While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of completely novel biological circuit components beyond naturally-derived templates remains challenging due to poorly understood design rules. Toehold switches, which are programmable nucleic acid sensors, face an analogous design bottleneck; our limited understanding of how sequence impacts functionality often necessitates expensive, time-consuming screens to identify effective switches. Here, we introduce Sequence-based Toehold Optimization and Redesign Model (STORM) and Nucleic-Acid Speech (NuSpeak), two orthogonal and synergistic deep learning architectures to characterize and optimize toeholds. Applying techniques from computer vision and natural language processing, we ‘un-box’ our models using convolutional filters, attention maps, and in silico mutagenesis. Through transfer-learning, we redesign sub-optimal toehold sensors, even with sparse training data, experimentally validating their improved performance. This work provides sequence-to-function deep learning frameworks for toehold selection and design, augmenting our ability to construct potent biological circuit components and precision diagnostics.

Women continue to be underrepresented in science, technology, engineering, and math (STEM). Gender discrimination and gender bias reinforce cultural stereotypes about women and their ability to perform in male-dominated STEM fields. Greater policy intervention can bolster national response to gender-based harassment and discrimination. There are four major efforts that individual institutions, local governments, and the federal government can support to combat gender discrimination in STEM: (1) invest in early education initiatives for increasing female representation, (2) institute stronger state and federal policies around gender discrimination, (3) foster workplace practices that promote diversity, and (4) develop better quantification and metrics for assessing gender discrimination to enact more meaningful policies.

In recent years, there has been a growing interest in the field of "AI Ethics" and related areas. This field is purposefully broad, allowing for the intersection of numerous subfields and disciplines. However, a lot of work in this area thus far has centered computational methods, leading to a narrow lens where technical tools are framed as solutions for broader sociotechnical problems. In this work, we discuss a less-explored mode of what it can mean to "do" AI Ethics: tech worker collective action. Through collective action, the employees of powerful tech companies can act as a countervailing force against strong corporate impulses to grow or make a profit to the detriment of other values. In this work, we ground these efforts in existing scholarship of social movements and labor organizing. We characterize 150 documented collective actions, and explore several case studies of successful campaigns. Looking forward, we also identify under-explored types of actions, and provide conceptual frameworks and inspiration for how to utilize worker organizing as an effective lever for change.

MIT Science Policy Review spoke with Mr. Ryan Morhard about the ties between bioeconomy development and pandemic preparedness. Mr. Morhard is the Director of Policy and Partnerships at Ginkgo Bioworks, a Boston-based synthetic biology company. He obtained a J.D. at the Washington University in St. Louis School of Law. Formerly, Mr. Morhard has served as the Head of COVID Action Platform and Lead of Global Health Security at the World Economic Forum. From 2014 to 2016, he was the Branch Chief of International Partnerships at the U.S. Department of Health and Human Services, where he engaged with international partners to improve collective capacities and handle public health emergencies. We spoke with Mr. Morhard’s about his professional experience in biosecurity, insights in capacity building, and outlooks about leveraging advancements in biotechnology and the growing bioeconomy to address future pandemics and other significant societal and environmental challenges.