The principles and computational methods to study the biological data generated by genome sequencing, gene expressions, protein profiles, and metabolic fluxes. Application of arithmetic, algebraic, graph, pattern matching, sorting and searching algorithms and statictical tools to genome analysis. Applications of Bioinformatics to metabolic engineering, drug design, and biotechnology.
Fluids classification; transport coefficients; momentum transfer and velocity profiles; energy and mass transfer for isothermal and multicomponent systems; mass transfer with chemical reaction; applications for chemical and biological systems.
Kinetics of homogeneous and heterogeneous chemical reactions; catalysts; design of chemical reactors; applications for chemical and biological systems.
Classical thermodynamics: enthalpy, entropy, free energies, equilibria; introduction to statistical thermodynamics to describe the properties of materials; kinetic processes; diffusion of mass, heat, energy; fundamentals of rate processes in materials, kinetics of transformations.
Principles of phase and chemical equilibria; computational methods for phase and chemical equilibria calculations; applications for chemical and biological systems.
Differences between small molecules and polymers; thermosets; thermoplastics. Relationships between molecular structure and properties. Major types of polymers. Supramolecular architectures, composites, copolymers.
The principles and computational methods to study the biological data generated by genome sequencing, gene expressions, protein profiles, and metabolic fluxes. Application of arithmetic, algebraic, graph, pattern matching, sorting and searching algorithms and statistical tools to genome analysis. Applications of Bioinformatics to metabolic engineering, drug design, and biotechnology.
Fundamentals of diffusion; primary mechanisms for mass transfer; mass transfer coupled with chemical reactions; membrane processes and controlled release phenomena.
Fundamentals of physicochemical and biological processes used for waste minimization, air pollution control, water pollution control, hazardous waste control; environmentally conscious design of chemical processes.
Key aspects of microbial physiology; exploring the versatility of microorganisms and their diverse metabolic activities and products; industrial microorganisms and the technology required for large-scale cultivation.
Examine the technologies, environmental impacts and economics of main energy sources of today and tomorrow including fossil fuels, nuclear power, biomass, geothermal energy, hydropower, wind energy, and solar energy. Comparison of different energy systems within the context of sustainability. Hydrogen economy and fuel cells.
Adsorption on surfaces, structural and dynamic considerations in adsorption, thermodynamics of adsorption, methods for catalyst characterization, pore structure and surface area, surface chemistry of catalysis, metals, highly dispersed catalysts, industrial examples with emphasis on energy production
Crude oil and biomass refining technologies. Fractionation, catalytic- and thermo- cracking, gasoline and diesel upgrading and other side processes in crude oil refining; gasification, pyrolysis, transesterification and condensation processes in biomass refining; economical and environmental factors in refining.
Recombinant DNA, enzymes and other biomolecules. Molecular genetics. Commercial use of microorganisms. Cellular reactors; bioseparation techniques. Transgenic systems. Gene therapy. Biotechnology applications in environmental, agricultural and pharmaceutical problems.
Drug design consists of identifying a target (DNA, RNA, proteins) that is known to cause a certain disease and selectively inhibiting or modifying its activity by binding a drug molecule to a specified location on that target. In this course, computational techniques for designing such a drug molecule will be taught. The topics to be covered are: Identification of the active part. Forces involved in drug-receptor interactions. Screening of drug libraries. Use of different software to determine binding energies. Identifying a lead molecule. Methods of refining a lead molecule for better suitability. Case studies: A survey of known drugs, success and failure stories.
Reconstruction of metabolic network from genome information and its structural and functional analysis, computational models of biochemical reaction networks; system biology in drug discovery and proteomics, flux balance analysis; modeling of gene expression; system biology in artificial intelligence. These concepts will be supported with statistic, thermodynamic, structural biology and learning machine
Modeling concepts and tools for chemical and biological systems. Steady state and transient modeling and simulation. MATLAB based case studies. Selected topics from the curriculum such as reaction stoichiometry, kinetics modeling, reactors, equation of state, phase equilibria, staged operations, fluxes, diffusion and convection, parameter estimation.
The fundamentals of tissue engineering at the molecular and cellular level; techniques in tissue engineering; problems and solution in tissue engineering; transplantation of tissues in biomedicine using sophisticated equipments and materials; investigation of methods for the preparation of component of cell, effect of growth factors on tissues.
Principles of molecular modeling in chemical engineering applications; fundamentals for molecular simulation of adsorption and diffusion processes in nanoporous materials; molecular dynamics methods for gas transport in nanopores; Monte Carlo methods for equilibrium based gas separations; molecular modeling of zeolites and metal organic frameworks for gas storage.
Independent research towards M.S. degree with thesis option.
Relationship between structure, function and dynamics in biomolecules. Overview of the biomolecular databases and application of computational methods to understand molecular interactions; networks. Principles of computational modeling and molecular dynamics of biological systems.
The following objectives will be met through extensive reading, writing and discussion both in and out of class.Build a solid background in academic discourse, both written and spoken. Improve intensive and extensive critical reading skills. Foster critical and creative thinking. Build fundamental academic writing skills including summary, paraphrase, analysis, synthesis. Master cohesiveness as well as proper academic citation when incorporating the work of others.
Optimization problems for dynamical systems. Pontryagin?s Maximum Principle. Optimality conditions for nonlinear dynamical systems. Linear Quadratic Optimal Control of continuous and discrete linear systems using finite and infinite time horizons. Stability and performance analysis of the properties of the optimal feedback solutions. Moving horizon optimal control of constrained systems using Model Predictive Control formulation. Applications from different disciplines and case studies.