MINISYMPOSIUM LOGICAL MODELLING OF (MULTI)CELLULAR NETWORKS

Similar documents
Synchronous state transition graph

arxiv: v1 [cs.dm] 13 Nov 2014

Networks in systems biology

Simulation of Gene Regulatory Networks

Logical modelling of cellular decisions

Logical modelling: Inferring structure from dynamics

arxiv: v1 [q-bio.mn] 2 Aug 2018

Developmental genetics: finding the genes that regulate development

Biological networks CS449 BIOINFORMATICS

Logic-Based Modeling in Systems Biology

Modelling the cell cycle regulatory network

Positive circuits and d-dimensional spatial differentiation: Application to the formation of sense organs in Drosophila

Level 3 Proposals/Qualitative Models

Unicellular: Cells change function in response to a temporal plan, such as the cell cycle.

GINsim: A software suite for the qualitative modelling, simulation and analysis of regulatory networks

Developmental Biology Lecture Outlines

Developmental Biology 3230 Midterm Exam 1 March 2006

Boolean models of gene regulatory networks. Matthew Macauley Math 4500: Mathematical Modeling Clemson University Spring 2016

Positive and negative cycles in Boolean networks

Classification of Random Boolean Networks

Types of biological networks. I. Intra-cellurar networks

Random Boolean Networks

Stability Depends on Positive Autoregulation in Boolean Gene Regulatory Networks

FUNDAMENTALS of SYSTEMS BIOLOGY From Synthetic Circuits to Whole-cell Models

Course plan Academic Year Qualification MSc on Bioinformatics for Health Sciences. Subject name: Computational Systems Biology Code: 30180

SCOTCAT Credits: 20 SCQF Level 7 Semester 1 Academic year: 2018/ am, Practical classes one per week pm Mon, Tue, or Wed

Qualitative Petri Net Modelling of Genetic Networks

Drosophila melanogaster- Morphogen Gradient

Cybergenetics: Control theory for living cells

purpose of this Chapter is to highlight some problems that will likely provide new

Towards a framework for multi-level modelling in Computational Biology

Cooperative development of logical modelling standards and tools with CoLoMoTo

New Computational Methods for Systems Biology

networks in molecular biology Wolfgang Huber

Introduction to Bioinformatics

Attractor computation using interconnected Boolean networks: testing growth rate models in E. Coli

Accepted Manuscript. Boolean Modeling of Biological Regulatory Networks: A Methodology Tutorial. Assieh Saadatpour, Réka Albert

MCDB 4777/5777 Molecular Neurobiology Lecture 29 Neural Development- In the beginning

Basins of Attraction, Commitment Sets and Phenotypes of Boolean Networks arxiv: v1 [math.ds] 26 Jul 2018

Classification of Random Boolean Networks

Basic Synthetic Biology circuits

Introduction to Bioinformatics

Hybrid Model of gene regulatory networks, the case of the lac-operon

Early Development in Invertebrates

Linear Models and Empirical Bayes Methods for Microarrays

Synthesis of Biological Models from Mutation Experiments

Biology 218, practise Exam 2, 2011

Mathematical Biology - Lecture 1 - general formulation

A&S 320: Mathematical Modeling in Biology

Petri net models. tokens placed on places define the state of the Petri net

Francisco M. Couto Mário J. Silva Pedro Coutinho

Evolutionary analysis of the well characterized endo16 promoter reveals substantial variation within functional sites

Introduction to Systems Biology

biological networks Claudine Chaouiya SBML Extention L3F meeting August

Basic modeling approaches for biological systems. Mahesh Bule

Discrete and Indiscrete Models of Biological Networks

Logical modelling of genetic regulatory networks Contents

Why Flies? stages of embryogenesis. The Fly in History

Complex Systems Theory

7.32/7.81J/8.591J: Systems Biology. Fall Exam #1

ACTA PHYSICA DEBRECINA XLVI, 47 (2012) MODELLING GENE REGULATION WITH BOOLEAN NETWORKS. Abstract

Rui Dilão NonLinear Dynamics Group, IST

Three different fusions led to three basic ideas: 1) If one fuses a cell in mitosis with a cell in any other stage of the cell cycle, the chromosomes

10-810: Advanced Algorithms and Models for Computational Biology. microrna and Whole Genome Comparison

Chaotic Dynamics in an Electronic Model of a Genetic Network

Written Exam 15 December Course name: Introduction to Systems Biology Course no

A New Kind of Language for Complex Engineering Systems:

Motivation. Evolution has rediscovered several times multicellularity as a way to build complex living systems

DYNAMIC MODELING OF BIOLOGICAL AND PHYSICAL SYSTEMS

Dynamical Systems in Biology

1. Supplemental figures and legends

Constraint and Contingency in Multifunctional Gene Regulatory Circuits

Chapter 1. Scientific Process and Themes of Biology

PRACTICE EXAM. 20 pts: 1. With the aid of a diagram, indicate how initial dorsal-ventral polarity is created in fruit fly and frog embryos.

Chapter 18 Regulation of Gene Expression

Mesoderm Induction CBT, 2018 Hand-out CBT March 2018

Network Biology: Understanding the cell s functional organization. Albert-László Barabási Zoltán N. Oltvai

Predictive Modeling of Signaling Crosstalk... Model execution and model checking can be used to test a biological hypothesis

Exam 1 ID#: October 4, 2007

Optimal State Estimation for Boolean Dynamical Systems using a Boolean Kalman Smoother

Correlation Networks

TUNING-RULES FOR FRACTIONAL PID CONTROLLERS

A Simple Protein Synthesis Model

Stochastic simulations

An introduction to SYSTEMS BIOLOGY

Computational Modelling in Systems and Synthetic Biology

Sorting Network Development Using Cellular Automata

Rigorous numerical computation of polynomial differential equations over unbounded domains

BINARY COUNTING WITH CHEMICAL REACTIONS

Neural development its all connected

Preface. Motivation and Objectives

BILD7: Problem Set. 2. What did Chargaff discover and why was this important?

SPA for quantitative analysis: Lecture 6 Modelling Biological Processes

Analog Electronics Mimic Genetic Biochemical Reactions in Living Cells

On the convergence of Boolean automata networks without negative cycles

A A A A B B1

Coexistence of Dynamics for Two- Dimensional Cellular Automata

Lecture 9: June 21, 2007

BIOINFORMATICS CS4742 BIOLOGICAL NETWORKS

Supplementary Materials for

Transcription:

Friday, July 27th, 11:00 MINISYMPOSIUM LOGICAL MODELLING OF (MULTI)CELLULAR NETWORKS Organizer Pedro T. Monteiro INESC-ID/IST - Univ. de Lisboa 1000-029 Lisboa, PT Pedro.Tiago.Monteiro@tecnico.ulisboa.pt Co-organizer Claudine Chaouiya Instituto Gulbenkian de Ciência 2780-156 Oeiras, PT chaouiya@igc.gulbenkian.pt Minisymposium Keywords: Boolean regulatory networks, Logical models, regulatory circuits, discrete dynamics Boolean and multilevel logical approaches are increasingly used to study the behaviours of biological regulatory networks, which govern essential cellular processes. Moreover, modellers can rely on a broad array of model definitions, simulation methods, computational algorithms and software tools. This mini-symposium aims at introducing these modelling approaches as well as discussing recent advances on both formal aspects and applications. It is organized in connection with the Consortium for Logical Modelling and Tools (CoLoMoTo, http://colomoto.org), which has been launched to promote the logical modelling framework and to provide scientists with dedicated standards, repositories and tools. First, an overview of the field will be presented, covering different model classes, current challenges and application ranges. Four speakers will then present their recent achievements. These will first concern the assessment of the dynamics relying on topological features namely regulatory circuits of the modelled regulatory networks. Appropriate abstractions of large asynchronous dynamics will be then discussed, as well as the concept of model sets. Finally, definition of multicellular logical models by specifying cell-cell communication rules will be illustrated through the dorsalventral axis specification during sea urchin embryogenesis. Acknowledgements: We received supported from Fundação Calouste Gulbenkian and from national funds through Fundação para a Ciência e a Tecnologia (FCT) with reference UID/CEC/50021/2013 and FCT project PTDC/EEI-CTP/2914/2014.

Friday, July 27th, 11:00 LOGICAL MODELLING OF CELLULAR NETWORKS, INTRODUCTION AND METHODOLOGICAL CHALLENGES Claudine Chaouiya chaouiya@igc.gulbenkian.pt Instituto Gulbenkian de Ciência Rua da Quinta Grande 6, 2780-156 Oeiras, PT Keywords: Regulatory networks, Logical modelling, Discrete dynamics. This introductory talk will provide the basics on the logical modelling formalism, covering some distinct variants. Logical models define discrete dynamics whose properties of interest will be introduced, including their counterparts for the biological networks under study. Distinctive features of signalling and regulatory networks amenable to such a qualitative approach will be discussed. For large regulatory networks, model definition and analysis are often hampered due to diverse sources of complexity. We will conclude by discussing how to tackle these issues, through methods that have been developed relying on diverse mathematical and computational approaches. Acknowledgements: CC acknowledges financial support from Fundação Calouste Gulbenkian.

Friday, July 27th, 11:20 LOCAL NEGATIVE CIRCUITS AND CYCLIC ATTRACTORS: A BOOLEAN SATISFIABILITY APPROACH Elisa Tonello Elisa.Tonello@nottingham.ac.uk School of Mathematical Sciences, University of Nottingham Nottingham, NG7 2RD, UK Keywords: Boolean networks, Negative regulatory circuits, Cyclic attractors, Boolean satisfiability problem. Negative circuits in Boolean networks are necessary for the system to display sustained oscillations. In dimension greater than 6, the negative circuits are not necessarily local. In this work we investigate the existence of Boolean maps with desired properties using Boolean satisfiability problems. We translate the absence of local negative circuits into an expression on n2 n variables. We then implement a necessary condition for the existence of a cyclic attractor. We verify with a satisfiability solver that, for networks of dimension less or equal to 5, a local negative circuit is required for sustained oscillations, and find new counterexamples for n 6.

Friday, July 27th, 11:40 BOOLEAN DYNAMICS OF COMPOUND REGULATORY CIRCUITS Elisabeth Remy elisabeth.remy@univ-amu.fr Institut de Mathématiques de Luminy, 13288 Marseille cedex 9, FR Keywords: Boolean networks, Regulatory circuits, Synchronous/asynchronous dynamics, Asymptotic behaviours. In biological regulatory networks represented in terms of signed, directed graphs, topological motifs such as circuits are known to play key dynamical roles. After reviewing established results on the roles of simple motifs, we present recent results on the impact of the addition of a short-cut in a regulatory circuit. More precisely, based on Boolean formalisation of regulatory graphs, we provide complete descriptions of the discrete dynamics of particular motifs, under the synchronous and asynchronous updating schemes. These motifs are made of a circuit of arbitrary length, combining positive and negative interactions in any sequence, and are including a short-cut, and hence a smaller embedded circuit. The asymptotical behavior of such motifs strongly depends on the coherence of the signs of the two involved circuits. Such results on the functionality of motifs allow to improve the dynamical analysis of large regulatory biological networks.

Friday, July 27th, 12:00 APPROXIMATING THE BEHAVIOR OF BOOLEAN NETWORKS Heike Siebert siebert@mi.fu-berlin.de DFG Research Center Matheon, Freie Universität Berlin Arnimallee 6, 14195 Berlin, DE Keywords: Boolean networks, Asynchronous dynamics, Invariant sets, Boolean model sets. One of the key advantages of Boolean models is their low complexity that allows for a more comprehensive analysis of the state space comparative to, e.g., ordinary differential equation (ODE) models. However, scalability is still an issue, in particular, when considering asynchronous update schemes that are often better suited for capturing the behaviour of biological systems and more comparable to ODE models. To tackle this issue, instead of considering the full state transition graph the model dynamics can be analysed on a coarser level. Rather than computing one trajectory step by step, sets of trajectories can be described by sequences of invariant sets finally leading to an attractor. In this talk, we will discuss some applications of this idea both in the context of single model analysis as well as for model sets.

Friday, July 27th, 12:20 LOGICAL MODELLING OF THE REGULATORY NETWORK GOVERNING DORSAL-VENTRAL AXIS SPECIFICATION IN THE SEA URCHIN P. LIVIDUS Denis Thieffry thieffry@ens.fr Institut de Biologie de l Ecole Normale Supérieure (IBENS) 46 Rue d Ulm, F-75005 Paris, FR Keywords: Developmental patterning, Logical modelling, Multicellular system. Located at the basis of the deuterostome branch, echinoderms occupy a unique position to study the regulatory networks governing embryo morphogenesis. The dorsal-ventral (D-V) axis specification in the sea urchin Paracentrotus lividus is controlled by various transcription factors, including two TGF-βs: Nodal and BMP2/4. However, the signalling network downstream of these key morphogens is not yet fully understood. To identify Nodal and BMP2/4 target genes, we have performed a systematic functional analysis using RNA sequencing and in situ hybridization screens. The analysis of these data enables to delineate novel interactions. To gain further insights into this developmental process, we have developed a predictive dynamical model of the corresponding signalling/regulatory network. More specifically, using a logical modelling framework, we account for the specification of three main ectodermal regions along the D-V axis (ventral, ciliary and dorsal ectoderm) in terms of specific marker gene expression patterns. In our model analysis, we first focused on the computation of stable states and on their reachability in single representative cells, depending on signalling inputs. Next, taking advantage of the software EpiLog (http://epilogtool.org), we have simulated grids of cells connected through signalling molecules. These model simulations correctly reproduces the wild-type pattern, as well as various reported embryo mutant phenotypes, including double Nodal mrna injections.