Graphical summary of the research conducted by the leadership team. From left to right: Drs. Watanabe, Dietrich, Zelinski, and Sluka

Leadership team

Karen H. Watanabe

Dr. Watanabe is an Associate Professor in the School of Mathematical and Natural Sciences at Arizona State University located on the West campus in Glendale, AZ.  As the PI for the MOTHER project, Dr. Watanabe coordinates the team’s efforts to meet project milestones and is responsible for administrative tasks related to budget and reporting.  In addition to supporting the project where needed, she supervises students scanning histology slides and identifying ovarian follicles in different stages of development to create a training dataset for machine learning algorithm development, prepares written project protocols to ensure data quality, oversees the evaluation of data segmentation results and obtains feedback from collaborators to continually improve MOTHER as a research and educational resource.

Dr. Watanabe’s research group develops biologically based mathematical/computational models through the integration of disparate datasets to recapitulate/predict observed phenomena and test hypotheses about processes where multiple theories exist.  She focuses on how chemicals in the environment affect biological systems and uses Bayesian methods of model parameter estimation to account for biological variability. Her reproduction research focuses on developing models for normal conditions and in response to endocrine active chemical exposure.  These include the first cell-based computational model of normal early ovarian development in mice; for fathead minnows (Pimephales promelas), a MATLAB®-based model of oocyte growth dynamics, and physiologically based models of the hypothalamic-pituitary-gonadal axis in males and females.  She is committed to training students interested in working at the interface of natural sciences, mathematics and computing and in broadening participation of underrepresented students in STEM disciplines.

Suzanne W. Dietrich

Dr. Dietrich is a Professor in the School of Mathematical and Natural Sciences at Arizona State University’s West campus. As a co-PI, Dr. Dietrich oversees the development of data transfer tools, data curation, MOTHER database design, and web portal development and searching.

Dr. Dietrich’s current database research emphasizes interdisciplinary collaborations, applying database technology for sharing scientific information. Dr. Dietrich’s database systems research over her career has emphasized distributed heterogeneous resources, including metadata, database design, and querying. Besides MOTHER, she has contributed to the design, implementation, and search of the database of NeuroML models ( for computational neuroscience. Dr. Dietrich’s research has also included computer science education, mostly in the field of databases. She is an author of several database textbooks, including Understanding Databases: Concepts and Practice, John Wiley & Sons, 2021, and a co-author of Fundamentals of Object Databases: Object-Oriented and Object-Relational Design, Morgan Claypool, 2011. Her database education research has also included the development of visualizations for introducing students in all majors to fundamental databases concepts (

Mary Zelinski

Dr. Zelinski is a Professor in the Division of Reproductive & Developmental Sciences, Oregon National Primate Research Center, Beaverton, OR and in the Department of Obstetrics & Gynecology, Oregon Health & Science University, Portland, OR.  She leads the reproductive science component of the MOTHER project including providing hundreds of macaque ovary histology slides and their metadata, preparing instructional videos on ovarian follicle identification and counting, and consulting with students on difficult follicle identifications.  She is also a primary liaison for outreach to collaborators willing to share slides with the MOTHER project.

Dr. Zelinski’s research centers on understanding the development and function of primate ovarian follicles, incorporating nonhuman primates as pre-clinical models for infertility, contraception, fertility preservation and ovarian aging as related to women’s reproductive health.  The primary goal of oncofertility research in the Zelinski lab is to merge the principles of tissue engineering and biomaterial science with ovarian biology to develop novel fertility preservation options for girls and young women who must undergo treatments that threaten their fertility. The rhesus monkey is being used as a model for ovarian protection, ovarian transplantation and 3-dimensional follicle culture as strategies for fertility preservation.  These experiments will be translated to the human clinic to offer ovarian follicle maturation before or after cryopreservation as fertility preservation options for cancer survivors.  More recent research is focused on pre-clinical trials in the rhesus monkey to test the in vivo efficacy of a potential intervention for ovarian aging.  Endpoints include evaluating the primordial follicle ovarian reserve, indicators of ovarian follicular health, oocyte quality and competence, as well as metabolic and immune functions in both young and aging macaques.  ​​Dr. Zelinski recently participated in the National Institutes of Child Health and Human Development (NICHD)-sponsored Workshop on Ovarian Nomenclature, serving on subcommittees regarding  gross ovarian morphology and follicle nomenclature.  Committee members consisted of both clinical and basic scientists who contributed to published manuscripts on gross ovarian morphology, ovarian subanatomy and follicle nomenclature (see Publications).  This information will be added to the anatomical databases, Uberon and HubMap.

James Sluka

Dr. Sluka is a Senior Scientist in the Intelligent Systems Engineering Department of the Luddy School of Informatics, Computing and Engineering at Indiana University in Bloomington Indiana.  He leads the data segmentation and image data analysis/machine learning components of the project .  This includes overseeing the development of machine learning algorithms to automatically classify folllicles in different stages of development, and writing scripts to downsize images, customizing QuPath software for ovarian follicle identification, and comparing follicle annotations between two independent counters. In addition, he collaborates with the other team leads on defining structured annotations for the data in MOTHER.

Dr. Sluka is a computational biologist and chemist that is also involved in the development of international standards in biomedical research. His research interests include computational modeling of the liver, lungs, ovary and the blood-brain barrier (BBB). His models span a range of biological scales from sub-cellular signaling and metabolism, to tissue scale multicellular models, to systemic Physiologically Based Pharmacokinetic (PBPK) models, and population models. His recent biological modeling research has focused on modeling infectious agents such as SARS-CoV2 and linking in vitro measures of BBB crossing to whole-body PBPK models of toxins and drugs. In the SARS-CoV2 domain, he is a founding member of a modeling group, Multiscale Modeling and Viral Pandemics, under the auspices of the Interagency Modeling and Analysis Group (IMAG). IMAG is a multi-agency government organization that promotes and supports biomedical modeling. His work also includes artificial intelligence and machine learning (ML) research including image segmentation in ovarian histology slides and ML approaches to the rapid calculation of diffusing fields in place of computationally expensive calculation of partial differential equations in spatial models of tissues and organs. In the standards domain, Dr. Sluka is a voting member of the US national committee (hosted by the US National institute of Standards and Technology, NIST) to the International Organization for Standardization (ISO) which is developing standards in data sharing and modeling in biomedical research.

Funding: MOTHER DB is funded by grant “CIBR Multispecies Ovary Tissue Histology Electronic Repository (MOTHER)” from the National Science Foundation (NSF DBI-2054061, 2021 – 2024).
Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.