KaDE: A Tool to Compile Kappa Rules into (Reduced) ODE Models

Camporesi, Ferdinanda; Feret, Jérôme; Ly, Kim Quyen

doi:10.1007/978-3-319-67471-1_18

Cited by 13 publications

(8 citation statements)

References 23 publications

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“…A set of rules can be interpreted as a dynamical system which describes the evolution of a soup of molecules. There are several choices: when the number of different kinds of molecular complexes is not too great, the set of rules may be translated into ODEs [Camporesi et al, 2017]. Each set of rules also induces a continuous time Markov chain the execution traces of which can be sampled by simulation [Danos et al, 2007b].…”

Section: Modeling Approaches For Tgf-β Signalingmentioning

confidence: 99%

Integrative Models for TGF-β Signaling and Extracellular Matrix

Théret

Feret

Hodgkinson

et al. 2020

Biology of Extracellular Matrix

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The extracellular matrix (ECM) is the most important regulator of cellcell communication within tissues. ECM is a complex structure, made up of a wide variety of molecules including proteins, proteglycans and glycoaminoglycans. It contributes to cell signaling through the action of both its constituents and their proteolytic cleaved fragments called matricryptins [Hynes and Naba, 2012, Ricard-Blum andVallet, 2019]. In addition, ECM acts as a "reservoir" of growth factors and cytokines and regulates their bioavailability at the cell surface. By controlling cell signaling inputs, ECM plays a key role in regulating cell phenotype (differentiation, proliferation, migration, etc.). In this context, signaling networks associated with the polypeptide transforming growth factor TGF-β are unique since their activation are controlled by ECM and TGF-β is a major regulator of ECM remodeling in return.

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Section: Modeling Approaches For Tgf-β Signalingmentioning

confidence: 99%

Integrative Models for TGF-β Signaling and Extracellular Matrix

Théret

Feret

Hodgkinson

et al. 2020

Biology of Extracellular Matrix

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“…Stochastic simulations were performed using . ODEs (in SBML format) corresponding to the Kappa program were generated using the KaDE utility (Camporesi et al, 2017) and simulated using the roadrunner library in Python (Somogyi et al, 2015). For steady-state simulations in the presence of fixed inputs (Fig.…”

Section: Stochastic and Deterministic Simulations Of Circuitmentioning

confidence: 99%

Embodying probabilistic inference in biochemical circuits

Katz,

Springer,

Fontana

2018

Preprint

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Probabilistic inference provides a language for describing how organisms may learn from and adapt to their environment.The computations needed to implement probabilistic inference often require specific representations, akin to having the suitable data structures for implementing certain algorithms in computer programming. Yet it is unclear how such representations can be instantiated in the stochastic, parallel-running biochemical machinery found in cells (such as single-celled organisms). Here, we show how representations for supporting inference in Markov models can be embodied in cellular circuits, by combining a concentration-dependent scheme for encoding probabilities with a mechanism for directional counting. We show how the logic of protein production and degradation constrains the computation we set out to implement. We argue that this process by which an abstract computation is shaped by its biochemical realization strikes a compromise between "rationalistic" information-processing perspectives and alternative approaches that emphasize embodiment.

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“…However, if we had a monomer such as B a.B * + b.B * * which dimerizes via reactions at two different sites which interact according to the pattern a b@ MA k , the bonding rate would be MA 2 . By these combinatorial considerations we are able to calculate the correct rates of reactions no matter whether the sites in a pattern occur in the same species or in different species and regardless of how may times a single site is repeated in an affinity pattern; see the discussions of reaction rates in [16,14].…”

Section: Symmetric Multi-way Bondingmentioning

confidence: 99%

The Bond-Calculus: A Process Algebra for Complex Biological Interaction Dynamics

Wright,

Stark

2018

Preprint

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We present the bond-calculus, a process algebra for modelling biological and chemical systems featuring nonlinear dynamics, multiway interactions, and dynamic bonding of agents. Mathematical models based on differential equations have been instrumental in modelling and understanding the dynamics of biological systems. Quantitative process algebras aim to build higher level descriptions of biological systems, capturing the agents and interactions underlying their behaviour, and can be compiled down to a range of lower level mathematical models. The bond-calculus builds upon the work of Kwiatkowski, Banks, and Stark's continuous pi-calculus by adding a flexible multiway communication operation based on affinity patterns and general kinetic laws. We develop a compositional semantics based on vector fields and linear operators, which we use to define the time evolution of this system. This enables simulation and analysis via differential equation generation or stochastic simulation. Finally, we apply our framework to an existing biological model: Kuznetsov's classic model of tumour immune interactions.

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KaDE: A Tool to Compile Kappa Rules into (Reduced) ODE Models

Cited by 13 publications

References 23 publications

Integrative Models for TGF-β Signaling and Extracellular Matrix

Integrative Models for TGF-β Signaling and Extracellular Matrix

Embodying probabilistic inference in biochemical circuits

The Bond-Calculus: A Process Algebra for Complex Biological Interaction Dynamics

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