On Cellular Automaton Approaches to Modeling Biological Cells
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Neurogenic Decisions Require a Cell Cycle Independent Function of The
RESEARCH ARTICLE Neurogenic decisions require a cell cycle independent function of the CDC25B phosphatase Fre´ de´ ric Bonnet1†, Angie Molina1†, Me´ lanie Roussat1, Manon Azais2, Sophie Bel-Vialar1, Jacques Gautrais2, Fabienne Pituello1*, Eric Agius1* 1Centre de Biologie du De´veloppement, Centre de Biologie Inte´grative, Universite´ de Toulouse, CNRS, UPS, Toulouse, France; 2Centre de Recherches sur la Cognition Animale, Centre de Biologie Inte´grative., Universite´ de Toulouse, CNRS, UPS, Toulouse, France Abstract A fundamental issue in developmental biology and in organ homeostasis is understanding the molecular mechanisms governing the balance between stem cell maintenance and differentiation into a specific lineage. Accumulating data suggest that cell cycle dynamics play a major role in the regulation of this balance. Here we show that the G2/M cell cycle regulator CDC25B phosphatase is required in mammals to finely tune neuronal production in the neural tube. We show that in chick neural progenitors, CDC25B activity favors fast nuclei departure from the apical surface in early G1, stimulates neurogenic divisions and promotes neuronal differentiation. We design a mathematical model showing that within a limited period of time, cell cycle length modifications cannot account for changes in the ratio of the mode of division. Using a CDC25B point mutation that cannot interact with CDK, we show that part of CDC25B activity is independent *For correspondence: of its action on the cell cycle. [email protected] (FP); [email protected] (EA) †These authors contributed equally to this work Introduction In multicellular organisms, managing the development, homeostasis and regeneration of tissues Competing interests: The requires the tight control of self-renewal and differentiation of stem/progenitor cells. -
Proteomic and Bioinformatic Investigation of Altered Pathways in Neuroglobin-Deficient Breast Cancer Cells
molecules Article Proteomic and Bioinformatic Investigation of Altered Pathways in Neuroglobin-Deficient Breast Cancer Cells Michele Costanzo 1,2 , Marco Fiocchetti 3 , Paolo Ascenzi 3, Maria Marino 3 , Marianna Caterino 1,2,* and Margherita Ruoppolo 1,2,* 1 Department of Molecular Medicine and Medical Biotechnology, School of Medicine, University of Naples Federico II, 80131 Naples, Italy; [email protected] 2 CEINGE—Biotecnologie Avanzate S.C.Ar.L., 80145 Naples, Italy 3 Department of Science, University Roma Tre, 00146 Rome, Italy; marco.fi[email protected] (M.F.); [email protected] (P.A.); [email protected] (M.M.) * Correspondence: [email protected] (M.C.); [email protected] (M.R.) Abstract: Neuroglobin (NGB) is a myoglobin-like monomeric globin that is involved in several processes, displaying a pivotal redox-dependent protective role in neuronal and extra-neuronal cells. NGB remarkably exerts its function upon upregulation by NGB inducers, such as 17β-estradiol (E2) and H2O2. However, the molecular bases of NGB’s functions remain undefined, mainly in non- neuronal cancer cells. Human MCF-7 breast cancer cells with a knocked-out (KO) NGB gene obtained using CRISPR/Cas9 technology were analyzed using shotgun label-free quantitative proteomics in comparison with control cells. The differential proteomics experiments were also performed after treatment with E2, H2O2, and E2 + H2O2. All the runs acquired using liquid chromatography–tandem mass spectrometry were elaborated within the same MaxQuant analysis, leading to the quantification Citation: Costanzo, M.; Fiocchetti, of 1872 proteins in the global proteomic dataset. Then, a differentially regulated protein dataset was M.; Ascenzi, P.; Marino, M.; Caterino, M.; Ruoppolo, M. -
Increased Processing of SINE B2 Ncrnas Unveils a Novel Type Of
RESEARCH ARTICLE Increased processing of SINE B2 ncRNAs unveils a novel type of transcriptome deregulation in amyloid beta neuropathology Yubo Cheng1,2,3,4†, Luke Saville1,2,3,4†, Babita Gollen1,2,3,4†, Christopher Isaac1,2,3,4†, Abel Belay1,2,3,4, Jogender Mehla3, Kush Patel1,2,3, Nehal Thakor1,2,3, Majid H Mohajerani3, Athanasios Zovoilis1,2,3,4* 1Department of Chemistry and Biochemistry, University of Lethbridge, Lethbridge, Canada; 2Southern Alberta Genome Sciences Centre, University of Lethbridge, Lethbridge, Canada; 3Canadian Centre for Behavioral Neuroscience, University of Lethbridge, Lethbridge, Canada; 4Alberta RNA Research and Training Institute, University of Lethbridge, Lethbridge, Canada Abstract The functional importance of many non-coding RNAs (ncRNAs) generated by repetitive elements and their connection with pathologic processes remains elusive. B2 RNAs, a class of ncRNAs of the B2 family of SINE repeats, mediate through their processing the transcriptional activation of various genes in response to stress. Here, we show that this response is dysfunctional during amyloid beta toxicity and pathology in the mouse hippocampus due to increased levels of B2 RNA processing, leading to constitutively elevated B2 RNA target gene expression and high Trp53 levels. Evidence indicates that Hsf1, a master regulator of stress response, mediates B2 RNA *For correspondence: processing in hippocampal cells and is activated during amyloid toxicity, accelerating the [email protected] processing of SINE RNAs and gene hyper-activation. Our study reveals that in mouse, SINE RNAs † constitute a novel pathway deregulated in amyloid beta pathology, with potential implications for These authors contributed equally to this work similar cases in the human brain, such as Alzheimer’s disease (AD). -
Genetic Algorithms and Their Application to In-Silico Evolution of Genetic Regulatory Networks
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of Hertfordshire Research Archive Genetic Algorithms and their Application to in-silico Evolution of Genetic Regulatory Networks Johannes F. Knabe‡†, Katja Wegner†, Chrystopher L. Nehaniv‡†, and Maria J. Schilstra†* †Biological and Neural Computation Laboratory, and ‡Adaptive Systems Research Group, STRI, University of Hertfordshire, Hatfield, AL10 9AB United Kingdom *To whom correspondence should be addressed ([email protected]) i. Abstract A genetic algorithm (GA) is a procedure that mimics processes occurring in Darwinian evolution to solve computational problems. A GA introduces variation through “mutation” and “recombination” in a “population” of possible solutions to a problem, encoded as strings of characters in “genomes”, and allows this population to evolve, using selection procedures that favor the gradual enrichment of the gene pool with the genomes of the “fitter” individuals. GA’s are particularly suitable for optimization problems in which an effective system design or set of parameter values is sought. In nature, genetic regulatory networks (GRN’s) form the basic control layer in the regulation of gene expression levels. GRN’s are composed of regulatory interactions between genes and their gene products, and are, inter alia, at the basis of the development of single fertilized cells into fully grown organisms. This paper describes how GA’s may be applied to find functional regulatory schemes and parameter values for models that capture the fundamental GRN characteristics. The central ideas behind evolutionary computation and GRN modeling, and the considerations in GA design and use are discussed, and illustrated with an extended example. -
Self-Reproduction Based on Turing Machines
MEASURES OF ORGANIZATION IN A MODEL OF CELLULAR SELF-REPRODUCTION BASED ON TURING MACHINES WALTER R. STAHL OREGON REGIONAL PRIMATE RESEARCH CENTER BEAVERTON, OREGON 1. Introductory summary The late John von Neumann first realized that self-reproduction, as observed in biological systems, was a legitimate problem of mathematics, specifically of automata and algorithm theory. He founded the study of self-reproduction in systems of "natural automata." This report deals with the self-organizing prop- erties of a new type of cellular self-reproduction model. The action of enzymes is simulated by Turing machines acting as molecular automata or computers, with their highly standardized coding corresponding to genes of the cell. Com- puter experiments have been done to test the stability and logical homeostatic properties of a 36 gene cell model. It is shown that the system will continue to organize itself and reproduce in spite of a variety of environmental deficiencies and disturbances, and also to repair itself after certain kinds of injury. The most pertinent organizational criteria for the described model are amounts of "energy" and "time cycles" needed to reach maturity and reproduce. Comparisons are drawn between these organizational measures and thermodynamic informa- tional parameters such as Gibbsean chemical potential and entropy. The total amount of genetic and cellular information can be quantified in the cell model and this helps clarify some confusing aspects of genetic information measures. The model is highly idealized. It bears somewhat the same relationship to real cells as computer circuits do to the brain, but shows that automata theory can be applied to molecular biology in a meaningful way. -
Metabolic Network Modeling with Model Organisms
Available online at www.sciencedirect.com ScienceDirect Metabolic network modeling with model organisms L Safak Yilmaz and Albertha JM Walhout Flux balance analysis (FBA) with genome-scale metabolic reconstruct a genome-scale metabolic network model network models (GSMNM) allows systems level predictions of (GSMNM), which is then used to calculate the flux metabolism in a variety of organisms. Different types of distribution over the entire network in any defined con- predictions with different accuracy levels can be made dition for the organism (see below). For model organisms, depending on the applied experimental constraints ranging high-quality genomic annotations allow the reconstruc- from measurement of exchange fluxes to the integration of tion of comprehensive GSMNMs that can be parame- gene expression data. Metabolic network modeling with model terized and validated with publically available experi- organisms has pioneered method development in this field. In mental datasets. In addition, since many properties of addition, model organism GSMNMs are useful for basic metabolic networks are conserved across taxa, model understanding of metabolism, and in the case of animal organism GSMNMs can be used to study human disease models, for the study of metabolic human diseases. Here, we with animal models, while plant models can instruct discuss GSMNMs of most highly used model organisms with agriculture. the emphasis on recent reconstructions. Here, we first summarize the basics of genome-scale meta- Address bolic network modeling, and then explore GSMNMs Program in Systems Biology, Program in Molecular Medicine, University and their applications in common model organisms [3] of Massachusetts Medical School, Worcester, MA, United States (Figure 1). -
Multicellular Models Bridging Intracellular Signaling and Gene Transcription to Population Dynamics
processes Article Multicellular Models Bridging Intracellular Signaling and Gene Transcription to Population Dynamics Mohammad Aminul Islam 1,† , Satyaki Roy 2,†, Sajal K. Das 2 and Dipak Barua 1,* 1 Department of Chemical and Biochemial Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA; [email protected] 2 Department of Computer Science, Missouri University of Science and Technology, Rolla, MO 65409, USA; [email protected] (S.R.); [email protected] (S.K.D.) * Correspondence: [email protected]; Tel.: +1-573-341-7560 † These authors contributed equally to this work. Received: 26 July 2018; Accepted: 31 October 2018; Published: 4 Novemeber 2018 Abstract: Cell signaling and gene transcription occur at faster time scales compared to cellular death, division, and evolution. Bridging these multiscale events in a model is computationally challenging. We introduce a framework for the systematic development of multiscale cell population models. Using message passing interface (MPI) parallelism, the framework creates a population model from a single-cell biochemical network model. It launches parallel simulations on a single-cell model and treats each stand-alone parallel process as a cell object. MPI mediates cell-to-cell and cell-to-environment communications in a server-client fashion. In the framework, model-specific higher level rules link the intracellular molecular events to cellular functions, such as death, division, or phenotype change. Cell death is implemented by terminating a parallel process, while cell division is carried out by creating a new process (daughter cell) from an existing one (mother cell). We first demonstrate these capabilities by creating two simple example models. In one model, we consider a relatively simple scenario where cells can evolve independently. -
Multisite Mechanisms for Ultrasensitivity in Signal Transduction
Noname manuscript No. (will be inserted by the editor) Multisite Mechanisms for Ultrasensitivity in Signal Transduction Germ´an A. Enciso the date of receipt and acceptance should be inserted later Abstract One of the key aspects in the study of cellular communication is understanding how cells receive a continuous input and transform it into a discrete, all-or-none output. Such so-called ultrasensitive dose responses can also be used in a variety of other contexts, from the efficient transport of oxygen in the blood to the regulation of the cell cycle and gene expression. This chapter provides a self contained mathematical review of the most important molecular models of ultrasensitivity in the literature, with an emphasis on mechanisms involving multisite modifications. The models described include two deeply influential systems based on allosteric behavior, the MWC and the KNF models. Also included is a description of more recent work by the author and colleagues of novel mechanisms using alternative hypotheses to create ultrasensitive behavior. Keywords Systems Biology · Ultrasensitivity · Allostery · Cooperativity · Signal Transduction 1 Introduction: Ultrasensitive Dose Responses Chemical reaction networks (CRN) lie at the heart of many biochemical pro- cesses inside the cell. They have been extensively modeled to understand the behavior of specific systems, and they have also been systematically studied at the theoretical level [5,14,21]. Although often implicitly assumed to con- verge globally towards a unique equilibrium in chemical engineering and other applications, CRNs can have exceedingly complex dynamical behavior. More- over, many biological systems have arguably evolved towards precisely such relatively rare complex examples, driven by a need to exhibit behaviors such as oscillations (e.g. -
Integrated Approach for Identifying the Molecular, Cellular, and Host Responses to Chemical Insults
Integrated Approach for Identifying the Molecular, Cellular, and Host Responses to Chemical Insults Audrey E. Fischer, Emily P. English, Julia B. Patrone, Kathlyn Santos, Jody B. G. Proescher, Rachel S. Quizon, Kelly A. Van Houten, Robert S. Pilato, Eric J. Van Gieson, and Lucy M. Carruth e performed a pilot study to characterize the molecular, cellular, and whole-organism response to nonlethal chemical agent exposure in the central nervous system. Multiple methodologies were applied to measure in vitro enzyme inhibition, neuronal cell pathway signaling, and in vivo zebrafish neural development in response to challenge with two different classes of chemical compounds. While all compounds tested exhibited expected enzyme inhibitory activity against acetylcholinesterase (AChE), a well-characterized target of chemical agents, distinct differences between chemical exposures were detected in cellular toxicity and pathway target responses and were tested in a zebrafish model. Some of these differences have not been detected using conventional chemical toxicity screening methods. Taken together, the data demonstrate the potential value of an integrated, multimethodological approach for improved target and pathway identification for subsequent diagnostic and therapeutic biomarker development. INTRODUCTION To build capability and leverage new and growing cell models to complete living organisms. Regardless of biology and chemistry expertise at APL, a collabora- the model selected, challenges exist in sample collection, tive, cross-departmental effort was established through a dose determination, and biases inherent in each assay/ series of related independent research and development technology. Therefore, multiple experimental methodol- (IR&D) projects. The focus of this effort was on mitiga- ogies brought to bear on a particular biological question tion of chemical and biological threat agents. -
Evolution and Analysis of Genetic Networks for Stable Cellular Growth and Regeneration
Honda Research Institute Europe GmbH https://www.honda-ri.de/ Evolution and Analysis of Genetic Networks for Stable Cellular Growth and Regeneration Lisa Schramm, Yaochu Jin, Bernhard Sendhoff 2012 Preprint: This is an accepted article published in Artificial Life. The final authenticated version is available online at: https://doi.org/[DOI not available] Powered by TCPDF (www.tcpdf.org) Evolution and Analysis of Genetic Lisa Schramm** Networks for Stable Cellular Technische Universität Darmstadt Growth and Regeneration Yaochu Jin† University of Surrey ‡ Bernhard Sendhoff*, Honda Research Institute Europe Abstract A computational model is presented that simulates stable growth of cellular structures that are in some cases capable of regeneration. In the model, cellular growth is governed by a Keywords gene regulatory network. By evolving the parameters and structure Cellular growth, development, of the genetic network using a modified evolution strategy, a regeneration, stability, gene regulatory network, motifs, evolutionary strategy dynamically stable state can be achieved in the developmental process, where cell proliferation and cell apoptosis reach an A version of this paper with color figures is equilibrium. The results of evolution with different setups in available online at http://dx.doi.org/10.1162/ fitness evaluation during the development are compared with artl_a_00075. Subscription required. respect to their regeneration capability as well as their gene regulatory network structure. Network motifs responsible for stable growth and regeneration that emerged from the evolution are also analyzed. We expect that our findings can help to gain a better understanding of the process of growth and regeneration inspired by biological systems, in order to solve complex engineering problems, such as the design of self-healing materials. -
A Cellular Potts Model of Single Cell Migration in Presence of Durotaxis Rachele Allena, Marco Scianna, Luigi Preziosi
A Cellular Potts Model of single cell migration in presence of durotaxis Rachele Allena, Marco Scianna, Luigi Preziosi To cite this version: Rachele Allena, Marco Scianna, Luigi Preziosi. A Cellular Potts Model of single cell migration in presence of durotaxis. Mathematical Biosciences, 2016, pp.57-70. hal-02375906 HAL Id: hal-02375906 https://hal.archives-ouvertes.fr/hal-02375906 Submitted on 22 Nov 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. A Cellular Potts Model of single cell migration in presence of durotaxis ∗ R. Allena a, , M. Scianna b, L. Preziosi b a Arts et Metiers ParisTech, LBM/Institut de Biomecanique Humaine Georges Charpak, 151 bd de l’Hopital, 75013 Paris, France b Dipartimento di Scienze Mathematiche, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy a b s t r a c t Cell migration is a fundamental biological phenomenon during which cells sense their surroundings and respond to different types of signals. In presence of durotaxis, cells preferentially crawl from soft to stiff substrates by reorganizing their cytoskeleton from an isotropic to an anisotropic distribution of actin fil- aments. In the present paper, we propose a Cellular Potts Model to simulate single cell migration over flat substrates with variable stiffness. -
Hypoxia Triggers Collective Aerotactic Migration in Dictyostelium Discoideum
RESEARCH ARTICLE Hypoxia triggers collective aerotactic migration in Dictyostelium discoideum Olivier Cochet-Escartin1†*, Mete Demircigil2†, Satomi Hirose3,4, Blandine Allais1, Philippe Gonzalo5,6, Ivan Mikaelian5, Kenichi Funamoto3,4,7, Christophe Anjard1, Vincent Calvez2†*, Jean-Paul Rieu1†* 1Institut Lumie`re Matie`re, UMR5306, Universite´ Lyon 1-CNRS, Universite´ de Lyon, Villeurbanne, France; 2Institut Camille Jordan, UMR5208, Universite´ Lyon 1-CNRS, Universite´ de Lyon, Villeurbanne, France; 3Graduate School of Biomedical Engineering, Tohoku University, Sendai, Japan; 4Institute of Fluid Science, Tohoku University, Sendai, Japan; 5Centre Le´on Be´rard, Centre de recherche en cance´rologie de Lyon, INSERM 1052, CNRS 5286, Universite´ Lyon 1, Universite´ de Lyon, Lyon, France; 6Laboratoire de Biochimie et Pharmacologie, Faculte´ de me´decine de Saint-Etienne, CHU de Saint-Etienne, Saint-Etienne, France; 7Graduate School of Engineering, Tohoku University, Sendai, Japan Abstract Using a self-generated hypoxic assay, we show that the amoeba Dictyostelium discoideum displays a remarkable collective aerotactic behavior. When a cell colony is covered, cells quickly consume the available oxygen (O2) and form a dense ring moving outwards at constant speed and density. To decipher this collective process, we combined two technological *For correspondence: developments: porphyrin-based O2 -sensing films and microfluidic O2 gradient generators. We olivier.cochet-escartin@univ-lyon1. showed that Dictyostelium cells exhibit aerotactic and aerokinetic response in a low range of O2 fr (OC-E); concentration indicative of a very efficient detection mechanism. Cell behaviors under self- [email protected] (VC); generated or imposed O2 gradients were modeled using an in silico cellular Potts model built on [email protected] (J-PR) experimental observations.