Approximate inference for stochastic dynamics in large biological networks
|
|
- Elmer Mosley
- 6 years ago
- Views:
Transcription
1 MID-TERM REVIEW Institut Henri Poincaré, Paris January 2014 Approximate inference for stochastic dynamics in large biological networks Ludovica Bachschmid Romano Supervisor: Prof. Manfred Opper Artificial Intelligence group, TU Berlin
2 The functions of most biological systems arise from complex interactions between the numerous system s components
3 protein
4 DNA protein
5 protein DNA Gene: information needed to produce one kind of proteins
6 protein RNA: copy of the genetic information TRANSCRIPTION DNA Gene: information needed to produce one kind of proteins
7 protein RNA: copy of the genetic information TRANSCRIPTION DNA Gene: information needed to produce one kind of proteins
8 protein Transcription factor RNA TRANSCRIPTION DNA Gene
9 protein sugar Transcription factor RNA TRANSCRIPTION DNA Gene
10 Protein that breaks down sugar into simpler molecules sugar Transcription factor RNA TRANSCRIPTION DNA Gene
11 Protein sugar Transcription factor RNA X TRANSCRIPTION DNA Gene
12 Protein Y sugar Transcription factor RNA X TRANSCRIPTION DNA Gene
13 X increases the production of Y Protein Y sugar Transcription factor RNA X TRANSCRIPTION DNA Gene
14 protein Y Envirometal signal Transcription factor RNA: copy of the genetic information X TRANSCRIPTION DNA Gene: information needed to produce one kind of proteins
15 protein Y Envirometal signal Transcription factor RNA: copy of the genetic information X TRANSCRIPTION DNA Gene: information needed to produce one kind of proteins
16 protein Y X decreases the production of Y Envirometal signal Transcription factor RNA: copy of the genetic information X TRANSCRIPTION DNA Gene: information needed to produce one kind of proteins
17 X Y
18 X Y X
19 X Y X W U
20 X Y X W K U
21 E. coli regulatory network
22 The development of high-throughput data-collection techniques allows for the simultaneous interrogation of the status of cell s components
23 The development of high-throughput data-collection techniques allows for the simultaneous interrogation of the status of cell s components Big data
24 Challenge: network reconstruction
25 Challenge: network reconstruction Difficulty: large size (many components) of biological systems
26 Challenge: network reconstruction Difficulty: large size (many components) of biological systems Statistical physics find approximate methods to solve the problem
27 Challenge: network reconstruction Difficulty: large size (many components) of biological systems Statistical physics find approximate methods to solve the problem Most of the current reconstruction techniques do not exploit the temporal information of the data
28 GOAL: Statistical mechanics improve the quality of biological network reconstruction
29 GOAL: Statistical mechanics develop new inference techniques that exploit the temporal structure of the data
30 GOAL: Statistical mechanics develop new inference techniques that exploit the temporal structure of the data improve the quality of biological network reconstruction
31
32 New method to predict the state of the system knowing the value of the links (results agree well with simulations of small systems)
33 New method to predict the state of the system knowing the value of the links (results agree well with simulations of small systems) Faster algorithm
34 New method to predict the state of the system knowing the value of the links (results agree well with simulations of small systems) Faster algorithm To do: Inference problem: optimally predict the links
35 Hidden nodes
36 Hidden nodes Optimally predict the state of the unobserved variables ( results agree well with simulations of small systems)
37 Hidden nodes Optimally predict the state of the unobserved variables ( results agree well with simulations of small systems) Quality of the prediction on the hidden nodes varying the sistem s parameters
38 Hidden nodes Optimally predict the state of the unobserved variables ( results agree well with simulations of small systems) Quality of the prediction on the hidden nodes varying the sistem s parameters Algorithm to infer the links
39 Hidden nodes Optimally predict the state of the unobserved variables ( results agree well with simulations of small systems) Quality of the prediction on the hidden nodes varying the sistem s parameters Algorithm to infer the links To do: Study different kinds of networks, continuous variables
40 Courses and schools (besides those organized by NETADIS): TU Berlin: - Stochastic Systems - Monte Carlo methods in Artificial Intelligence and Machine Learning - Probabilistic and Bayesian Modelling in Machine Learning and Artificial Intelligence Bernstein Center for Computational Neuroscience, Berlin: Benjamin Lindner s seminars on "Stochastic Aspects of Neurobiological Problems" ICTP, Trieste: School on Large Scale Problems in Machine Learning and Workshop on Common Concepts in Machine Learning and Statistical Physics, August Main collaborations: Prof. Sollich s group and Dr. Roudi group
41 Thanks for your attention!
Efficient inference of interactions from non-equilibrium data and application to multi-electrode neural recordings
Efficient inference of interactions from non-equilibrium data and application to multi-electrode neural recordings ESR: 1 Supervisor: Dr. Yasser Roudi 1, 2 1 Kavli Institute for Systems Neuroscience, Trondheim
More informationIntroduction to Bioinformatics
CSCI8980: Applied Machine Learning in Computational Biology Introduction to Bioinformatics Rui Kuang Department of Computer Science and Engineering University of Minnesota kuang@cs.umn.edu History of Bioinformatics
More informationComputational Biology: Basics & Interesting Problems
Computational Biology: Basics & Interesting Problems Summary Sources of information Biological concepts: structure & terminology Sequencing Gene finding Protein structure prediction Sources of information
More informationInference in kinetic Ising models: mean field and Bayes estimators
Inference in kinetic Ising models: mean field and Bayes estimators Ludovica Bachschmid-Romano, Manfred Opper Artificial Intelligence group, Computer Science, TU Berlin, Germany October 23, 2015 L. Bachschmid-Romano,
More informationLearning in Bayesian Networks
Learning in Bayesian Networks Florian Markowetz Max-Planck-Institute for Molecular Genetics Computational Molecular Biology Berlin Berlin: 20.06.2002 1 Overview 1. Bayesian Networks Stochastic Networks
More informationImproved TBL algorithm for learning context-free grammar
Proceedings of the International Multiconference on ISSN 1896-7094 Computer Science and Information Technology, pp. 267 274 2007 PIPS Improved TBL algorithm for learning context-free grammar Marcin Jaworski
More information3.B.1 Gene Regulation. Gene regulation results in differential gene expression, leading to cell specialization.
3.B.1 Gene Regulation Gene regulation results in differential gene expression, leading to cell specialization. We will focus on gene regulation in prokaryotes first. Gene regulation accounts for some of
More informationComputational Genomics. Systems biology. Putting it together: Data integration using graphical models
02-710 Computational Genomics Systems biology Putting it together: Data integration using graphical models High throughput data So far in this class we discussed several different types of high throughput
More informationNetwork motifs in the transcriptional regulation network (of Escherichia coli):
Network motifs in the transcriptional regulation network (of Escherichia coli): Janne.Ravantti@Helsinki.Fi (disclaimer: IANASB) Contents: Transcription Networks (aka. The Very Boring Biology Part ) Network
More informationGene Regulation and Expression
THINK ABOUT IT Think of a library filled with how-to books. Would you ever need to use all of those books at the same time? Of course not. Now picture a tiny bacterium that contains more than 4000 genes.
More informationGenetic Networks. Korbinian Strimmer. Seminar: Statistical Analysis of RNA-Seq Data 19 June IMISE, Universität Leipzig
Genetic Networks Korbinian Strimmer IMISE, Universität Leipzig Seminar: Statistical Analysis of RNA-Seq Data 19 June 2012 Korbinian Strimmer, RNA-Seq Networks, 19/6/2012 1 Paper G. I. Allen and Z. Liu.
More informationGLOBEX Bioinformatics (Summer 2015) Genetic networks and gene expression data
GLOBEX Bioinformatics (Summer 2015) Genetic networks and gene expression data 1 Gene Networks Definition: A gene network is a set of molecular components, such as genes and proteins, and interactions between
More informationBiology. Biology. Slide 1 of 26. End Show. Copyright Pearson Prentice Hall
Biology Biology 1 of 26 Fruit fly chromosome 12-5 Gene Regulation Mouse chromosomes Fruit fly embryo Mouse embryo Adult fruit fly Adult mouse 2 of 26 Gene Regulation: An Example Gene Regulation: An Example
More information15-381: Artificial Intelligence. Hidden Markov Models (HMMs)
15-381: Artificial Intelligence Hidden Markov Models (HMMs) What s wrong with Bayesian networks Bayesian networks are very useful for modeling joint distributions But they have their limitations: - Cannot
More informationBME 5742 Biosystems Modeling and Control
BME 5742 Biosystems Modeling and Control Lecture 24 Unregulated Gene Expression Model Dr. Zvi Roth (FAU) 1 The genetic material inside a cell, encoded in its DNA, governs the response of a cell to various
More information2. Mathematical descriptions. (i) the master equation (ii) Langevin theory. 3. Single cell measurements
1. Why stochastic?. Mathematical descriptions (i) the master equation (ii) Langevin theory 3. Single cell measurements 4. Consequences Any chemical reaction is stochastic. k P d φ dp dt = k d P deterministic
More information13.4 Gene Regulation and Expression
13.4 Gene Regulation and Expression Lesson Objectives Describe gene regulation in prokaryotes. Explain how most eukaryotic genes are regulated. Relate gene regulation to development in multicellular organisms.
More information12-5 Gene Regulation
12-5 Gene Regulation Fruit fly chromosome 12-5 Gene Regulation Mouse chromosomes Fruit fly embryo Mouse embryo Adult fruit fly Adult mouse 1 of 26 12-5 Gene Regulation Gene Regulation: An Example Gene
More informationBayesian Networks BY: MOHAMAD ALSABBAGH
Bayesian Networks BY: MOHAMAD ALSABBAGH Outlines Introduction Bayes Rule Bayesian Networks (BN) Representation Size of a Bayesian Network Inference via BN BN Learning Dynamic BN Introduction Conditional
More informationComputational Systems Biology
Computational Systems Biology Vasant Honavar Artificial Intelligence Research Laboratory Bioinformatics and Computational Biology Graduate Program Center for Computational Intelligence, Learning, & Discovery
More informationCELL BIOLOGY. by the numbers. Ron Milo. Rob Phillips. illustrated by. Nigel Orme
CELL BIOLOGY by the numbers Ron Milo Rob Phillips illustrated by Nigel Orme viii Detailed Table of Contents List of Estimates xii Preface xv Acknowledgments xiii The Path to Biological Numeracy Why We
More informationUsing phylogenetics to estimate species divergence times... Basics and basic issues for Bayesian inference of divergence times (plus some digression)
Using phylogenetics to estimate species divergence times... More accurately... Basics and basic issues for Bayesian inference of divergence times (plus some digression) "A comparison of the structures
More informationIntrinsic Noise in Nonlinear Gene Regulation Inference
Intrinsic Noise in Nonlinear Gene Regulation Inference Chao Du Department of Statistics, University of Virginia Joint Work with Wing H. Wong, Department of Statistics, Stanford University Transcription
More informationProkaryotic Gene Expression (Learning Objectives)
Prokaryotic Gene Expression (Learning Objectives) 1. Learn how bacteria respond to changes of metabolites in their environment: short-term and longer-term. 2. Compare and contrast transcriptional control
More informationGraph Alignment and Biological Networks
Graph Alignment and Biological Networks Johannes Berg http://www.uni-koeln.de/ berg Institute for Theoretical Physics University of Cologne Germany p.1/12 Networks in molecular biology New large-scale
More informationUnderstanding Science Through the Lens of Computation. Richard M. Karp Nov. 3, 2007
Understanding Science Through the Lens of Computation Richard M. Karp Nov. 3, 2007 The Computational Lens Exposes the computational nature of natural processes and provides a language for their description.
More informationAP Bio Module 16: Bacterial Genetics and Operons, Student Learning Guide
Name: Period: Date: AP Bio Module 6: Bacterial Genetics and Operons, Student Learning Guide Getting started. Work in pairs (share a computer). Make sure that you log in for the first quiz so that you get
More information(Lys), resulting in translation of a polypeptide without the Lys amino acid. resulting in translation of a polypeptide without the Lys amino acid.
1. A change that makes a polypeptide defective has been discovered in its amino acid sequence. The normal and defective amino acid sequences are shown below. Researchers are attempting to reproduce the
More informationBoolean models of gene regulatory networks. Matthew Macauley Math 4500: Mathematical Modeling Clemson University Spring 2016
Boolean models of gene regulatory networks Matthew Macauley Math 4500: Mathematical Modeling Clemson University Spring 2016 Gene expression Gene expression is a process that takes gene info and creates
More informationName: SBI 4U. Gene Expression Quiz. Overall Expectation:
Gene Expression Quiz Overall Expectation: - Demonstrate an understanding of concepts related to molecular genetics, and how genetic modification is applied in industry and agriculture Specific Expectation(s):
More informationA DNA Sequence 2017/12/6 1
A DNA Sequence ccgtacgtacgtagagtgctagtctagtcgtagcgccgtagtcgatcgtgtgg gtagtagctgatatgatgcgaggtaggggataggatagcaacagatgagc ggatgctgagtgcagtggcatgcgatgtcgatgatagcggtaggtagacttc gcgcataaagctgcgcgagatgattgcaaagragttagatgagctgatgcta
More informationLatent Variable models for GWAs
Latent Variable models for GWAs Oliver Stegle Machine Learning and Computational Biology Research Group Max-Planck-Institutes Tübingen, Germany September 2011 O. Stegle Latent variable models for GWAs
More informationProbabilistic Machine Learning
Probabilistic Machine Learning Bayesian Nets, MCMC, and more Marek Petrik 4/18/2017 Based on: P. Murphy, K. (2012). Machine Learning: A Probabilistic Perspective. Chapter 10. Conditional Independence Independent
More informationIntroduction to Probabilistic Graphical Models
Introduction to Probabilistic Graphical Models Kyu-Baek Hwang and Byoung-Tak Zhang Biointelligence Lab School of Computer Science and Engineering Seoul National University Seoul 151-742 Korea E-mail: kbhwang@bi.snu.ac.kr
More informationClustering and Network
Clustering and Network Jing-Dong Jackie Han jdhan@picb.ac.cn http://www.picb.ac.cn/~jdhan Copy Right: Jing-Dong Jackie Han What is clustering? A way of grouping together data samples that are similar in
More informationAP Curriculum Framework with Learning Objectives
Big Ideas Big Idea 1: The process of evolution drives the diversity and unity of life. AP Curriculum Framework with Learning Objectives Understanding 1.A: Change in the genetic makeup of a population over
More informationDensity Propagation for Continuous Temporal Chains Generative and Discriminative Models
$ Technical Report, University of Toronto, CSRG-501, October 2004 Density Propagation for Continuous Temporal Chains Generative and Discriminative Models Cristian Sminchisescu and Allan Jepson Department
More informationGeert Geeven. April 14, 2010
iction of Gene Regulatory Interactions NDNS+ Workshop April 14, 2010 Today s talk - Outline Outline Biological Background Construction of Predictors The main aim of my project is to better understand the
More informationIntroduction to Artificial Intelligence (AI)
Introduction to Artificial Intelligence (AI) Computer Science cpsc502, Lecture 9 Oct, 11, 2011 Slide credit Approx. Inference : S. Thrun, P, Norvig, D. Klein CPSC 502, Lecture 9 Slide 1 Today Oct 11 Bayesian
More informationLecture 7: Simple genetic circuits I
Lecture 7: Simple genetic circuits I Paul C Bressloff (Fall 2018) 7.1 Transcription and translation In Fig. 20 we show the two main stages in the expression of a single gene according to the central dogma.
More information86 Part 4 SUMMARY INTRODUCTION
86 Part 4 Chapter # AN INTEGRATION OF THE DESCRIPTIONS OF GENE NETWORKS AND THEIR MODELS PRESENTED IN SIGMOID (CELLERATOR) AND GENENET Podkolodny N.L. *1, 2, Podkolodnaya N.N. 1, Miginsky D.S. 1, Poplavsky
More informationControl of Gene Expression in Prokaryotes
Why? Control of Expression in Prokaryotes How do prokaryotes use operons to control gene expression? Houses usually have a light source in every room, but it would be a waste of energy to leave every light
More informationIntroduction to Bioinformatics
Systems biology Introduction to Bioinformatics Systems biology: modeling biological p Study of whole biological systems p Wholeness : Organization of dynamic interactions Different behaviour of the individual
More informationChapter 15 Active Reading Guide Regulation of Gene Expression
Name: AP Biology Mr. Croft Chapter 15 Active Reading Guide Regulation of Gene Expression The overview for Chapter 15 introduces the idea that while all cells of an organism have all genes in the genome,
More informationHub Gene Selection Methods for the Reconstruction of Transcription Networks
for the Reconstruction of Transcription Networks José Miguel Hernández-Lobato (1) and Tjeerd. M. H. Dijkstra (2) (1) Computer Science Department, Universidad Autónoma de Madrid, Spain (2) Institute for
More informationStatistical Physics approach to Gene Regulatory Networks
Statistical Physics approach to Gene Regulatory Networks Jonathan Fiorentino Supervisors: Andrea Crisanti, Andrea De Martino February 16, 2017 Scuola di dottorato Vito Volterra, XXXI ciclo 1 Introduction
More informationTopic 4 - #14 The Lactose Operon
Topic 4 - #14 The Lactose Operon The Lactose Operon The lactose operon is an operon which is responsible for the transport and metabolism of the sugar lactose in E. coli. - Lactose is one of many organic
More informationPredicting Protein Functions and Domain Interactions from Protein Interactions
Predicting Protein Functions and Domain Interactions from Protein Interactions Fengzhu Sun, PhD Center for Computational and Experimental Genomics University of Southern California Outline High-throughput
More informationGaussian Process Approximations of Stochastic Differential Equations
Gaussian Process Approximations of Stochastic Differential Equations Cédric Archambeau Centre for Computational Statistics and Machine Learning University College London c.archambeau@cs.ucl.ac.uk CSML
More informationUnravelling the biochemical reaction kinetics from time-series data
Unravelling the biochemical reaction kinetics from time-series data Santiago Schnell Indiana University School of Informatics and Biocomplexity Institute Email: schnell@indiana.edu WWW: http://www.informatics.indiana.edu/schnell
More informationEuropean Conference on Mathematical and Theoretical Biology Gothenburg
European Conference on Mathematical and Theoretical Biology 2014 - Gothenburg Minisymposium: Numerical methods for high-dimensional problems in biology Date: Monday, 16 th of June Time: 16:00-19:00 Organizers:
More informationA A A A B B1
LEARNING OBJECTIVES FOR EACH BIG IDEA WITH ASSOCIATED SCIENCE PRACTICES AND ESSENTIAL KNOWLEDGE Learning Objectives will be the target for AP Biology exam questions Learning Objectives Sci Prac Es Knowl
More informationComplete all warm up questions Focus on operon functioning we will be creating operon models on Monday
Complete all warm up questions Focus on operon functioning we will be creating operon models on Monday 1. What is the Central Dogma? 2. How does prokaryotic DNA compare to eukaryotic DNA? 3. How is DNA
More informationIdentifying Signaling Pathways
These slides, excluding third-party material, are licensed under CC BY-NC 4.0 by Anthony Gitter, Mark Craven, Colin Dewey Identifying Signaling Pathways BMI/CS 776 www.biostat.wisc.edu/bmi776/ Spring 2018
More information15-780: Graduate Artificial Intelligence. Bayesian networks: Construction and inference
15-780: Graduate Artificial Intelligence ayesian networks: Construction and inference ayesian networks: Notations ayesian networks are directed acyclic graphs. Conditional probability tables (CPTs) P(Lo)
More informationThe Saguaro Genome. Toward the Ecological Genomics of a Sonoran Desert Icon. Dr. Dario Copetti June 30, 2015 STEMAZing workshop TCSS
The Saguaro Genome Toward the Ecological Genomics of a Sonoran Desert Icon Dr. Dario Copetti June 30, 2015 STEMAZing workshop TCSS Why study a genome? - the genome contains the genetic information of an
More informationBSc MATHEMATICAL SCIENCE
Overview College of Science Modules Electives May 2018 (2) BSc MATHEMATICAL SCIENCE BSc Mathematical Science Degree 2018 1 College of Science, NUI Galway Fullscreen Next page Overview [60 Credits] [60
More informationMolecular evolution - Part 1. Pawan Dhar BII
Molecular evolution - Part 1 Pawan Dhar BII Theodosius Dobzhansky Nothing in biology makes sense except in the light of evolution Age of life on earth: 3.85 billion years Formation of planet: 4.5 billion
More informationBioinformatics Chapter 1. Introduction
Bioinformatics Chapter 1. Introduction Outline! Biological Data in Digital Symbol Sequences! Genomes Diversity, Size, and Structure! Proteins and Proteomes! On the Information Content of Biological Sequences!
More informationA variational radial basis function approximation for diffusion processes
A variational radial basis function approximation for diffusion processes Michail D. Vrettas, Dan Cornford and Yuan Shen Aston University - Neural Computing Research Group Aston Triangle, Birmingham B4
More informationBiological Pathways Representation by Petri Nets and extension
Biological Pathways Representation by and extensions December 6, 2006 Biological Pathways Representation by and extension 1 The cell Pathways 2 Definitions 3 4 Biological Pathways Representation by and
More informationWarm-Up. Explain how a secondary messenger is activated, and how this affects gene expression. (LO 3.22)
Warm-Up Explain how a secondary messenger is activated, and how this affects gene expression. (LO 3.22) Yesterday s Picture The first cell on Earth (approx. 3.5 billion years ago) was simple and prokaryotic,
More informationCHAPTER 13 PROKARYOTE GENES: E. COLI LAC OPERON
PROKARYOTE GENES: E. COLI LAC OPERON CHAPTER 13 CHAPTER 13 PROKARYOTE GENES: E. COLI LAC OPERON Figure 1. Electron micrograph of growing E. coli. Some show the constriction at the location where daughter
More informationBayesian Networks Inference with Probabilistic Graphical Models
4190.408 2016-Spring Bayesian Networks Inference with Probabilistic Graphical Models Byoung-Tak Zhang intelligence Lab Seoul National University 4190.408 Artificial (2016-Spring) 1 Machine Learning? Learning
More informationBig Idea 1: The process of evolution drives the diversity and unity of life.
Big Idea 1: The process of evolution drives the diversity and unity of life. understanding 1.A: Change in the genetic makeup of a population over time is evolution. 1.A.1: Natural selection is a major
More informationWhat is Systems Biology
What is Systems Biology 2 CBS, Department of Systems Biology 3 CBS, Department of Systems Biology Data integration In the Big Data era Combine different types of data, describing different things or the
More informationComputational Cell Biology Lecture 4
Computational Cell Biology Lecture 4 Case Study: Basic Modeling in Gene Expression Yang Cao Department of Computer Science DNA Structure and Base Pair Gene Expression Gene is just a small part of DNA.
More informationTopic 4: Equilibrium binding and chemical kinetics
Topic 4: Equilibrium binding and chemical kinetics Outline: Applications, applications, applications use Boltzmann to look at receptor-ligand binding use Boltzmann to look at PolII-DNA binding and gene
More informationRevisiting the Central Dogma The role of Small RNA in Bacteria
Graduate Student Seminar Revisiting the Central Dogma The role of Small RNA in Bacteria The Chinese University of Hong Kong Supervisor : Prof. Margaret Ip Faculty of Medicine Student : Helen Ma (PhD student)
More informationGene Expression as a Stochastic Process: From Gene Number Distributions to Protein Statistics and Back
Gene Expression as a Stochastic Process: From Gene Number Distributions to Protein Statistics and Back June 19, 2007 Motivation & Basics A Stochastic Approach to Gene Expression Application to Experimental
More informationSTA 4273H: Statistical Machine Learning
STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Statistics! rsalakhu@utstat.toronto.edu! http://www.utstat.utoronto.ca/~rsalakhu/ Sidney Smith Hall, Room 6002 Lecture 7 Approximate
More informationSelf Similar (Scale Free, Power Law) Networks (I)
Self Similar (Scale Free, Power Law) Networks (I) E6083: lecture 4 Prof. Predrag R. Jelenković Dept. of Electrical Engineering Columbia University, NY 10027, USA {predrag}@ee.columbia.edu February 7, 2007
More informationInferring Protein-Signaling Networks
Inferring Protein-Signaling Networks Lectures 14 Nov 14, 2011 CSE 527 Computational Biology, Fall 2011 Instructor: Su-In Lee TA: Christopher Miles Monday & Wednesday 12:00-1:20 Johnson Hall (JHN) 022 1
More informationCS 5522: Artificial Intelligence II
CS 5522: Artificial Intelligence II Bayes Nets: Independence Instructor: Alan Ritter Ohio State University [These slides were adapted from CS188 Intro to AI at UC Berkeley. All materials available at http://ai.berkeley.edu.]
More informationTowards a Bayesian model for Cyber Security
Towards a Bayesian model for Cyber Security Mark Briers (mbriers@turing.ac.uk) Joint work with Henry Clausen and Prof. Niall Adams (Imperial College London) 27 September 2017 The Alan Turing Institute
More information26 : Spectral GMs. Lecturer: Eric P. Xing Scribes: Guillermo A Cidre, Abelino Jimenez G.
10-708: Probabilistic Graphical Models, Spring 2015 26 : Spectral GMs Lecturer: Eric P. Xing Scribes: Guillermo A Cidre, Abelino Jimenez G. 1 Introduction A common task in machine learning is to work with
More information56:198:582 Biological Networks Lecture 8
56:198:582 Biological Networks Lecture 8 Course organization Two complementary approaches to modeling and understanding biological networks Constraint-based modeling (Palsson) System-wide Metabolism Steady-state
More informationTópicos Especiais em Modelagem e Análise - Aprendizado por Máquina CPS863
Tópicos Especiais em Modelagem e Análise - Aprendizado por Máquina CPS863 Daniel, Edmundo, Rosa Terceiro trimestre de 2012 UFRJ - COPPE Programa de Engenharia de Sistemas e Computação Bayesian Networks
More informationCHAPTER : Prokaryotic Genetics
CHAPTER 13.3 13.5: Prokaryotic Genetics 1. Most bacteria are not pathogenic. Identify several important roles they play in the ecosystem and human culture. 2. How do variations arise in bacteria considering
More informationMachine Learning. Neural Networks. (slides from Domingos, Pardo, others)
Machine Learning Neural Networks (slides from Domingos, Pardo, others) Human Brain Neurons Input-Output Transformation Input Spikes Output Spike Spike (= a brief pulse) (Excitatory Post-Synaptic Potential)
More informationBayesian Clustering of Multi-Omics
Bayesian Clustering of Multi-Omics for Cardiovascular Diseases Nils Strelow 22./23.01.2019 Final Presentation Trends in Bioinformatics WS18/19 Recap Intermediate presentation Precision Medicine Multi-Omics
More informationBig Idea 3: Living systems store, retrieve, transmit, and respond to information essential to life processes.
Big Idea 3: Living systems store, retrieve, transmit, and respond to information essential to life processes. Enduring understanding 3.A: Heritable information provides for continuity of life. Essential
More informationEnduring understanding 1.A: Change in the genetic makeup of a population over time is evolution.
The AP Biology course is designed to enable you to develop advanced inquiry and reasoning skills, such as designing a plan for collecting data, analyzing data, applying mathematical routines, and connecting
More informationProkaryotic Gene Expression (Learning Objectives)
Prokaryotic Gene Expression (Learning Objectives) 1. Learn how bacteria respond to changes of metabolites in their environment: short-term and longer-term. 2. Compare and contrast transcriptional control
More informationAPGRU6L2. Control of Prokaryotic (Bacterial) Genes
APGRU6L2 Control of Prokaryotic (Bacterial) Genes 2007-2008 Bacterial metabolism Bacteria need to respond quickly to changes in their environment STOP u if they have enough of a product, need to stop production
More informationDiffusion. CS/CME/BioE/Biophys/BMI 279 Nov. 15 and 20, 2016 Ron Dror
Diffusion CS/CME/BioE/Biophys/BMI 279 Nov. 15 and 20, 2016 Ron Dror 1 Outline How do molecules move around in a cell? Diffusion as a random walk (particle-based perspective) Continuum view of diffusion
More informationBiology 105/Summer Bacterial Genetics 8/12/ Bacterial Genomes p Gene Transfer Mechanisms in Bacteria p.
READING: 14.2 Bacterial Genomes p. 481 14.3 Gene Transfer Mechanisms in Bacteria p. 486 Suggested Problems: 1, 7, 13, 14, 15, 20, 22 BACTERIAL GENETICS AND GENOMICS We still consider the E. coli genome
More informationLecture 1 Modeling in Biology: an introduction
Lecture 1 in Biology: an introduction Luca Bortolussi 1 Alberto Policriti 2 1 Dipartimento di Matematica ed Informatica Università degli studi di Trieste Via Valerio 12/a, 34100 Trieste. luca@dmi.units.it
More informationCell Organelles Tutorial
1 Name: Cell Organelles Tutorial TEK 7.12D: Differentiate between structure and function in plant and animal cell organelles, including cell membrane, cell wall, nucleus, cytoplasm, mitochondrion, chloroplast,
More informationCS 343: Artificial Intelligence
CS 343: Artificial Intelligence Bayes Nets: Sampling Prof. Scott Niekum The University of Texas at Austin [These slides based on those of Dan Klein and Pieter Abbeel for CS188 Intro to AI at UC Berkeley.
More informationPROTEIN SYNTHESIS INTRO
MR. POMERANTZ Page 1 of 6 Protein synthesis Intro. Use the text book to help properly answer the following questions 1. RNA differs from DNA in that RNA a. is single-stranded. c. contains the nitrogen
More informationChapter 4 Dynamic Bayesian Networks Fall Jin Gu, Michael Zhang
Chapter 4 Dynamic Bayesian Networks 2016 Fall Jin Gu, Michael Zhang Reviews: BN Representation Basic steps for BN representations Define variables Define the preliminary relations between variables Check
More informationDevelopment Team. Regulation of gene expression in Prokaryotes: Lac Operon. Molecular Cell Biology. Department of Zoology, University of Delhi
Paper Module : 15 : 23 Development Team Principal Investigator : Prof. Neeta Sehgal Department of Zoology, University of Delhi Co-Principal Investigator : Prof. D.K. Singh Department of Zoology, University
More informationBayesian Hierarchical Classification. Seminar on Predicting Structured Data Jukka Kohonen
Bayesian Hierarchical Classification Seminar on Predicting Structured Data Jukka Kohonen 17.4.2008 Overview Intro: The task of hierarchical gene annotation Approach I: SVM/Bayes hybrid Barutcuoglu et al:
More informationFUNDAMENTALS of SYSTEMS BIOLOGY From Synthetic Circuits to Whole-cell Models
FUNDAMENTALS of SYSTEMS BIOLOGY From Synthetic Circuits to Whole-cell Models Markus W. Covert Stanford University 0 CRC Press Taylor & Francis Group Boca Raton London New York Contents /... Preface, xi
More informationUNIT 6 PART 3 *REGULATION USING OPERONS* Hillis Textbook, CH 11
UNIT 6 PART 3 *REGULATION USING OPERONS* Hillis Textbook, CH 11 REVIEW: Signals that Start and Stop Transcription and Translation BUT, HOW DO CELLS CONTROL WHICH GENES ARE EXPRESSED AND WHEN? First of
More informationCreative Genomic Webs -Kapil Rajaraman PHY 498BIO, HW 4
Creative Genomic Webs -Kapil Rajaraman (rajaramn@uiuc.edu) PHY 498BIO, HW 4 Evolutionary progress is generally considered a result of successful accumulation of mistakes in replication of the genetic code.
More informationL3.1: Circuits: Introduction to Transcription Networks. Cellular Design Principles Prof. Jenna Rickus
L3.1: Circuits: Introduction to Transcription Networks Cellular Design Principles Prof. Jenna Rickus In this lecture Cognitive problem of the Cell Introduce transcription networks Key processing network
More informationEVOLUTIONARY DISTANCE MODEL BASED ON DIFFERENTIAL EQUATION AND MARKOV PROCESS
August 0 Vol 4 No 005-0 JATIT & LLS All rights reserved ISSN: 99-8645 wwwjatitorg E-ISSN: 87-95 EVOLUTIONAY DISTANCE MODEL BASED ON DIFFEENTIAL EUATION AND MAKOV OCESS XIAOFENG WANG College of Mathematical
More informationNetwork Biology: Understanding the cell s functional organization. Albert-László Barabási Zoltán N. Oltvai
Network Biology: Understanding the cell s functional organization Albert-László Barabási Zoltán N. Oltvai Outline: Evolutionary origin of scale-free networks Motifs, modules and hierarchical networks Network
More information