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  1. Article ; Online: An information theoretic approach for the inference of Boolean networks and functions from data: BoCSE.

    Murrugarra, David / Veliz-Cuba, Alan

    Patterns (New York, N.Y.)

    2022  Volume 3, Issue 11, Page(s) 100617

    Abstract: Building predictive models from data is an important and challenging task in many fields including biology, medicine, engineering, and economy. In this issue, Sun et al. ...

    Abstract Building predictive models from data is an important and challenging task in many fields including biology, medicine, engineering, and economy. In this issue, Sun et al.
    Language English
    Publishing date 2022-11-11
    Publishing country United States
    Document type News
    ISSN 2666-3899
    ISSN (online) 2666-3899
    DOI 10.1016/j.patter.2022.100617
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  2. Article ; Online: Modular Control of Biological Networks.

    Murrugarra, David / Veliz-Cuba, Alan / Dimitrova, Elena / Kadelka, Claus / Wheeler, Matthew / Laubenbacher, Reinhard

    ArXiv

    2024  

    Abstract: The concept of control is central to understanding and applications of biological network models. Some of their key structural features relate to control functions, through gene regulation, signaling, or metabolic mechanisms, and computational models ... ...

    Abstract The concept of control is central to understanding and applications of biological network models. Some of their key structural features relate to control functions, through gene regulation, signaling, or metabolic mechanisms, and computational models need to encode these. Applications of models often focus on model-based control, such as in biomedicine or metabolic engineering. This paper presents an approach to model-based control that exploits two common features of biological networks, namely their modular structure and canalizing features of their regulatory mechanisms. The paper focuses on intracellular regulatory networks, represented by Boolean network models. A main result of this paper is that control strategies can be identified by focusing on one module at a time. This paper also presents a criterion based on canalizing features of the regulatory rules to identify modules that do not contribute to network control and can be excluded. For even moderately sized networks, finding global control inputs is computationally very challenging. The modular approach presented here leads to a highly efficient approach to solving this problem. This approach is applied to a published Boolean network model of blood cancer large granular lymphocyte (T-LGL) leukemia to identify a minimal control set that achieves a desired control objective.
    Language English
    Publishing date 2024-01-23
    Publishing country United States
    Document type Preprint
    ISSN 2331-8422
    ISSN (online) 2331-8422
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Modularity of biological systems: a link between structure and function.

    Kadelka, Claus / Wheeler, Matthew / Veliz-Cuba, Alan / Murrugarra, David / Laubenbacher, Reinhard

    Journal of the Royal Society, Interface

    2023  Volume 20, Issue 207, Page(s) 20230505

    Abstract: This paper addresses two topics in systems biology, the hypothesis that biological systems are modular and the problem of relating structure and function of biological systems. The focus here is on gene regulatory networks, represented by Boolean network ...

    Abstract This paper addresses two topics in systems biology, the hypothesis that biological systems are modular and the problem of relating structure and function of biological systems. The focus here is on gene regulatory networks, represented by Boolean network models, a commonly used tool. Most of the research on gene regulatory network modularity has focused on network structure, typically represented through either directed or undirected graphs. But since gene regulation is a highly dynamic process as it determines the function of cells over time, it is natural to consider functional modularity as well. One of the main results is that the structural decomposition of a network into modules induces an analogous decomposition of the dynamic structure, exhibiting a strong relationship between network structure and function. An extensive simulation study provides evidence for the hypothesis that modularity might have evolved to increase phenotypic complexity while maintaining maximal dynamic robustness to external perturbations.
    MeSH term(s) Computer Simulation ; Systems Biology ; Gene Regulatory Networks ; Models, Biological
    Language English
    Publishing date 2023-10-25
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2156283-0
    ISSN 1742-5662 ; 1742-5689
    ISSN (online) 1742-5662
    ISSN 1742-5689
    DOI 10.1098/rsif.2023.0505
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  4. Article: Modularity of biological systems: a link between structure and function.

    Kadelka, Claus / Wheeler, Matthew / Veliz-Cuba, Alan / Murrugarra, David / Laubenbacher, Reinhard

    bioRxiv : the preprint server for biology

    2023  

    Abstract: This paper addresses two topics in systems biology, the hypothesis that biological systems are modular and the problem of relating structure and function of biological systems. The focus here is on gene regulatory networks, represented by Boolean network ...

    Abstract This paper addresses two topics in systems biology, the hypothesis that biological systems are modular and the problem of relating structure and function of biological systems. The focus here is on gene regulatory networks, represented by Boolean network models, a commonly used tool. Most of the research on gene regulatory network modularity has focused on network structure, typically represented through either directed or undirected graphs. But since gene regulation is a highly dynamic process as it determines the function of cells over time, it is natural to consider functional modularity as well. One of the main results is that the structural decomposition of a network into modules induces an analogous decomposition of the dynamic structure, exhibiting a strong relationship between network structure and function. An extensive simulation study provides evidence for the hypothesis that modularity might have evolved to increase phenotypic complexity while maintaining maximal dynamic robustness to external perturbations.
    Language English
    Publishing date 2023-09-12
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.09.11.557227
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Book ; Online: Algebraic network reconstruction of discrete dynamical systems

    Harrington, Heather A. / Stillman, Mike / Veliz-Cuba, Alan

    2022  

    Abstract: We present a computational algebra solution to reverse engineering the network structure of discrete dynamical systems from data. We use monomial ideals to determine dependencies between variables that encode constraints on the possible wiring diagrams ... ...

    Abstract We present a computational algebra solution to reverse engineering the network structure of discrete dynamical systems from data. We use monomial ideals to determine dependencies between variables that encode constraints on the possible wiring diagrams underlying the process generating the discrete-time, continuous-space data. Our work assumes that each variable is either monotone increasing or decreasing. We prove that with enough data, even in the presence of small noise, our method can reconstruct the correct unique wiring diagram.

    Comment: 19 pages, 5 figures
    Keywords Quantitative Biology - Quantitative Methods ; Mathematics - Algebraic Geometry ; Quantitative Biology - Molecular Networks ; 13P25 ; 37N25 ; 92B05 ; 05E40 ; 46N60 ; 92C42 ; 68R10 ; 90B10 ; 97N70 ; 62-07
    Publishing date 2022-12-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: A unified approach to reverse engineering and data selection for unique network identification

    Veliz-Cuba, Alan / Newsome-Slade, Vanessa / Dimitrova, Elena S.

    2022  

    Abstract: Due to cost concerns, it is optimal to gain insight into the connectivity of biological and other networks using as few experiments as possible. Data selection for unique network connectivity identification has been an open problem since the introduction ...

    Abstract Due to cost concerns, it is optimal to gain insight into the connectivity of biological and other networks using as few experiments as possible. Data selection for unique network connectivity identification has been an open problem since the introduction of algebraic methods for reverse engineering for almost two decades. In this manuscript we determine what data sets uniquely identify the unsigned wiring diagram corresponding to a system that is discrete in time and space. Furthermore, we answer the question of uniqueness for signed wiring diagrams for Boolean networks. Computationally, unsigned and signed wiring diagrams have been studied separately, and in this manuscript we also show that there exists an ideal capable of encoding both unsigned and signed information. This provides a unified approach to studying reverse engineering that also gives significant computational benefits.

    Comment: 21 pages
    Keywords Mathematics - Algebraic Geometry ; Mathematics - Dynamical Systems ; Quantitative Biology - Quantitative Methods
    Subject code 000
    Publishing date 2022-12-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Algebraic Experimental Design

    Dimitrova, Elena S. / Fredrickson, Cameron H. / Rondoni, Nicholas A. / Stigler, Brandilyn / Veliz-Cuba, Alan

    Theory and Computation

    2022  

    Abstract: Over the past several decades, algebraic geometry has provided innovative approaches to biological experimental design that resolved theoretical questions and improved computational efficiency. However, guaranteeing uniqueness and perfect recovery of ... ...

    Abstract Over the past several decades, algebraic geometry has provided innovative approaches to biological experimental design that resolved theoretical questions and improved computational efficiency. However, guaranteeing uniqueness and perfect recovery of models are still open problems. In this work we study the problem of uniqueness of wiring diagrams. We use as a modeling framework polynomial dynamical systems and utilize the correspondence between simplicial complexes and square-free monomial ideals from Stanley-Reisner theory to develop theory and construct an algorithm for identifying input data sets $V\subset \mathbb F_p^n$ that are guaranteed to correspond to a unique minimal wiring diagram regardless of the experimental output. We apply the results on a tumor-suppression network mediated by epidermal derived growth factor receptor and demonstrate how careful experimental design decisions can lead to a unique minimal wiring diagram identification. One of the insights of the theoretical work is the connection between the uniqueness of a wiring diagram for a given $V\subset \mathbb F_p^n$ and the uniqueness of the reduced Gr\"obner basis of the polynomial ideal $I(V)\subset \mathbb F_p[x_1,\ldots, x_n]$. We discuss existing results and introduce a new necessary condition on the points in $V$ for uniqueness of the reduced Gr\"obner basis of $I(V)$. These results also point to the importance of the relative proximity of the experimental input points on the number of minimal wiring diagrams, which we then study computationally. We find that there is a concrete heuristic way to generate data that tends to result in fewer minimal wiring diagrams.

    Comment: 16 pages, 2 figures (one in color but prints well in b&w), 2 tables
    Keywords Mathematics - Algebraic Geometry ; Quantitative Biology - Molecular Networks ; 11T06 ; 92C42 ; 37N25 ; 13F55
    Subject code 000 ; 004
    Publishing date 2022-08-04
    Publishing country us
    Document type Book ; Online
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  8. Book ; Online: Decomposition of Boolean networks

    Kadelka, Claus / Laubenbacher, Reinhard / Murrugarra, David / Veliz-Cuba, Alan / Wheeler, Matthew

    An approach to modularity of biological systems

    2022  

    Abstract: This paper presents the foundation for a decomposition theory for Boolean networks, a type of discrete dynamical system that has found a wide range of applications in the life sciences, engineering, and physics. Given a Boolean network satisfying certain ...

    Abstract This paper presents the foundation for a decomposition theory for Boolean networks, a type of discrete dynamical system that has found a wide range of applications in the life sciences, engineering, and physics. Given a Boolean network satisfying certain conditions, there is a unique collection of subnetworks so that the network can be reconstructed from these subnetworks by an extension operation. The main result of the paper is that this structural decomposition induces a corresponding decomposition of the network dynamics. The theory is motivated by the search for a mathematical framework to formalize the hypothesis that biological systems are modular, widely accepted in the life sciences, but not well-defined and well-characterized. As an example of how dynamic modularity could be used for the efficient identification of phenotype control, the control strategies for the network can be found by identifying controls in its modules, one at a time.

    Comment: 31 pages, 7 figures
    Keywords Mathematics - Dynamical Systems ; Nonlinear Sciences - Adaptation and Self-Organizing Systems ; Physics - Biological Physics ; Quantitative Biology - Molecular Networks ; 37N25 ; 94C10 ; 92B05
    Subject code 000
    Publishing date 2022-06-08
    Publishing country us
    Document type Book ; Online
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  9. Article: Performance of normative and approximate evidence accumulation on the dynamic clicks task.

    Radillo, Adrian E / Veliz-Cuba, Alan / Josić, Krešimir / Kilpatrick, Zachary P

    Neurons, behavior, data analysis and theory

    2019  Volume 3, Issue 1

    Abstract: The aim of a number of psychophysics tasks is to uncover how mammals make decisions in a world that is in flux. Here we examine the characteristics of ideal and near-ideal observers in a task of this type. We ask when and how performance depends on task ... ...

    Abstract The aim of a number of psychophysics tasks is to uncover how mammals make decisions in a world that is in flux. Here we examine the characteristics of ideal and near-ideal observers in a task of this type. We ask when and how performance depends on task parameters and design, and, in turn, what observer performance tells us about their decision-making process. In the dynamic clicks task subjects hear two streams (left and right) of Poisson clicks with different rates. Subjects are rewarded when they correctly identify the side with the higher rate, as this side switches unpredictably. We show that a reduced set of task parameters defines regions in parameter space in which optimal, but not near-optimal observers, maintain constant response accuracy. We also show that for a range of task parameters an approximate normative model must be finely tuned to reach near-optimal performance, illustrating a potential way to distinguish between normative models and their approximations. In addition, we show that using the negative log-likelihood and the 0/1-loss functions to fit these types of models is not equivalent: the 0/1-loss leads to a bias in parameter recovery that increases with sensory noise. These findings suggest ways to tease apart models that are hard to distinguish when tuned exactly, and point to general pitfalls in experimental design, model fitting, and interpretation of the resulting data.
    Language English
    Publishing date 2019-10-09
    Publishing country United States
    Document type Journal Article
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  10. Article ; Online: Reduction of Boolean network models.

    Veliz-Cuba, Alan

    Journal of theoretical biology

    2011  Volume 289, Page(s) 167–172

    Abstract: Boolean networks have been successfully used in modelling gene regulatory networks. However, for large networks, analysis by simulation becomes unfeasible. In this paper we propose a reduction method for Boolean networks that decreases the size of the ... ...

    Abstract Boolean networks have been successfully used in modelling gene regulatory networks. However, for large networks, analysis by simulation becomes unfeasible. In this paper we propose a reduction method for Boolean networks that decreases the size of the network, while preserving important dynamical properties and topological features. As a result, the reduced network can be used to infer properties about the original network and to gain a better understanding of the role of network topology on the dynamics. In particular, we use the reduction method to study steady states of Boolean networks and apply our results to models of Th-lymphocyte differentiation and the lac operon.
    MeSH term(s) Algorithms ; Cell Differentiation/immunology ; Feedback, Physiological ; Gene Regulatory Networks/physiology ; Humans ; Lac Operon/genetics ; Models, Biological ; Signal Transduction/immunology ; Systems Biology/methods ; T-Lymphocytes/cytology
    Language English
    Publishing date 2011-11-21
    Publishing country England
    Document type Journal Article
    ZDB-ID 2972-5
    ISSN 1095-8541 ; 0022-5193
    ISSN (online) 1095-8541
    ISSN 0022-5193
    DOI 10.1016/j.jtbi.2011.08.042
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