Systems Biology, Women in Science 2021/22: Data and Model Integration

Gender equality within science is essential to ensure sustainable development and to realize the fullest potential of humankind in the advancement of scientific knowledge. Long-standing gender biases and stereotypes continue to discourage young women away from science-related fields and STEM research in particular. Such traditional mindsets must be changed by actively encouraging women to pursue STEM careers and to equitably promote their professional development.

Here, we showcase the tremendous science being done by women researchers throughout the world that are breaking these mindsets. In doing so, we hope to encourage young women to pursue STEM careers especially in systems biology. Frontiers in Systems Biology is proud to promote the work of women scientists across the field of Systems Biology with an emphasis on Data and Model Integration.

We particularly invite submissions from those women researchers coming from less privileged countries and communities to help emphasize the cross-cultural and cross-communal aspects of scientific research.

The work presented here highlights the diversity of research performed across Systems Biology with a focus on Data and Model Integration research. It spans advances in theory, experiment, and methodology with applications to compelling problems.

Please note that to be considered for this collection, the first or last author should be a researcher who identifies as a woman

Keywords: Women in Science, Women in STEM, Systems Biology, Data Mining, Stochastic Modeling, Dynamic Models, Spatio-temporal Modeling, Non-linear Programming, Experimental Design, Machine Learning, Artificial Intelligence, multi-scale

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.