Giulia Palermo, Ph.D.

Talk Title: Dynamics and mechanisms of CRISPR-Cas9 through the lens of computational methods Abstract:  The clustered regularly interspaced short palindromic repeat (CRISPR) genome-editing revolution established the beginning of a new era in life sciences. I will review the role of state-of-the-art computations in the CRISPR-Cas9 revolution, from the early refinement of cryo-EM data to enhanced simulations of large-scale conformational transitions. Molecular simulations reported a mechanism for RNA binding and the formation of a catalytically competent Cas9 enzyme, in agreement with subsequent structural studies. Inspired by single-molecule experiments, molecular dynamics offered a rationale for the onset of off-target effects, while graph theory unveiled the allosteric regulation. Finally, the use of a mixed quantum-classical approach established the catalytic mechanism of DNA cleavage. Overall, molecular simulations have been instrumental in understanding the dynamics and mechanism of CRISPR-Cas9, contributing to understanding function, catalysis, allostery, and specificity. Speaker Bio:

• Associate Professor, Department of Bioengineering and Chemistry; University of California, Riverside
• Ph.D. (Computational Drug Discover) Italian Institute of Technology, Genova

The Palermo lab employs advanced computer simulations to study new genome editing systems that are transforming life sciences. Their approach bridges the scales from classical and quantum mechanical methods to enhanced simulations and cryo-EM processing techniquest to push the boundaries of computational biophysics and tackle the most exciting problems in biology. Research:

• Our lab uses computer simulations to unravel the function and improve biological applications of emerging genome editing systems that are transforming life sciences. Using state-of-the-art computational methods, we described conformational activation, catalysis, allostery and selectivity of CRISPR-Cas9 and other large macromolecular machines acting on genes.
• We apply and develop hybrid computational approaches based on molecular dynamics and free energy simulations, quantum mechanical methods and cryo-electron microscopy (cryo-EM) processing techniques. We complement these methods with graph theory and network models, to describe the intricate communication mechanisms in biological systems. Our findings are fully integrated with data obtained from experimental techniques, including cryo-EM, single-molecule spectroscopy and NMR.
• Our interest is in pushing the frontiers of computational biophysics to access large spatio-temporal scales and tackle dynamics and mechnisms of emerging biological systems obtained through cryo-EM.

Ning Zhao, Ph.D.

Talk Title:  Expanding tagging toolbox for visualizing translation live Bio:

• Associate Professor, University of Colorado Anschutz Medical Campus
• Ph.D. (Chemical Engineering) Colorado State University, Fort Collins

The Zhao Lab focuses on tracking protein translation and co-translational folding of disease and aging-related targets with a high spatiotemporal resolution in living cells. They apply the state-of-art single-particle tracking approach to study the dynamics and uncover any heterogeneity in individual mRNAs. Current projects include: Visualizing the translation of proteins in living cells. The translation of a single mRNA in living cells was first visualized in 2016. Our lab mainly applies a technology named Nascent Chain Tracking (NCT) to study protein translation. Visualizing co-translational folding. Proteins, the building blocks of life, need to fold correctly to function in living cells. We are developing a technology that overcomes previous technological hurdles to enable the visualization of co-translational protein folding with single-molecule spatial resolution and sub-second temporal resolution in living cells. Developing live-cell imaging tools that enable tracking protein translation and folding. To expand the toolbox of tracking translation and enable tracking protein folding in a living cell, our lab focuses on developing genetically-encodable intrabodies as live-cell imaging tools. We aim to develop a high throughput in vitro display technology for generating intrabodies.

Janani Ravi, Ph.D.

Talk Title:  A bug’s life: a data integration view of microbial genotypes, phenotypes, and diseases Bio:

• Assistant Professor, Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus
• Ph.D. (Computational Biology) Virginia Tech, Blacksburg

Dr. Janani Ravi is an Assistant Professor in the Dept. of Biomedical Informatics with ties to the Dept. of Immunology and Microbiology. She completed her PhD in Computational Biology at Virginia Tech, postdoctoral research at the Rutgers Public Health Research Institute, and started her research group at Michigan State University prior to moving to CU in late 2022. Dr. Ravi’s research group (JRaviLab, jravilab.github.io) develops general-purpose computational approaches that integrate large-scale heterogeneous public datasets for mechanistic understanding of microbial genotypes, phenotypes, and diseases. The JRaviLab asks:

• How do we link (microbial) pathogen genotypes to phenotypes?
• How can host responses to infection inform disease mechanisms and therapeutics?

Her group provides open data/software and easy-to-use web applications for biomedical researchers, and the methods are developed to be pathogen- and disease-agnostic. Dr. Ravi is currently supported by an NIH NIAID U01 (antimicrobial resistance prediction) and R21 (host responses, host-directed therapeutics), two CU CPMR-DBMI dyads (bone health, implant corrosion), and an NIH NLM T15 supporting her postdoc. Dr. Ravi is engaged and committed to mentoring/training, education, and outreach, and creating and sustaining diversity and inclusivity in data science for learners and professionals alike, focused on increasing the participation of underrepresented minorities in the field. She founded R-Ladies East Lansing and R-Ladies Aurora, and co-founded Women+ Data Science and AsiaR. She also co-chairs the R/Bioconductor Community Advisory Board.

Philip Nelson, Ph.D.

Bio:

• Professor, Department of Physics and Astronomy, University of Pennsylvania School of Arts & Sciences
• Ph.D. (Physics) Harvard University

Dr. Nelson is the author of numerous books including “Physical Models of Living Systems” and “Student’s Guide to Python for Physical Modeling.”  He recently served as Chair of the American Physical Society’s Division of Biological Physics, and currently serves on the editorial board of The Biophysicist journal. Research: I’m currently studying the physics of artificial biomembranes, biopolymers such as DNA, and other “soft” condensed matter systems. There is a great cultural gap between physics, which seeks to understand extremely simple systems, and biology, which is obliged to study the overall behavior of extremely complex systems. I think that one area where simplicity and life intersect is the study of biomembranes and biopolymers, both in isolation and as active elements of cells. Membranes and polymers display the sort of universal behavior amenable to the powerful tools of statistical physics, thus making these systems are a bit more inevitable, less accidental, than more complex architectural elements of life. Nature has made extensive use of these generic properties in constructing useful materials and molecular machines; moreover, artificial analogs of biomaterials are subjects of intense investigation for a wide variety of bioengineering applications. Of course, physicists have been studying membranes and polymers for much of this century. Much physical research on these systems has concerned bulk systems, in thermal equilibrium. But the activities of many molecular actors are obscured when we look at them in bulk; new, single-molecule techniques reveal much that was previously hidden. Also, life processes take place far from equilibrium. So we have been studying mainly experiments involving the manipulation of single molecules of DNA, and dynamical phenomena involving membranes. I have some interest as well in general nonlinear dynamics (including pattern formation), self-assembly, electrostatics at the colloidal level, and the general area of geometrical methods in physics.

Kaan Öcal, Ph.D.

Talk Title: Modelling global mRNA-protein dynamics in cells Abstract:  Biological organisms produce proteins by transcribing RNA, but establishing quantitative relationships between RNA and protein numbers in general has proven rather difficult. Quantitative methods like RNA sequencing commonly rely on the premise that measured mRNA numbers are indicative of protein numbers and cell state in general, but a number of studies measuring joint mRNA and protein expression have found these to be weakly correlated in practice. This is a result of a complex interplay of factors such as noisy gene expression, post-transcriptional regulation, cell size and cell cycle effects, as well as technical noise that remains notoriously hard to quantify. In this talk I will give a brief overview over existing research on the correlation between mRNA and protein numbers and present some current work on building a “global” model of mRNA and protein expression, with the goal of understanding how these factors interrelate. Such models of global gene expression will hopefully give us a better understanding of cellular dynamics like signal processing and differentiation, as well as helping us extract more precise information from biological experiments. Speaker Bio:

• Postdoctoral Researcher in Systems Biology Biosciences, University of Melbourne
• Ph.D., University of Edinburgh

Kaan Öcal is a postdoctoral researcher in the research group of Michael Stumpf at the University of Melbourne. His work focuses on mathematical and statistical methods in biology, particularly applying Bayesian methods and Machine Learning to model cells. He recently completed his PhD at the University of Edinburgh under the supervision of Guido Sanguinetti and Ramon Grima. Prior to that he was a research assistant in Jakob Macke’s research group, working on likelihood-free inference for biophysical models of neurons. Research:  Randomness in biology: My research focuses on stochastic models in biology. It is nowadays well-established that random events occur at all levels in biology, from individual genetics (what eye colour would my children have? could they inherit genetic diseases? ) to populations and ecosystems (how do species evolve or become extinct? ). Some of these can have significant impact on our lives, such as what are my chances of getting cancer? and when is the next pandemic going to happen?. Many of these processes start at the cellular and molecular level, and one of my research topics is understanding the role and importance of randomness at this stage. Stochastic modelling: My current work centers on the Chemical Master Equation, which provides a rigorous way of describing many biological systems subject to randomness. The Chemical Master Equation is notoriously tricky to handle, and I am interested in finding ways to approximate/simplify it – this will researchers make better predictions with less effort. To this end I focus on automated computational tools based on statistical learning and inference. Inference: Since observing biological organisms at cellular level is difficult, my work involves a lot of mathematics and statistics to extract information from what we can measure. I am interested in developing and adapting inference methods that are scalable and easy to use, so that researchers without a degree in Machine Learning (or a spare grad student) can feel confident about applying them. I am particularly interested in Likelihood-Free Inference (e.g. Approximate Bayesian Computation) as many real-life problems in biology are too difficult to solve using classical likelihood-based approaches.

Travis Sanders (Watchmaker Genomics) – June 17th, 11:05 – 11:20 AM

Talk Title:  Advanced Enzyme Engineering for Improved Single Enzymes and NGS Library Prep Abstract:  The Watchmaker platform draws upon five decades of spectacular achievements in molecular biology and sequencing, laboratory automation and bioinformatics and computational analysis. Recent advances in these fields have enabled exponential improvements in the breadth, depth and throughput of scientific interrogation. Existing enzymes and reagents are not keeping pace with the evolving performance requirements of high-stringency clinical applications enabled by next generation sequencing technologies. Our extensive experience with the distinct challenges in inherited disease, somatic oncology, transcriptomics and epigenomics allows us to purpose-design enzymes and workflows to support emerging applications. We have established an innovative, computationally driven, and vertically integrated protein engineering and production platform to create best-in-class, tailor-made solutions for reading, writing and editing of DNA and RNA.

Ivy Brown (Avantor) – June 17th, 2:35 – 2:50 PM

Talk Title:  TBD