Browsing by Author "Bertolusso, Roberto"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Computational models of signaling processes in cells with applications: Influence of stochastic and spatial effects
    (2012) Bertolusso, Roberto; Kimmel, Marek
    The usual approach to the study of signaling pathways in biological systems is to assume that high numbers of cells and of perfectly mixed molecules within cells are involved. To study the temporal evolution of the system averaged over the cell population, ordinary differential equations are usually used. However, this approach has been shown to be inadequate if few copies of molecules and/or cells are present. In such situation, a stochastic or a hybrid stochastic/deterministic approach needs to be used. Moreover, considering a perfectly mixed system in cases where spatial effects are present can be an over-simplifying assumption. This can be corrected by adding diffusion terms to the ordinary differential equations describing chemical reactions and proliferation kinetics. However, there exist cases in which both stochastic and spatial effects have to be considered. We study the relevance of differential equations, stochastic Gillespie algorithm, and deterministic and stochastic reaction-diffusion models for the study of important biological processes, such as viral infection and early carcinogenesis. To that end we have developed two optimized libraries of C functions for R (r-project.org) to simulate biological systems using Petri Nets, in a pure deterministic, pure stochastic, or hybrid deterministic/stochastic fashion, with and without spatial effects. We discuss our findings in the terms of specific biological systems including signaling in innate immune response, early carcinogenesis and spatial spread of viral infection.
  • Thumbnail Image
    Item
    Dynamic Cross Talk Model of the Epithelial Innate Immune Response to Double-Stranded RNA Stimulation: Coordinated Dynamics Emerging from Cell-Level Noise
    (Public Library of Science, 2014) Bertolusso, Roberto; Tian, Bing; Zhao, Yingxin; Vergara, Leoncio; Sabree, Aqeeb; Iwanaszko, Marta; Lipniacki, Tomasz; Brasier, Allan R.; Kimmel, Marek
    We present an integrated dynamical cross-talk model of the epithelial innate immune reponse (IIR) incorporating RIG-I and TLR3 as the two major pattern recognition receptors (PRR) converging on the RelA and IRF3 transcriptional effectors. bioPN simulations reproduce biologically relevant gene-and protein abundance measurements in response to time course, gene silencing and dose-response perturbations both at the population and single cell level. Our computational predictions suggest that RelA and IRF3 are under auto- and cross-regulation. We predict, and confirm experimentally, that RIG-I mRNA expression is controlled by IRF7. We also predict the existence of a TLR3-dependent, IRF3-independent transcription factor (or factors) that control(s) expression of MAVS, IRF3 and members of the IKK family. Our model confirms the observed dsRNA dose-dependence of oscillatory patterns in single cells, with periods of 1-3 hr. Model fitting to time series, matched by knockdown data suggests that the NF-kB module operates in a different regime (with different coefficient values) than in the TNFa-stimulation experiments. In future studies, this model will serve as a foundation for identification of virus-encoded IIR antagonists and examination of stochastic effects of viral replication. Our model generates simulated time series, which reproduce the noisy oscillatory patterns of activity (with 1-3 hour period) observed in individual cells. Our work supports the hypothesis that the IIR is a phenomenon that emerged by evolution despite highly variable responses at an individual cell level.
  • Thumbnail Image
    Item
    Mathematical modelling reveals unexpected inheritance and variability patterns of cell cycle parameters in mammalian cells
    (Public Library of Science, 2019) Mura, Marzena; Feillet, Céline; Bertolusso, Roberto; Delaunay, Franck; Kimmel, Marek
    The cell cycle is the fundamental process of cell populations, it is regulated by environmental cues and by intracellular checkpoints. Cell cycle variability in clonal cell population is caused by stochastic processes such as random partitioning of cellular components to progeny cells at division and random interactions among biomolecules in cells. One of the important biological questions is how the dynamics at the cell cycle scale, which is related to family dependencies between the cell and its descendants, affects cell population behavior in the long-run. We address this question using a “mechanistic” model, built based on observations of single cells over several cell generations, and then extrapolated in time. We used cell pedigree observations of NIH 3T3 cells including FUCCI markers, to determine patterns of inheritance of cell-cycle phase durations and single-cell protein dynamics. Based on that information we developed a hybrid mathematical model, involving bifurcating autoregression to describe stochasticity of partitioning and inheritance of cell-cycle-phase times, and an ordinary differential equation system to capture single-cell protein dynamics. Long-term simulations, concordant with in vitro experiments, demonstrated the model reproduced the main features of our data and had homeostatic properties. Moreover, heterogeneity of cell cycle may have important consequences during population development. We discovered an effect similar to genetic drift, amplified by family relationships among cells. In consequence, the progeny of a single cell with a short cell cycle time had a high probability of eventually dominating the population, due to the heritability of cell-cycle phases. Patterns of epigenetic heritability in proliferating cells are important for understanding long-term trends of cell populations which are either required to provide the influx of maturing cells (such as hematopoietic stem cells) or which started proliferating uncontrollably (such as cancer cells).
  • Thumbnail Image
    Item
    Mutation, drift and selection in single-driver hematologic malignancy: Example of secondary myelodysplastic syndrome following treatment of inherited neutropenia
    (Public Library of Science, 2019) Wojdyla, Tomasz; Mehta, Hrishikesh; Glaubach, Taly; Bertolusso, Roberto; Iwanaszko, Marta; Braun, Rosemary; Corey, Seth J.; Kimmel, Marek
    Cancer development is driven by series of events involving mutations, which may become fixed in a tumor via genetic drift and selection. This process usually includes a limited number of driver (advantageous) mutations and a greater number of passenger (neutral or mildly deleterious) mutations. We focus on a real-world leukemia model evolving on the background of a germline mutation. Severe congenital neutropenia (SCN) evolves to secondary myelodysplastic syndrome (sMDS) and/or secondary acute myeloid leukemia (sAML) in 30–40%. The majority of SCN cases are due to a germline ELANE mutation. Acquired mutations in CSF3R occur in >70% sMDS/sAML associated with SCN. Hypotheses underlying our model are: an ELANE mutation causes SCN; CSF3R mutations occur spontaneously at a low rate; in fetal life, hematopoietic stem and progenitor cells expands quickly, resulting in a high probability of several tens to several hundreds of cells with CSF3R truncation mutations; therapeutic granulocyte colony-stimulating factor (G-CSF) administration early in life exerts a strong selective pressure, providing mutants with a growth advantage. Applying population genetics theory, we propose a novel two-phase model of disease development from SCN to sMDS. In Phase 1, hematopoietic tissues expand and produce tens to hundreds of stem cells with the CSF3R truncation mutation. Phase 2 occurs postnatally through adult stages with bone marrow production of granulocyte precursors and positive selection of mutants due to chronic G-CSF therapy to reverse the severe neutropenia. We predict the existence of the pool of cells with the mutated truncated receptor before G-CSF treatment begins. The model does not require increase in mutation rate under G-CSF treatment and agrees with age distribution of sMDS onset and clinical sequencing data.