Learning on knowledge graph dynamics provides an early warning of impactful research.

The scientific ecosystem relies on citation-based metrics that provide only imperfect, inconsistent and easily manipulated measures of research quality. Here we describe DELPHI (Dynamic Early-warning by Learning to Predict High Impact), a framework that provides an early-warning signal for 'impactful' research by autonomously learning high-dimensional relationships among features calculated across time from the scientific literature. We prototype this framework and deduce its performance and scaling properties on time-structured publication graphs from 1980 to 2019 drawn from 42 biotechnology-related journals, including over 7.8 million individual nodes, 201 million relationships and 3.8 billion calculated metrics. We demonstrate the framework's performance by correctly identifying 19/20 seminal biotechnologies from 1980 to 2014 via a blinded retrospective study and provide 50 research papers from 2018 that DELPHI predicts will be in the top 5% of time-rescaled node centrality in the future. We propose DELPHI as a tool to aid in the construction of diversified, impact-optimized funding portfolios.

Machine Learning Identifies Chemical Characteristics That Promote Enzyme Catalysis.

Despite tremendous progress in understanding and engineering enzymes, knowledge of how enzyme structures and their dynamics induce observed catalytic properties is incomplete, and capabilities to engineer enzymes fall far short of industrial needs. Here, we investigate the structural and dynamic drivers of enzyme catalysis for the rate-limiting step of the industrially important enzyme ketol-acid reductoisomerase (KARI) and identify a region of the conformational space of the bound enzyme-substrate complex that, when populated, leads to large increases in reactivity. We apply computational statistical mechanical methods that implement transition interface sampling to simulate the kinetics of the reaction and combine this with machine learning techniques from artificial intelligence to select features relevant to reactivity and to build predictive models for reactive trajectories. We find that conformational descriptors alone, without the need for dynamic ones, are sufficient to predict reactivity with greater than 85% accuracy (90% AUC). Key descriptors distinguishing reactive from almost-reactive trajectories quantify substrate conformation, substrate bond polarization, and metal coordination geometry and suggest their role in promoting substrate reactivity. Moreover, trajectories constrained to visit a region of the reactant well, separated from the rest by a simple hyperplane defined by ten conformational parameters, show increases in computed reactivity by many orders of magnitude. This study provides evidence for the existence of reactivity promoting regions within the conformational space of the enzyme-substrate complex and develops methodology for identifying and validating these particularly reactive regions of phase space. We suggest that identification of reactivity promoting regions and re-engineering enzymes to preferentially populate them may lead to significant rate enhancements.

Tackling Regional Public Health Issues Using Mobile Health Technology: Event Report of an mHealth Hackathon in Thailand.

Hackathons are intense, short, collaborative events focusing on solving real world problems through interdisciplinary teams. This is a report of the mHealth hackathon hosted by Khon Kaen University in collaboration with MIT Sana and faculty members from Harvard Medical School with the aim to improve health care delivery in the Northeast region of Thailand. Key health challenges, such as improving population health literacy, tracking disease trajectory and outcomes among rural communities, and supporting the workflow of overburdened frontline providers, were addressed using mHealth. Many modifications from the usual format of hackathon were made to tailor the event to the local context and culture, such as the process of recruiting participants and how teams were matched and formed. These modifications serve as good learning points for hosting future hackathons. There are also many lessons learned about how to achieve a fruitful collaboration despite cultural barriers, how to best provide mentorship to the participants, how to instill in the participants a sense of mission, and how to match the participants in a fair and efficient manner. This event showcases how interdisciplinary collaboration can produce results that are unattainable by any discipline alone and demonstrates that innovations are the fruits of collective wisdom of people from different fields of expertise who work together toward the same goals.

Genotype Specification Language.

We describe here the Genotype Specification Language (GSL), a language that facilitates the rapid design of large and complex DNA constructs used to engineer genomes. The GSL compiler implements a high-level language based on traditional genetic notation, as well as a set of low-level DNA manipulation primitives. The language allows facile incorporation of parts from a library of cloned DNA constructs and from the "natural" library of parts in fully sequenced and annotated genomes. GSL was designed to engage genetic engineers in their native language while providing a framework for higher level abstract tooling. To this end we define four language levels, Level 0 (literal DNA sequence) through Level 3, with increasing abstraction of part selection and construction paths. GSL targets an intermediate language based on DNA slices that translates efficiently into a wide range of final output formats, such as FASTA and GenBank, and includes formats that specify instructions and materials such as oligonucleotide primers to allow the physical construction of the GSL designs by individual strain engineers or an automated DNA assembly core facility.