Overcoming adversity, one optimized model at a time.
Who We Are
Cultured out of Baltimore Underground Science Space, Common Ground – Synthesis of Biology focuses on two objectives: expanding awareness of the Systems Biology field through outreach, and refinement of ground truth through Math Modeling and Machine Learning cooperative competitions.
What We Do
We aim to achieve the first by sourcing guest speakers from the Systems Biology community to speak at high school and undergraduate science classes about some of the exciting topics being worked on in the field, and the countless possibilities that stem from the mastery of Systems Biology. Feedback from students will ideally aid institutions in gaining data-driven insights, and thus, will better position institutions on the decision of implementing a Systems Biology program as well as design and course optimization. Our approach to the second category entails hosting co-opetitions focused on merging traditional Math Modeling techniques such as Constraint-Based Modeling with newer approaches in Machine Learning. System identification, multi-scale integration, data-driven discovery, knowledge refinement, model scoring, and applicable validation methods are a few important features we focus on.
How We Do It
Accomplishing these goals takes collaboration with both new and prominent entities in the Systems Biology Ecosystem. Successful collaboration and progress requires an alignment of our objectives with those of other value providers in the space. With institutions sourcing out speakers we can tap from, open access to datasets by multi-omics data providers, streamlined funding for participants by established foundations, etc., the burden to remain sustainable becomes dispersed and makes for a more robust network.
Unfortunately, we do not provide funding. An exception to this is a paid internship (for work on projects we undertake internally) we provide students facing adversity. We generally align our objectives with those of external entities that provide funding opportunities. For now, this is mostly rooted in the design of the math modeling and machine learning co-opetitions. For example, we research and keep an open line of communication with program directors at the National Science Foundation. This enables us to gain insights into unsolicited proposals they might be looking for, and in turn, design co-opetitions accordingly to position participants on a streamlined path to funding.