by Molecular Biologists. Laboratory techniques and projects will focus on recombinant DNA technology and the detection and tracking of biomolecules such as DNA, RNA and proteins. (Prerequisite:(BIOL-101&BIOL-102&BIOL-103&BIOL-104)or(BIOL-121&BIOL-122)or(BIOL-123&BIOL-124&BIOL-125&BIOL-126)or equivalent courses w/ grade of C- or higher. Co-requisite:BIOL-206&((CHMG-141&CHMG-145)or(CHEM-151&CHEM-155)orCHMG-131)or equivalent courses.) Lab 3 (Fall, Spring).

This is an introductory course in languages commonly used in bioinformatics and their application to biological data. We will investigate the use of multiple languages for processing sequence and "-omics" data, building analysis pipelines, integrating languages, managing a variety of biological data types, and providing effective interfaces to existing tools for analysis of these data. The course is largely based around live-code demonstration, in-class assisted coding assignments, and a student-designed final class project. (Prerequisites: BIOL-135 or equivalent course.) Lecture 2, Studio 2 (Spring).

Introduction to the principles of inheritance; the study of genes and chromosomes at molecular, cellular, organismal, and population levels. (Prerequisites: (BIOL-206 and BIOL-216) or BIOL-201 or BIOL-202 or BIOG-240 or equivalent courses.) Lecture 3, Recitation 1 (Fall, Spring, Summer).

This course is a study of the structure, nomenclature, reactions and synthesis of the following functional groups: alkanes, alkenes, alkynes. This course also introduces chemical bonding, IR and NMR spectroscopy, acid and base reactions, stereochemistry, nucleophilic substitution reactions, and alkene and alkyne reactions. In addition, the course provides an introduction to the use of mechanisms in describing and predicting organic reactions. (Prerequisites: CHMG-142 or CHMG-131 or equivalent course. Corequisites: CHMO-235 or equivalent course.) Lecture 3 (Fall, Spring, Summer).

This course trains students to perform techniques important in an organic chemistry lab. The course also covers reactions from the accompanying lecture CHMO-231. (Corequisite: CHMO-231 or equivalent course.) Lab 3 (Fall, Spring, Summer).

This course introduces students to ideas and techniques from discrete mathematics that are widely used in Computer Science. Students will learn about the fundamentals of propositional and predicate calculus, set theory, relations, recursive structures and counting. This course will help increase students’ mathematical sophistication and their ability to handle abstract problems. (Co-requisites: MATH-182 or MATH-182A or MATH-172 or equivalent courses.) Lecture 3, Recitation 1 (Fall, Spring).

This course introduces statistical methods of extracting meaning from data, and basic inferential statistics. Topics covered include data and data integrity, exploratory data analysis, data visualization, numeric summary measures, the normal distribution, sampling distributions, confidence intervals, and hypothesis testing. The emphasis of the course is on statistical thinking rather than computation. Statistical software is used. (Prerequisites: Any 100 level MATH course, or NMTH-260 or NMTH-272 or NMTH-275 or (NMTH-250 with a C- or better) or a Math Placement Exam score of at least 35.) Lecture 3 (Fall, Spring, Summer).

Computer programming in the life sciences is used for modeling and data analysis across all fields. In this course, students will learn more advanced techniques to solve life sciences modeling problems efficiently using parallelization and distributed computing. Common methods and thoughtful decisions on proper use of tools will be emphasized. (Prerequisites: BIOL-230 or equivalent course and students in COS Majors.) Lab 2, Lecture 2 (Fall).

This course explores major ethical issues in medicine and biology via lecture, readings, films, and presentation and discussion of cases. Students report on current events in ethics as researched on the internet or other news media. The first portion of the course is in a lecture format. Students learn about various theories of ethical analysis that are in current use. Subsequent classes are devoted to particular ethical areas. Relevant cases are given to the students for presentation in both written and oral formats. Any additional background material that may be required to discuss the cases is presented by the instructor and the remainder of the period is discussion based on the philosophical foundation provided at the beginning of the course. (Prerequisites: (BIOL-101 and BIOL-102 and BIOL-103 and BIOL-104) or (BIOL-121 and BIOL-122) or (BIOL-123 and BIOL-124 and BIOL-125 and BIOL-126) or equivalent courses.) Lecture 3 (Spring).

This course addresses the fundamental concepts of bioinformatics, focusing on computational analysis of nucleic acids and proteins. Utilization of computational programs for analysis of individual and multiple sequences for functional and evolutionary information will be discussed. The computational laboratory will highlight the applications available for analysis of molecular sequences. (Prerequisite: BIOL-201 or BIOL-202 or BIOL-206 or BIOG-240 or equivalent course.) Lecture 2, Studio 2 (Fall).

Cooperative education experience for undergraduate biological sciences students. CO OP (Fall, Spring, Summer).

Students will utilize commonly used bioinformatics tools to analyze a real High Throughput Sequencing data set starting with raw data, proceeding with quality control, either aligning to a reference genome or performing de novo assembly, assessing differential gene expression determination, and finally annotating their results. Weekly lab reports will be required, and a group manuscript is expected at the end of the semester. (Prerequisite: BIOL-201 or BIOL-202 or BIOL-206 or BIOG-240 or equivalent course.) Lab 2, Lecture 2 (Spring).

This course introduces the structure and function of biological macromolecules and their metabolic pathways. The relationship between the three-dimensional structure of proteins and their function in enzymatic catalysis will be examined. Membrane structure and the physical laws that apply to metabolic processes will also be discussed. (Prerequisite: CHMO-231 or CHMO-331 or equivalent course.) Lecture 3 (Fall, Spring, Summer).

A presentation of the fundamental concepts and theories used in organizing and structuring data. Coverage includes the data modeling process, basic relational model, normalization theory, relational algebra, and mapping a data model into a database schema. Structured Query Language is used to illustrate the translation of a data model to physical data organization. Modeling and programming assignments will be required. Note: students should have one course in object-oriented programming. (Prerequisites: ISTE-120 or ISTE-200 or IGME-101 or IGME-105 or CSCI-140 or CSCI-142 or NACA-161 or NMAD-180 or BIOL-135 or GCIS-123 or GCIS-127 or equivalent course.) Lec/Lab 3 (Fall, Spring).

The overall goal of this course is to familiarize students with the theory and analysis of genomics data. Students will survey topics including the structure, organization, and expression of the genome in a diverse array of organisms ranging from microbes to humans. Students will also become familiar with the analysis of next generation ‘omics-type data through a series of computational activities and problem sets. A hands-on laboratory component will guide students through a rigorous investigation of genomes. (Prerequisites: BIOL-321 or equivalent course.) Lab 3, Lecture 3 (Fall).

This course is an introduction to the probabilistic models and statistical techniques used in computational molecular biology. Examples include Markov models, such as the Jukes-Cantor and Kimura evolutionary models and hidden Markov models, and multivariate models use for discrimination and classification. (Prerequisites: (MATH-161 or MATH-173 or MATH-182) and (STAT-145 or MATH-251) or equivalent courses.) Lecture 3 (Spring).

The experiential learning (EL) requirement may be fulfilled through a variety of methods including co-op, undergraduate research, summer research experiences, study abroad relevant to the major, designated EL courses, etc. All experiences must be approved by the GSOLS EL Committee. Lecture (Fall, Spring, Summer).

Bioinformatics Algorithms will focus on the types of analyses, tools, and databases that are available and commonly used in Bioinformatics. The labs will apply the lecture material in the analysis of real data through computer programming. (This course is restricted to students in the BIOINFO-MS, BIOINFO-BS/MS program.) Lab 3, Lecture 2 (Fall).

This course will explore two facets of protein molecules: their separation and their structure. The structure component will build upon information from earlier bioinformatics courses. Protein separation techniques will be addressed in lectures with descriptions of 2D gel electrophoresis and chromatography. Algorithms of protein secondary structure prediction will be implemented. Experimental techniques for tertiary structure determination such as NMR will be covered. The course will also include the analysis of inter-molecular interactions, such as ligand/receptor pairing, by employing software that permits modeling of molecular docking experiments. (Prerequisite: BIOL-327 or equivalent course or student standing in BIOINFO-MS.) Lab 2, Lecture 2 (Spring).

Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (This course requires permission of the Instructor to enroll.) Thesis (Fall, Spring, Summer).

This course will be focused on individual and organizational responsibilities in bioinformatics research, product development, product commercialization and clinical and consumer genetic testing. (This course is restricted to students in the BIOINFO-MS, BIOINFO-BS/MS program.) Lecture 3 (Fall).

The course provides opportunities for students and faculty to develop and share professional interests while discussing current trends and developments in bioinformatics. Material for this course will be drawn from the current scientific literature. (This course is restricted to students in the BIOINFO-MS, BIOINFO-BS/MS program.) Lecture 3 (Fall).

This course will introduce traditional multivariate statistical methods and multi-model inference, as well as iterative computational algorithms (i.e. Bayesian methods and machine learning) appropriate for graduate students conducting or planning to conduct a graduate research project. The course will focus on the proper application of methods to a sample data sets using statistical programming software and graphics and will forego the more in-depth analytical mathematical exposition that you might see in a math course, so that we can cover a larger variety of methods and spend more time implementing them in code. Practical examples will often derive from the fields of biology, environmental science, or medicine, however the statistical methods we cover will also have much broader application within modern data science. The ultimate goal will be to learn when and where to correctly apply a given method to real questions about real data. Class time will be devoted to introductory lecture, programming language demonstrations with a common dataset, and open discussions of potential applications, including in-class studio hours to help with homework. Students should be prepared to learn to write code scripts that will manipulate statistical tests and graphical output. However, no background experience with programming is assumed. All software used in the course is open-source and students will be required to set up and run weekly assignments on their own laptop computer or on a computer borrowed from the library or RIT’s computer lab. (Prerequisites: STAT-145 or equivalent course or graduate student standing.) Lecture 2, Studio 2 (Fall, Spring).

Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (This course requires permission of the Instructor to enroll.) Thesis (Fall, Spring, Summer).