Jennifer Linderman | University of Michigan (original) (raw)

Papers by Jennifer Linderman

Pharmaceutical research, May 28, 2024

npj systems biology and applications, Apr 18, 2024

Single cancer cells within a tumor exhibit variable levels of resistance to drugs, ultimately lea... more Single cancer cells within a tumor exhibit variable levels of resistance to drugs, ultimately leading to treatment failures. While tumor heterogeneity is recognized as a major obstacle to cancer therapy, standard dose-response measurements for the potency of targeted kinase inhibitors aggregate populations of cells, obscuring intercellular variations in responses. In this work, we develop an analytical and experimental framework to quantify and model dose responses of individual cancer cells to drugs. We first explore the connection between population and single-cell dose responses using a computational model, revealing that multiple heterogeneous populations can yield nearly identical population dose responses. We demonstrate that a single-cell analysis method, which we term a threshold inhibition surface, can differentiate among these populations. To demonstrate the applicability of this method, we develop a dose-titration assay to measure dose responses in single cells. We apply this assay to breast cancer cells responding to phosphatidylinositol-3-kinase inhibition (PI3Ki), using clinically relevant PI3Kis on breast cancer cell lines expressing fluorescent biosensors for kinase activity. We demonstrate that MCF-7 breast cancer cells exhibit heterogeneous dose responses with some cells requiring over tenfold higher concentrations than the population average to achieve inhibition. Our work reimagines dose-response relationships for cancer drugs in an emerging paradigm of single-cell tumor heterogeneity. Despite numerous advances in cancer biology, target identification, and drug discovery and development, existing chemotherapy drugs generally fail to yield durable responses. Kinase inhibitors represent one promising new class of chemotherapeutic agents. These drugs are designed to inhibit the activity of an oncogenic kinase critical to transformation, proliferation, and/ or survival. While kinase inhibitors have had some clinical success 1-3 , a variety of cell-intrinsic and cell-extrinsic resistance mechanisms have been identified, including variable drug distribution 4 , microenvironmental heterogeneity 5 , compensatory activation of other oncogenic signaling pathways 6-8 , and heterogeneity in the underlying population of cancer cells. Population heterogeneity can have multiple origins, including genetic and non-genetic differences among cells 9. Non-genetic heterogeneity

Frontiers in Systems Biology, Jan 21, 2024

Computational models of disease progression have been constructed for a myriad of pathologies. Ty... more Computational models of disease progression have been constructed for a myriad of pathologies. Typically, the conceptual implementation for pathology-related in silico intervention studies has been ad hoc and similar in design to experimental studies. We introduce a multi-scale interventional design (MID) framework toward two key goals: tracking of disease dynamics from within-body to patient to population scale; and tracking impact(s) of interventions across these same spatial scales. Our MID framework prioritizes investigation of impact on individual patients within virtual pre-clinical trials, instead of replicating the design of experimental studies. We apply a MID framework to develop, organize, and analyze a cohort of virtual patients for the study of tuberculosis (TB) as an example disease. For this study, we use HostSim: our next-generation whole patient-scale computational model of individuals infected with Mycobacterium tuberculosis. HostSim captures infection within lungs by tracking multiple granulomas, together with dynamics occurring with blood and lymph node compartments, the compartments involved during pulmonary TB. We extend HostSim to include a simple drug intervention as an example of our approach and use our MID framework to quantify the impact of treatment at cellular and tissue (granuloma), patient (lungs, lymph nodes and blood), and population scales. Sensitivity analyses allow us to determine which features of virtual patients are the strongest predictors of intervention efficacy across scales. These insights allow us to identify patient-heterogeneous mechanisms that drive outcomes across scales.

Frontiers in systems biology, Mar 8, 2024

Cellular heterogeneity is a ubiquitous aspect of biology and a major obstacle to successful cance... more Cellular heterogeneity is a ubiquitous aspect of biology and a major obstacle to successful cancer treatment. Several techniques have emerged to quantify heterogeneity in live cells along axes including cellular migration, morphology, growth, and signaling. Crucially, these studies reveal that cellular heterogeneity is not a result of randomness or a failure in cellular control systems, but instead is a predictable aspect of multicellular systems. We hypothesize that individual cells in complex tissues can behave as reward-maximizing agents and that differences in reward perception can explain heterogeneity. In this perspective, we introduce inverse reinforcement learning as a novel approach for analyzing cellular heterogeneity. We briefly detail experimental approaches for measuring cellular heterogeneity over time and how these experiments can generate datasets consisting of cellular states and actions. Next, we show how inverse reinforcement learning can be applied to these datasets to infer how individual cells choose different actions based on heterogeneous states. Finally, we introduce potential applications of inverse reinforcement learning to three cell biology problems. Overall, we expect inverse reinforcement learning to reveal why cells behave heterogeneously and enable identification of novel treatments based on this new understanding.

Wiley Interdisciplinary Reviews: Systems Biology and Medicine, Jul 1, 2014

The use of multi-scale mathematical and computational models to study complex biological processe... more The use of multi-scale mathematical and computational models to study complex biological processes is becoming increasingly productive. Multi-scale models span a range of spatial and/or temporal scales and can encompass multi-compartment (e.g., multi-organ) models. Modeling advances are enabling virtual experiments to explore and answer questions that are problematic to address in the wet-lab. Wet-lab experimental technologies now allow scientists to observe, measure, record, and analyze experiments focusing on different system aspects at a variety of biological scales. We need the technical ability to mirror that same flexibility in virtual experiments using multi-scale models. Here we present a new approach, tuneable resolution, which can begin providing that flexibility. Tuneable resolution involves fine-or coarse-graining existing multi-scale models at the user's discretion, allowing adjustment of the level of resolution specific to a question, an experiment, or a scale of interest. Tuneable resolution expands options for revising and validating mechanistic multi-scale models, can extend the longevity of multi-scale models, and may increase computational efficiency. The tuneable resolution approach can be applied to many model types, including differential equation, agent-based, and hybrid models. We demonstrate our tuneable resolution ideas with examples relevant to infectious disease modeling, illustrating key principles at work.

PLOS Computational Biology, Dec 23, 2010

This paper introduces the concept of phase-locking analysis of oscillatory cellular signaling sys... more This paper introduces the concept of phase-locking analysis of oscillatory cellular signaling systems to elucidate biochemical circuit architecture. Phase-locking is a physical phenomenon that refers to a response mode in which system output is synchronized to a periodic stimulus; in some instances, the number of responses can be fewer than the number of inputs, indicative of skipped beats. While the observation of phase-locking alone is largely independent of detailed mechanism, we find that the properties of phase-locking are useful for discriminating circuit architectures because they reflect not only the activation but also the recovery characteristics of biochemical circuits. Here, this principle is demonstrated for analysis of a G-protein coupled receptor system, the M3 muscarinic receptor-calcium signaling pathway, using microfluidic-mediated periodic chemical stimulation of the M3 receptor with carbachol and real-time imaging of resulting calcium transients. Using this approach we uncovered the potential importance of basal IP3 production, a finding that has important implications on calcium response fidelity to periodic stimulation. Based upon our analysis, we also negated the notion that the Gq-PLC interaction is switch-like, which has a strong influence upon how extracellular signals are filtered and interpreted downstream. Phase-locking analysis is a new and useful tool for model revision and mechanism elucidation; the method complements conventional genetic and chemical tools for analysis of cellular signaling circuitry and should be broadly applicable to other oscillatory pathways.

Biophysical Journal, 2003

Long-term treatment with a drug to a G-protein-coupled receptor (GPCR) often leads to receptor-me... more Long-term treatment with a drug to a G-protein-coupled receptor (GPCR) often leads to receptor-mediated desensitization, limiting the therapeutic lifetime of the drug. To better understand how this therapeutic window might be controlled, we created a mechanistic Monte Carlo model of the early steps in GPCR signaling and desensitization. Using this model we found that the rates of G-protein activation and receptor phosphorylation can be partially decoupled by varying the drug-receptor dissociation rate constant, k off , and the drug's efficacy, a. The maximum ratio of G-protein activation to receptor phosphorylation (GARP) was found for drugs with an intermediate k off value and small a-value. Changes to the cellular environment, such as changes in the diffusivity of membrane molecules and the G-protein inactivation rate constant, affected the GARP value of a drug but did not change the characteristic shape of the GARP curve. These model results are examined in light of experimental data for a number of GPCRs and are found to be in good agreement, lending support to the idea that the desensitization properties of a drug might be tailored to suit a specific application.

Biomaterials, Apr 1, 2006

The adhesion ligand RGD has been coupled to various materials to be used as tissue culture matric... more The adhesion ligand RGD has been coupled to various materials to be used as tissue culture matrices or cell transplantation vehicles, and recent studies indicate that nanopatterning RGD into high-density islands alters cell adhesion, proliferation, and differentiation. However, elucidating the impact of nanopattern parameters on cellular responses has been stymied by a lack of understanding of the actual ligand presentation within these systems. We have developed a multi-scale predictive modeling approach to characterize the adhesion ligand nanopatterns within an alginate hydrogel matrix. The models predict the distribution of ligand islands, the spacing between ligands within an island and the fraction of ligands accessible for cell binding. These model predictions can be used to select pattern parameter ranges for experiments on the effects of individual parameters on cellular responses. Additionally, our technique could also be applied to other polymer systems presenting peptides or other signaling molecules.

Integrative Biology, 2015

Many biological processes are rhythmic and proper timing is increasingly appreciated as being cri... more Many biological processes are rhythmic and proper timing is increasingly appreciated as being critical for development and maintenance of physiological functions. To understand how temporal modulation of an input signal influences downstream responses, we employ microfluidic pulsatile stimulation of a G-Protein coupled receptor, the muscarinic M 3 receptor, in single cells with simultaneous real-time imaging of both intracellular calcium and NFAT nuclear localization. Interestingly, we find that reduced stimulation with pulses of ligand can give more efficient transcription factor activation, if stimuli are timed appropriately. Our experiments and computational analyses show that M 3 receptor-induced calcium oscillations form a low pass filter while calcium-induced NFAT translocation forms a high pass filter. The combination acts as a band-pass filter optimized for intermediate frequencies of stimulation. We demonstrate that receptor desensitization and NFAT translocation rates determine critical features of the band-pass filter and that the band-pass may be shifted for different receptors or NFAT dynamics. As an example, we show that the two NFAT isoforms (NFAT4 and NFAT1) have shifted band-pass windows for the same receptor. While we focus specifically on the M 3 muscarinic receptor and NFAT translocation, band-pass processing is expected to be a general theme that applies to multiple signaling pathways.

Frontiers in Pharmacology, Mar 24, 2020

Tuberculosis (TB) remains as one of the world's deadliest infectious diseases despite the use of ... more Tuberculosis (TB) remains as one of the world's deadliest infectious diseases despite the use of standardized antibiotic therapies. Recommended therapy for drug-susceptible TB is up to 6 months of antibiotics. Factors that contribute to lengthy regimens include antibiotic underexposure in lesions due to poor pharmacokinetics (PK) and complex granuloma compositions, but it is difficult to quantify how individual antibiotics are affected by these factors and to what extent these impact treatments. We use our next-generation multi-scale computational model to simulate granuloma formation and function together with antibiotic pharmacokinetics and pharmacodynamics, allowing us to predict conditions leading to granuloma sterilization. In this work, we focus on how PK variability, determined from human PK data, and granuloma heterogeneity each quantitatively impact granuloma sterilization. We focus on treatment with the standard regimen for TB of four first-line antibiotics: isoniazid, rifampin, ethambutol, and pyrazinamide. We find that low levels of antibiotic concentration due to naturally occurring PK variability and complex granulomas leads to longer granuloma sterilization times. Additionally, the ability of antibiotics to distribute in granulomas and kill different subpopulations of bacteria contributes to their specialization in the more efficacious combination therapy. These results can inform strategies to improve antibiotic therapy for TB.

Journal of Theoretical Biology, Sep 1, 2017

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  We construct a hybrid multi-scale model of fibrotic granuloma formation in the lung  We predict local cytokine concentration gradients drive fibrotic granuloma outcomes  We identify differences between centrally vs. peripherally fibrotic granulomas  We elucidate a role for fibroblast movement in driving fibrotic granuloma formation  We propose computational histology to improve understanding of granuloma formation

PLOS ONE, Aug 11, 2009

G-protein coupled receptors (GPCRs) are the largest family of cell surface receptors; they activa... more G-protein coupled receptors (GPCRs) are the largest family of cell surface receptors; they activate heterotrimeric G-proteins in response to ligand stimulation. Although many GPCRs have been shown to form homo-and/or heterodimers on the cell membrane, the purpose of this dimerization is not known. Recent research has shown that receptor dimerization may have a role in organization of receptors on the cell surface. In addition, microdomains on the cell membrane termed lipid rafts have been shown to play a role in GPCR localization. Using a combination of stochastic (Monte Carlo) and deterministic modeling, we propose a novel mechanism for lipid raft partitioning of GPCRs based on reversible dimerization of receptors and then demonstrate that such localization can affect GPCR signaling. Modeling results are consistent with a variety of experimental data indicating that lipid rafts have a role in amplification or attenuation of G-protein signaling. Thus our work suggests a new mechanism by which dimerization-inducing or inhibiting characteristics of ligands can influence GPCR signaling by controlling receptor organization on the cell membrane.

Journal of Theoretical Biology, Apr 1, 2022

Tuberculosis (TB), caused by infection with Mycobacterium tuberculosis (Mtb), is one of the world... more Tuberculosis (TB), caused by infection with Mycobacterium tuberculosis (Mtb), is one of the world's deadliest infectious diseases and remains a significant global health burden. TB disease and pathology can present clinically across a spectrum of outcomes, ranging from total sterilization of infection to active disease. Much remains unknown about the biology that drives an individual towards various clinical outcomes as it is challenging to experimentally address specific mechanisms driving clinical outcomes. Furthermore, it is unknown whether numbers of immune cells in the blood accurately reflect ongoing events during infection within human lungs. Herein, we utilize a systems biology approach by developing a whole-host model of the immune response to Mtb across multiple physiologic and time scales. This model, called HostSim, tracks events at the cellular, granuloma, organ, and host scale and represents the first whole-host, multi-scale model of the immune response following Mtb infection. We show that this model can capture various aspects of human and non-human primate TB disease and predict that biomarkers in the blood may only faithfully represent events in the lung at early time points after infection. We posit that HostSim, as a first step toward personalized digital twins in TB research, offers a powerful computational tool that can be used in concert with experimental approaches to understand and predict events about various aspects of TB disease and therapeutics.

Journal of Biological Chemistry, Feb 1, 2009

G-protein-coupled receptors (GPCRs) 2 are the largest family of cell membrane receptors. An estim... more G-protein-coupled receptors (GPCRs) 2 are the largest family of cell membrane receptors. An estimated 50% of current pharmaceuticals target GPCRs (1), suggesting that further increases in our understanding of GPCRs and the signaling pathways they initiate will lead to new drug targets. Mathematical and computational modeling (here, simply "modeling") has a substantial history in modern biology and pharmacology (2, 3) and offers a powerful tool for examining GPCR pathways. Such models can be used to better understand hypothesized mechanisms, run virtual (in silico) experiments, interpret data, suggest new drug targets, motivate experiments, and offer new explanations for observed phenomena.

Nano Letters, Jul 13, 2004

It was hypothesized that nanoscale adhesion ligand spacing regulates cell adhesion, proliferation... more It was hypothesized that nanoscale adhesion ligand spacing regulates cell adhesion, proliferation, and differentiation, and that this control can be decoupled from the overall ligand density. Alginate was chemically modified with a peptide containing the cell adhesion sequence arginineglycine-aspartic acid (RGD), and the nanoscale spacing of RGD ligands in alginate gels was varied. A decrease in the RGD island spacing from 78 to 36 nm upregulated the proliferation rates of MC3T3-E1 cells from 0.59 ± 0.08 to 0.73 ± 0.03 day −1 and resulted in 4-fold increase of the osteocalcin secretion rate. This finding was independent of the bulk ligand density of gels. These results indicate that nanoscale ligand organization may provide an important variable to regulate cell functions in many biomedical applications, including tissue engineering.

Journal of Theoretical Biology, Dec 1, 2022

CRC Press eBooks, Sep 19, 2000

Cellular Immunology, Jun 1, 1995

Bulletin of Mathematical Biology, Mar 4, 2019

Often in mathematical and computational biology, assumptions are made (e.g. symmetry) to reduce t... more Often in mathematical and computational biology, assumptions are made (e.g. symmetry) to reduce the problem from three spatial dimensions (3D) to two (2D). However, some experimental datasets, such as cell counts obtained via flow cytometry, represent the entire 3D biological object. For purposes of model calibration and validation, it is sometimes necessary to compare these biological datasets with model outputs. We propose a methodology for scaling 2D model outputs to compare with 3D experimental datasets, and we discuss the application of this methodology to two examples: agent-based models of granuloma formation and skeletal muscle tissue. The accuracy of the method is evaluated in artificially generated scenarios.