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Conference on Causal Learning and Reasoning

作者： Bhattacharjya, Debarun Shanmugam, Karthikeyan Gao, Tian Subramanian, Dharmashankar Thomas J Watson Res Ctr IBM Res Yorktown Hts NY 10598 USA

Datasets involving irregular occurrences of different types of events over the timeline are increasingly commonly available. Proximal graphical event models (PGEMs) are a recent graphical representation for modeling relationships between different event types in such datasets. Existing algorithms for learning PGEMs from event datasets perform poorly on the task of structure discovery, which is particularly important for causal inference since the underlying graph determines the effect of interventions. In this paper, we explore causal semantics in PGEMs and study process independencies implied by the graphical structure of the model. We introduce (conditional) process independence tests for causal PGEMs, deploying them using variations of constraint-based structure discovery algorithms for Bayesian networks. Through experiments with synthetic and real datasets, we show that the proposed approaches are better at balancing precision and recall, demonstrating improved F1 scores over state-of-the-art baselines.

关键词： Event datasets Structure discovery Event models Graphical event models Process independence Local independence constraint-based methods

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Causal Discovery Toolbox: Uncovering causal relationships in Python

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JOURNAL OF MACHINE LEARNING RESEARCH 2020年第1期21卷 1-5页

作者： Kalainathan, Diviyan Goudet, Olivier Dutta, Ritik Univ Paris Sud Fen Tech TAU LRIINRIA F-75013 Paris France Univ Angers LERIA 2 Blvd Lavoisier F-49045 Angers France IIT Gandhinagar Gandhinagar 382355 Gujarat India

This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling. The CDT package implements an end-to-end approach, recovering the direct dependencies (the skeleton of the causal graph) and the causal relationships between variables. It includes algorithms from the 'BNLEARN' (Scutari, 2018) and 'PCALG' (Kalisch et al., 2018) packages, together with algorithms for pairwise causal discovery such as ANM (Hover et al., 2009). CDT is available under the MIT License at https://***/FenTechSolutions/CausalDiscoveryToolbox.

关键词： Causal Discovery Graph recovery open source constraint-based methods score-based methods pairwise causality Markov blanket

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Learning Bayesian Networks with Cops and Robbers 10

Learning Bayesian Networks with Cops and Robbers

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10th International Conference on Probabilistic Graphical Models (PGM)

作者： Talvitie, Topi Parviainen, Pekka Univ Helsinki Dept Comp Sci Helsinki Finland Univ Bergen Dept Informat Bergen Norway

constraint-based methods for learning structures of Bayesian networks are based on testing conditional independencies between variables and constructing a structure that expresses the same conditional independencies as indicated by the tests. We present a constraint-based algorithm that learns the structure of a Bayesian network by simulating a cops-and-a-robber game. The algorithm is designed for learning structures of low treewidth distributions and in such case it conducts conditional independence tests only with small conditioning sets.

关键词： Bayesian networks Structure learning constraint-based methods Treewidth

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Causal discovery toolbox: uncovering causal relationships in Python

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 1406-1410页

作者： Diviyan Kalainathan Olivier Goudet Ritik Dutta FenTech TAU LRI INRIA Université Paris-Sud Paris France LERIA Université d'Angers Angers France IIT Gandhinagar Gandhinagar Gujarat India

This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling. The CDT package implements an end-to-end approach, recovering the direct dependencies (the skeleton of the causal graph) and the causal relationships between variables. It includes algorithms from the 'Bnlearn' (Scutari, 2018) and 'Pcalg' (Kalisch et al., 2018) packages, together with algorithms for pairwise causal discovery such as ANM (Hoyer et al., 2009). CDT is available under the MIT License at https://***/FenTechSolutions/CausalDiscoveryToolbox.

关键词： causal discovery graph recovery open source constraint-based methods score-based methods pairwise causality Markov blanket

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Expanding Metabolic Capabilities Using Novel Pathway Designs: Computational Tools and Case Studies

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BIOTECHNOLOGY JOURNAL 2019年第9期14卷 e1800734页

作者： Saa, Pedro A. Cortes, Maria P. Lopez, Javiera Bustos, Diego Maass, Alejandro Agosin, Eduardo Pontificia Univ Catolica Chile Dept Ingn Quim & Bioproc Ave Vicuna Mackenna 4860 Santiago 7820436 Chile Univ Chile Ctr Modelamiento Matemat Ave Beaucheff 851 Santiago 8370456 Chile Univ Chile Ctr Regulac Genoma Ave Beaucheff 851 Santiago 8370456 Chile DICTUC SA Ctr Aromas & Sabores Ave Vicuna Mackenna 4860 Santiago 7820436 Chile Univ Chile Dept Ingn Matemat Ave Beaucheff 851 Santiago 8370456 Chile

Design and selection of efficient metabolic pathways is critical for the success of metabolic engineering endeavors. Convenient pathways should not only produce the target metabolite in high yields but also are required to be thermodynamically feasible under production conditions, and to prefer efficient enzymes. To support the design and selection of such pathways, different computational approaches have been proposed for exploring the feasible pathway space under many of the above constraints. In this review, an overview of recent constraint-based optimization frameworks for metabolic pathway prediction, as well as relevant pathway engineering case studies that highlight the importance of rational metabolic designs is presented. Despite the availability and suitability of in silico design tools for metabolic pathway engineering, scarce-although increasing-application of computational outcomes is found. Finally, challenges and limitations hindering the broad adoption and successful application of these tools in metabolic engineering projects are discussed.

关键词： constraint-based methods metabolic engineering optimization pathway designs

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Optimization of bioprocess productivity based on metabolic-genetic network models with bilevel dynamic programming

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BIOTECHNOLOGY AND BIOENGINEERING 2018年第7期115卷 1829-1841页

作者： Jabarivelisdeh, Banafsheh Waldherr, Steffen Int Max Planck Res Sch IMPRS Adv Methods Proc & S Magdeburg Germany Ctr Dynam Syst Inst Automat Engn Magdeburg Saxony Anhalt Germany Katholieke Univ Leuven Dept Chem Engn Celestijnenlaan 200F Bus 2424 B-3001 Leuven Belgium

One of the main goals of metabolic engineering is to obtain high levels of a microbial product through genetic modifications. To improve the productivity of such a process, the dynamic implementation of metabolic engineering strategies has been proven to be more beneficial compared to static genetic manipulations in which the gene expression is not controlled over time, by resolving the trade-off between growth and production. In this work, a bilevel optimization framework based on constraint-based models is applied to identify an optimal strategy for dynamic genetic and process level manipulations to increase productivity. The dynamic enzyme-cost flux balance analysis (deFBA) as underlying metabolic network model captures the network dynamics and enables the analysis of temporal regulation in the metabolic-genetic network. We apply our computational framework to maximize ethanol productivity in a batch process with Escherichia coli. The results highlight the importance of integrating the genetic level and enzyme production and degradation processes for obtaining optimal dynamic gene and process manipulations.

关键词： bilevel optimization constraint-based methods dynamic enzyme-cost flux balance analysis metabolic engineering resource allocation models

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Integration of enzymatic data in Bacillus subtilis genome-scale metabolic model improves phenotype predictions and enables in silico design of poly--glutamic acid production strains

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MICROBIAL CELL FACTORIES 2019年第1期18卷 3-3页

作者： Massaiu, Ilaria Pasotti, Lorenzo Sonnenschein, Nikolaus Rama, Erlinda Cavaletti, Matteo Magni, Paolo Calvio, Cinzia Herrgard, Markus J. Univ Pavia Dept Elect Comp & Biomed Engn Lab Bioinformat Math Modelling & Synthet Biol Via Ferrata 5 I-27100 Pavia Italy Univ Pavia Ctr Hlth Technol Via Ferrata 5 I-27100 Pavia Italy Tech Univ Denmark Novo Nordisk Fdn Ctr Biosustainabil DK-2800 Lyngby Denmark Univ Pavia Dept Biol & Biotechnol Lazzaro Spallanzani Via Ferrata 9 I-27100 Pavia Italy

BackgroundGenome-scale metabolic models (GEMs)allow predicting metabolic phenotypes from limited data on uptake and secretion fluxes by defining the space of all the feasible solutions and excluding physio-chemically and biologically unfeasible behaviors. The integration of additional biological information in genome-scale models, e.g., transcriptomic or proteomic profiles, has the potential to improve phenotype prediction accuracy. This is particularly important for metabolic engineering applications where more accurate model predictions can translate to more reliable model-based strain *** we present a GEM with Enzymatic constraints using Kinetic and Omics data (GECKO) model of Bacillus subtilis, which uses publicly available proteomic data and enzyme kinetic parameters for central carbon (CC) metabolic reactions to constrain the flux solution space. This model allows more accurate prediction of the flux distribution and growth rate of wild-type and single-gene/operon deletion strains compared to a standard genome-scale metabolic model. The flux prediction error decreased by 43% and 36% for wild-type and mutants respectively. The model additionally increased the number of correctly predicted essential genes in CC pathways by 2.5-fold and significantly decreased flux variability in more than 80% of the reactions with variable flux. Finally, the model was used to find new gene deletion targets to optimize the flux toward the biosynthesis of poly--glutamic acid (-PGA) polymer in engineered B. subtilis. We implemented the single-reaction deletion targets identified by the model experimentally and showed that the new strains have a twofold higher -PGA concentration and production rate compared to the ancestral *** work confirms that integration of enzyme constraints is a powerful tool to improve existing genome-scale models, and demonstrates the successful use of enzyme-constrained models in B. subtilis metabolic engineering. We expect

关键词： Genome-scale metabolic model Enzymatic data Bacillus subtilis constraint-based methods Poly--glutamic acid

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ParallelPC: An R Package for Efficient Causal Exploration in Genomic Data 22nd

ParallelPC: An R Package for Efficient Causal Exploration in...

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22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Thuc Duy Le Xu, Taosheng Liu, Lin Shu, Hu Hoang, Tao Li, Jiuyong Univ South Australia Mawson Lakes SA 5095 Australia Chinese Acad Sci Inst Intelligent Machines Hefei Inst Phys Sci Hefei 230031 Peoples R China

ISBN: (纸本)9783030045036;9783030045029

Discovering causal relationships from genomic data is the ultimate goal in gene regulation research. constraint based causal exploration algorithms, such as PC, FCI, RFCI, PC-simple, IDA and Joint-IDA have achieved significant progress and have many applications. However, their applications in bioinformatics are still limited due to their high computational complexity. In this paper, we present an R package, ParallelPC, that includes the parallelised versions of these causal exploration algorithms and 12 different conditional independence tests for each. The parallelised algorithms help speed up the procedure of experimenting large biological datasets and reduce the memory used when running the algorithms. Our experiment results on a real gene expression dataset show that using the parallelised algorithms it is now practical to explore causal relationships in high dimensional datasets with thousands of variables in a personal multicore computer. We present some typical applications in bioinformatics using different algorithms in ParallelPC. ParallelPC is available in CRAN repository at https://***/web/packages/ParallelPC/***.

关键词： Causality discovery Bayesian networks Parallel computing constraint-based methods

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Improving Bioprocess Productivity Using constraint-based Models in a Dynamic Optimization Scheme

Improving Bioprocess Productivity Using Constraint-Based Mod...

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6th IFAC Conference on Foundations of Systems Biology in Engineering (FOSBE)

作者： Jabarivelisdeh, Banafsheh Waldherr, Steffen Int Max Planck Res Sch IMPRS Adv Methods Proc & S Magdeburg Germany Otto Von Guericke Univ Ctr Dynam Syst Inst Automat Engn Magdeburg Germany Katholieke Univ Leuven Dept Chem Engn Leuven Belgium Harvard Univ Sch Engn & Appl Sci Cambridge MA 02138 USA

Modulating the expression of target genes is an effective metabolic engineering approach to increase bioprocess productivity. In this work, a bilevel optimization framework is applied for dynamic manipulation of gene expression based on constraint-based models. A dynamic model in the inner problem captures the network dynamics and enables temporal regulation of the metabolic network. Through linearization of nonlinear constraints, this framework is based on linear optimization which saves on computation time especially in a bilevel structure. A case study involving batch fermentation of Escherichia coli for ethanol production is considered to find an optimal manipulation strategy of metabolic networks for maximal productivity. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Productivity constraint-based methods Metabolic engineering Bilevel optimization Dynamic flux balance analysis

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Dynamic optimization of metabolic networks coupled with gene expression

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JOURNAL OF THEORETICAL BIOLOGY 2015年 365卷 469-485页

作者： Waldherr, Steffen Oyarzun, Diego A. Bockmayr, Alexander Univ Magdeburg Inst Automat Engn D-39106 Magdeburg Germany Univ London Imperial Coll Sci Technol & Med Dept Math London SW7 2AZ England Free Univ Berlin DFG Res Ctr Matheon D-14195 Berlin Germany

The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition. An approximation by a timescale separation leads to a coupled model of quasi-steady state constraints on the metabolic reactions, and differential equations for the substrate concentrations and biomass composition. We propose a dynamic optimization approach to determine reaction fluxes for this model, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. Discretization of the optimization problems leads to a linear program that can be efficiently solved. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle. (C) 2014 Else

关键词： Flux optimization constraint-based methods Metabolic genetic networks Bacterial growth

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