Avida-ED allows users to design and perform experiments to test hypotheses about evolutionary mechanisms using evolving digital organisms. Avida-ED is an NSF-funded project to develop a digital evolution educational software platform for use in biology courses. The co-PIs on the project are Charles Ofria, Richard Lenski, and Diane Ebert-May. There are several on-line tools to help with problems with the Avida-ED program
" This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include: Genomes: biological sequence analysis, hidden Markov models, gene finding, RNA folding, sequence alignment, genome assembly Networks: gene expression analysis, regulatory motifs, graph algorithms, scale-free networks, network motifs, network evolution Evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory, rapid evolution "
Introduction to computational biology including the fundamentals of protein and nucleic acid sequence analysis, phylogenetic analysis, motif finding, hidden Markov models, and 3D structure prediction and modeling. An overview of emerging fields including expression profiling, quantitative image analysis and the modeling of cellular signal transduction networks are also included. Subject designed for advanced undergraduates and graduate students with strong backgrounds in either molecular biology or computer science but not necessarily both. Two self-study tracks are offered, introducing either basic statistical methods and programming (to biologists) or the fundamentals of molecular biology (to computer scientists). Lectures combine both perspectives to illustrate how computation is having a significant impact on modern biology. Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.
Phylogenetic Investigator (PI) is a software package designed to facilitate creative problem-solving in phylogenetic analysis for the purpose of teaching and learning phylogenetic inference. Users can identify characters and states, polarize characters, and engage in directed-search phylogenetic tree construction.
PI also allows the user to * make inferences and represent them one step at a time * vary representational features of their trees (such as angle of divergence and time between speciation events) * create reticulate tree patterns * view all of the character transformations at one time.
In addition, PI can generate plausible data stochastically for modeling and practicing tree construction.
A survey of problems at the interface of statistical physics and modern biology: Bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, phylogenetic trees. Physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, elements of protein folding. Considerations of force, motion, and packaging; protein motors, membranes. Collective behavior of biological elements; cellular networks, neural networks, and evolution.
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