Data-Driven Uncertainty Quantification Interpretation with High Density Regions

In a time when data is being constantly generated by phones, vehicles, sensor net- works, social media, etc. detecting anomalies with in the data can be very crucial. In cases where we know little prior knowledge about the data, it becomes difficult to extract uncertainty about our results. In this thesis, we will propose a framework in which we can extract uncertainty distributions from data-driven modeling prob- lems. We will show some concrete examples of how to apply framework and provide some insight into what the uncertainty distributions are telling us using High Density Regions (HDRs)

Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations

Machine learning (ML)-based steering can improve the performance of ensemble-based simulations by allowing for online selection of more scientifically meaningful computations. We present DeepDriveMD, a framework for ML-driven steering of scientific simulations that we have used to achieve orders-of-magnitude improvements in molecular dynamics (MD) performance via effective coupling of ML and HPC on large parallel computers. We discuss the design of DeepDriveMD and characterize its performance. We demonstrate that DeepDriveMD can achieve between 100-1000x acceleration for protein folding simulations relative to other methods, as measured by the amount of simulated time performed, while covering the same conformational landscape as quantified by the states sampled during a simulation. Experiments are performed on leadership-class platforms on up to 1020 nodes. The results establish DeepDriveMD as a high-performance framework for ML-driven HPC simulation scenarios, that supports diverse MD simulation and ML back-ends, and which enables new scientific insights by improving the length and time scales accessible with current computing capacity

Three-dimensional Folding of Eukaryotic Genomes

Chromatin packages eukaryotic genomes via a hierarchical series of folding steps, encrypting multiple layers of epigenetic information, which are capable of regulating nuclear transactions in response to complex signals in environment. Besides the 1-dimensinal chromatin landscape such as nucleosome positioning and histone modifications, little is known about the secondary chromatin structures and their functional consequences related to transcriptional regulation and DNA replication. The family of chromosomal conformation capture (3C) assays has revolutionized our understanding of large-scale chromosome folding with the ability to measure relative interaction probability between genomic loci in vivo. However, the suboptimal resolution of the typical 3C techniques leaves the levels of nucleosome interactions or 30 nm structures inaccessible, and also restricts their applicability to study gene level of chromatin folding in small genome organisms such as yeasts, worm, and plants. To uncover the “blind spot” of chromatin organization, I developed an innovative method called Micro-C and an improved protocol, Micro-C XL, which enable to map chromatin structures at all range of scale from single nucleosome to the entire genome. Several fine-scale aspects of chromatin folding in budding and fission yeasts have been identified by Micro-C, including histone tail-mediated tri-/tetra-nucleosome stackings, gene crumples/globules, and chromosomally-interacting domains (CIDs). CIDs are spatially demarcated by the boundaries, which are colocalized with the promoters of actively transcribed genes and histone marks for active transcription or turnover. The levels of chromatin compaction are regulated via transcription-dependent or transcription-independent manner – either the perturbations of transcription or the mutations of chromatin regulators strongly affect the global chromatin folding. Taken together, Micro-C further reveals chromatin folding behaviors below the sub-kilobase scale and opens an avenue to study chromatin organization in many biological systems

#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized

Thirty years of molecular dynamics simulations on posttranslational modifications of proteins

Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.Comment: 64 pages, 11 figure

LifeTime and improving European healthcare through cell-based interceptive medicine

Here we describe the LifeTime Initiative, which aims to track, understand and target human cells during the onset and progression of complex diseases, and to analyse their response to therapy at single-cell resolution. This mission will be implemented through the development, integration and application of single-cell multi-omics and imaging, artificial intelligence and patient-derived experimental disease models during the progression from health to disease. The analysis of large molecular and clinical datasets will identify molecular mechanisms, create predictive computational models of disease progression, and reveal new drug targets and therapies. The timely detection and interception of disease embedded in an ethical and patient-centred vision will be achieved through interactions across academia, hospitals, patient associations, health data management systems and industry. The application of this strategy to key medical challenges in cancer, neurological and neuropsychiatric disorders, and infectious, chronic inflammatory and cardiovascular diseases at the single-cell level will usher in cell-based interceptive medicine in Europe over the next decade

Theoretical Circular Dichroism Of Lysozyme, Myoglobin And Collagen And Experimental Circular Dichroism Of Myoglobin And Pea Lectin

The need to understand protein structure and interaction is ever-growing and this has led to scientist and investigators utilizing numerous different techniques in order to obtain substantial insights and explanation to these structures and their interactions. Circular dichroism (CD), which is one of these techniques, is a powerful structural biology technique used to study protein and nucleic acid structures and their dynamics. This technique is important because it identifies the secondary, tertiary and even quaternary structures in proteins and can be used to study folding patterns in proteins. Theoretical and experimental methods are used to better understand and teach the phenomenon of circular dichroism. First, molecular mechanics allows for the energy calculations of different conformations in large molecules like peptides and proteins. Theoretical CD via the dipole interaction model (DInaMo) is used to relate the structural nature of peptides and proteins to the experimental CD observed. Minimization was done on lysozyme, myoglobin and collagen, using the molecular modeling software package, Insight®II to obtain minimum energy structures suitable for CD calculations. Molecular dynamics simulations were performed in water at 300K to create an ensemble of conformations. The program CDCALC was used to predict the CD spectrum of the proteins for comparison with experiment. The output from CDCALC was analyzed using OriginPro Version 7.5 and the analyzed data reported as plots with data from synchrotron radiation circular dichroism (SRCD). Theoretical CD plots showed agreement with SRCD for location, sign, and bandwidths of the peaks. Experimental CD spectra of horse heart myoglobin and pea lectin were measured on a JASCO spectropolarimeter and compared to that obtained from the Protein Circular Dichroism Data Bank (PCDDB) in the development of a physical chemistry laboratory exercise to teach secondary structure analysis