Browsing by Author "Igoshin, Oleg"

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    Agent-based model for developmental aggregation in Myxococcus xanthus bacteria
    (2020-04-23) Zhang, Zhaoyang; Igoshin, Oleg; Onuchic, Jose
    Collective behavior refers to social processes and events which do not reflect existing social structure (laws, conventions, and institutions), but which emerge in a”spontaneous” way. It is a common phenomenon in microbiology: a group of cells can spontaneously form different structures under different conditions. How cells interact with each other and achieve this kind of coordinated cell movement is of active scientific interest. As a model organism for bacterial collective behavior,Myxococcus xanthus is widely studied to uncover the mechanism behind bacterial collective behavior. In this work, we applied agent-based models to study the aggregation behavior of M. xanthus cells under starvation and the important cell behaviors for csgA and pilC mutants aggregation.Experiments have shown that WT M. xanthus cells perform a biased walk to-wards aggregation center and this biased walk helps aggregation. To uncover the mechanism of the biased walk, we first developed a model where each cell is modeled as an agent, represented by a point-particle and characterized by its position and moving direction. At low agent density, the model recapitulates the dynamic pat-terns observed by experiments and a previous biophysical model. At high cell density,we extended the model based on the experimental data of the biased movement to-wards aggregates. We tested two possible mechanisms for this biased movement and demonstrate that a chemotax is model with adaptation can reproduce the observed experimental results leading to the formation of stable aggregates. Furthermore, our model reproduces the experimentally observed patterns of cell alignment around aggregates. Next, we applied a data-driven agent-based model to investigate what cell behaviors are important for the rescue of aggregation in two mutants: csgA and pilC, which cannot aggregate unless mixed with wild type (WT) cells. We discovered that when mixed with WT cells, both mutants show biased movements and reduced motility inside aggregates. These behaviors are shown to be important to aggregation in our agent-based simulations. However, some mutant behaviors remain different from WT cells demonstrating that perfect recreation of WT behavior is unnecessary.This work proposes a possible mechanism of the aggregation of M. xanthus bacteria and has shown that some cell behaviors are more important than others in aggregation. Our agent-based model provides a general framework that can be used to study self-organization behaviors in other n other surface motile bacteria
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    Data-driven modeling to infer the function of viral replication in a counting-based decision
    (2021-04-05) Coleman, Seth; Igoshin, Oleg; Golding, Ido
    Cells use gene regulatory networks, sets of genes connected through a web of biochemical interactions, to select a developmental pathway based on signals from their environment. These processes, called cell-fate decisions, are ubiquitous in biology. Yet efforts to study cell-fate decisions are often stymied by the inherent complexity of organisms. Simple model systems provide attractive alternative platforms to study cell-fate decisions and gain insights which may be broadly applicable. Infection of E. coli by the virus lambda is one such model system. The outcome of this viral infection is dependent on the number of initially coinfecting viruses (multiplicity of infection, or MOI), which the viral regulatory network appears to ‘count’. Yet precisely how the viral regulatory network responds to MOI is still unclear, as is how the system is able to achieve sensitivity to MOI despite viral replication, which quickly obfuscates initial viral copy number. In this thesis, I used mathematical modeling of the network dynamics, calibrated by experimental measurements of viral replication and gene expression during infection, to demonstrate how the network responds to MOI and to show that viral replication actually facilitates, rather than hinders, a counting-based decision. This work provides an example of how complex behaviors can emerge from the interplay between gene/network copy number and gene expression, whose coupling cannot be ignored in developing a predictive description of cellular decision-making
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    Feedback loops in bacterial signaling networks
    (2020-04-24) Rao, Satyajit; Igoshin, Oleg
    To understand how bacteria adapt to hostile environments we need to understand the complex networks that process information and control gene-expression programs in response to stress. These complex networks called stress-response networks are a mesh of interconnected components that contain certain conserved structural elements such as feedback loops. This work aims to identify design principles that relate network structural elements to functional requirements in stress response networks using two known bacterial systems. First, we investigate dynamical properties of the Mycobacterium tuberculosis network that is responds to surface stress. In this network transcriptional master regulators MprAB two-component system (TCS) and alternative sigma factor sigma-E mutually activate each other creating positive feedback loops. In this project we build on previous mathematical models that predict bistability, and as a consequence hysteresis, in the network due to presence of positive feedback loops. Experimental observations confirm hysteresis but reveal surprisingly rapid response to stress, uncharacteristic of a bistable network. We seek to reconcile this seemingly discrepant experimental observations using a mathematical model of the known surface stress response network, but discover a trade-off between hysteresis and speed of response to stress. To resolve this trade-off we hypothesize a novel mechanism of activation of the MprAB TCS. Our new models of the stress-response network fit experimental observations, thereby resolving the trade-off. Follow-up experiments perturbing this hypothesized mechanism are consistent with predictions from our models. Thus, we show that synergistic use of experiments and modeling can improve our understanding of stress-response in clinically relevant bacterial species. In the second project, we investigate the E. coli network centered around the PhoPQ TCS which senses low extracellular Mg2+. Experiments indicate that the output of PhoPQ is relatively invariant to overexpression of the two components. While this is consistent with a model of the TCS, a later discovered negative feedback loop limiting PhoPQ activity raises questions about the results. Here we propose a mechanism that allows for invariance of output to PhoPQ overexpression despite the negative feedback loop. While dynamical properties of PhoPQ TCS have been published extensively, the role played by the overlaid positive and negative feedback loops in shaping the observed behavior has not been well understood. We show that the negative feedback leads to a plateau in steady state expression of genes in the PhoPQ regulon even as magnesium concentration decreases. We also describe how positive feedback is activated only when magnesium concentration decreases to growth-limiting levels. This resultant dose-response activation of PhoPQ in two phases is explained by a detailed model of PhoPQ hypothesizing a specific role played by MgrB. Further, we also find that this overlaid feedback loop structure enables cells to be sensitive to a wide range of magnesium concentrations. Finally, in a pivot from stress-response networks found in wild-type organisms, we investigate unexpected behavior of synthetic circuits designed to control gene expression noise independent of the mean. We build deterministic models to understand mechanisms that exploit positive feedback to yield ultrasensitivity and high cell-to-cell variance in gene expression. Stochastic models of the circuit accurately explain experimentally observed mean and noise properties, confirming our proposed mechanisms. Structural features like positive and negative feedback loops are common to a wide range of biological networks. Using mathematical modeling, we extract design principles of stress-response networks featuring feedback loops. The design principles identified in these studies should be widely applicable.
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    Mechanistic modeling of pathological biomarkers to study Alzheimer’s disease progression
    (2021-04-19) Peláez Soní, María José; Igoshin, Oleg; Cristini, Vittorio
    Alzheimer’s disease (AD) is one of the leading causes of death in the United States. It is a neurodegenerative disorder that affects cognitive abilities, characterized by deterioration of the brain tissue due to synaptic loss caused by the abnormal accumulation of amyloid-β (Aβ) peptide and hyperphosphorylated tau proteins, leading to the formation of senile plaques and neurofibrillary tangles, respectively. Mathematical models based on AD biomarkers can be used as a tool to estimate disease progression kinetics, make disease prognosis, determine novel treatment strategies, and develop patient-specific treatment regimens. In this thesis, I developed a novel kinetic model, formulated as a system of differential equations, and a dynamic network diffusion model to characterize the spatiotemporal evolution of pathological biomarkers during AD progression. The kinetic model was calibrated with the ADNI database and simulated the temporal evolution of the five variables of the model: CSF tau, CSF phosphorylated tau, neuronal activity, CSF soluble Aβ, and Aβ plaques. Additionally, parametric analysis of the model highlighted key parameters responsible for disease progression, which hold the potential to design new treatment strategies.
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    Modeling Mechanical And Chemical Signals In Cellular Biophysics
    (2023-11-30) Deng, Youyuan; Igoshin, Oleg; Levine, Herbert
    Mechanical and chemical signaling are essential in shaping cellular biophysics. This thesis covers computational models for this field with different emphases. First, a mechanical model for collective cell motility is discussed. It assumes a contraction-protrusion motile cycle modulated by a molecular clutch which can be stalled by resistant forces. It also includes simplistic pictures of cell-substrate and cell-cell interactions. A number of experimental observations receive a unified explanation under this model, including the tensile stress, edge-confining traction force, and mechanical waves during tissue expansion; the spontaneous revolving of cells along a narrow annulus. After accounting for substrate stiffness gradient, this model also explains collective durotaxis when isolated cells are not durotactic. More specifically, it shows that durotactically biased tissue expansion can be achieved without cell polarity flips, as long as disruptions such as cell divisions keep interior cells non-stalled. Next is a multi-scale model for the coupling between epithelial-mesenchymal transition (EMT) and extracellular matrix (ECM) stiffness. EMT plays a critical role in cancer progression, and has traditionally been associated with chemical signals, but more evidence has established the importance of biomechanical features of tumor microenvironment such as ECM stiffness. A coupled positive feedback loop is proposed whereby mesenchymal cells secretes more LOXL2 that increases crosslinking of collagen fibers and stiffen the ECM, only to produce mechanosensing signals that further drive EMT. Implications of spatial-temporal heterogeneity are also discussed. Last, a dynamical model for the ecology of tumor microenvironment is studied. The microenvironment consists of both cancer and immune cells, among others, all competing for limited resource. Cancer is a systems disease that involves failure of immune surveillance where a large fraction of immune cells become pro-tumor. The ordinary differential equations (ODE) model includes the dynamics of resource influx/consumption, and growth of tumor, pro- and anti-tumor immune cells. A reduced variant model with constant resource is compared. The bifurcation diagrams show that aggressively proliferative tumors or overly pro-tumor immune systems would lead to cancer progression. It also sets a mathematical example that Jacobian matrix is not representative of the stability against small but finite perturbations.
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    The Psp system ofﾠMycobacterium tuberculosisﾠintegrates envelope stress-sensing and envelope-preserving functions
    (Wiley, 2015) Datta, Pratik; Ravi, Janani; Guerrini, Valentina; Chauhan, Rinki; Neiditch, Matthew B.; Shell, Scarlet S.; Fortune, Sarah M.; Hancioglu, Baris; Igoshin, Oleg; Gennaro, Maria Laura
    The bacterial envelope integrates essential stress-sensing and adaptive functions; thus, envelope-preserving functions are important for survival. In Gram-negative bacteria, envelope integrity during stress is maintained by the multi-gene Psp response. Mycobacterium tuberculosis was thought to lack the Psp system since it encodes only pspA and no other psp ortholog. Intriguingly, pspA maps downstream from clgR, which encodes a transcription factor regulated by the MprAB-σE envelope-stress-signaling system. clgR inactivation lowered ATP concentration during stress and protonophore treatment-induced clgR-pspA expression, suggesting that these genes express Psp-like functions. We identified a four-gene set – clgR, pspA (rv2744c), rv2743c, rv2742c – that is regulated by clgR and in turn regulates ClgR activity. Regulatory and protein–protein interactions within the set and a requirement of the four genes for functions associated with envelope integrity and surface-stress tolerance indicate that a Psp-like system has evolved in mycobacteria. Among Actinobacteria, the four-gene module occurred only in tuberculous mycobacteria and was required for intramacrophage growth, suggesting links between its function and mycobacterial virulence. Additionally, the four-gene module was required for MprAB-σE stress-signaling activity. The positive feedback between envelope-stress-sensing and envelope-preserving functions allows sustained responses to multiple, envelope-perturbing signals during chronic infection, making the system uniquely suited to tuberculosis pathogenesis.
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    Understanding Collective Cell Motility From Modulated Single-Cell Motility Cycles
    (2022-04-21) Deng, Youyuan; Igoshin, Oleg; Levine, Herbert
    Mechanical signals are believed to play a major role in organizing the collective motility of epithelial cell clusters on a substrate. A number of experimental observations in these systems await a comprehensive explanation: the interior is tensile even for clusters that expand by proliferation; the tractions on the substrate are often confined to the cluster edges; mechanical waves can propagate within the cluster; cells can spontaneously fill an annulus by proliferation and initiate unidirectional rotation around it; cell clusters can durotax much more efficiently than individual cells. We formulate a mechanical model to examine these effects. We include cell motility cycles comprised of active contraction and protrusion, and use a molecular clutch picture allowing “stalling” --- inhibition of cell contraction by external forces. By attaching cells to the substrate and to each other, and taking into account contact inhibition of locomotion, we obtain a simple picture underlying many of these findings.