R-3 Repository :: Browsing by Author "Levine, Herbert"

Browsing by Author "Levine, Herbert"

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Alignment and nonlinear elasticity in biopolymer gels
(American Physical Society, 2015) Feng, Jingchen; Levine, Herbert; Mao, Xiaoming; Sander, Leonard M.; Bioengineering; Center for Theoretical Biological Physics
We present a Landau-type theory for the nonlinear elasticity of biopolymer gels with a part of the order parameter describing induced nematic order of fibers in the gel. We attribute the nonlinear elastic behavior of these materials to fiber alignment induced by strain. We suggest an application to contact guidance of cell motility in tissue. We compare our theory to simulation of a disordered lattice model for biopolymers. We treat homogeneous deformations such as simple shear, hydrostatic expansion, and simple extension, and obtain good agreement between theory and simulation. We also consider a localized perturbation which is a simple model for a contracting cell in a medium.
Alignment of cellular motility forces with tissue flow as a mechanism for efficient wound healing
(National Academy of Science, 2012) Basan, Markus; Elgeti, Jens; Hannezo, Edouard; Rappel, Wouter-Jan; Levine, Herbert; Center for Theoretical Biological Physics; Bioengineering; Center for Theoretical Biological Physics
Recent experiments have shown that spreading epithelial sheets exhibit a long-range coordination of motility forces that leads to a buildup of tension in the tissue, which may enhance cell division and the speed of wound healing. Furthermore, the edges of these epithelial sheets commonly show finger-like protrusions whereas the bulk often displays spontaneous swirls of motile cells. To explain these experimental observations, we propose a simple flocking-type mechanism, in which cells tend to align their motility forceswith their velocity. Implementing this idea in amechanical tissue simulation, the proposed model gives rise to efficient spreading and can explain the experimentally observed long-range alignment of motility forces in highly disordered patterns, as well as the buildup of tensile stress throughout the tissue. Our model also qualitatively reproduces the dependence of swirl size and swirl velocity on cell density reported in experiments and exhibits an undulation instability at the edge of the spreading tissue commonly observed in vivo. Finally, we study the dependence of colony spreading speed on important physical and biological parameters and derive simple scaling relations that show that coordination of motility forces leads to an improvement of the wound healing process for realistic tissue parameters.
Analysis of Hierarchical Organization in Gene Expression Networks Reveals Underlying Principles of Collective Tumor Cell Dissemination and Metastatic Aggressiveness of Inflammatory Breast Cancer
(Frontiers, 2018) Tripathi, Shubham; Jolly, Mohit Kumar; Woodward, Wendy A.; Levine, Herbert; Deem, Michael W.; Bioengineering; Physics and Astronomy
Clusters of circulating tumor cells (CTCs), despite being rare, may account for more than 90% of metastases. Cells in these clusters do not undergo a complete epithelial-to-mesenchymal transition (EMT), but retain some epithelial traits as compared to individually disseminating tumor cells. Determinants of single cell dissemination versus collective dissemination remain elusive. Inflammatory breast cancer (IBC), a highly aggressive breast cancer subtype that chiefly metastasizes via CTC clusters, is a promising model for studying mechanisms of collective tumor cell dissemination. Previous studies, motivated by a theory that suggests physical systems with hierarchical organization tend to be more adaptable, have found that the expression of metastasis-associated genes is more hierarchically organized in cases of successful metastases. Here, we used the cophenetic correlation coefficient (CCC) to quantify the hierarchical organization in the expression of two distinct gene sets, collective dissemination-associated genes and IBC-associated genes, in cancer cell lines and in tumor samples from breast cancer patients. Hypothesizing that a higher CCC for collective dissemination-associated genes and for IBC-associated genes would be associated with retention of epithelial traits enabling collective dissemination and with worse disease progression in breast cancer patients, we evaluated the correlation of CCC with different phenotypic groups. The CCC of both the abovementioned gene sets, the collective dissemination-associated genes and the IBC-associated genes, was higher in (a) epithelial cell lines as compared to mesenchymal cell lines and (b) tumor samples from IBC patients as compared to samples from non-IBC breast cancer patients. A higher CCC of both gene sets was also correlated with a higher rate of metastatic relapse in breast cancer patients. In contrast, neither the levels of CDH1 gene expression nor gene set enrichment analysis (GSEA) of the abovementioned gene sets could provide similar insights. These results suggest that retention of some epithelial traits in disseminating tumor cells as IBC progresses promotes successful breast cancer metastasis. The CCC provides additional information regarding the organizational complexity of gene expression in comparison to GSEA. We have shown that the CCC may be a useful metric for investigating the collective dissemination phenotype and a prognostic factor for IBC.
cell motility and machine learning
(2020-03-11) Yu, Guangyuan; Wolynes, Peter; Levine, Herbert
Cell migration is a necessary function in organisms. It is relevant to wound healing, immune reaction, cancer expansion. One example is durotaxis: Cells exhibit qualitatively different behaviors on substrates with different rigidities. The fact that cells are more polarized on the stiffer substrate motivates us to construct a two-dimensional cell with the distribution of focal adhesions dependent on substrate rigidities. This distribution affects the forces exerted by the cell and thereby determines its motion. Our model reproduces the experimental observation that the persistence time is higher on the stiffer substrate. This stiffness-dependent persistence will lead to durotaxis, the preference in moving towards stiffer substrates. We derive and validate a two-dimensional corresponding Fokker-Planck equation associated with our model. Another example is the chemotaxis: Leukotriene B4 is secreted as exosomes by neutrophils, serving as a secondary gradient to amplify the chemical attraction of primary chemoattractants. We introduce a model to compare the enhancement effect between directly releasing Leukotriene B4 and generating as exosomes. We attribute this advantage to the longer lifetime of exosomes which leads to a larger range of attraction. The third example is in the immune system: The infiltrations of T are different in patients, which could be a tool for the prognosis. High CD8+ T cell counts (both overall and inside cancer-cell islands) is associated with better patient outcome. However, a cut-off of the T-cell count has to be selected manually to separate groups of patients. In this work, we propose a method to classify the small patch of triple-negative breast cancer (TNBC) tumor and use the overall percentage of good patches as a marker to predict the prognosis, which is an automatic method of prognosis and could also be used for other cancers. The result shows that the machine learns the importance of cell count and cell infiltration and use the combination as an indicator for prognosis. We also applied the machine learning method on sperm classification and quantum many body problem. In sperm classification, we could get around 70\% accuracy with balanced good and poor example. In quantum many body problem, we proposed a deep neural network to calculate the ground-state energies and it shows excellent agreement with the Bethe-Ansatz exact solution. Furthermore, we also calculate the loop correlation function using the wave function obtained.
Collective Signal Processing in Cluster Chemotaxis: Roles of Adaptation, Amplification, and Co-attraction in Collective Guidance
(Public Library of Science, 2016) Camley, Brian A.; Zimmermann, Juliane; Levine, Herbert; Rappel, Wouter-Jan; Bioengineering; Center for Theoretical Biological Physics
Single eukaryotic cells commonly sense and follow chemical gradients, performing chemotaxis. Recent experiments and theories, however, show that even when single cells do not chemotax, clusters of cells may, if their interactions are regulated by the chemoattractant. We study this general mechanism of “collective guidance” computationally with models that integrate stochastic dynamics for individual cells with biochemical reactions within the cells, and diffusion of chemical signals between the cells. We show that if clusters of cells use the well-known local excitation, global inhibition (LEGI) mechanism to sense chemoattractant gradients, the speed of the cell cluster becomes non-monotonic in the cluster’s size—clusters either larger or smaller than an optimal size will have lower speed. We argue that the cell cluster speed is a crucial readout of how the cluster processes chemotactic signals; both amplification and adaptation will alter the behavior of cluster speed as a function of size. We also show that, contrary to the assumptions of earlier theories, collective guidance does not require persistent cell-cell contacts and strong short range adhesion. If cell-cell adhesion is absent, and the cluster cohesion is instead provided by a co-attraction mechanism, e.g. chemotaxis toward a secreted molecule, collective guidance may still function. However, new behaviors, such as cluster rotation, may also appear in this case. Co-attraction and adaptation allow for collective guidance that is robust to varying chemoattractant concentrations while not requiring strong cell-cell adhesion.
Computational Modeling of the Crosstalk Between Macrophage Polarization and Tumor Cell Plasticity in the Tumor Microenvironment
(Frontiers, 2019) Li, Xuefei; Jolly, Mohit Kumar; George, Jason T.; Pienta, Kenneth J.; Levine, Herbert; Bioengineering; Physics and Astronomy
Tumor microenvironments contain multiple cell types interacting among one another via different signaling pathways. Furthermore, both cancer cells and different immune cells can display phenotypic plasticity in response to these communicating signals, thereby leading to complex spatiotemporal patterns that can impact therapeutic response. Here, we investigate the crosstalk between cancer cells and macrophages in a tumor microenvironment through in silico (computational) co-culture models. In particular, we investigate how macrophages of different polarization (M1 vs. M2) can interact with epithelial-mesenchymal plasticity of cancer cells, and conversely, how cancer cells exhibiting different phenotypes (epithelial vs. mesenchymal) can influence the polarization of macrophages. Based on interactions documented in the literature, an interaction network of cancer cells and macrophages is constructed. The steady states of the network are then analyzed. Various interactions were removed or added into the constructed-network to test the functions of those interactions. Also, parameters in the mathematical models were varied to explore their effects on the steady states of the network. In general, the interactions between cancer cells and macrophages can give rise to multiple stable steady-states for a given set of parameters and each steady state is stable against perturbations. Importantly, we show that the system can often reach one type of stable steady states where cancer cells go extinct. Our results may help inform efficient therapeutic strategies.
Confluent and nonconfluent phases in a model of cell tissue
(American Physical Society, 2018) Teomy, Eial; Kessler, David A.; Levine, Herbert
The Voronoi-based cellular model is highly successful in describing the motion of two-dimensional confluent cell tissues. In the homogeneous version of this model, the energy of each cell is determined solely by its geometric shape and size, and the interaction between adjacent cells is a by-product of this additive energy. We generalize this model so as to allow zero or partial contact between cells. We identify several phases, two of which (solid confluent and liquid confluent) were found in previous studies that imposed confluency as well as others that were not. Transitions in this model may be relevant for understanding both normal development as well as cancer metastasis.
Contact map dependence of a T-cell receptor binding repertoire
(American Physical Society, 2022) Chau, Kevin Ng; George, Jason T.; Onuchic, José N.; Lin, Xingcheng; Levine, Herbert; Biosciences; Chemistry; Physics and Astronomy; Center for Theoretical Biological Physics
The T-cell arm of the adaptive immune system provides the host protection against unknown pathogens by discriminating between host and foreign material. This discriminatory capability is achieved by the creation of a repertoire of cells each carrying a T-cell receptor (TCR) specific to non-self-antigens displayed as peptides bound to the major histocompatibility complex (pMHC). The understanding of the dynamics of the adaptive immune system at a repertoire level is complex, due to both the nuanced interaction of a TCR-pMHC pair and to the number of different possible TCR-pMHC pairings, making computationally exact solutions currently unfeasible. To gain some insight into this problem, we study an affinity-based model for TCR-pMHC binding in which a crystal structure is used to generate a distance-based contact map that weights the pairwise amino acid interactions. We find that the TCR-pMHC binding energy distribution strongly depends both on the number of contacts and the repeat structure allowed by the topology of the contact map of choice; this in turn influences T-cell recognition probability during negative selection, with higher variances leading to higher survival probabilities. In addition, we quantify the degree to which neoantigens with mutations in sites with higher contacts are recognized at a higher rate.
Coupling the modules of EMT and stemness: A tunable ‘stemness window’ model
(Impact Journals, LLC., 2015) Jolly, Mohit Kumar; Jia, Dongya; Boareto, Marcelo; Mani, Sendurai A.; Pienta, Kenneth J.; Ben-Jacob, Eshel; Levine, Herbert; Bioengineering; Biosciences; Physics and Astronomy; Center for Theoretical Biological Physics
Metastasis of carcinoma involves migration of tumor cells to distant organs and initiate secondary tumors. Migration requires a complete or partial Epithelial-to-Mesenchymal Transition (EMT), and tumor-initiation requires cells possessing stemness. Epithelial cells (E) undergoing a complete EMT to become mesenchymal (M) have been suggested to be more likely to possess stemness. However, recent studies suggest that stemness can also be associated with cells undergoing a partial EMT (hybrid E/M phenotype). Therefore, the correlation between EMT and stemness remains elusive. Here, using a theoretical framework that couples the core EMT and stemness modules (miR-200/ZEB and LIN28/let-7), we demonstrate that the positioning of 'stemness window' on the 'EMT axis' need not be universal; rather it can be fine-tuned. Particularly, we present OVOL as an example of a modulating factor that, due to its coupling with miR-200/ZEB/LIN28/let-7 circuit, fine-tunes the EMT-stemness interplay. Coupling OVOL can inhibit the stemness likelihood of M and elevate that of the hybrid E/M (partial EMT) phenotype, thereby pulling the 'stemness window' away from the M end of 'EMT axis'. Our results unify various apparently contradictory experimental findings regarding the interconnection between EMT and stemness, corroborate the emerging notion that partial EMT associates with stemness, and offer new testable predictions.
Decoding the coupled decision-making of the epithelial-mesenchymal transition and metabolic reprogramming in cancer
(Cell Press, 2023) Galbraith, Madeline; Levine, Herbert; Onuchic, José N.; Jia, Dongya; Biosciences; Chemistry; Physics and Astronomy; Center for Theoretical Biological Physics
Cancer metastasis relies on an orchestration of traits driven by different interacting functional modules, including metabolism and epithelial-mesenchymal transition (EMT). During metastasis, cancer cells can acquire a hybrid metabolic phenotype (W/O) by increasing oxidative phosphorylation without compromising glycolysis and they can acquire a hybrid epithelial/mesenchymal (E/M) phenotype by engaging EMT. Both the W/O and E/M states are associated with high metastatic potentials, and many regulatory links coupling metabolism and EMT have been identified. Here, we investigate the coupled decision-making networks of metabolism and EMT. Their crosstalk can exhibit synergistic or antagonistic effects on the acquisition and stability of different coupled metabolism-EMT states. Strikingly, the aggressive E/M-W/O state can be enabled and stabilized by the crosstalk irrespective of these hybrid states’ availability in individual metabolism or EMT modules. Our work emphasizes the mutual activation between metabolism and EMT, providing an important step toward understanding the multifaceted nature of cancer metastasis.
Designing bacterial signaling interactions with coevolutionary landscapes
(Public Library of Science, 2018) Cheng, Ryan R.; Haglund, Ellinor; Tiee, Nicholas S.; Morcos, Faruck; Levine, Herbert; Adams, Joseph A.; Jennings, Patricia A.; Onuchic, José N.
Selecting amino acids to design novel protein-protein interactions that facilitate catalysis is a daunting challenge. We propose that a computational coevolutionary landscape based on sequence analysis alone offers a major advantage over expensive, time-consuming brute-force approaches currently employed. Our coevolutionary landscape allows prediction of single amino acid substitutions that produce functional interactions between non-cognate, interspecies signaling partners. In addition, it can also predict mutations that maintain segregation of signaling pathways across species. Specifically, predictions of phosphotransfer activity between the Escherichia coli histidine kinase EnvZ to the non-cognate receiver Spo0F from Bacillus subtilis were compiled. Twelve mutations designed to enhance, suppress, or have a neutral effect on kinase phosphotransfer activity to a non-cognate partner were selected. We experimentally tested the ability of the kinase to relay phosphate to the respective designed Spo0F receiver proteins against the theoretical predictions. Our key finding is that the coevolutionary landscape theory, with limited structural data, can significantly reduce the search-space for successful prediction of single amino acid substitutions that modulate phosphotransfer between the two-component His-Asp relay partners in a predicted fashion. This combined approach offers significant improvements over large-scale mutations studies currently used for protein engineering and design.
Distinguishing mechanisms underlying EMT tristability
(Springer International Publishing, 2017) Jia, Dongya; Jolly, Mohit K.; Tripathi, Satyendra C.; Den Hollander, Petra; Huang, Bin; Lu, Mingyang; Celiktas, Muge; Ramirez-Peña, Esmeralda; Ben-Jacob, Eshel; Onuchic, José Nelson; Hanash, Samir M.; Mani, Sendurai A.; Levine, Herbert; Bioengineering; Biosciences; Chemistry; Physics and Astronomy
Abstract Background The Epithelial-Mesenchymal Transition (EMT) endows epithelial-looking cells with enhanced migratory ability during embryonic development and tissue repair. EMT can also be co-opted by cancer cells to acquire metastatic potential and drug-resistance. Recent research has argued that epithelial (E) cells can undergo either a partial EMT to attain a hybrid epithelial/mesenchymal (E/M) phenotype that typically displays collective migration, or a complete EMT to adopt a mesenchymal (M) phenotype that shows individual migration. The core EMT regulatory network - miR-34/SNAIL/miR-200/ZEB1 - has been identified by various studies, but how this network regulates the transitions among the E, E/M, and M phenotypes remains controversial. Two major mathematical models – ternary chimera switch (TCS) and cascading bistable switches (CBS) - that both focus on the miR-34/SNAIL/miR-200/ZEB1 network, have been proposed to elucidate the EMT dynamics, but a detailed analysis of how well either or both of these two models can capture recent experimental observations about EMT dynamics remains to be done. Results Here, via an integrated experimental and theoretical approach, we first show that both these two models can be used to understand the two-step transition of EMT - E→E/M→M, the different responses of SNAIL and ZEB1 to exogenous TGF-β and the irreversibility of complete EMT. Next, we present new experimental results that tend to discriminate between these two models. We show that ZEB1 is present at intermediate levels in the hybrid E/M H1975 cells, and that in HMLE cells, overexpression of SNAIL is not sufficient to initiate EMT in the absence of ZEB1 and FOXC2. Conclusions These experimental results argue in favor of the TCS model proposing that miR-200/ZEB1 behaves as a three-way decision-making switch enabling transitions among the E, hybrid E/M and M phenotypes.
DNA supercoiling-mediated collective behavior of co-transcribing RNA polymerases
(Oxford University Press, 2022) Tripathi, Shubham; Brahmachari, Sumitabha; Onuchic, José N.; Levine, Herbert; Biosciences; Chemistry; Physics and Astronomy; Center for Theoretical Biological Physics
Multiple RNA polymerases (RNAPs) transcribing a gene have been known to exhibit collective group behavior, causing the transcription elongation rate to increase with the rate of transcription initiation. Such behavior has long been believed to be driven by a physical interaction or ‘push’ between closely spaced RNAPs. However, recent studies have posited that RNAPs separated by longer distances may cooperate by modifying the DNA segment under transcription. Here, we present a theoretical model incorporating the mechanical coupling between RNAP translocation and the DNA torsional response. Using stochastic simulations, we demonstrate DNA supercoiling-mediated long-range cooperation between co-transcribing RNAPs. We find that inhibiting transcription initiation can slow down the already recruited RNAPs, in agreement with recent experimental observations, and predict that the average transcription elongation rate varies non-monotonically with the rate of transcription initiation. We further show that while RNAPs transcribing neighboring genes oriented in tandem can cooperate, those transcribing genes in divergent or convergent orientations can act antagonistically, and that such behavior holds over a large range of intergenic separations. Our model makes testable predictions, revealing how the mechanical interplay between RNAPs and the DNA they transcribe can govern transcriptional dynamics.
Dynamical Heterogeneity of the Glassy State
(2014-04-24) Wisitsorasak, Apiwat; Wolynes, Peter G.; Levy, Eugene H.; Levine, Herbert
The understanding and the complete description of the glass transition are impeded by the complexity of nature of the glass. Parts of this complexity come from the emergence of long-lived inherent structures of a liquid at a temperature below which the activated reconfiguration events play a dominant role. Molecules in a glass change their locations through the activated process at a rate which varies throughout the glass owing to these local and aperiodic structures. Motions in one location also cause or relieve constrains, thereby altering the rate of transitions of neighboring regions. The key to understanding this problem is the interplay between the activated events that generate mobility and the transport of mobility. In the following we explore fluctuating mobility generation and transport in glasses to understand the dynamics of the glassy state within the framework of the random first order transition theory of glass. Fluctuating mobility generation and transport in the glass that arise from there being a distribution of local stability and thus effective temperature are treated by numerically solving stochastic continuum equations for mobility and fictive temperature fields. Fluctuating spatiotemporal structures in aging and rejuvenating glasses lead to dynamical heterogeneity in glasses with characteristics that are distinct from those found in the equilibrium liquid. We illustrate in this thesis how the heterogeneity in glasses gives rises of a non-Gaussian distribution of activation free energies, the stretching exponent, and the growth of characteristic lengths. These are studied along with the four-point dynamic correlation function. Asymmetric thermodynamic responses upon heating and cooling are also predicted to be the results of the heterogeneity and the out-of-equilibrium behavior of glasses below the glass transition temperature. Moreover the dynamical heterogeneity can lead to a growth front of mobility in rejuvenating glasses that emanates from the surface where stable glasses are heated. Noticeably bimodal distributions of local fictive temperatures in aging glasses are also investigate. This result explains recent experimental observations that have been interpreted as coming from these being two distinct equilibration mechanisms in glasses. Finally we study the mechanical properties of glasses. The remarkable strength of glasses is examined using the random first order transition theory. The theory predicts that the strength not only depends on the elastic modulus but also depends on the amount of configurational energy frozen in when the glass is prepared. The stress catalysis of cooperative rearrangements of the same type as those responsible for the supercooled liquid's high viscosity account quantitatively for the measured strength of a range of metallic glasses, silica and a polymer glass.
Elucidating the Metabolic Plasticity of Cancer: Mitochondrial Reprogramming and Hybrid Metabolic States
(MDPI, 2018) Jia, Dongya; Park, Jun Hyoung; Jung, Kwang Hwa; Levine, Herbert; Kaipparettu, Benny Abraham; Bioengineering; Biosciences; Physics and Astronomy
Aerobic glycolysis, also referred to as the Warburg effect, has been regarded as the dominant metabolic phenotype in cancer cells for a long time. More recently, it has been shown that mitochondria in most tumors are not defective in their ability to carry out oxidative phosphorylation (OXPHOS). Instead, in highly aggressive cancer cells, mitochondrial energy pathways are reprogrammed to meet the challenges of high energy demand, better utilization of available fuels and macromolecular synthesis for rapid cell division and migration. Mitochondrial energy reprogramming is also involved in the regulation of oncogenic pathways via mitochondria-to-nucleus retrograde signaling and post-translational modification of oncoproteins. In addition, neoplastic mitochondria can engage in crosstalk with the tumor microenvironment. For example, signals from cancer-associated fibroblasts can drive tumor mitochondria to utilize OXPHOS, a process known as the reverse Warburg effect. Emerging evidence shows that cancer cells can acquire a hybrid glycolysis/OXPHOS phenotype in which both glycolysis and OXPHOS can be utilized for energy production and biomass synthesis. The hybrid glycolysis/OXPHOS phenotype facilitates metabolic plasticity of cancer cells and may be specifically associated with metastasis and therapy-resistance. Moreover, cancer cells can switch their metabolism phenotypes in response to external stimuli for better survival. Taking into account the metabolic heterogeneity and plasticity of cancer cells, therapies targeting cancer metabolic dependency in principle can be made more effective.
EMT and MET: necessary or permissive for metastasis?
(Wiley, 2017) Jolly, Mohit Kumar; Ware, Kathryn E.; Gilja, Shivee; Somarelli, Jason A.; Levine, Herbert; Bioengineering; Center for Theoretical Biological Physics
Epithelial-to-mesenchymal transition (EMT) and its reverse mesenchymal-to-epithelial transition (MET) have been suggested to play crucial roles in metastatic dissemination of carcinomas. These phenotypic transitions between states are not binary. Instead, carcinoma cells often exhibit a spectrum of epithelial/mesenchymal phenotype(s). While epithelial/mesenchymal plasticity has been observed preclinically and clinically, whether any of these phenotypic transitions are indispensable for metastatic outgrowth remains an unanswered question. Here, we focus on epithelial/mesenchymal plasticity in metastatic dissemination and propose alternative mechanisms for successful dissemination and metastases beyond the traditional EMT/MET view. We highlight multiple hypotheses that can help reconcile conflicting observations, and outline the next set of key questions that can offer valuable insights into mechanisms of metastasis in multiple tumor models.
Epithelial/mesenchymal plasticity: how have quantitative mathematical models helped improve our understanding?
(Wiley, 2017) Jolly, Mohit Kumar; Tripathi, Satyendra C.; Somarelli, Jason A.; Hanash, Samir M.; Levine, Herbert; Bioengineering; Center for Theoretical Biological Physics
Phenotypic plasticity, the ability of cells to reversibly alter their phenotypes in response to signals, presents a significant clinical challenge to treating solid tumors. Tumor cells utilize phenotypic plasticity to evade therapies, metastasize, and colonize distant organs. As a result, phenotypic plasticity can accelerate tumor progression. A well-studied example of phenotypic plasticity is the bidirectional conversions among epithelial, mesenchymal, and hybrid epithelial/mesenchymal (E/M) phenotype(s). These conversions can alter a repertoire of cellular traits associated with multiple hallmarks of cancer, such as metabolism, immune evasion, invasion, and metastasis. To tackle the complexity and heterogeneity of these transitions, mathematical models have been developed that seek to capture the experimentally verified molecular mechanisms and act as ‘hypothesis-generating machines’. Here, we discuss how these quantitative mathematical models have helped us explain existing experimental data, guided further experiments, and provided an improved conceptual framework for understanding how multiple intracellular and extracellular signals can drive E/M plasticity at both the single-cell and population levels. We also discuss the implications of this plasticity in driving multiple aggressive facets of tumor progression.
Excitable waves and direction-sensing inﾠDictyostelium discoideum: steps towards a chemotaxis model
(IOP Publishing Ltd, 2016) Bhowmik, Arpan; Rappel, Wouter-Jan; Levine, Herbert; Bioengineering; Center for Theoretical Biological Physics
In recent years, there have been significant advances in our understanding of the mechanisms underlying chemically directed motility by eukaryotic cells such as Dictyostelium. In particular, the local excitation and global inhibition (LEGI) model has proven capable of providing a framework for quantitatively explaining many experiments that present Dictyostelium cells with tailored chemical stimuli and monitor their subsequent polarization. In their natural setting, cells generate their own directional signals via the detection and secretion of cyclic adenosine monophosphate (cAMP). Here, we couple the LEGI approach to an excitable medium model of the cAMP wave-field that is propagated by the cells and investigate the possibility for this class of models to enable accurate chemotaxis to the cAMP waveforms expected in vivo. Our results indicate that the ultra-sensitive version of the model does an excellent job in providing natural wave rectification, thereby providing a compelling solution to the 'back-of-the-wave paradox' during cellular aggregation.
Exploring Cancer Cell Plasticity: Epithelial-Mesenchymal Transition and Metabolic Reprogramming
(2018-04-12) Jia, Dongya; Levine, Herbert
Metastasis is a hallmark of cancer. Cancer cells can utilize epithelial-mesenchymal transition (EMT) to facilitate metastasis. During metastasis, cells do not always undergo a complete EMT, instead a partial EMT, leading to a hybrid epithelial/mesenchymal (E/M) phenotype has often been observed. Cells in the hybrid E/M phenotype tend to migrate as clusters of circulating tumor cells, that serve as the 'chief instigators' of metastasis. Typically, the hybrid E/M phenotype was assumed to be transient. Here we identify mechanisms underlying EMT tristability – epithelial, hybrid E/M, mesenchymal - through integrated theoretical and experimental approach. We further identify several phenotypic stability factors that may stabilize the hybrid E/M phenotype, and associate it with stem-like properties. To extend our understanding of EMT dynamics, we develop a new computational method, generalized random circuit perturbation (RACIPE), by which multiple hybrid E/M phenotypes are characterized. Abnormal metabolism is another hallmark of cancer. Cancer cells were considered to utilize primarily glycolysis for ATP production even in the presence of oxygen, referred to as the Warburg effect. Increasing evidence shows that mitochondria are actively functioning in cancer cells and oxidative phosphorylation (OXPHOS) may be specifically associated with metastasis. However, it remains elusive how cancer cells take advantage of both glycolysis and OXPHOS to facilitate malignancy. Through integrating mathematical modeling with bioinformatics, we show that cancer cells can acquire a stable hybrid metabolic phenotype, characterized by high activity of AMPK and HIF-1, and high metabolic activity of glycolysis and glucose/fatty acid oxidation. Guided by the model, we develop the AMPK and HIF-1 signatures by evaluating the expression of their downstream targets, to quantify the activity of AMPK and HIF-1. The AMPK and HIF-1 signatures can capture the significant metabolic features of both bulk tumors and single cells. In summary, our systems biology analysis of EMT and metabolic reprogramming serves as a platform to identify certain underlying basic principles pertaining to different hallmarks of cancer and design therapies targeting cancer cell plasticity.
Gene expression profiles of inflammatory breast cancer reveal high heterogeneity across the epithelial-hybrid-mesenchymal spectrum
(Elsevier, 2021) Chakraborty, Priyanka; George, Jason T; Woodward, Wendy A; Levine, Herbert; Jolly, Mohit Kumar; Center for Theoretical Biological Physics
Inflammatory breast cancer (IBC) is a highly aggressive breast cancer that metastasizes largely via tumor emboli, and has a 5-year survival rate of less than 30%. No unique genomic signature has yet been identified for IBC nor has any specific molecular therapeutic been developed to manage the disease. Thus, identifying gene expression signatures specific to IBC remains crucial. Here, we compare various gene lists that have been proposed as molecular footprints of IBC using different clinical samples as training and validation sets and using independent training algorithms, and determine their accuracy in identifying IBC samples in three independent datasets. We show that these gene lists have little to no mutual overlap, and have limited predictive accuracy in identifying IBC samples. Despite this inconsistency, single-sample gene set enrichment analysis (ssGSEA) of IBC samples correlate with their position on the epithelial-hybrid-mesenchymal spectrum. This positioning, together with ssGSEA scores, improves the accuracy of IBC identification across the three independent datasets. Finally, we observed that IBC samples robustly displayed a higher coefficient of variation in terms of EMT scores, as compared to non-IBC samples. Pending verification that this patient-to-patient variability extends to intratumor heterogeneity within a single patient, these results suggest that higher heterogeneity along the epithelial-hybrid-mesenchymal spectrum can be regarded to be a hallmark of IBC and a possibly useful biomarker.

Browsing by Author "Levine, Herbert"

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