We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byOswald Caldwell
Modified about 1 year ago
Inferring regulatory networks from spatial and temporal gene expression patterns Y. Fomekong Nanfack¹, Boaz Leskes¹, Jaap Kaandorp¹ and Joke Blom² ¹) Section Computational Science, University of Amsterdam, Kruislaan 403, NL-1098 SJ Amsterdam ²)Center for Mathematics and Computer Science (CWI), Kruislaan 413, NL- 1098 SJ Amsterdam 5. Parameter Optimization Real expression data procured the quantitative value of the concentration of a number of proteins in a large number of nuclei cells at a number of different developmental time. It is a main issue to have a set of parameters for Equation 4.1 that gives the closest possible fit to the real expression data. The optimization process allows to find such parameters. We are using a simulated annealing approach to find the value of these parameters. The simulated annealing used is based on the Lam schedule cooling temperature  which is accurate and rapid as it decreases the temperature after every move (Equation 5.1) The Cost function, which evaluates the quality of the current solution used is the least squares means of the deviation of the solution of the expression data. (Equation 5.2). Fig 6.1. 5-genes network simulation. Left: Simulation of Eq 4.1 from a set of target initial parameters. Right: simulation of Eq 4.1 with parameters obtain from optimization 3. Acquisition of quantitative Data From an image of a Drosophila embryo stained for three different genes, we construct a binary nuclear mask and find the protein concentration of each gene within all nuclei. The binary nuclear mask is an image in which all the different nuclei have been isolated by an image segmentation and edge detection. The nuclei contour is found mainly by a watershed mathematical morphology operation. The resulting image is the nuclei without overlap. 2. Early development of Drosophila embryo Here, we briefly discuss the body plan formation of Drosophila embryo. Fig 2.1 shows the basic outline of the anteroposterior (A/P) axis regulatory hierarchical network of genetic interactions that together regulate the process of development. In our model we simulate the formation of the second stripe eve 2 (Fig 2.2) 1. Introduction Regulatory networks play a fundamental role in many biological processes. In this project we aim to develop a model for simulating regulatory networks that are capable of quantitatively reproducing spatial and temporal expression patterns in developmental processes (case studies: Drosophila and sponges) An important option for the analysis of regulatory control systems are simulation models in combination with an optimization algorithm, due to the uncertainty value of parameters. Here we present a model based approach for inferring networks from spatial and temporal expression patterns. 6. Discussion The model presented here is capable of reproducing the formation of eve stripe 2 in Drosophila in 2D (Fig. 4.1) and support 3D. We need to improve our model and find an answer to the non- uniqueness problem. So far only artificial data have been used and it is important to move to realistic biological data. Thus, in the data acquisition process, we’ll need to integrate different images stained for 3 genes to obtain quantitative data for networks with more than 3 genes and used a more appropriate cost function in the optimization process. References 1) S.B Carroll & al: From DNA to diversity: molecular genetics and the evolution of animal design (2001) 2) E. Mjolness & al: Connectionist model of development. J. Theo Biol 152 (1991) 429-453 3) J. Reinitz & al: Mechanism of eve stripe formation. Mechanism of Development 49 (1995) 133-158 4) Krul & al: Modelling Developmental Regulatory Networks. In Proceedings of Computational Science - ICCS 2003, 5) J. Lam & J.M. Deslome: An efficient simulated annealing: Derivation. Technical report 8816. Yale Electrical Engineering Department (1988a) Fig 2.1. left: different stages of development of five classes of A-P axis genes. Right: selected members of the regulatory network © 2001 by S.B Caroll Fig 5.1 Construction of a binary nuclei mask. Above: left, an original image of a drosophila embryo stained for three genes. Right, the final nuclear mask after segmentation. Below a crop of the binary nuclear mask where we can see the isolated nuclei. Fig 5.2 This figures gives the gene concentration along the A-P axis of the drosophila image in figure 5.1. This is obtain by quantifying the pixel value in each nuclei for all the 3 channels. The Bicoid is concentrate on the left and Caudal on the right (see Fig 2.2, b) 4. Mathematical Model The model is a generalization of the standard connectionist model used for modeling genetic interaction [2, 3]. It assumes that three basic processes govern gene product concentration: Fig 4.1 concentration gradients for a 6-genes network. This 3D graph shows that our model is capable of reproducing the spatial temporal expression patterns in 2D Fig 2.2. Illustration of the Eve stripe 2 formation. Left: formation of the eve stripe 2 controlled by the gene eve-skipped. Right: location of Bicoid, Hunchback (who activate eve) and Giant and Krüppel (repress eve). © 2001 by S.B Caroll Model Abstraction: here we decouple in time the regulation and diffusion. The genetic regulation is modelled by a set of differential equation (eq. 4.1) in which protein can activate/inhibit each other, and diffuse between the cells. Cells are discrete and allow us to support the general case in two or three dimensions and integrate migrating cells. In Fig 6.1, the two plots illustrate the simulation of the model described by equation 4.1. The left figure gives the solution from an initial set of parameters considered as the target. The right figure illustrates the simulation from a given set of parameters optimized. A close look at the parameter W from the model and the data (see yellow box) shows that in some cases, there is a significant difference between the values. From this observation, it is interesting to see if the non- uniqueness of the parameters solution comes from the robustness of the model. Acknowledgement This project (6184.108.40.206), is part of the Computational Life Science’s project and is financed by the NWO as 3D-RegNet : simulation of developmental regulatory networks Research team is composed as follow: PhD Students: Yves Fomekong Nanfack (UvA) & Maksat Ashrylaliyev (CWI). Staff: Jaap Kaandorp (UvA), Joke Blom (CWI), Peter Sloot (UvA) & Werner Müller (Johanes Gutenberg Universität-Mainz) website: http://www.science.uva.nl/research/scs/3D-RegNet/
9.17 Generalized model of Drosophila anterior-posterior pattern formation (Part 1)
February 06 Developmental biology: Drosophila segmentation and repeated units 1 * egg: generate the system * larva: eat and grow * pupa: structures in.
Systems Biology of Pattern Formation, Canalization and Transcription in the Drosophila Blastoderm John Reinitz STAT Applied Math Retreat Gleacher Center.
Gene regulation Ch 8 pp What will I learn? THAT; 1.Gene regulation is at the heart of development 2.The most important part of a gene is its.
MSc GBE Course: Genes: from sequence to function Brief Introduction to Systems Biology Sven Bergmann Department of Medical Genetics University of Lausanne.
Development and Genes Part 1. 2 Development is the process of timed genetic controlled changes that occurs in an organism’s life cycle. Mitosis Cell differentiation.
Comparison of methods for reconstruction of models for gene expression regulation A.A. Shadrin 1, *, I.N. Kiselev, 1 F.A. Kolpakov 2,1 1 Technological.
Lecture 10 Gene Control in Development Cell type specification Development of an organism Reading: Chapter 11:471-2 Chapter 15.1; 15.3; 15.4 Chapter 22.2.
Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae Vu, T. T.,
Announcements Exam this Wednesday: my “half” is 40%. Gerry Prody’s “half” is 60%. Exam regrade policy: if you have a question about how I graded an answer,
Robustness in biology Eörs Szathmáry Eötvös University Collegium Budapest.
Modelisation and Dynamical Analysis of Genetic Regulatory Networks Claudine Chaouiya Denis Thieffry LGPD Laboratoire de Génétique et Physiologie du Développement.
Anterior-posterior patterning in Drosophila. The fly body plan: Each segment has a unique identity and produces distinct structures 3 head 3 thorax 8.
Chapter 9 - Axis specification in Drosophila Drosophila genetics is the groundwork for _______________l genetics Cheap, easy to breed and maintain Drosophila.
Extraction and comparison of gene expression patterns from 2D RNA in situ hybridization images BIOINFORMATICS Gene expression Vol. 26, no. 6, 2010, pages.
Pedigree Analysis & Developmental Genetics. The Story of ‘Eve’ RThis example illustrates why gene regulation is fundamental to development RThe Players.
Network Morphospace Andrea Avena-Koenigsberger, Joaquin Goni Ricard Sole, Olaf Sporns Tung Hoang Spring 2015.
Applications of Visualization and Data Clustering to 3D Gene Expression Data Oliver Rübel 1,2,3,7, Gunther H. Weber 3,7, Min-Yu Huang 1,7, E. Wes Bethel.
I can’t wait to grow up! Laugh now.
1. Elements of the Genetic Algorithm Genome: A finite dynamical system model as a set of d polynomials over 2 (finite field of 2 elements) Fitness.
Image Analysis Phases Image pre-processing –Noise suppression, linear and non-linear filters, deconvolution, etc. Image segmentation –Detection of objects.
Guo-Wei. Wei 1 Department of Mathematics, Michigan State University, East Lansing, MI 48824, US High Order Geometric and Potential Driving PDEs for Image.
Computing correspondences in order to study spatial and temporal patterns of gene expression Charless Fowlkes UC Berkeley, Computer Science.
Chapter 21: The Genetic Basis of Development Model organisms for study of development.
CONCEPT 18.4: A PROGRAM OF DIFFERENTIAL GENE EXPRESSION LEADS TO THE DIFFERENT CELL TYPES IN A MULTICELLULAR ORGANISM.
Gaussian Processes for Transcription Factor Protein Inference Neil D. Lawrence, Guido Sanguinetti and Magnus Rattray.
BsysE595 Lecture Basic modeling approaches for engineering systems – Summary and Review Shulin Chen January 10, 2013.
Chapter 16 The Genetic Basis of Development. Determination is a multistep process Pluripotent embryonic cells MesodermEctoderm Nerve cells Skin cells.
Flies are quick!. The fly body plan: each segment has a unique identity and produces distinctive structures 3 head 3 thorax 8 abdomen.
Robustness analysis and tuning of synthetic gene networks February 15, 2008 Eyad Lababidi Based on the paper “Robustness analysis and tuning of synthetic.
Embryonic Development & Cell Differentiation. During embryonic development, a fertilized egg gives rise to many different cell types Cell types are organized.
UNDERSTANDING DYNAMIC BEHAVIOR OF EMBRYONIC STEM CELL MITOSIS Shubham Debnath 1, Bir Bhanu 2 Embryonic stem cells are derived from the inner cell mass.
© 2011 Pearson Education, Inc. Ch 21 Introduction How does a single fertilized egg cell develop into an embryo and then into a baby and eventually an adult?
MATH 499 VIGRE Seminar: Mathematical Models in Developmental Biology Drosophila development 101.
SCB : 1 Department of Computer Science Simulation and Complexity SCB : Simulating Complex Biosystems Susan Stepney Department of Computer Science Leo Caves.
Some physical aspects of embryogenesis Ana Hočevar Department of Theoretical Physics (F-1) “Jožef Stefan” Institute Adviser: doc. dr. Primož Ziherl January.
MCDB 4650 Developmental Genetics in Drosophila. In the mature Drosophila egg before fertilization a) Neither the A-P nor the D-V polarity of the future.
Boyce/DiPrima 10 th ed, Ch 10.5: Separation of Variables; Heat Conduction in a Rod Elementary Differential Equations and Boundary Value Problems, 10 th.
Embryonic Development Timing and coordination of gene activation.
The Hardwiring of development: organization and function of genomic regulatory systems Maria I. Arnone and Eric H. Davidson.
BE/APh161 – Physical Biology of the Cell Rob Phillips Applied Physics and Bioengineering California Institute of Technology.
Vegetation Science Lecture 4 Non-Linear Inversion Lewis, Disney & Saich UCL.
Embryonic development OvumFertilised ovum Cell Division.
欢迎随时提问、切磋！ How does a simple cell turn into a complicated organism? How do genes coordinate and orchestrate the body planning?
The Helmholtz Machine P Dayan, GE Hinton, RM Neal, RS Zemel Computational Modeling of Intelligence (Fri) Summarized by Joon Shik Kim.
Introduction to molecular networks Sushmita Roy BMI/CS 576 Nov 6 th, 2014.
Modeling the Gene Expression of Saccharomyces cerevisiae Δcin5 Under Cold Shock Conditions Kevin McKay Laura Terada Department of Biology Loyola Marymount.
CompuCell Software Current capabilities and Research Plan Rajiv Chaturvedi Jesús A. Izaguirre With Patrick M. Virtue.
Assignment for the course: “Introduction to Statistical Thermodynamics of Soft and Biological Matter” Dima Lukatsky In the first.
© 2017 SlidePlayer.com Inc. All rights reserved.