Posts classified under: Fall 2022

BIODS 260 presents:
Dr. Adrian Buganza-Tepole
10/27/22
130 pm-2:30 pm
Location: MSOB x303

Dr. Adrian Buganza-Tepole is an Associate Professor of Mechanical Engineering and Biomedical Engineering (courtesy) at Purdue University. He obtained his Ph.D. in Mechanical Engineering from Stanford University in 2015 and was a postdoctoral fellow at Harvard University for a year before joining Purdue as a faculty member in 2016. He was also a Miller Visiting Professor at UC Berkeley during Spring 2022. His group studies the interplay between mechanics and mechanobiology of skin. Using computational simulation, machine learning, and experimentation, his group seeks to characterize the multi-scale mechanics of skin to understand the fundamental mechanisms of this tissue’s mechano-adaptation in order to improve clinical diagnostics and interventional tools.

Title: Data-driven skin biophysics  

Abstract: 

The recent explosion in machine learning (ML) and artificial intelligence (AI) algorithms has started a revolution in many engineering fields, including computational biophysics. This talk focuses on our recent efforts to leverage ML methods to increase our fundamental understanding of skin and its unique ability to adapt to mechanical cues. The first project that will be described is skin growth in tissue expansion, a popular reconstructive surgery technique that grows new skin in response to sustained supra-physiological loading. We have created computational models that combine mechanics and mechanobiology to describe the deformation and growth of expanded skin. Together with experiments on a porcine model, and leveraging ML tools such as multi-fidelity Gaussian processes, we have performed Bayesian inference to learn mechanistically how skin grows in response to stretch. The second half of the talk will explore how mechanical cues can be key drivers of wound healing pathologies such as fibrosis. I will show computational models of reconstructive surgery and wound healing for a murine model of wound healing and in patient-specific cases. Once again, ML methods enable new kinds of analyses such as optimization under uncertainty and inverse parameter calibration which are not achieved with traditional approaches.

Suggested readings: 

Han T, et al. Bayesian calibration of a computational model of tissue expansion based on a porcine animal model. Acta Biomaterialia. 2022;137:136-46.

Tac V, Costabal FS, Tepole AB. Data-driven tissue mechanics with polyconvex neural ordinary differential equations. Comput Method Appl Mech Eng. 2022;398:115248.

Sohutskay DO, Tepole AB, Voytik-Harbin SL. Mechanobiological wound model for improved design and evaluation of collagen dermal replacement scaffolds. Acta Biomaterialia. 2021;135:368-82.

Lee T, Bilionis I, Tepole AB. Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression. Comput Method Appl Mech Eng. 2020;359:112724.

Zoom info:

• • Meeting ID: 937 0175 0367
  • Password: 249596
  • https://stanford.zoom.us/j/93701750367?pwd=VnlZeCt3NkVYUkRhK2VYSURtN2laQT09

BIODS 260
11/3/22
1:30 pm-2:50 pm
David Van Valen
MSOB x303

Title: Everything as Code

Bio: David Van Valen is an Assistant Professor in the Division of Biology and Bioengineering at Caltech. Before becoming faculty, he studied mathematics (B.S. 2003) and physics (B.S. 2003) at the Massachusetts Institute of Technology, applied physics (Ph.D. 2011) at Caltech, medicine (M.D. 2013) at UCLA, and bioengineering as a postdoctoral fellow at Stanford University. At Caltech, his research group develops new technologies at the intersection of imaging, genomics, and machine learning to produce quantitative measurements of living systems with single-cell resolution. David is the recipient of several awards, including a Hertz Fellowship (2005), a Rita Allen Scholar award (2020), A Pew-Stewart Cancer Research Scholar award (2021), a Heritage Medical Research Investigator award (2021), a Moore Inventor Fellowship (2021), and the NIH New Innovator award (2022).

Abstract: Biological systems are difficult to study because they consist of tens of thousands of parts, vary in space and time, and their fundamental unit—the cell—displays remarkable variation in its behavior. These challenges have spurred the development of genomics and imaging technologies over the past 30 years that have revolutionized our ability to capture information about biological systems in the form of images. Excitingly, these advances are poised to place the microscope back at the center of the modern biologist’s toolkit. Because we can now access temporal, spatial, and “parts list” variation via imaging, images have the potential to be a standard data type for biology.

For this vision to become reality, biology needs a new data infrastructure. Imaging methods are of little use if it is too difficult to convert the resulting data into quantitative, interpretable information. New deep learning methods are proving to be essential to reliable interpretation of imaging data. These methods differ from conventional algorithms in that they learn how to perform tasks from labeled data; they have demonstrated immense promise, but they are challenging to use in practice. The expansive training data required to power them are sorely lacking, as are easy-to-use software tools for creating and deploying new models. Solving these challenges through open software is a key goal of the Van Valen lab. In this talk, I describe DeepCell, a collection of software tools that meet the data, model, and deployment challenges associated with deep learning. These include tools for distributed labeling of biological imaging data, a collection of modern deep learning architectures tailored for biological image analysis tasks, and cloud-native software for making deep learning methods accessible to the broader life science community. I discuss how we have used DeepCell to label large-scale imaging datasets to power deep learning methods that achieve human level performance and enable new experimental designs for imaging-based experiments.

Website: https://vanvalen.caltech.edu

Zoom info:

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BIOMEDICAL DATA SCIENCE PRESENTS:
BIODS 260
11/10/22 1:30PM-2:50PM
MSOB X303 (SEE ZOOM DETAILS BELOW)
Liana Lareau Assistant Professor, Department of Bioengineering University of California, Berkeley

TITLE:

Revealing patterns of alternative splicing in single cells

ABSTRACT:

Alternative splicing shapes the output of the genome and contributes to each cell’s unique identity, but single-cell RNA sequencing has struggled to capture its impact. We have shown that low recovery of mRNAs from single cells can lead to misleading conclusions about alternative splicing and its regulation. To address this, we have developed a method, Psix, to confidently identify splicing that changes across a landscape of single cells, using a probabilistic model that is robust against the data limitations of scRNA-seq. Its autocorrelation-inspired approach finds patterns of alternative splicing that correspond to patterns of cell identity, such as cell type or developmental stage, without the need for explicit cell clustering, labeling, or trajectory inference. Psix reveals cell type-dependent splicing patterns and the wiring of the splicing regulatory networks that control them, enabling scRNA-seq analysis to go beyond transcription to understand the roles of post-transcriptional regulation in determining cell identity.

SUGGESTED READINGS:

CF Buen Abad Najar, N Yosef, LF Lareau. Coverage-dependent bias creates the appearance of binary splicing in single cells. eLife, 2020. https://elifesciences.org/articles/54603

CF Buen Abad Najar, P Burra, N Yosef, LF Lareau. Identifying cell state–associated alternative splicing events and their coregulation. Genome Research, 2022 https://genome.cshlp.org/content/32/7/1385.short

Zoom link: https://stanford.zoom.us/j/92874055477pwd=aThzNmpmNEQ1L2FjV0E5ZXF5SDR 1UT09&from=addon
Password: 705300

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BIOMEDICAL DATA SCIENCE PRESENTS:
BIODS 260
11/17/22 1:30PM-2:50PM
MSOB X303 (SEE ZOOM DETAILS BELOW)
Lorin Crawford
Principal Researcher, Microsoft Research New England; Associate Professor of Biostatistics, Brown University http://lorincrawford.com/

TITLE:

Machine Learning for Human Genetics: A Multi-Scale View on Complex Traits and Disease

ABSTRACT:

A common goal in genome-wide association (GWA) studies is to characterize the relationship between genotypic and phenotypic variation. Linear models are widely used tools in GWA analyses, in part, because they provide significance measures which detail how individual single nucleotide polymorphisms (SNPs) are statistically associated with a trait or disease of interest. However, traditional linear regression largely ignores non-additive genetic variation, and the univariate SNP-level mapping approach has been shown to be underpowered and challenging to interpret for certain trait architectures. While machine learning (ML) methods such as neural networks are well known to account for complex data structures, these same algorithms have also been criticized as “black box” since they do not naturally carry out statistical hypothesis testing like classic linear models. This limitation has prevented ML approaches from being used for association mapping tasks in GWA applications. In this talk, we present flexible and scalable classes of Bayesian feedforward models which provide interpretable probabilistic summaries such as posterior inclusion probabilities and credible sets which allows researchers to simultaneously perform (i) fine- mapping with SNPs and (ii) enrichment analyses with SNP-sets on complex traits. While analyzing real data assayed in diverse self-identified human ancestries from the UK Biobank, the Biobank Japan, and the PAGE consortium we demonstrate that interpretable ML has the power to increase the return on investment in multi-ancestry biobanks. Furthermore, we highlight that by prioritizing biological mechanism we can identify associations that are robust across ancestries—suggesting that ML can play a key role in making personalized medicine a reality for all.

SUGGESTED READINGS:

A.R. Martin, M. Kanai, Y. Kamatani, Y. Okada, B.M. Neale, and M.J. Daly (2019). Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics. 51: 584–591.

S.P. Smith, S. Shahamatdar, W. Cheng, S. Zhang, J. Paik, M. Graff, C. Haiman, T.C. Matise, K.E. North, U. Peters, E. Kenny, C. Gignoux, G. Wojcik, L. Crawford, and S. Ramachandran (2022). Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries. American Journal of Human Genetics. 109: 871-884.

P. Demetci, W. Cheng, G. Darnell, X. Zhou, S. Ramachandran, and L. Crawford (2021). Multi-scale inference of genetic architecture using biologically annotated neural networks. PLOS Genetics. 17(8): e1009754.

Zoom link: https://stanford.zoom.us/j/92874055477pwd=aThzNmpmNEQ1L2FjV0E5ZXF5SDR1UT09 &from=addon
Password: 705300

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