The Biology I Course was developed through the Ohio Department of Higher Education OER Innovation Grant. The course is part of the Ohio Transfer Assurance Guides and is also named OSC003. This work was completed and the course was posted in October 2019. For more information about credit transfer between Ohio colleges and universities, please visit: www.ohiohighered.org/transfer.Team LeadCathy Sistilli                                         Eastern Gateway Community CollegeContent ContributorsLisa Aschemeier                                 Northwest State Community CollegeShaun Blevins                                     Rhodes State CollegeRachel Detraz                                     Edison State Community College                                     Sara Finch                                          Sinclair Community CollegeWendy Gagliano                                 Clark State Community College AJ Snow                                             University of Akron Wayne CollegeLibrarianAmanda Rinehart                               Ohio State UniversityReview TeamJessica Hall                                        Ohio Dominican UniversitySanhita Gupta                                    Kent State UniversityErica Mersfelder                                 Sinclair Community College

In scientific terms, phylogeny is the evolutionary history and relationship of an organism or group of organisms. A phylogeny describes the organism's relationships, such as from which organisms it may have evolved, or to which species it is most closely related. Scientists must collect accurate information that allows them to make evolutionary connections among organisms. It is a highly dynamic field of biology because phylogenetic modeling concepts are constantly changing as new information is collected.  Over the last several decades, new research has challenged scientists’ ideas about how organisms are related. 

This course covers the analytical, graphical, and numerical methods supporting the analysis and design of integrated biological systems. Topics include modularity and abstraction in biological systems, mathematical encoding of detailed physical problems, numerical methods for solving the dynamics of continuous and discrete chemical systems, statistics and probability in dynamic systems, applied local and global optimization, simple feedback and control analysis, statistics and probability in pattern recognition.