A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and Simulation

Accurate molecular concentrations are essential for reliable analyses of biochemical networks and the creation of predictive models for molecular and systems biology, yet protein and metabolite concentrations used in such models are often poorly constrained or irreproducible. Challenges of using data from different sources include conflicts in nomenclature and units, as well as discrepancies in experimental procedures, data processing and implementation of the model. To obtain a consistent estimate of protein and metabolite levels, we integrated and normalized data from a large variety of sources to calculate Adjusted Molecular Concentrations. We found a high degree of reproducibility and consistency of many molecular species across brain regions and cell types, consistent with tight homeostatic regulation. We demonstrated the value of this normalization with differential protein expression analyses related to neurodegenerative diseases, brain regions and cell types. We also used the results in proof-of-concept simulations of brain energy metabolism. The standardized Brain Molecular Atlas overcomes the obstacles of missing or inconsistent data to support systems biology research and is provided as a resource for biomolecular modeling.

Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field

We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy and scope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners. Expected final online publication date for the Annual Review of Biophysics, Volume 50 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Principles of synthetic biology: a MOOC for an emerging field

Synthetic biology requires students and scientists to draw upon knowledge and expertise from many disciplines. While this diversity is one of the field’s primary strengths, it also makes it challenging for newcomers to acquire the background knowledge necessary to thrive. To address this gap, we developed a course that provides a structured approach to learning the biological principles and theoretical underpinnings of synthetic biology. Our course, Principles of Synthetic Biology (PoSB), was released on the massively open online course platform edX in 2016. PoSB seeks to teach synthetic biology through five key fundamentals: (i) parts and layers of abstraction, (ii) biomolecular modeling, (iii) digital logic abstraction, (iv) circuit design principles and (v) extended circuit modalities. In this article, we describe the five fundamentals, our formulation of the course, and impact and metrics data from two runs of the course through the edX platform.

Planarity and out-of-plane vibrational modes of tryptophan and tyrosine in biomolecular modeling

Vibrations of tryptophan and tyrosine are analyzed to quantify magnitudes of heteroaromatic ring out-of-plane fluctuations in MD of aqueous solutions. Frequencies from normal-mode analysis are compared to experimental data for modified force fields.