+

WO2007000379A1 - Procede de simulation assistee par informatique d'experiences biologiques d'interference d'arn - Google Patents

Procede de simulation assistee par informatique d'experiences biologiques d'interference d'arn Download PDF

Info

Publication number
WO2007000379A1
WO2007000379A1 PCT/EP2006/062393 EP2006062393W WO2007000379A1 WO 2007000379 A1 WO2007000379 A1 WO 2007000379A1 EP 2006062393 W EP2006062393 W EP 2006062393W WO 2007000379 A1 WO2007000379 A1 WO 2007000379A1
Authority
WO
WIPO (PCT)
Prior art keywords
rna
cell
activity
network
computer
Prior art date
Application number
PCT/EP2006/062393
Other languages
German (de)
English (en)
Inventor
Mathäus Dejori
Bernd SCHÜRMANN
Martin Stetter
Original Assignee
Siemens Aktiengesellschaft
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens Aktiengesellschaft filed Critical Siemens Aktiengesellschaft
Publication of WO2007000379A1 publication Critical patent/WO2007000379A1/fr

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression

Definitions

  • the invention relates to a method for the computer-aided simulation of biological RNA interference experiments and to a corresponding computer program product.
  • RNA molecules ribonucleic acid molecules
  • DNA genes of the genetic material
  • protein molecules which perform the manifold tasks in the cell.
  • the interplay or interactions of the genes with each other and with the proteins constitute a so-called regulatory genetic network, which is based on the development of the human body from a fertilized egg cell and all bodily functions.
  • the process of protein synthesis from a gene via the intermediate step of RNA production is called gene expression. If more protein (or more RNA than intermediate) is produced from a gene at a certain time than in a reference state, it is called overexpression, too little protein (or too little RNA) from a lower expression.
  • the expression state of all genes taken together at a time is referred to herein as the gene expression pattern.
  • the gene expression pattern changes over time depending on the condition of the cell, the cellular environment as well as the type of cell considered. Since a cell undergoes different and very complex states during its lifetime, its gene expression pattern also changes continuously. A gene expression pattern thus represents a snapshot of the condition of the cell.
  • ribonucleic acids were considered to be only an intermediate in protein synthesis in a cell.
  • recent years' genetic research has shown that ribonucleic acids play a much more important role in the cell system and control basic mechanisms in the biological processes in a cell.
  • the regulation of gene expression of a cell is primarily controlled by the proteins produced in the cell.
  • Research results of recent years show, however, that short single-stranded RNA molecules can inhibit the translation of genes into proteins (so-called gene silencing) by binding the RNA molecules generated as an intermediate in the protein synthesis and thus preventing reading into the corresponding protein ,
  • RNA interference RNA interference
  • RNAi is of particular interest for therapeutic and pharmaceutical applications. For example, by switching off individual disease-relevant genes (eg oncogenes), disease-causing mechanisms can be inhibited and new drugs can be found.
  • RNAi studies are performed as in vivo and in vitro experiments that are laborious and expensive.
  • additional experimental data is needed z. For example, DNA microarray data to select the RNA molecules of interest for an RNAi experiment.
  • WO 2005/003368 A2 describes a method for the computer-aided simulation of gene expression patterns, wherein a causal network is used, which describes the regulatory genetic network of a cell.
  • the object of the invention is to provide a method and a corresponding computer program product with which computer-assisted RNAi experiments can be simulated.
  • RNA activity patterns of a cell are determined by a) using a causal network which describes the regulatory genetic network of the cell in such a way that nodes of the causal network in each case monitor the activity of an RNA molecule species represent the cell and edges of the causal network represent regulatory interactions between the RNA molecule types of the cell; b) the activity of one or more RNA molecule types of the cell is blocked by setting their activity substantially to zero; c) using the causal network for the blocked ones
  • RNA molecule species one or more RNA activity patterns of the cell are generated.
  • Activity refers to the concentration or a measure of the concentration of the corresponding RNA molecule species in the cell.
  • the method essentially represents a further development of the method described in the above-mentioned publication WO 2005/003368 A2, the disclosure of which Reference is hereby made by reference to the content of the present application.
  • the method according to the invention is based on the finding that the method for the simulation of gene expression patterns according to WO 2005/003368 A2 can also be used for the simulation of RNAi experiments in that the variables of the method as activities of the individual RNA molecule types in the Cell are interpreted and the activities of individual RNA molecules are essentially eliminated.
  • RNAi experiments can thus be simulated on the computer, whereby the experimental and time expenditure can be minimized.
  • the method according to the invention preferably uses as a causal network a Bayesian network (also known as Bayesian network), which is well known in the art.
  • the causal network is also preferably of a DAG (Directed Acylic Graph) type.
  • the activities of the RNA molecule species can be represented by discrete states which represent measures of particular concentrations of the RNA molecule species in the cell.
  • the discrete states may include an overexpressed, normal-ex- pressed, and under-expressed state.
  • an overexpressed state stands for a high activity exceeding a normal range, a normal-expressed state for a normal-range activity, and a sub-expressed state for an activity below a normal range.
  • the causal network is trained using one or more known RNA activity patterns, with the nodes and edges of the causal network being adjusted.
  • a computer-aided comparison of the one or more generated RNA activity pattern with one or more predetermined RNA activity patterns performed, for example, to draw conclusions about the influence of certain types of RNA molecules on RNA activity pattern.
  • the computer-assisted comparison is preferably carried out using a statistical method and / or a statistical index, in particular a distance measure.
  • the one or more known and / or predetermined RNA activity patterns with which the network is trained or the computer-aided comparison is performed are patterns measured using DNA microarray technology.
  • the one or more known and / or predefined RNA activity patterns with which the network is trained or the computer-aided comparison are performed , from diseased cells.
  • the method according to the invention can be used, in particular, as a preliminary examination for wet-biochemical RNA interference experiments, the method extracting RNA molecule species with great influence on the RNA activity patterns, so that in the subsequent RNAi experiments, preferably the extracted ones RNA molecule types are blocked.
  • the invention also relates to a computer program product with a program code stored on a machine-readable carrier for carrying out the method according to the invention, when the program runs on a computer.
  • FIG. 1 shows the sequence of an embodiment of the method according to the invention
  • FIG. 2 shows the sequence of a method for generating a data set of samples from a Bayesian network
  • FIG. 3 shows the sequence of a method of interventional sampling according to a sub-step of FIG
  • FIG. 1 An exemplary embodiment of the invention will be described below with reference to FIG. 1, in which a Bayesian cell is used as a causal network for simulating an RNAi experiment
  • a Bayesian network B is a special type of representation of a common multivariate probability density function (WDF) of a set of variables X by a graphical model.
  • WDF probability density function
  • GAG directed acyclic graph
  • the edges between the nodes represent statistical dependencies and can be interpreted as causal relationships between them.
  • the second component of the Bayesian network is the set of conditional DFs ⁇ P (X ⁇ ⁇ Pa ⁇ , ⁇ , G), which are parameterized by means of a vector ⁇ .
  • conditional WDFs specify the nature of the dependencies of the individual variables i on the set of parent parents (Parents) Pa ⁇ .
  • the common WDF in the product form
  • the DAG of a Bayesian network uniquely describes the conditional dependency and independence relationships between a set of variables, but in contrast, a given statistical structure of the WDF does not result in a unique DAG.
  • two DAGs describe one and the same WDF, if and only if they have the same set of edges and the same set of "colliders", where a collider is a constellation in which at least two directed edges are to lead the same node.
  • knots represent the activity of an RNA species in the cell and the edges describe the
  • Control mechanisms between two nodes which can be interpreted in a causal way.
  • the network is initially learned structurally in accordance with step 101 of FIG.
  • the structure G of the Bayesian network is found which best matches D, ie which the Bayes score (Bayes score) P (D 1 G) P (G 1 P (D)
  • P (D ⁇ G) is the edge probability
  • P (G) is the apriori probability of the structure
  • P (D) is the evidence.
  • the data set D consists of N microarray experiments, eg. From cell samples from different patients, and each data vector ⁇ d 1 ! , d 1 2 f ••• / C ⁇ n ] corresponds to the activity of n RNA molecule species in the microarray experiment.
  • a Bayesian network learned from such data encodes the probability distribution of n RNA molecule species obtained from these N microarray experiments.
  • a so-called interventional sampling (B, E, N) is carried out, with which data sets of N independent samples are generated in the learned Bayesian network given a given evidence.
  • the procedure of interventional sampling will be explained in more detail below with reference to FIG.
  • the evidence here represents the amount of observations of the RNAi experiment to be simulated in the method according to the invention.
  • E represents one or more blocked RNA species whose activity value is set to substantially zero.
  • variable set X is first ordered according to the condition that parent (s) Pa ⁇ are arranged in front of the X ⁇ .
  • the node X 1 with the highest order number in the bar sample that is not instantiated is selected (step 202). If X 1 is a root node (ie a node without parent node), a random state with the probability P (state) is selected (step 203). Otherwise, a random state with conditional probability P (state
  • extracted states of Pa ⁇ ) is selected (step 204). Finally, in step 205, the node X 1 is instantiated with the random state, ie X 1 state. After instantiation of all X ⁇ for all samples N, a data set DB of N independent samples was obtained.
  • variable set Xq is first ordered according to the condition that parent (s) Pa ⁇ are arranged in front of the X ⁇ .
  • the node X 1 with the highest order number in the bar sample that is not instantiated is selected (step 302). If X 1 is a root node (ie, a node without parent node), a random state with probability P (state
  • extracted states of Pa ⁇ , E) is selected (step 304). Finally, in step 305, the node X 1 is instantiated with the random state, ie X 1 state.
  • This data set represents the simulated activities of the RNA molecule species of an RNAi experiment in which, according to Evidence E, certain types of RNA molecules were blocked.
  • the simulation method described above can be extended by estimating the quality of the influence of the evidence E on the behavior of the Bayesian network B in order to obtain biological or medical findings from the method.
  • E compared to a set of data sets D of known states S.
  • the influence of observed evidence in the form of blocked RNA species on cancer characteristic behavior of the model is measurable.
  • N ES is the number of samples of D B ⁇ E which statistically come closest to data set D 5
  • N is the total number of samples of D B ⁇ E.

Landscapes

  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • Genetics & Genomics (AREA)
  • Biotechnology (AREA)
  • Evolutionary Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Theoretical Computer Science (AREA)
  • Physiology (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

Procédé de simulation assistée par informatique d'expériences biologiques d'interférence d'ARN pour déterminer des modèles d'activité d'ARN d'une cellule. Selon ledit procédé, (a) un réseau causal est utilisé, qui décrit le réseau génétique régulateur de la cellule de manière telle que des noeuds du réseau causal représentent chacun l'activité d'un type de molécule d'ARN dans la cellule et que les bords du réseau causal représentent des interactions régulatrices entre les types de molécules d'ARN de la cellule, (b) l'activité d'un ou plusieurs types de molécules d'ARN de la cellule est bloquée, ladite activité étant réduite à zéro et (c) un ou plusieurs modèles d'activité d'ARN de la cellule sont produits pour les types de molécules d'ARN bloquées, à l'aide du réseau causal.
PCT/EP2006/062393 2005-06-28 2006-05-17 Procede de simulation assistee par informatique d'experiences biologiques d'interference d'arn WO2007000379A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102005030136.3 2005-06-28
DE102005030136A DE102005030136B4 (de) 2005-06-28 2005-06-28 Verfahren zur rechnergestützten Simulation von biologischen RNA-Interferenz-Experimenten

Publications (1)

Publication Number Publication Date
WO2007000379A1 true WO2007000379A1 (fr) 2007-01-04

Family

ID=36809053

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2006/062393 WO2007000379A1 (fr) 2005-06-28 2006-05-17 Procede de simulation assistee par informatique d'experiences biologiques d'interference d'arn

Country Status (2)

Country Link
DE (1) DE102005030136B4 (fr)
WO (1) WO2007000379A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107391961A (zh) * 2011-09-09 2017-11-24 菲利普莫里斯生产公司 用于基于网络的生物活性评估的系统与方法

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005003368A2 (fr) * 2003-07-04 2005-01-13 Siemens Aktiengesellschaft Procede, programme informatique avec moyens de codage de programme et produit programme informatique pour l'analyse du reseau genetique regulatoire d'une cellule

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10159262B4 (de) * 2001-12-03 2007-12-13 Siemens Ag Identifizieren pharmazeutischer Targets

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2005003368A2 (fr) * 2003-07-04 2005-01-13 Siemens Aktiengesellschaft Procede, programme informatique avec moyens de codage de programme et produit programme informatique pour l'analyse du reseau genetique regulatoire d'une cellule

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
"Analyzing gene-expression data with bayesian networks", DIPLOMARBEIT TECHNISCHE UNIVERSITAET GRAZ. TUG, XX, XX, June 2002 (2002-06-01), pages 1 - 55, XP002320819 *
FRIEDMAN N ET AL: "Using bayesian networks to analyze expression data", JOURNAL OF COMPUTATIONAL BIOLOGY, MARY ANN LIEBERT, LARCHMONT, NY, US, vol. 7, no. 3/4, 2000, pages 601 - 620, XP002963504, ISSN: 1066-5277 *
YOO C ET AL: "Discovery of causal relationships in a gene-regulation pathway from a mixture of experimental and observational DNA microarray data", PROCEEDINGS OF THE PACIFIC SYMPOSIUM ON BIOCOMPUTING, 2002, pages 498 - 509, XP002320820 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107391961A (zh) * 2011-09-09 2017-11-24 菲利普莫里斯生产公司 用于基于网络的生物活性评估的系统与方法

Also Published As

Publication number Publication date
DE102005030136B4 (de) 2010-09-23
DE102005030136A1 (de) 2007-01-11

Similar Documents

Publication Publication Date Title
DE102009032649B4 (de) Massenspektrometrische Identifizierung von Mikroben nach Unterarten
DE112005002331T5 (de) Verfahren, System und Vorrichtung zur Zusammenstellung und Nutzung von biologischem Wissen
EP1934895A2 (fr) Procede d'apprentissage assiste par ordinateur d'un reseau neuronal, et reseau neuronal correspondant
EP3540632A1 (fr) Procédé et appareil d'analyse destinés à la classification des échantillons tissulaires
DE10162927A1 (de) Auswerten von mittels funktionaler Magnet-Resonanz-Tomographie gewonnenen Bildern des Gehirns
DE10159262B4 (de) Identifizieren pharmazeutischer Targets
WO2007000379A1 (fr) Procede de simulation assistee par informatique d'experiences biologiques d'interference d'arn
DE112018006190T5 (de) Subtypisierung von tnbc und methoden
EP3425519A1 (fr) Procédé de configuration assistée par ordinateur d'un modèle commandé par des données en fonction de données d'apprentissage
WO2005003368A2 (fr) Procede, programme informatique avec moyens de codage de programme et produit programme informatique pour l'analyse du reseau genetique regulatoire d'une cellule
DE102006033267B4 (de) Verfahren zur rechnergestützten Ermittlung von quantitativen Vorhersagen aus qualitativen Informationen mit Hilfe von Bayesianischen Netzwerken
DE102005028975B4 (de) Verfahren zur Ermittlung eines Biomarkers zur Kennzeichnung eines spezifischen biologischen Zustands eines Organismus aus mindestens einem Datensatz
DE102005015000A1 (de) Verfahren und System zur Analyse von arraybasierten Komparativhybridisierungsdaten
DE10350525A1 (de) Verfahren zur Visualisierung der ADME-Eigenschaften chemischer Substanzen
DE102004030296A1 (de) Verfahren, Computerprogramm mit Programmcode-Mitteln und Computerprogramm-Produkt zur Analyse eines regulatorischen genetischen Netzwerks einer Zelle
Richter et al. Does weed control by precision spray technology favour the emergence of resistance
DE102007039917A1 (de) Verfahren zur rechnergestützten Analyse eines Interaktionsnetzwerks von biomedizinischen Entitäten
EP2491508A2 (fr) Procédés de production de motifs de référence pour biomarqueurs
EP1527406A2 (fr) Procede, dispositif, programme informatique comprenant un systeme de code de programmation et produit de programme informatique pour analyser des activites neuronales dans des zones neuronales
WO2006013131A2 (fr) Procede pour analyser un reseau de regulation genetique d'une cellule
DE102022212416A1 (de) Verfahren und Vorrichtung zur Positionsrekonstruktion von Zellen in einem dreidimensionalen Gewebe
WO2010060746A2 (fr) Procédé et dispositif d'analyse automatique de modèles
EP1451750B1 (fr) Procede pour identifier des pharmacophores
DE102007022170A1 (de) Verfahren zur rechnergestützten Verarbeitung von biomedizinischen Daten
DE102023200020A1 (de) Verfahren zum Trainieren eines Algorithmus des maschinellen Lernens und Verfahren zum Einstellen mindestens eines in einem induktiven Härteverfahren mindestens eines Bauteils einstellbaren Prozessparameters

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application
NENP Non-entry into the national phase

Ref country code: DE

WWW Wipo information: withdrawn in national office

Country of ref document: DE

122 Ep: pct application non-entry in european phase

Ref document number: 06777223

Country of ref document: EP

Kind code of ref document: A1

点击 这是indexloc提供的php浏览器服务，不要输入任何密码和下载