TY - JOUR
TI - BIOCHAM: an environment for modeling biological systems and formalizing experimental knowledge
AU - Fages, F.
AU - Calzone, L.
AU - Soliman, S.
T2 - Bioinformatics
DA - 2006/07/15/
PY - 2006
DO - 10/dfv
DP - Crossref
VL - 22
IS - 14
SP - 1805
EP - 1807
LA - en
SN - 1367-4803, 1460-2059
ST - BIOCHAM
UR - https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btl172
Y2 - 2019/11/23/07:28:51
KW - Abstract machines
KW - Biology
KW - Implementation
KW - Rewriting theory
KW - Symbolic logic
KW - Systems biology
ER -
TY - CONF
TI - Cells as Machines: Towards Deciphering Biochemical Programs in the Cell
AU - Fages, François
A2 - Natarajan, Raja
T3 - Lecture Notes in Computer Science
AB - 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.
C1 - Cham
C3 - Distributed Computing and Internet Technology
DA - 2014///
PY - 2014
DO - 10/ggdf96
DP - Springer Link
SP - 50
EP - 67
LA - en
PB - Springer International Publishing
SN - 978-3-319-04483-5
ST - Cells as Machines
KW - Biology
KW - Rewriting theory
KW - Symbolic logic
KW - Systems biology
ER -
TY - JOUR
TI - Influence Networks Compared with Reaction Networks: Semantics, Expressivity and Attractors
AU - Fages, Francois
AU - Martinez, Thierry
AU - Rosenblueth, David A.
AU - Soliman, Sylvain
T2 - IEEE/ACM Trans. Comput. Biol. Bioinformatics
AB - 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.
DA - 2018/07//
PY - 2018
DO - 10/ggdf94
DP - ACM Digital Library
VL - 15
IS - 4
SP - 1138
EP - 1151
SN - 1545-5963
ST - Influence Networks Compared with Reaction Networks
UR - https://doi.org/10.1109/TCBB.2018.2805686
Y2 - 2019/11/23/07:40:24
KW - Biology
KW - Rewriting theory
KW - Symbolic logic
KW - Systems biology
ER -
TY - CONF
TI - Rule-Based Modelling, Symmetries, Refinements
AU - Danos, Vincent
AU - Feret, Jérôme
AU - Fontana, Walter
AU - Harmer, Russell
AU - Krivine, Jean
A2 - Fisher, Jasmin
T3 - Lecture Notes in Computer Science
AB - 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.
C1 - Berlin, Heidelberg
C3 - Formal Methods in Systems Biology
DA - 2008///
PY - 2008
DO - 10/dc5k68
DP - Springer Link
SP - 103
EP - 122
LA - en
PB - Springer
SN - 978-3-540-68413-8
KW - Biology
KW - Rewriting theory
KW - Systems biology
ER -
TY - BOOK
TI - Stochastic Modelling for Systems Biology
AU - Wilkinson, Darren J.
AB - 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.
DA - 2006/04/18/
PY - 2006
DP - Google Books
SP - 296
LA - en
PB - CRC Press
SN - 978-1-58488-540-5
KW - Bayesian inference
KW - Biology
KW - Implementation
KW - Probabilistic programming
KW - Rewriting theory
KW - Systems biology
KW - Transition systems
ER -
TY - JOUR
TI - The Kappa platform for rule-based modeling
AU - Boutillier, Pierre
AU - Maasha, Mutaamba
AU - Li, Xing
AU - Medina-Abarca, Héctor F.
AU - Krivine, Jean
AU - Feret, Jérôme
AU - Cristescu, Ioana
AU - Forbes, Angus G.
AU - Fontana, Walter
T2 - Bioinformatics
AB - AbstractMotivation. We present an overview of the Kappa platform, an integrated suite of analysis and visualization techniques for building and interactively e
DA - 2018/07/01/
PY - 2018
DO - 10/gdrhw6
DP - academic.oup.com
VL - 34
IS - 13
SP - i583
EP - i592
J2 - Bioinformatics
LA - en
SN - 1367-4803
UR - https://academic.oup.com/bioinformatics/article/34/13/i583/5045802
Y2 - 2019/11/23/07:43:12
KW - Biology
KW - Implementation
KW - Rewriting theory
KW - Systems biology
ER -