CoNtRol: an open source framework for the analysis of chemical reaction networks

Donnell, Pete; Banaji, Murad; Marginean, Anca; Pantea, Casian

doi:10.1093/bioinformatics/btu063

Cited by 40 publications

(35 citation statements)

References 22 publications

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“…A test with CoNtRol by Donnell et al [26] confirms that none of the networks considered in the interferon-receptor complex formation is multistationary. Here it is important to remark that we can not preclude the existence of multiple steady states based on the results obtained by the optimization method.…”

Section: Resultsmentioning

confidence: 98%

“…Here it is important to remark that we can not preclude the existence of multiple steady states based on the results obtained by the optimization method. For precluding multistationarity, other injectivity-based methods [26–28, 56] provide conclusive results.…”

Section: Resultsmentioning

confidence: 99%

“…Although Chemical Reaction Network Theory appeared to be a powerful framework, to the best of our knowledge, there was no unified method for multi-stationarity detection based on CRNT that provides parameter sets leading to multiple steady states and that could also be applied to the majority of signaling pathways. As mentioned in the introduction, other methods for detection of complex nonlinear behavior in biochemical reaction networks based on different network graphs have been developed recently, including the software GraTelPy by Walther et al [25] which uses graph-theoretic analysis of the bipartite graph to find sources of multistability, oscillations or Turing instabilities, and the toolbox CoNtRol by Donnell et al based on the DSR graph [26]. Importantly, the two methods presented here can be fully automated in an algorithmic manner, combining numerical with simple symbolic computations (basically simple symbolic derivatives).…”

Section: Discussionmentioning

confidence: 99%

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Chemical Reaction Network Theory elucidates sources of multistability in interferon signaling

2017

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Bistability has important implications in signaling pathways, since it indicates a potential cell decision between alternative outcomes. We present two approaches developed in the framework of the Chemical Reaction Network Theory for easy and efficient search of multiple steady state behavior in signaling networks (both with and without mass conservation), and apply them to search for sources of bistability at different levels of the interferon signaling pathway. Different type I interferon subtypes and/or doses are known to elicit differential bioactivities (ranging from antiviral, antiproliferative to immunomodulatory activities). How different signaling outcomes can be generated through the same receptor and activating the same JAK/STAT pathway is still an open question. Here, we detect bistability at the level of early STAT signaling, showing how two different cell outcomes are achieved under or above a threshold in ligand dose or ligand-receptor affinity. This finding could contribute to explain the differential signaling (antiviral vs apoptotic) depending on interferon dose and subtype (α vs β) observed in type I interferons.

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Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Chemical Reaction Network Theory elucidates sources of multistability in interferon signaling

2017

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“…In the special case that kinetics are mono-or bimolecular, the RLF is piecewise linear or piecewise quadratic on species, respectively. Computationally, we complement previous reaction network toolboxes [24,25] and we provide a Lyapunov-Enabled Analysis of Reaction Networks (LEARN) toolbox to implement the results on any given network by searching for an RLF and checking the appropriate conditions via four main methods: a graphical algorithm, a linear program, an iterative procedure, and a semi-definite program. Additionally, LEARN checks for conditions that rule out the existence of an RLF.…”

Section: Introductionmentioning

confidence: 99%

A computational framework for a Lyapunov-enabled analysis of biochemical reaction networks

Ali

Angeli

Sontag

2019

Preprint

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Complex molecular biological processes such as transcription and translation, signal transduction, post-translational modification cascades, and metabolic pathways can be described in principle by biochemical reactions that explicitly take into account the sophisticated network of chemical interactions regulating cell life. The ability to deduce the possible qualitative behaviors of such networks from a set of reactions is a central objective and an ongoing challenge in the field of systems biology. Unfortunately, the construction of complete mathematical models is often hindered by a pervasive problem: despite the wealth of qualitative graphical knowledge about network interactions, the form of the governing nonlinearities and/or the values of kinetic constants are hard to uncover experimentally. The kinetics can also change with environmental variations.This work addresses the following question: given a set of reactions and without assuming a particular form for the kinetics, what can we say about the asymptotic behavior of the network? Specifically, it introduces a class of networks that are "structurally (mono) attractive" meaning that they are incapable of exhibiting multiple steady states, oscillation, or chaos by virtue of their reaction graphs. These networks are characterized by the existence of a universal energy-like function called a Robust Lyapunov function (RLF). To find such functions, a finite set of rank-one linear systems is introduced, which form the extremals of a linear convex cone. The problem is then reduced to that of finding a common Lyapunov function for this set of extremals. Based on this characterization, a computational package, Lyapunov-Enabled Analysis of Reaction Networks (LEARN), is provided that constructs such functions or rules out their existence.An extensive study of biochemical networks demonstrates that LEARN offers a new unified framework. Basic motifs, three-body binding, and genetic networks are studied first. The work then focuses on cellular signalling networks including various post-translational modification cascades, phosphotransfer and phosphorelay networks, T-cell kinetic proofreading, and ERK signalling. The Ribosome Flow Model is also studied.A theoretical and computational framework is developed for the identification of 1 biochemical networks that are "structurally attractive". This means that they only 2 allow global point attractors and they cannot exhibit any other asymptotic behavior 3 such as multi-stability, oscillations, or chaos for any choice of the kinetics. They are 4 characterized by the existence of energy-like functions. A computational package is 5 made available for usage by a wider community. Many relevant networks in molecular 6 biology satisfy the assumptions, and some are analyzed for the first time. 7 Introduction 8 Many biological systems are known for the ability to operate precisely and consistently 9 subject to potentially large disruptions and uncertainties [1-5]. Examples are 10 homeostasis, understood as the maintenance of a desir...

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“… 11 An elegant internet-based tool for online-drawing of a variant of the SR Graph is available in [14]. …”

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confidence: 99%

Sharper graph-theoretical conditions for the stabilization of complex reaction networks

Knight¹,

Shinar²,

Feinberg

2015

Mathematical Biosciences

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Across the landscape of all possible chemical reaction networks there is a surprising degree of stable behavior, despite what might be substantial complexity and nonlinearity in the governing differential equations. At the same time there are reaction networks, in particular those that arise in biology, for which richer behavior is exhibited. Thus, it is of interest to understand network-structural features whose presence enforces dull, stable behavior and whose absence permits the dynamical richness that might be necessary for life. We present conditions on a network’s Species-Reaction Graph that ensure a high degree of stable behavior, so long as the kinetic rate functions satisfy certain weak and natural constraints. These graph-theoretical conditions are considerably more incisive than those reported earlier.

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CoNtRol: an open source framework for the analysis of chemical reaction networks

Abstract: Reference implementation: reaction-networks.net/control/. Source code and binaries, released under the GPLv3: reaction-networks.net/control/download/. Documentation: reaction-networks.net/wiki/CoNtRol.

Cited by 40 publications

References 22 publications

Chemical Reaction Network Theory elucidates sources of multistability in interferon signaling

Chemical Reaction Network Theory elucidates sources of multistability in interferon signaling

A computational framework for a Lyapunov-enabled analysis of biochemical reaction networks

Sharper graph-theoretical conditions for the stabilization of complex reaction networks

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