Publish in OALib Journal

ISSN: 2333-9721

APC: Only $99


Any time

2019 ( 2 )

2018 ( 21 )

2017 ( 14 )

2016 ( 28 )

Custom range...

Search Results: 1 - 10 of 11682 matches for " Julio Saez-Rodriguez "
All listed articles are free for downloading (OA Articles)
Page 1 /11682
Display every page Item
Using Python to Dive into Signalling Data with CellNOpt and BioServices
Thomas Cokelaer,Julio Saez-Rodriguez
Computer Science , 2014,
Abstract: Systems biology is an inter-disciplinary field that studies systems of biological components at different scales, which may be molecules, cells or entire organism. In particular, systems biology methods are applied to understand functional deregulations within human cells (e.g., cancers). In this context, we present several python packages linked to CellNOptR (R package), which is used to build predictive logic models of signalling networks by training networks (derived from literature) to signalling (phospho-proteomic) data. The first package (cellnopt.wrapper) is a wrapper based on RPY2 that allows a full access to CellNOptR functionalities within Python. The second one (cellnopt.core) was designed to ease the manipulation and visualisation of data structures used in CellNOptR, which was achieved by using Pandas, NetworkX and matplotlib. Systems biology also makes extensive use of web resources and services. We will give an overview and status of BioServices, which allows one to access programmatically to web resources used in life science and how it can be combined with CellNOptR.
Structural and functional analysis of cellular networks with CellNetAnalyzer
Steffen Klamt, Julio Saez-Rodriguez, Ernst D Gilles
BMC Systems Biology , 2007, DOI: 10.1186/1752-0509-1-2
Abstract: Herein we introduce CellNetAnalyzer, a toolbox for MATLAB facilitating, in an interactive and visual manner, a comprehensive structural analysis of metabolic, signalling and regulatory networks. The particular strengths of CellNetAnalyzer are methods for functional network analysis, i.e. for characterising functional states, for detecting functional dependencies, for identifying intervention strategies, or for giving qualitative predictions on the effects of perturbations. CellNetAnalyzer extends its predecessor FluxAnalyzer (originally developed for metabolic network and pathway analysis) by a new modelling framework for examining signal-flow networks. Two of the novel methods implemented in CellNetAnalyzer are discussed in more detail regarding algorithmic issues and applications: the computation and analysis (i) of shortest positive and shortest negative paths and circuits in interaction graphs and (ii) of minimal intervention sets in logical networks.CellNetAnalyzer provides a single suite to perform structural and qualitative analysis of both mass-flow- and signal-flow-based cellular networks in a user-friendly environment. It provides a large toolbox with various, partially unique, functions and algorithms for functional network analysis.CellNetAnalyzer is freely available for academic use.Systems biology aims at a holistic analysis of biological networks. Mathematical modelling plays a pivotal role for this integrative approach. The arguably most common formalism for cellular networks is kinetic modelling, which has been successfully applied to the study of single pathways and networks of moderate size (e.g. [1,2]). However, building dynamic models with high predictive power requires an amount of reliable quantitative data which is often not available in large-scale networks with hundreds of players and interactions. Structural or qualitative (parameter-free) models relying solely on the often well-known network structure provide an alternative approach still
Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli
Melody K. Morris,Julio Saez-Rodriguez,David C. Clarke,Peter K. Sorger,Douglas A. Lauffenburger
PLOS Computational Biology , 2011, DOI: 10.1371/journal.pcbi.1001099
Abstract: Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone.
The Logic of EGFR/ErbB Signaling: Theoretical Properties and Analysis of High-Throughput Data
Regina Samaga,Julio Saez-Rodriguez,Leonidas G. Alexopoulos,Peter K. Sorger,Steffen Klamt
PLOS Computational Biology , 2009, DOI: 10.1371/journal.pcbi.1000438
Abstract: The epidermal growth factor receptor (EGFR) signaling pathway is probably the best-studied receptor system in mammalian cells, and it also has become a popular example for employing mathematical modeling to cellular signaling networks. Dynamic models have the highest explanatory and predictive potential; however, the lack of kinetic information restricts current models of EGFR signaling to smaller sub-networks. This work aims to provide a large-scale qualitative model that comprises the main and also the side routes of EGFR/ErbB signaling and that still enables one to derive important functional properties and predictions. Using a recently introduced logical modeling framework, we first examined general topological properties and the qualitative stimulus-response behavior of the network. With species equivalence classes, we introduce a new technique for logical networks that reveals sets of nodes strongly coupled in their behavior. We also analyzed a model variant which explicitly accounts for uncertainties regarding the logical combination of signals in the model. The predictive power of this model is still high, indicating highly redundant sub-structures in the network. Finally, one key advance of this work is the introduction of new techniques for assessing high-throughput data with logical models (and their underlying interaction graph). By employing these techniques for phospho-proteomic data from primary hepatocytes and the HepG2 cell line, we demonstrate that our approach enables one to uncover inconsistencies between experimental results and our current qualitative knowledge and to generate new hypotheses and conclusions. Our results strongly suggest that the Rac/Cdc42 induced p38 and JNK cascades are independent of PI3K in both primary hepatocytes and HepG2. Furthermore, we detected that the activation of JNK in response to neuregulin follows a PI3K-dependent signaling pathway.
Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling
Bree B. Aldridge,Julio Saez-Rodriguez,Jeremy L. Muhlich,Peter K. Sorger,Douglas A. Lauffenburger
PLOS Computational Biology , 2009, DOI: 10.1371/journal.pcbi.1000340
Abstract: When modeling cell signaling networks, a balance must be struck between mechanistic detail and ease of interpretation. In this paper we apply a fuzzy logic framework to the analysis of a large, systematic dataset describing the dynamics of cell signaling downstream of TNF, EGF, and insulin receptors in human colon carcinoma cells. Simulations based on fuzzy logic recapitulate most features of the data and generate several predictions involving pathway crosstalk and regulation. We uncover a relationship between MK2 and ERK pathways that might account for the previously identified pro-survival influence of MK2. We also find unexpected inhibition of IKK following EGF treatment, possibly due to down-regulation of autocrine signaling. More generally, fuzzy logic models are flexible, able to incorporate qualitative and noisy data, and powerful enough to produce quantitative predictions and new biological insights about the operation of signaling networks.
Visual setup of logical models of signaling and regulatory networks with ProMoT
Julio Saez-Rodriguez, Sebastian Mirschel, Rebecca Hemenway, Steffen Klamt, Ernst Gilles, Martin Ginkel
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-506
Abstract: Herein we present a flexible framework for setting up large logical models in a visual manner with the software tool ProMoT. An easily extendible library, ProMoT's inherent modularity and object-oriented concept as well as adaptive visualization techniques provide a versatile environment. Both the graphical and the textual description of the logical model can be exported to different formats.New features of ProMoT facilitate an efficient set-up of large Boolean models of biochemical interaction networks. The modeling environment is flexible; it can easily be adapted to specific requirements, and new extensions can be introduced. ProMoT is freely available from http://www.mpi-magdeburg.mpg.de/projects/promot/ webcite.The analysis of regulatory mechanisms using Boolean formalisms is an important technique [1], and has been successfully applied to systems of moderate size, e.g. [2-4]. Furthermore, a tool (GINSim) has been developed to set up and analyze logical networks [5].Recently, new techniques based on a logical formalism – in combination with graph-theoretical methods applied to the underlying interaction graph – have been proposed for the analysis of large-scale signaling and regulatory networks [6]. These methods have been implemented in CellNetAnalyzer (CNA), allowing structural analysis of large networks within a GUI [7].In CNA, the user should provide a graphical map of the network, a mathematical (textual) input of the network structure, and a mapping from the latter to the earlier. However, the procedure for setting up large-scale networks by hand, of both the graph and text, can be a cumbersome and error-prone task. There are many tools available to set up models describing signaling networks as a biochemical reaction network, such as CellDesigner, JDesigner, and ProMoT [8-10]. However, to the best of our knowledge, there is currently no tool available that allows the visual setup of large logical networks, and has the ability to export both the mathemati
A domain-oriented approach to the reduction of combinatorial complexity in signal transduction networks
Holger Conzelmann, Julio Saez-Rodriguez, Thomas Sauter, Boris N Kholodenko, Ernst D Gilles
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-34
Abstract: Our results show that under realistic assumptions on domain interactions, the dynamics of signaling pathways can be exactly described by reduced, hierarchically structured models. The method presented here provides a rigorous way to model a large class of signaling networks using macro-states (macroscopic quantities such as the levels of occupancy of the binding domains) instead of micro-states (concentrations of individual species). The method is described using generic multidomain proteins and is applied to the molecule LAT.The presented method is a systematic and powerful tool to derive reduced model structures describing the dynamics of multiprotein complex formation accurately.Receptor-mediated signal transduction is the subject of intense research since it plays a crucial role in the regulation of a variety of cellular functions. The ligand binding to a receptor triggers conformational changes that allow for receptor dimerization and phosphorylation of numerous residues. The subsequent formation of multiprotein signaling complexes on these receptors and their scaffolding adaptor proteins initiates a variety of signaling pathways. The number of feasible different multiprotein species grows exponentially with the number of binding domains, and can easily reach thousands or even millions [1,2]. In the past years, a large number of mathematical models have attempted to describe signaling phenomena and to get deeper insights into the dynamics of cellular responses [3-11]. Most of these models did not consider the combinatorial variety of all possible species and interactions [1,2]. The obvious advantage of models neglecting the combinatorial complexity is there are less ordinary differential equations (ODEs) that have to be considered. For example, if all possible species were included in the model of Schoeberl et al. [6], the number of variables would grow from around 100 to almost 40000 [2]. However, there is no general method to decide a priori which species pla
A methodology for the structural and functional analysis of signaling and regulatory networks
Steffen Klamt, Julio Saez-Rodriguez, Jonathan A Lindquist, Luca Simeoni, Ernst D Gilles
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-56
Abstract: We propose formalisms and methods, relying on adapted and partially newly introduced approaches, which facilitate a structural analysis of signaling and regulatory networks with focus on functional aspects. We use two different formalisms to represent and analyze interaction networks: interaction graphs and (logical) interaction hypergraphs. We show that, in interaction graphs, the determination of feedback cycles and of all the signaling paths between any pair of species is equivalent to the computation of elementary modes known from metabolic networks. Knowledge on the set of signaling paths and feedback loops facilitates the computation of intervention strategies and the classification of compounds into activators, inhibitors, ambivalent factors, and non-affecting factors with respect to a certain species. In some cases, qualitative effects induced by perturbations can be unambiguously predicted from the network scheme. Interaction graphs however, are not able to capture AND relationships which do frequently occur in interaction networks. The consequent logical concatenation of all the arcs pointing into a species leads to Boolean networks. For a Boolean representation of cellular interaction networks we propose a formalism based on logical (or signed) interaction hypergraphs, which facilitates in particular a logical steady state analysis (LSSA). LSSA enables studies on the logical processing of signals and the identification of optimal intervention points (targets) in cellular networks. LSSA also reveals network regions whose parametrization and initial states are crucial for the dynamic behavior.We have implemented these methods in our software tool CellNetAnalyzer (successor of FluxAnalyzer) and illustrate their applicability using a logical model of T-Cell receptor signaling providing non-intuitive results regarding feedback loops, essential elements, and (logical) signal processing upon different stimuli.The methods and formalisms we propose herein are anot
Revisiting the Training of Logic Models of Protein Signaling Networks with a Formal Approach based on Answer Set Programming
Santiago Videla,Carito Guziolowski,Federica Eduati,Sven Thiele,Niels Grabe,Julio Saez-Rodriguez,Anne Siegel
Computer Science , 2012, DOI: 10.1007/978-3-642-33636-2_20
Abstract: A fundamental question in systems biology is the construction and training to data of mathematical models. Logic formalisms have become very popular to model signaling networks because their simplicity allows us to model large systems encompassing hundreds of proteins. An approach to train (Boolean) logic models to high-throughput phospho-proteomics data was recently introduced and solved using optimization heuristics based on stochastic methods. Here we demonstrate how this problem can be solved using Answer Set Programming (ASP), a declarative problem solving paradigm, in which a problem is encoded as a logical program such that its answer sets represent solutions to the problem. ASP has significant improvements over heuristic methods in terms of efficiency and scalability, it guarantees global optimality of solutions as well as provides a complete set of solutions. We illustrate the application of ASP with in silico cases based on realistic networks and data.
Knot numbers used as labels for identifying subject matter of a khipu
Alberto Saez-Rodriguez
Revista Latinoamericana de Etnomatemática , 2013,
Abstract: This investigation presents a new way to look at the numerical khipu, a knotted-string recording device from Pachacamac (Peru), and the types of information it contains. In addition to celestial coordinates, khipu knots apparently pertain to an early form of double-entry accounting. This study hypothesizes that the khipu sample has the recording capacity needed to register double-entry-like accounts. After the identification of its subject matter, the khipu sample was studied in an attempt to ascertain whether the knot values could represent instructions from the Inca state administration to a local accounting center. The results indicate that the numerical information in the pairing quadrants (determined by the distribution of S- and Z-knots) should be read from top to bottom along the full length of the string and can then provide certain complementary details regarding the projected corn stocks of the Inca stat
Page 1 /11682
Display every page Item

Copyright © 2008-2017 Open Access Library. All rights reserved.