@article{boutillier_kappa_2018,
title = {The {Kappa} platform for rule-based modeling},
volume = {34},
issn = {1367-4803},
url = {https://academic.oup.com/bioinformatics/article/34/13/i583/5045802},
doi = {10/gdrhw6},
abstract = {AbstractMotivation. We present an overview of the Kappa platform, an integrated suite of analysis and visualization techniques for building and interactively e},
language = {en},
number = {13},
urldate = {2019-11-23},
journal = {Bioinformatics},
author = {Boutillier, Pierre and Maasha, Mutaamba and Li, Xing and Medina-Abarca, Héctor F. and Krivine, Jean and Feret, Jérôme and Cristescu, Ioana and Forbes, Angus G. and Fontana, Walter},
month = jul,
year = {2018},
note = {ZSCC: 0000017},
keywords = {Biology, Implementation, Rewriting theory, Systems biology},
pages = {i583--i592}
}
@article{fages_influence_2018,
title = {Influence {Networks} {Compared} with {Reaction} {Networks}: {Semantics}, {Expressivity} and {Attractors}},
volume = {15},
issn = {1545-5963},
shorttitle = {Influence {Networks} {Compared} with {Reaction} {Networks}},
url = {https://doi.org/10.1109/TCBB.2018.2805686},
doi = {10/ggdf94},
abstract = {Biochemical reaction networks are one of the most widely used formalisms in systems biology to describe the molecular mechanisms of high-level cell processes. However, modellers also reason with influence diagrams to represent the positive and negative influences between molecular species and may find an influence network useful in the process of building a reaction network. In this paper, we introduce a formalism of influence networks with forces, and equip it with a hierarchy of Boolean, Petri net, stochastic and differential semantics, similarly to reaction networks with rates. We show that the expressive power of influence networks is the same as that of reaction networks under the differential semantics, but weaker under the discrete semantics. Furthermore, the hierarchy of semantics leads us to consider a positive Boolean semantics that cannot test the absence of a species, that we compare with the negative Boolean semantics with test for absence of a species in gene regulatory networks à la Thomas. We study the monotonicity properties of the positive semantics and derive from them an algorithm to compute attractors in both the positive and negative Boolean semantics. We illustrate our results on models of the literature about the p53/Mdm2 DNA damage repair system, the circadian clock, and the influence of MAPK signaling on cell-fate decision in urinary bladder cancer.},
number = {4},
urldate = {2019-11-23},
journal = {IEEE/ACM Trans. Comput. Biol. Bioinformatics},
author = {Fages, Francois and Martinez, Thierry and Rosenblueth, David A. and Soliman, Sylvain},
month = jul,
year = {2018},
note = {ZSCC: 0000002},
keywords = {Biology, Rewriting theory, Symbolic logic, Systems biology},
pages = {1138--1151}
}
@inproceedings{fages_cells_2014,
address = {Cham},
series = {Lecture {Notes} in {Computer} {Science}},
title = {Cells as {Machines}: {Towards} {Deciphering} {Biochemical} {Programs} in the {Cell}},
isbn = {978-3-319-04483-5},
shorttitle = {Cells as {Machines}},
doi = {10/ggdf96},
abstract = {Systems biology aims at understanding complex biological processes in terms of their basic mechanisms at the molecular level in cells. The bet of applying theoretical computer science concepts and software engineering methods to the analysis of distributed biochemical reaction systems in the cell, designed by natural evolution, has led to interesting challenges in computer science, and new model-based insights in biology. In this paper, we review the development over the last decade of the biochemical abstract machine (Biocham) software environment for modeling cell biology molecular reaction systems, reasoning about them at different levels of abstraction, formalizing biological behaviors in temporal logic with numerical constraints, and using them to infer non-measurable kinetic parameter values, evaluate robustness, decipher natural biochemical processes and implement new programs in synthetic biology.},
language = {en},
booktitle = {Distributed {Computing} and {Internet} {Technology}},
publisher = {Springer International Publishing},
author = {Fages, François},
editor = {Natarajan, Raja},
year = {2014},
note = {ZSCC: NoCitationData[s0]},
keywords = {Biology, Rewriting theory, Symbolic logic, Systems biology},
pages = {50--67}
}
@inproceedings{danos_rule-based_2008,
address = {Berlin, Heidelberg},
series = {Lecture {Notes} in {Computer} {Science}},
title = {Rule-{Based} {Modelling}, {Symmetries}, {Refinements}},
isbn = {978-3-540-68413-8},
doi = {10/dc5k68},
abstract = {Rule-based modelling is particularly effective for handling the highly combinatorial aspects of cellular signalling. The dynamics is described in terms of interactions between partial complexes, and the ability to write rules with such partial complexes -i.e., not to have to specify all the traits of the entitities partaking in a reaction but just those that matter- is the key to obtaining compact descriptions of what otherwise could be nearly infinite dimensional dynamical systems. This also makes these descriptions easier to read, write and modify.In the course of modelling a particular signalling system it will often happen that more traits matter in a given interaction than previously thought, and one will need to strengthen the conditions under which that interaction may happen. This is a process that we call rule refinement and which we set out in this paper to study. Specifically we present a method to refine rule sets in a way that preserves the implied stochastic semantics.This stochastic semantics is dictated by the number of different ways in which a given rule can be applied to a system (obeying the mass action principle). The refinement formula we obtain explains how to refine rules and which choice of refined rates will lead to a neutral refinement, i.e., one that has the same global activity as the original rule had (and therefore leaves the dynamics unchanged). It has a pleasing mathematical simplicity, and is reusable with little modification across many variants of stochastic graph rewriting. A particular case of the above is the derivation of a maximal refinement which is equivalent to a (possibly infinite) Petri net and can be useful to get a quick approximation of the dynamics and to calibrate models. As we show with examples, refinement is also useful to understand how different subpopulations contribute to the activity of a rule, and to modulate differentially their impact on that activity.},
language = {en},
booktitle = {Formal {Methods} in {Systems} {Biology}},
publisher = {Springer},
author = {Danos, Vincent and Feret, Jérôme and Fontana, Walter and Harmer, Russell and Krivine, Jean},
editor = {Fisher, Jasmin},
year = {2008},
note = {ZSCC: NoCitationData[s0]},
keywords = {Biology, Rewriting theory, Systems biology},
pages = {103--122}
}
@article{fages_biocham:_2006,
title = {{BIOCHAM}: an environment for modeling biological systems and formalizing experimental knowledge},
volume = {22},
issn = {1367-4803, 1460-2059},
shorttitle = {{BIOCHAM}},
url = {https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btl172},
doi = {10/dfv},
language = {en},
number = {14},
urldate = {2019-11-23},
journal = {Bioinformatics},
author = {Fages, F. and Calzone, L. and Soliman, S.},
month = jul,
year = {2006},
note = {ZSCC: 0000264},
keywords = {Abstract machines, Biology, Implementation, Rewriting theory, Symbolic logic, Systems biology},
pages = {1805--1807}
}
@book{wilkinson_stochastic_2006,
title = {Stochastic {Modelling} for {Systems} {Biology}},
isbn = {978-1-58488-540-5},
abstract = {Although stochastic kinetic models are increasingly accepted as the best way to represent and simulate genetic and biochemical networks, most researchers in the field have limited knowledge of stochastic process theory. The stochastic processes formalism provides a beautiful, elegant, and coherent foundation for chemical kinetics and there is a wealth of associated theory every bit as powerful and elegant as that for conventional continuous deterministic models. The time is right for an introductory text written from this perspective. Stochastic Modelling for Systems Biology presents an accessible introduction to stochastic modelling using examples that are familiar to systems biology researchers. Focusing on computer simulation, the author examines the use of stochastic processes for modelling biological systems. He provides a comprehensive understanding of stochastic kinetic modelling of biological networks in the systems biology context. The text covers the latest simulation techniques and research material, such as parameter inference, and includes many examples and figures as well as software code in R for various applications.While emphasizing the necessary probabilistic and stochastic methods, the author takes a practical approach, rooting his theoretical development in discussions of the intended application. Written with self-study in mind, the book includes technical chapters that deal with the difficult problems of inference for stochastic kinetic models from experimental data. Providing enough background information to make the subject accessible to the non-specialist, the book integrates a fairly diverse literature into a single convenient and notationally consistent source.},
language = {en},
publisher = {CRC Press},
author = {Wilkinson, Darren J.},
month = apr,
year = {2006},
note = {ZSCC: NoCitationData[s1]
Google-Books-ID: roHTk4m8JGAC},
keywords = {Bayesian inference, Biology, Implementation, Probabilistic programming, Rewriting theory, Systems biology, Transition systems}
}
@inproceedings{fages_machine_2006,
address = {Berlin, Heidelberg},
series = {Lecture {Notes} in {Computer} {Science}},
title = {Machine {Learning} {Biochemical} {Networks} from {Temporal} {Logic} {Properties}},
isbn = {978-3-540-46236-1},
doi = {10/dd8},
abstract = {One central issue in systems biology is the definition of formal languages for describing complex biochemical systems and their behavior at different levels. The biochemical abstract machine BIOCHAM is based on two formal languages, one rule-based language used for modeling biochemical networks, at three abstraction levels corresponding to three semantics: boolean, concentration and population; and one temporal logic language used for formalizing the biological properties of the system. In this paper, we show how the temporal logic language can be turned into a specification language. We describe two algorithms for inferring reaction rules and kinetic parameter values from a temporal specification formalizing the biological data. Then, with an example of the cell cycle control, we illustrate how these machine learning techniques may be useful to the modeler.},
language = {en},
booktitle = {Transactions on {Computational} {Systems} {Biology} {VI}},
publisher = {Springer},
author = {Fages, François and Calzone, Laurence and Chabrier-Rivier, Nathalie and Soliman, Sylvain},
editor = {Priami, Corrado and Plotkin, Gordon},
year = {2006},
note = {ZSCC: NoCitationData[s0]},
keywords = {Abstract machines, Biology, Classical ML, Machine learning, Symbolic logic, Systems biology},
pages = {68--94}
}
@article{boutillier_kappa_nodate,
title = {The {Kappa} {Language} and {Kappa} {Tools}},
language = {en},
author = {Boutillier, Pierre and Feret, Jérôme and Krivine, Jean and Fontana, Walter},
note = {ZSCC: NoCitationData[s0]},
keywords = {Biology, Implementation, Systems biology},
pages = {52}
}