Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session M14: Hierarchical Models for Omics Data |
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Sponsoring Units: DBIO Chair: Justin Kinney, Cold Spring Harbor Laboratory Room: Room 206 |
Wednesday, March 8, 2023 8:00AM - 8:12AM |
M14.00001: Growing a network via oscillations on nodes Hayden S Nunley, Matthew Smart, Stanislav Y Shvartsman Small cell networks, if not formed by cell aggregation, arise when a founder cell and its progeny divide with incomplete cytokinesis. These incomplete divisions result in intercellular bridges that allow the cells to share cytoplasm, enabling coordinated cellular behaviors like apoptosis and mitosis. Motivated by germline cysts in insect ovaries, we construct a minimal model for generating small cell networks: each node is an oscillator, and completion of a cycle causes a node to generate a daughter to which it remains connected. Importantly, oscillations on the nodes are transient -- under the control of a pulse -- and coupled via diffusion over the edges of the network. We, first, verify that our simple model can generate the diverse germline cyst structures observed in nature and identify where, in the space of model parameters, these naturally observed cyst structures appear. Inspired by cases like the lacewing C. perla, where a few different cyst structures are found in the same ovary, we test if a simple variation in model parameters can rationalize the observed variability. We conclude by proposing extensions of our model to the cases of germline cysts in mammals and annelid worms. Our simple framework, one of the first reported model for growing finite-sized networks based on oscillations, can be extended to study many naturally grown networks. |
Wednesday, March 8, 2023 8:12AM - 8:24AM |
M14.00002: Mechanisms of mammalian drug resistance acquired during long-term evolution Yiming Wan, Quanhua Mu, Rafa? Krzyszto?, Joseph Cohen, Damiano Coraci, Christopher Helenek, Annie Lin, Kevin Farquhar, Jiguang Wang, Gabor Balazsi Drug resistance emerges through cellular evolution, where selection pressure during treatment enables the spreading of increasingly drug-resistant cell variants. Despite the generality of this process, its underlying biological mechanisms and events are diverse, complex, and improperly understood. Previously, we applied synthetic gene circuits integrated into Chinese hamster ovary (CHO) cells to reveal antagonistic roles of gene expression stochasticity in mammalian drug resistance. After long-term exposure to Puromycin, many replicate cell populations evolved to be stably drug resistant. Yet, the biological mechanisms underlying drug resistance were unknown. Here, through transcriptome profiling by bulk RNA sequencing, we observed high up-regulation of drug resistance and other genes in the synthetic constructs, together with other native transcriptome changes. We verified both experimentally and computationally DNA amplification of gene circuit components during cellular evolution as a major cause of drug resistance. We propose DNA amplification around resistance genes as one of the major causes of drug resistance in cancer and other diseases, and suggest corresponding measures to improve treatment efficiency. |
Wednesday, March 8, 2023 8:24AM - 8:36AM |
M14.00003: Specificity, cooperativity, synergy, and mechanisms of splice-modifying drugs Justin B Kinney Drugs that target pre-mRNA splicing hold great therapeutic potential, but the quantitative understanding of how these drugs function is limited. Here we introduce a biophysical modeling framework that quantitatively describes the sequence-specific and concentration-dependent behavior of splice-modifying drugs. Using massively parallel splicing assays, RNA-seq experiments, and precision dose-response curves, we apply this framework to drugs developed for treating spinal muscular atrophy. The results quantitatively define the specificities of risdiplam and branaplam for 5’ splice site sequences, suggest that branaplam recognizes 5’ splice sites in two distinct molecular conformations, and disprove the prevailing two-site hypothesis for risdiplam activity at SMN2 exon 7. The results also show that anomalous cooperativity and multi-drug synergy are widespread among splice-modifying drugs more generally. Our biophysical modeling approach thus clarifies the mechanisms of existing splice-modifying treatments and provides a quantitative basis for the rational development of new therapies. |
Wednesday, March 8, 2023 8:36AM - 8:48AM |
M14.00004: Experimental quantification of model identifiability and information loss due to distortions in fluorescence microscopy and image processing Michael P May Single-molecule imaging (e.g., using smFISH or MS2-MCP labeling) and fluorescence microscopy can observe and count mRNA molecules within single cells. By quantifying mRNA expression distributions over many cells in different environmental conditions, we can infer discrete stochastic models and discover precise insight into the mechanisms and parameters of gene regulation. However, recent theoretical advances combining Finite State Projection analyses and the Fisher Information Matrix (FSP-FIM) show that inference results depend heavily on experiment design (Fox 2020) and on measurement distortions associated with the microscopy (Vo 2022). In this presentation, we experimentally analyze single-cell preparations using smFISH and MS2-MCP labeling and for many different microscopy and image processing settings. We then use these data to learn probabilistic models for how labeling and imaging settings distort the observation of mRNA spot counts in real cells. Finally, we show how empirically determined distortion operators can be combined with the FSP-FIM to design optimal smFISH experiments that mitigate distortion effects and improve the identification of gene regulation models. |
Wednesday, March 8, 2023 8:48AM - 9:00AM |
M14.00005: Computational agent-based modelling reveals the role of tumour microenvironment on the success of combination chemotherapy/immunotherapy to treat glioblastoma Anudeep Surendran Glioblastoma (GBM) is one of the most aggressive and deadly brain cancer, with a survival time of only 12 to 18 months. Unfortunately, the current standard-of-care for GBM with chemotherapy (temozolomide or TMZ) result in recurrences and treatment failure, leading to very poor therapeutic outcomes. Immunotherapies such as immune checkpoint blockade, a class of biologics that leverage the body's own defences against tumours, have shown durable benifits in many types of cancers. Immune checkpoints are proteins that prevent the immune response from being too strong (i.e. preventing destruction of cancer cells by immune cells). The blocking of immune checkpoints therefore enables tumour eradication. Though this treatment method is quite promising, recent clinical trials of immune checkpoint blockade in GBM have been largely disappointing, suggesting an absence of mechanistic understanding of the role of immune system in this disease. To address this, we need to understand the immune content of glioblastoma and the interactions between immune and cancer cells. To this end, we develop a novel agent-based model of glioblastoma dynamics that accounts for the interactions between GBM and immune cells and its effects on the treatment efficacy. |
Wednesday, March 8, 2023 9:00AM - 9:12AM |
M14.00006: Using the Finite State Projection based Fisher Information Matrix to optimize single-cell experiment designs under different combinations of discrete stochastic models and measurement errors Joshua Cook When combined with discrete stochastic models, single-molecule Fluorescence in situ Hybridization (smFISH) can reveal quantitative insight into gene regulation mechanisms. In principle, infinite smFISH experiment designs are possible (e.g., with different induction levels, measurement times, or observed biological species). Moreover, each experiment can be time consuming or expensive to perform and will result in labeling, imaging, or data processing errors. To find which experiments are best suited to identify a model, we adopt the chemical master equation framework to define likelihood functions, and we calculate the Finite State Projection based Fisher Information Matrix (FSP-FIM) to estimate and compare information for different experiment designs (Fox, 2019, Fox 2020). We extend the FSP-FIM with a probabilistic distortion operator to estimate how errors affect model identification (Vo, 2022). By analyzing different combinations of models, experiment designs, and image distortions, we find practical working principles to optimize smFISH experiments despite inexact imaging. Finally, we validate our FSP-FIM approach using new smFISH data for Dusp1 gene regulation upon Dexamethasone stimulation. |
Wednesday, March 8, 2023 9:12AM - 9:24AM |
M14.00007: Live imaging of gut-associated innate immune cell motion Piyush Amitabh, Jonah Sokoloff, Raghuveer Parthasarathy Immune responses involve complex dynamics at multiple scales. Even in the absence of pathogens, the immune system recognizes and actively regulates commensal microbes, including the large populations resident in the gut whose presence contributes to the host health. This recognition is typically assessed in terms of the number or activation state of immune cells, but in vitro studies imply that cellular motility and morphology should also be altered by microbial cues. How these physical behaviors are manifested inside a living host remains unclear. We therefore examined innate immune cells – neutrophils and macrophages – in larval zebrafish, a model vertebrate. Using light sheet fluorescence microscopy to obtain three-dimensional images, we tracked the positions of gut-associated neutrophils and macrophages over few hour durations. We compare dynamics in germ-free fish to those in fish inoculated with native gut bacteria, characterizing average speeds, measures of random-walk motion, and cellular morphology, illuminating search strategies employed by these immune cells in response to bacterial colonization. |
Wednesday, March 8, 2023 9:24AM - 9:36AM |
M14.00008: Identifying the transition genes and state specific gene regulation from single-cell transcriptome data with spliceJAC Federico Bocci Extracting dynamical information from single cell transcriptomics is a novel task with the promise to advance our understanding of cell state transition and interactions between genes. Yet, theory-oriented, bottom-up approaches that consider differences among cell states are largely lacking. Here, we present spliceJAC, a method to quantify the multivariate mRNA splicing from single cell RNA sequencing (scRNA-seq). spliceJAC utilizes the unspliced and spliced mRNA count matrices to constructs cell state-specific gene-gene regulatory interactions and applies stability analysis to predict putative driver genes critical to the transitions between cell states. By applying spliceJAC to biological systems including pancreas endothelium development and Epithelia- Mesenchymal Transition (EMT) in A549 lung cancer cells, we predict genes that serve specific signaling roles in different cell states, recover important differentially expressed genes in agreement with pre-existing analysis, and predict new transition genes that are either exclusive or shared between different cell state transitions. |
Wednesday, March 8, 2023 9:36AM - 9:48AM |
M14.00009: Analytical model for vaccination protocols that optimally produce broadly neutralizing antibodies Saeed Mahdisoltani, Mehran Kardar, Arup K Chakraborty One way that the adaptive immune system responds to infectious pathogens is by creating antibodies (Ab) that can bind specifically to the associated antigens (Ag). In order to generate such Abs, B cells go through many rounds of Darwinian mutation and selection during the affinity maturation (AM) process. Successful vaccination guides the AM to produce B cells that elicit neutralizing Abs against the pathogen of concern. For highly mutable pathogens such as HIV, however, B cells that respond to the Ags presented during natural infection or vaccination generally neutralize a small number of mutant strains. The desired outcome of vaccination in these cases is to generate optimal amounts of the so-called broadly neutralizing Abs (bnAbs) that protect against various strains of the fast-mutating pathogen. Our goal is to describe the mechanisms via which bnAbs might be elicited by properly designed vaccination procedures. We devise a minimal model of the B cell population dynamics that focuses on their mutations and also the selection forces imposed by the vaccine. Using an analytical approach based on operator formalism, we show that to maximize the bnAb production, the selection forces imposed by sequential vaccination rounds over time need to become more focused on the B cells that have a chance to reach the high breadth state by mutation only. We also investigate how the initial distribution of the B cells may modify the optimal vaccination protocol. |
Wednesday, March 8, 2023 9:48AM - 10:00AM |
M14.00010: A model for how T cell-mediated autoimmunity can be triggered by persistent viral infections Rose Yin, Sam Melton, Arup K Chakraborty, Mehran Kardar, Eric Huseby It has long been known that certain persistent infections can trigger the onset of T cell-mediated autoimmune diseases, but the reasons underlying this phenomenon remain unclear. T cell development in the thymus contributes to creating a largely self-tolerant and pathogen-specific mature repertoire. Some autoreactive T cells survive thymic development and circulate in peripheral tissues, but they usually do not result in autoimmunity. Theoretical and experimental studies suggest that activation of autoreactive T cells does not necessarily lead to a full-blown autoimmune response because a threshold number of T cells need to be activated in response to antigen for T cells to proliferate and differentiate into effector cells. Activation of such a “quorum” of T cells is more likely for foreign antigens compared to self-antigens because of the bias against autoreactive T cells conferred during thymic development. We developed a model for thymic development and then challenged the resulting mature T cell repertoire with varying intensities of infection. Persistent or intense infections were modeled by presenting increasing numbers of foreign antigens. Our results describe key parameters and conditions that result in persistent infections triggering T cell-mediated autoimmunity. Specifically, we describe how T cell activation by multiple foreign antigens can result in weakly autoreactive T cells exceeding the quorum threshold and mounting a response to self-antigens. These results highlight the importance of collective effects for T cell-mediated immunity and its aberrant regulation. Implications of our findings for phenomena as well as possible experimental tests will also be discussed. |
Wednesday, March 8, 2023 10:00AM - 10:12AM |
M14.00011: Optimal design of cocktail boosters to elicit a polyclonal response against related viral strains Federica Ferretti, Arup K Chakraborty, Mehran Kardar Immune escape from previous antibody responses by variants of a pathogen is a common threat from frequently mutating viruses, like influenza or SARS-CoV2. A recently developed strategy for epidemic control of the latter is the administration of a cocktail vaccine booster made of ancestral strain and omicron strain (1:1), which has already undergone clinical trials and has been approved in some countries. By exploiting a mapping of models describing the evolutionary dynamics of B cells during affinity maturation to a simple quantum mechanical analog, we investigate the optimal antigen composition of the cocktail vaccine in order to best exploit immune memory generated by previous encounters of related pathogen strains. |
Wednesday, March 8, 2023 10:12AM - 10:24AM |
M14.00012: Stochastic modeling for studying the effects of BET inhibitors on the modulation of P-TEFb levels Miranda D Harkess, Niraj Kumar Latent reservoir of HIV is the major obstacle in eradicating HIV from infected patients. Reversing this latency is an important goal for developing effective treatment strategy. Recent studies have shown that BET protein inhibitors can successfully reverse this latency by inhibiting the binding of BET proteins with cellular cofactor P-TEFb. Such inhibition leads to enhanced association of P-TEFb with viral Tat proteins which can lead to HIV transactivation. However, in cells of our immune system which are primarily infected by the virus, number of P-TEFb is very low and is considered as one of the factors in inducing viral latency. At such small numbers of P-TEFb, the internal fluctuations can have a decisive role in the cell fate decision and corresponding noise in the P-TEFb levels can switch the HIV to either a state of active replication or to a state of latency. Aimed at quantitative understanding of how BET inhibitors affect the statistics of PTEFb level, we develop a coarse-grained stochastic model. The interaction between P-TEFb and BET proteins makes the problem analytically challenging. Based on biologically relevant approximations, we derive analytical results for the mean and noise associated with P-TEFb levels in the steady state. The results derived will be helpful in estimating the model parameters as well in identifying the pathways that can intervened for effective HIV transactivation. |
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