Algorithms, models, representations, and databases for collecting and analyzing biological data to draw inferences. Overview of available molecular biological databases. Sequence analysis, alignment, database similarity searches. Phylogenetic trees. Discovering patterns in protein sequences and structures. Protein 3D structure prediction: homology modeling, protein folding, representation for macromolecules, simulation methods. Protein-protein interaction networks, regulatory networks, models and databases for signaling networks, data mining for signaling networks.

Review of 2-D linear system theory and 2-D Fourier transforms. Integral transforms used in optical signal processing; Fundamentals of physical optics and diffraction theory; Fourier and imaging properties of optical systems; Coherent and Incoherent optical image processing; Fundamental architectures for correlation and spectrum analysis; Interferometry; Discrete analog optical processors; Holography; Review of 3D Display technologies.

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.

Bioengineering, engineering materials, medicine, polymers, metals, smart materials, surgical implants, surgical instruments, cell and tissue mechanics, microsurgery, self-expanding stents, physical therapy, phase transformations, shape memory alloys.

The advanced methodology used for modern biological science research. Topics include the interpretation of data gained from both hypothesis-driven and high-throughput experiments from research articles focusing on DNA repair, DNA replication, transcription, cell cycle, organelle biogenesis, proteomics and genetics.

Entropy, Relative Entropy and Mutual Information; Asymptotic Equipartition Theory; Entropy Rates of a Stochastic Process; Data Compression; Kolmogorov Complexity; Channel Capacity; Differential Entropy; The Gaussian Channel; Maximum Entropy and Spectral Estimation; Rate Distortion Theory, Network Information Theory.

Fabrication and characterization techniques for micro and nano electro mechanical systems, MEMS & NEMS (including: microlithography; wet & dry etching techniques; physical & chemical vapor deposition processes; electroplating; bonding; focused ion beams; top-down approaches - electron-beam lithography, SPM, soft lithography - ; bottom-up techniques based on self-assembly). Semiconductor nanotechnology. Nanotubes & nanowires. Biological systems. Molecular electronics.

Intermolecular forces which govern self-organization of biological and synthetic nanostructures. Thermodynamic aspects of strong (covalent and coulomb interactions) and weak forces (dipolar, hydrogen bonding). Self-assembling systems: micelles, bilayers, and biological membranes. Computer simulations for ôhands-onö experience with nanostructures.

A graduate level introduction to matrix-based computing. Stable and efficient algorithms for linear equations, least squares and eigenvalue problems. Both direct and iterative methods are considered and MATLAB is used as a computing environment.

This course will discuss the applications of mechanics to biological systems. We will cover the basic principles of mechanics (force-moment, stress-strain, work, energy, rigid body dynamics), analysis of human movement, musculoskeletal mechanics, tissue mechanics, motor control system, sports biomechanics, and rehabilitation engineering.