Members of the Sindi Lab come from a variety of disciplines but are united by their common desire to use mathematical and statistical tools to gain insights in into complex biological processes.

Below we overview some of our current and on-going research topics. 

 Mathematical Modeling of Complex Biological Systems

Multi-Scale Modeling of Prion Aggregation and Dynamics in Yeast

Graduate Students: Jordan Collignon, Theo Loureaux, Fabian Santiago

Collaborators: Dr. Laurent Pujo-Menjouet (Lyon), Dr. Tricia R. Serio (U Washington), Dr. Mikahl Banwarth-Kuhn (CSU East Bay),
Dr. Maxime Theillard (UC Merced)

Prions are responsible for a host of fatal, mammalian diseases – including notably bovine spongiform encephalopathy (mad cow disease), fatal familial insomnia, and Creutzfeldt-Jakob disease. These diseases arise when a misfolded (prion) form of a protein appears and aggregates. Aggregates of the misfolded form act as templates to convert the normally folded protein to its misfolded state. Fragmentation of prion aggregates amplifies the number of templates facilitating the spread of the disease. Beyond prions, these linear aggregates (amyloids) formed of other proteins are associated with over 20 non-transmissible neurodegenerative diseases such as Alzheimer's and Parkinson's disease. Yeast has emerged as an ideal model system for studying prions, and the more general protein misfolding process, because there are many distinct non-fatal prion proteins whose dynamics can be studied in living cells, and prion phenotypes appear quickly. A major challenge in prion biology has been linking experimental observables (often of populations of cells) with basic processes (occurring inside individual cells). The major focus of the Sindi Lab has been to address gaps in biological knowledge by developing mathematical models that depict both single cell and population processes and, through doing so, generate novel hypotheses for biologists to explore experimentally. 

This research combines multi-scale modeling, intracellular signaling, mechanobiology, scientific computing, uncertainty quantification and sensitivity analysis to develop computational tools that integrate biological data from experiments and produce predictive simulations that motivate the development of future experiments. 

As part of this work we have a number of on-going projects, 

PhD Student Ali Heydari and Dr. Sindi collaborate with Dr. Maxime Theillard to use level-set methods for studying reaction/diffusion of prion aggregates in actively dividing cells.

Recent Publications

Heydari, AA; Sindi, S; Theillard, M (2021). Conservative Finite Volume Method on Deforming Geometries: the Case of Protein Aggregation in Dividing Yeast Cells. Journal of Computational Physics.

Villali, J., Dark, J., Brechtel, T. M., Pei, F., Sindi, S. S., & Serio, T. R. (2020). Nucleation seed size determines amyloid clearance and establishes a barrier to prion appearance in yeast. Nature Structural & Molecular Biology, 1-10.  

Lemarre, P., Pujo-Menjouet, L., & Sindi, S. S. (2020). A unifying model for the propagation of prion proteins in yeast brings insight into the [PSI+] prion. PLOS Computational Biology, 16(5), e1007647.

Banwarth-Kuhn, M., Collignon, J., & Sindi, S. (2020). Quantifying the Biophysical Impact of Budding Cell Division on the Spatial Organization of Growing Yeast Colonies. Applied Sciences, 10(17), 5780.

Sensitivity Analysis and Uncertainty Quantification in Blood Coagulation

Postdoctoral Scholar: Fabian Santiago 

Collaborators: Dr. Aaron Fogelson (Utah), Dr. Karin Leiderman (UNC), Dr. Dougald Monroe (UNC), Dr. Michael Stobb (Coe College)

Blood coagulation is a complex biochemical process in which dozens of plasma proteins take part in a nearly 100 enzymatic reactions involving positive feedback (where a blood clot is initiated) and negative feedback (where the growth of the blood clot is stopped). Both overclotting and uncerclotting are associated with diseases and, as such, understanding the regulation of this system is important. However, comparisons between mathematical models of coagulation and coagulation experiments and assays were vague and qualitative. Moreover, mathematical models were only consistent with these experimental assays for a relatively narrow range of experimental conditions. In order for more informative comparisons between models and experiments it was clear that interdisciplinary collaboration and the use of statistical approaches, including sensitivity analysis and uncertainty quantification were necessary. 

As part of this work we have a number of on-going projects, 

Recent Publications

Stobb, M. T., Monroe, D. M., Leiderman, K., & Sindi, S. S. (2019). Assessing the impact of product inhibition in a chromogenic assay. Analytical biochemistry, 580, 62-71.

Link, K.G., Stobb, M.T., Sorrells, M.G., Bortot, M., Ruegg, K., Manco‐Johnson, M.J., Di Paola, J.A., Sindi, S.S., Fogelson, A.L., Leiderman, K. and Neeves, K.B. (2020). A mathematical model of coagulation under flow identifies factor V as a modifier of thrombin generation in hemophilia A. Journal of Thrombosis and Haemostasis, 18(2).306-317.

Link, K. G., Stobb, M. T., Di Paola, J., Neeves, K. B., Fogelson, A. L., Sindi, S. S., & Leiderman, K. (2018). A local and global sensitivity analysis of a mathematical model of coagulation and platelet deposition under flow. PloS one, 13(7), e0200917.

Deep and Statistical Learning 

Deep Learning and Mathematical Modeling For Complex Biological Systems

Graduate Researcher: Ali Heydari

Applied Mathematics

This research lies at the intersection of deep learning, computer vision, and bioinformatics. The goal of our work is to develop computational methods for better downstream analysis of complex biological systems and datasets (such as single-cell RNA Sequencing and Spatial Transcriptomics). To achieve this, we have used a deep generative model to generate realistic synthetic single-cell data. Creating realistic in-silico data will help enable robust analysis of single-cell data and foster greater reproducibility in research no such data. The next aspect of this research focuses on developing large-scale deep learning models and transfer learning models for enhancing learning from small datasets.  Our goal is to design an "all-in-one" network that can be quickly fine-tuned on small datasets to accurately perform domain-specific tasks, such as clustering, cell-type identification, and data generation. This pre-trained core enables researchers to transfer the vast existing knowledge to their downstream analyses, allowing efficient and accurate predictions for datasets that are orders of magnitude smaller than the training data. 

Recent Publications

Heydari A. A, & Sindi SS. Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing, Biophysics Rev. 4, 011306. 

Heydari, A. A,  Davalos, OA., Hoyer, KK., & Sindi SS. N-ACT: An Interpretable Deep Learning Model for Automatic Cell Type and Salient Gene Identification. Proceedings of the 2022 International Conference on Machine Learning (ICML) Workshop on Computational Biology.

Heydari A. A, Davalos OA, Zhao L, Hoyer KK, Sindi SS. ACTIVA: realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders. Bioinformatics. 2022 Feb 18:btac095. DOI: 

Computational and Evolutionary Biology

Identification of Structural Variation from Sequencing Data

Collaborators: Dr. Mario Banuelos (Fresno State), Dr. Roummel Marcia (UC Merced) 

Many genetic disorders – including cancer – are caused by structural modifications of an individual’s genome. Structural variation (SV) in genomes consists of rearrangements ranging anywhere from a few nucleotides in length to millions of nucleotides in length. Originally, SVs such as inversions, insertions and deletions were thought to be rare, but today SVs have been be linked to some heritable diseases and implicated in a number of cancers. With continually decreasing costs of DNA sequencing and the availability of high-quality reference genonmes for a variety of species, the common paradigm for SV discovery has been to sequence reads from an individual genome and map these reads to the reference genome. Regions in the individual genome corresponding to an SV will be revealed by discordant configurations of mapped fragments. Unfortunately, deciphering the resulting data is complicated by both errors in the data and computational complexities arising from millions (and even billions) of observed data points.

Because of the high-volume of data and the error-prone and noisy sequencing data, sophisticated mathematical approaches are required to successfully predict SVs. What distinguishes my work from others is use of statistical modeling to consider the configuration of a set of reads suggesting a potential SV.

We have several projects in this area including:

Recent Publications

Sindi, S., Helman, E., Bashir, A., & Raphael, B. J. (2009). A geometric approach for classification and comparison of structural variants. Bioinformatics, 25(12), i222-i230.

Sindi, S. S., Önal, S., Peng, L. C., Wu, H. T., & Raphael, B. J. (2012). An integrative probabilistic model for identification of structural variation in sequencing data. Genome biology, 13(3), R22

Spence, M., Banuelos, M., Marcia, R. F., & Sindi, S. (2020). Detecting inherited and novel structural variants in low-coverage parent-child sequencing data. Methods, 173, 61-68.

Anzules, J.M., Valentine, K.M., Mullins, G.M., Diep, A., Hoyer, K.K., Sindi, S.S. Modeling Homeostatic Expansion in Wild Type and Autoimmune System. Journal of Immunology (In print)

Zhao, L., Santiago, F., Rutter E.M., Khatri S., Sindi S.S. Modeling and Global Sensitivity Analysis of Strategies to Mitigate Covid-19 Transmission on a Structured College Campus. MedRxiv