Genetics, Bioinformatics and Systems Biology Colloquium
Thursdays, 12:00 pm – 1:00 pm
UC San Diego, Powell-Focht Bioengineering Hall, Fung Auditorium
Complete schedule here
Understanding the emergent properties of tissue-level organization is a fundamental problem in biology. These properties emerge from the many different interactions of individual cells within a tissue. Yet, there are no methods that use the spatial distribution of cells and their signaling state to build a computational model that can make specific, testable predictions on tissue-level phenotypes. In this project we will use the maps-to-models paradigm to study two models of biological tissue organization: (1) antibiotic resistance of a biofilm of Bacillus subtilis; and (2) induction of viral protection through type I interferon response in lung epithelial cells during influenza infection. Through the use of two separate model systems we will demonstrate the utility of our approach and show how it could be further adapted to the analysis of tissue-level biological organization.
A key benefit of the Maps-to-Models paradigm is the generation of two interdependent deliverables: (1) a map depicting interactions (edges) between building blocks (node); and (2) a computational model generating testable predictions on the functional output of the network. In this project, the basic biological unit is the cell. We will generate detailed, quantitative network maps characterizing heterogeneous cells within a tissue and their communication network. Cells states will be determined by analyzing the signaling states of each individual cell. The interaction network between individual cells depends on their strength of paracrine communication. An edge will connect any two sender and receiver cells within the tissue and the weight of the edge will denote the strength of the diffusion limited communication. The two maps generated by this project will provide information on the spatiotemporal organization of bacillus biofilms and epithelium barriers and the extent of cell-to-cell communication within these tissues. These maps will be used to develop quantitative, predictive models.
|One part of this project will focus on the emergence of antibiotic resistance during biofilm formation of B. subtilis strain NCIB-3610. Numerous genes involved in biofilm formation have been identified in this strain, and, as a result, much of our understanding of biofilm formation was obtained in this model system. Despite this wealth of information, many fundamental questions remain unaddressed. The Suel laboratory has developed extensive expertise in quantitative measurements and genetic manipulation of NCIB-3610 (Asally et al., Proc Natl Acad Sci U S A 2012) including the use of multicolor fluorescence microscopy to simultaneously measure the activities of multiple intracellular processers in individual cells (Cagatay et al., Cell 2009. Fluorescent proteins and reporter dyes with distinct spectral will be used to track specific molecules and reactions in B. subtilis. These fluorescent reagents will be tracked using our fully automated, multicolor fluorescence time-lapse microscopy systems, which also provide temperature, humidity and gas control. Having tested many microscope objectives, we have identified special long distance objectives that are capable of measuring biofilms over a centimeter in diameter. We have also developed custom software, which can quantify microscopy images to track cell lineages or other movements within biofilms.||
|The second part of this project will focus on the epithelium, a critical barrier that protects our bodies from infections by harmful pathogens. Communication between cells within the epithelium is important for initiating and managing innate immune responses. It is not clear though how the epithelium balances generating enough of an immune response to combat the pathogen while not damaging itself. One possible mechanism is that individual cellular responses are stochastic and might thus determine the extent of cell-to-cell communication. Previously, the Wollman laboratory explored the role of stochastic cellular responses during NF-κB signaling in response to LPS (Selimkhanov et al., Science 2014). In the course of this work, we developed a suite of computational tools capable of automatically analyzing fluorescent microscopy images for nuclear and cytoplasmic levels of p65, a subunit of NF-κB subunit. In this project, we will continue to study this pathway, using p65 translocation as readout of cellular response to viral infection.||
Recent Publications by these New SDCSB Investigators:
- Galera-Laporta, L, Comerci, CJ, Garcia-Ojalvo, J, Süel, GM. IonoBiology: The functional dynamics of the intracellular metallome, with lessons from bacteria. Cell Syst. 2021;12 (6):497-508. doi: 10.1016/j.cels.2021.04.011. PubMed PMID:34139162 .
- Littman, R, Hemminger, Z, Foreman, R, Arneson, D, Zhang, G, Gómez-Pinilla, F et al.. Joint cell segmentation and cell type annotation for spatial transcriptomics. Mol Syst Biol. 2021;17 (6):e10108. doi: 10.15252/msb.202010108. PubMed PMID:34057817 PubMed Central PMC8166214.
- Oyler-Yaniv, J, Oyler-Yaniv, A, Maltz, E, Wollman, R. TNF controls a speed-accuracy tradeoff in the cell death decision to restrict viral spread. Nat Commun. 2021;12 (1):2992. doi: 10.1038/s41467-021-23195-9. PubMed PMID:34016976 PubMed Central PMC8137918.
- Nagle, MP, Tam, GS, Maltz, E, Hemminger, Z, Wollman, R. Bridging scales: From cell biology to physiology using in situ single-cell technologies. Cell Syst. 2021;12 (5):388-400. doi: 10.1016/j.cels.2021.03.002. PubMed PMID:34015260 .
- Pinter-Wollman, N, Keiser, CN, Wollman, R. Correction. Am Nat. 2021;197 (3):390-391. doi: 10.1086/712423. PubMed PMID:33625973 .
- Zhang, T, Foreman, R, Wollman, R. Identifying chromatin features that regulate gene expression distribution. Sci Rep. 2020;10 (1):20566. doi: 10.1038/s41598-020-77638-2. PubMed PMID:33239733 PubMed Central PMC7688950.
- Maity, A, Wollman, R. Information transmission from NFkB signaling dynamics to gene expression. PLoS Comput Biol. 2020;16 (8):e1008011. doi: 10.1371/journal.pcbi.1008011. PubMed PMID:32797040 PubMed Central PMC7478807.
- Yang, CY, Bialecka-Fornal, M, Weatherwax, C, Larkin, JW, Prindle, A, Liu, J et al.. Encoding Membrane-Potential-Based Memory within a Microbial Community. Cell Syst. 2020;10 (5):417-423.e3. doi: 10.1016/j.cels.2020.04.002. PubMed PMID:32343961 PubMed Central PMC7286314.
- Zhang, T, Pilko, A, Wollman, R. Loci specific epigenetic drug sensitivity. Nucleic Acids Res. 2020;48 (9):4797-4810. doi: 10.1093/nar/gkaa210. PubMed PMID:32246716 PubMed Central PMC7229858.
- Foreman, R, Wollman, R. Mammalian gene expression variability is explained by underlying cell state. Mol Syst Biol. 2020;16 (2):e9146. doi: 10.15252/msb.20199146. PubMed PMID:32043799 PubMed Central PMC7011657.