1,721,150 research outputs found
Reconstructing species-based dynamics from reduced stochastic rule-based models
No full textMany bio-molecular reactions inside the cell are characterized by complex-formation and mutual modification of a few constituent molecules that give rise to a combinatorial number of reachable complexes or species. For such cases rule-based models (or site-graph-rewrite rules), offer a compact model description, by enumerating only the necessary context of interacting molecules. Such a model specification induces symmetries in the underlying Markov chain, which we have recently exploited for model reduction, based on a backward Markovian bisimulation. Interestingly, the method showed a theoretical possibility of reconstructing the high-dimensional species-based dynamics from the aggregate state. In this paper, we present a procedure for reconstructing the high-dimensional species-based dynamics from the aggregate state, and we provide an algorithm for computing such de-aggregation functions explicitly. The algorithm involves counting the automorphisms of a connected site-graph, and has a quadratic time complexity in the number of molecules which constitute the site-graphs of interest. We provide illustrating case studies. © 2012 IEEE
Stochastic fragments: A framework for the ex- act reduction of the stochastic semantics of rule-based models
No full textApproximate model reductions for combinatorial reaction systems
No full textThe paper considers a model reduction technique that is well-suited for biochemical reaction systems giving rise to the assembly of a large number of different molecular species. The reduction is performed by grouping species with common properties, directly from the model specification in terms of a rule-based language. In recent works, general algorithms for the exact reductions of rule-based models were established, but the state space often remains combinatorial. We extend this line of research by introducing approximate reductions, and an error measure which allows us to quantitatively study the effect of approximate model reductions
Model Decomposition and Stochastic Fragments
No full textAbstractIn this paper, we discuss a method for decomposition, abstraction and reconstruction of the stochastic semantics of rule-based systems with conserved number of agents. Abstraction is induced by counting fragments instead of the species, which are the standard entities of information in molecular signaling. The rule-set can be decomposed to smaller rule-sets, so that the fragment-based dynamics of the whole rule-set is exactly a composition of species-based dynamics of smaller rule-sets. The reconstruction of the transient species-based dynamics is possible for certain initial distributions. We show that, if all the rules in a rule set are reversible, the reconstruction of the species-based dynamics is always possible at the stationary distribution. We use a case study of colloidal aggregation to demonstrate that the method can reduce the state space exponentially with respect to the standard, species-based description
Stochastic Semantics of Signaling as a Composition of Agent-view Automata
Full text linkAbstractIn this paper we present a formalism based on stochastic automata to describe the stochastic dynamics of signal transduction networks that are specified by rule-sets. Our formalism gives a modular description of the underlying stochastic process, in the sense that it is a composition of smaller units, agent-views. The view of an agent is an automaton that identifies all local modification changes of that agent (internal state modifications, binding and unbinding), but also those of interacting agents, which are tested within the same rule. We show how to represent the generator matrix of the underlying Markov process of the whole rule-set as Kronecker sums of the rate matrices belonging to individual view-automata. In the absence of birth the automata are finite, since the number of different contexts in which one agent can appear in
Markov chain aggregation and its applications to combinatorial reaction networks
No full textWe consider a continuous-time Markov chain (CTMC) whose state space is partitioned into aggregates, and each aggregate is assigned a probability measure. A sufficient condition for defining a CTMC over the aggregates is presented as a variant of weak lumpability, which also characterizes that the measure over the original process can be recovered from that of the aggregated one. We show how the applicability of de-aggregation depends on the initial distribution. The application section is devoted to illustrate how the developed theory aids in reducing CTMC models of biochemical systems particularly in connection to protein-protein interactions. We assume that the model is written by a biologist in form of site-graph-rewrite rules. Site-graph-rewrite rules compactly express that, often, only a local context of a protein (instead of a full molecular species) needs to be in a certain configuration in order to trigger a reaction event. This observation leads to suitable aggregate Markov chains with smaller state spaces, thereby providing sufficient reduction in computational complexity. This is further exemplified in two case studies: simple unbounded polymerization and early EGFR/insulin crosstalk
Coarse-grained Brownian dynamics simulation of rule-based models
No full textStudying spatial effects in signal transduction, such as colocalization along scaffold molecules, comes at a cost of complexity. In this paper, we propose a coarse-grained, particle-based spatial simulator, suited for large signal transduction models. Our approach is to combine the particle-based reaction and diffusion method, and (non-spatial) rule-based modeling: the location of each molecular complex is abstracted by a spheric particle, while its internal structure in terms of a site-graph is maintained explicit. The particles diffuse inside the cellular compartment and the colliding complexes stochastically interact according to a rule-based scheme. Since rules operate over molecular motifs (instead of full complexes), the rule set compactly describes a combinatorial or even infinite number of reactions. The method is tested on a model of Mitogen Activated Protein Kinase (MAPK) cascade of yeast pheromone response signaling. Results demonstrate that the molecules of the MAPK cascade co-localize along scaffold molecules, while the scaffold binds to a plasma membrane bound upstream component, localizing the whole signaling complex to the plasma membrane. Especially we show, how rings stabilize the resulting molecular complexes and derive the effective dissociation rate constant for it
Combining model reductions
No full textMolecular biological models usually suffer from a large combinatorial explosion. Indeed, proteins form complexes and modify each others, which leads to the formation of a huge number of distinct chemical species (i.e. non-isomorphic connected components of proteins). Thus we cannot generate explicitly the quantitative semantics of these models, and even less compute their properties. Model reduction aims at reducing this complexity by providing another grain of observation. In this paper, we propose two unifying frameworks for combining model reductions: we propose a symmetric product operator for combining model reductions for stochastic semantics and we show how to abstract further existing reduced differential systems by the means of linear projections. We apply both frameworks so as to abstract further existing reduced quantitative semantics of the models that are written in Kappa, by taking into account symmetries among binding sites in proteins. © 2010 Elsevier B.V. All rights reserved
Lumpability abstractions of rule-based systems
No full textThe induction of a signaling pathway is characterized by transient complex formation and mutual posttranslational modification of proteins. To faithfully capture this combinatorial process in a mathematical model is an important challenge in systems biology. Exploiting the limited context on which most binding and modification events are conditioned, attempts have been made to reduce the combinatorial complexity by quotienting the reachable set of molecular species, into species aggregates while preserving the deterministic semantics of the thermodynamic limit. Recently we proposed a quotienting that also preserves the stochastic semantics and that is complete in the sense that the semantics of individual species can be recovered from the aggregate semantics. In this paper we prove that this quotienting yields a sufficient condition for weak lumpability and that it gives rise to a backward Markov bisimulation between the original and aggregated transition system. We illustrate the framework on a case study of the EGF/insulin receptor crosstalk
Optimal Kullback-Leibler Aggregation via Information Bottleneck
No full textIn this paper, we present a method for reducing a regular, discrete-time Markov chain (DTMC) to another DTMC with a given, typically much smaller number of states. The cost of reduction is defined as the Kullback-Leibler divergence rate between a projection of the original process through a partition function and the a DTMC on the correspondingly partitioned state space. Finding the reduced model with minimal cost is computationally expensive, as it requires exhaustive search among all state space partitions, and exact evaluation of the reduction cost for each candidate partition. In our approach, we optimize an upper bound on the reduction cost instead of the exact cost; The proposed upper bound is easy to compute and it is tight in the case when the original chain is lumpable with respect to the partition. Then, we express the problem in form of information bottleneck optimization, and we propose the agglomerative information bottleneck algorithm for finding a locally optimal solution. The theory is illustrated with examples and one application scenario in the context of modeling bio-molecular interactions
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