@inproceedings{ehresmann_applications_2018,
title = {Applications of {Categories} to {Biology} and {Cognition}},
doi = {10/ggdf93},
author = {Ehresmann, Andrée C.},
year = {2018},
note = {ZSCC: NoCitationData[s0]},
keywords = {Biology, Emergence, Neuroscience}
}
@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}
}
@article{tuyeras_category_2018,
title = {Category {Theory} for {Genetics}},
url = {http://arxiv.org/abs/1708.05255},
abstract = {We introduce a categorical language in which it is possible to talk about DNA sequencing, alignment methods, CRISPR, homologous recombination, haplotypes, and genetic linkage. This language takes the form of a class of limit-sketches whose categories of models can model different concepts of Biology depending on what their categories of values are. We discuss examples of models in the category of sets and in the category of modules over the Boolean semi-ring \${\textbackslash}\{0,1{\textbackslash}\}\$. We identify a subclass of models in sets that models the genetic material of living beings and another subclass of models in modules that models haplotypes. We show how the two classes are related via a universal property.},
urldate = {2019-11-28},
journal = {arXiv:1708.05255 [math]},
author = {Tuyéras, Rémy},
month = may,
year = {2018},
note = {ZSCC: 0000001
arXiv: 1708.05255},
keywords = {Biology}
}
@article{tuyeras_category_2018,
title = {Category theory for genetics {I}: mutations and sequence alignments},
shorttitle = {Category theory for genetics {I}},
url = {http://arxiv.org/abs/1805.07002},
abstract = {The present article is the first of a series whose goal is to define a logical formalism in which it is possible to reason about genetics. In this paper, we introduce the main concepts of our language whose domain of discourse consists of a class of limit-sketches and their associated models. While our program will aim to show that different phenomena of genetics can be modeled by changing the category in which the models take their values, in this paper, we study models in the category of sets to capture mutation mechanisms such as insertions, deletions, substitutions, duplications and inversions. We show how the proposed formalism can be used for constructing multiple sequence alignments with an emphasis on mutation mechanisms.},
urldate = {2019-11-28},
journal = {arXiv:1805.07002 [math]},
author = {Tuyéras, Rémy},
month = dec,
year = {2018},
note = {ZSCC: 0000001
arXiv: 1805.07002},
keywords = {Biology}
}
@article{tuyeras_category_2018,
title = {Category theory for genetics {II}: genotype, phenotype and haplotype},
shorttitle = {Category theory for genetics {II}},
url = {http://arxiv.org/abs/1805.07004},
abstract = {In this paper, we use the language of pedigrads, introduced in previous work, to formalize the relationship between genotypes, phenotypes and haplotypes. We show how this formalism can help us localize the variations in the genotype that cause a given phenotype. We then use the concept of haplotype to formalize the process of predicting a certain phenotype for a given set of genotypes.},
urldate = {2019-11-28},
journal = {arXiv:1805.07004 [math]},
author = {Tuyéras, Rémy},
month = aug,
year = {2018},
note = {ZSCC: 0000001
arXiv: 1805.07004},
keywords = {Biology}
}
@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}
}
@article{ehresmann_conciliating_2015,
series = {Integral {Biomathics}: {Life} {Sciences}, {Mathematics}, and {Phenomenological} {Philosophy}},
title = {Conciliating neuroscience and phenomenology via category theory},
volume = {119},
issn = {0079-6107},
url = {http://www.sciencedirect.com/science/article/pii/S0079610715001005},
doi = {10/f75jzr},
abstract = {The paper discusses how neural and mental processes correlate for developing cognitive abilities like memory or spatial representation and allowing the emergence of higher cognitive processes up to embodied cognition, consciousness and creativity. It is done via the presentation of MENS (for Memory Evolutive Neural System), a mathematical methodology, based on category theory, which encompasses the neural and mental systems and analyzes their dynamics in the process of ‘becoming’. Using the categorical notion of a colimit, it describes the generation of mental objects through the iterative binding of distributed synchronous assemblies of neurons, and presents a new rationale of spatial representation in the hippocampus (Gómez-Ramirez and Sanz, 2011). An important result is that the degeneracy of the neural code (Edelman, 1989) is the property allowing for the formation of mental objects and cognitive processes of increasing complexity order, with multiple neuronal realizabilities; it is essential “to explain certain empirical phenomena like productivity and systematicity of thought and thinking (Aydede 2010)”. Rather than restricting the discourse to linguistics or philosophy of mind, the formal methods used in MENS lead to precise notions of Compositionality, Productivity and Systematicity, which overcome the dichotomic debate of classicism vs. connectionism and their multiple facets. It also allows developing the naturalized phenomenology approach asked for by Varela (1996) which “seeks articulations by mutual constraints between phenomena present in experience and the correlative field of phenomena established by the cognitive sciences”, while avoiding their pitfalls.},
language = {en},
number = {3},
urldate = {2019-11-28},
journal = {Progress in Biophysics and Molecular Biology},
author = {Ehresmann, Andrée C. and Gomez-Ramirez, Jaime},
month = dec,
year = {2015},
note = {ZSCC: 0000018},
keywords = {Biology, Emergence, Neuroscience, Psychology, Sketchy},
pages = {347--359}
}
@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_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}
}
@inproceedings{baez_operads_2015,
title = {Operads and {Phylogenetic} {Trees}},
abstract = {We construct an operad \${\textbackslash}mathrm\{Phyl\}\$ whose operations are the edge-labelled trees used in phylogenetics. This operad is the coproduct of \${\textbackslash}mathrm\{Com\}\$, the operad for commutative semigroups, and \$[0,{\textbackslash}infty)\$, the operad with unary operations corresponding to nonnegative real numbers, where composition is addition. We show that there is a homeomorphism between the space of \$n\$-ary operations of \${\textbackslash}mathrm\{Phyl\}\$ and \${\textbackslash}mathcal\{T\}\_n{\textbackslash}times [0,{\textbackslash}infty){\textasciicircum}\{n+1\}\$, where \${\textbackslash}mathcal\{T\}\_n\$ is the space of metric \$n\$-trees introduced by Billera, Holmes and Vogtmann. Furthermore, we show that the Markov models used to reconstruct phylogenetic trees from genome data give coalgebras of \${\textbackslash}mathrm\{Phyl\}\$. These always extend to coalgebras of the larger operad \${\textbackslash}mathrm\{Com\} + [0,{\textbackslash}infty]\$, since Markov processes on finite sets converge to an equilibrium as time approaches infinity. We show that for any operad \$O\$, its coproduct with \$[0,{\textbackslash}infty]\$ contains the operad \$W(O)\$ constucted by Boardman and Vogt. To prove these results, we explicitly describe the coproduct of operads in terms of labelled trees.},
author = {Baez, John C. and Otter, Nina},
year = {2015},
note = {ZSCC: NoCitationData[s1]
arXiv: 1512.03337},
keywords = {Biology, Coalgebras}
}
@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}
}
@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{mascari_symetries_2017,
title = {Symetries and asymetries of the immune system response: {A} categorification approach},
shorttitle = {Symetries and asymetries of the immune system response},
doi = {10/ggdnd3},
abstract = {A new modeling approach and conceptual framework to the immune system response and its dual role with respect to cancer is proposed based on Applied Category Theory. States of cells and pathogenes are structured as mathematical structures (categories), the interactions, at a given phase, between cells of the immune system and pathogenes, correspond to a pair of adjunctions (adjoint functors), the interaction process consisting of the sequential composition of an identification phase, a preparation phase and an activation phase is modeled by the composition of maps of adjunctions: the approach is illustrated by considering the Cancer-Immunity Cycle. A third dimension is needed to model Cancer Immunoediting. The categorical foundations of our approach is based on Marco Grandis and Rober Paré theory of Intercategories.},
booktitle = {2017 {IEEE} {International} {Conference} on {Bioinformatics} and {Biomedicine} ({BIBM})},
author = {Mascari, Jean-François and Giacchero, Damien and Sfakianakis, Nikolaos},
month = nov,
year = {2017},
note = {ZSCC: 0000000
ISSN: null},
keywords = {Biology, Sketchy},
pages = {1451--1454}
}
@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}
}