Results 1  10
of
25
A Bayesian Approach to Model Checking Biological Systems ⋆
"... Abstract. Recently, there has been considerable interest in the use of Model Checking for Systems Biology. Unfortunately, the state space of stochastic biological models is often too large for classical Model Checking techniques. For these models, a statistical approach to Model Checking has been sh ..."
Abstract

Cited by 52 (15 self)
 Add to MetaCart
(Show Context)
Abstract. Recently, there has been considerable interest in the use of Model Checking for Systems Biology. Unfortunately, the state space of stochastic biological models is often too large for classical Model Checking techniques. For these models, a statistical approach to Model Checking has been shown to be an effective alternative. Extending our earlier work, we present the first algorithm for performing statistical Model Checking using Bayesian Sequential Hypothesis Testing. We show that our Bayesian approach outperforms current statistical Model Checking techniques, which rely on tests from Classical (aka Frequentist) statistics, by requiring fewer system simulations. Another advantage of our approach is the ability to incorporate prior Biological knowledge about the model being verified. We demonstrate our algorithm on a variety of models from the Systems Biology literature and show that it enables faster verification than stateoftheart techniques, even when no prior knowledge is available. 1
Statistical model checking: An overview
 RV 2010
, 2010
"... Quantitative properties of stochastic systems are usually specified in logics that allow one to compare the measure of executions satisfying certain temporal properties with thresholds. The model checking problem for stochastic systems with respect to such logics is typically solved by a numerical a ..."
Abstract

Cited by 29 (6 self)
 Add to MetaCart
(Show Context)
Quantitative properties of stochastic systems are usually specified in logics that allow one to compare the measure of executions satisfying certain temporal properties with thresholds. The model checking problem for stochastic systems with respect to such logics is typically solved by a numerical approach [31,8,35,22,21,5] that iteratively computes (or approximates) the exact measure of paths satisfying relevant subformulas; the algorithms themselves depend on the class of systems being analyzed as well as the logic used for specifying the properties. Another approach to solve the model checking problem is to simulate the system for finitely many executions, and use hypothesis testing to infer whether the samples provide a statistical evidence for the satisfaction or violation of the specification. In this tutorial, we survey the statistical approach, and outline its main advantages in terms of efficiency, uniformity, and simplicity.
Symbolic approaches to finding control strategies in boolean networks
 Proceedings of The Sixth AsiaPacific Bioinformatics Conference, (APBC
, 2008
"... We present an exact algorithm, based on techniques from the field of Model Checking, for finding control policies for Boolean networks (BN) with control nodes. Given a BN, a set of starting states, I, a set of goal states, F, and a target time, t, our algorithm automatically finds a sequence of cont ..."
Abstract

Cited by 13 (4 self)
 Add to MetaCart
(Show Context)
We present an exact algorithm, based on techniques from the field of Model Checking, for finding control policies for Boolean networks (BN) with control nodes. Given a BN, a set of starting states, I, a set of goal states, F, and a target time, t, our algorithm automatically finds a sequence of control signals that deterministically drives the BN from I to F at, or before time t, or else guarantees that no such policy exists. Despite recent hardnessresults for finding control policies for BNs, we show that, in practice, our algorithm runs in seconds to minutes on over 13,400 BNs of varying sizes and topologies, including a BN model of embryogenesis in D. melanogaster with 15,360 Boolean variables. We then extend our method to automatically identify a set of Boolean transfer functions that reproduce the qualitative behavior of gene regulatory networks. Specifically, we automatically (re)learn a BN model of D. melanogaster embryogenesis in 5.3 seconds, from a Computational cellular and systems modeling is playing an increasingly important role in biology, bioengineering, and medicine. The promise of computer modeling is that it becomes a conduit through which reductionist data can be translated into scientific discoveries, clinical practice, and the design of new technologies. The reality of modeling is that there are still a number of unmet
Proving stabilization of biological systems
"... Abstract. We describe an efficient procedure for proving stabilization of biological systems modeled as qualitative networks or genetic regulatory networks. For scalability, our procedure uses modular proof techniques, where statespace exploration is applied only locally to small pieces of the syst ..."
Abstract

Cited by 13 (7 self)
 Add to MetaCart
(Show Context)
Abstract. We describe an efficient procedure for proving stabilization of biological systems modeled as qualitative networks or genetic regulatory networks. For scalability, our procedure uses modular proof techniques, where statespace exploration is applied only locally to small pieces of the system rather than the entire system as a whole. Our procedure exploits the observation that, in practice, the form of modular proofs can be restricted to a very limited set. For completeness, our technique falls back on a noncompositional counterexample search. Using our new procedure, we have solved a number of challenging published examples, including: a 3D model of the mammalian epidermis; a model of metabolic networks operating in type2 diabetes; a model of fate determination of vulval precursor cells in the C. elegans worm; and a model of pairrule regulation during segmentation in the Drosophila embryo. Our results show many orders of magnitude speedup in cases where previous stabilization proving techniques were known to succeed, and new results in cases where tools had previously failed. 1
Generalized Queries and Bayesian Statistical Model Checking in Dynamic Bayesian Networks: Application to Personalized Medicine
 In: Proc. 8th Ann. Intnl Conf. on Comput. Sys. Bioinf. (CSB
, 2009
"... We introduce the concept of generalized probabilistic queries in Dynamic Bayesian Networks (DBN) — computing P (φ1φ2), where φi is a formula in temporal logic encoding an equivalence class of trajectories through the variables of the model. Generalized queries include as special cases traditional ..."
Abstract

Cited by 9 (1 self)
 Add to MetaCart
(Show Context)
We introduce the concept of generalized probabilistic queries in Dynamic Bayesian Networks (DBN) — computing P (φ1φ2), where φi is a formula in temporal logic encoding an equivalence class of trajectories through the variables of the model. Generalized queries include as special cases traditional query types for DBNs (i.e., filtering, smoothing, prediction, and classification), but can also be used to express inference problems that are either impossible, or impractical to answer using traditional algorithms for inference in DBNs. We then discuss the relationship between answering generalized queries and the Probabilistic Model Checking Problem and introduce two novel algorithms for efficiently estimating P (φ1φ2) in a Bayesian fashion. Finally, we demonstrate our method by answering generalized queries that arise in the context of critical care medicine. Specifically, we show that our approach can be used to make treatment decisions for a cohort of 1,000 simulated sepsis patients, and that it outperforms Support Vector Machines, Neural Networks, and Random Forests on the same task.
On Coupling Models using ModelChecking: Effects of Irinotecan Injections on the Mammalian Cell Cycle
"... Abstract. In systems biology, the number of models of cellular processes increases rapidly, but reusing models in different contexts or for different questions remains a challenging issue. In this paper, we show how the validation of a coupled model and the optimization of its parameters with respe ..."
Abstract

Cited by 7 (4 self)
 Add to MetaCart
(Show Context)
Abstract. In systems biology, the number of models of cellular processes increases rapidly, but reusing models in different contexts or for different questions remains a challenging issue. In this paper, we show how the validation of a coupled model and the optimization of its parameters with respect to biological properties formalized in temporal logics, can be done automatically by modelchecking. More specifically, we illustrate this approach with the coupling of existing models of the mammalian cell cycle, the p53based DNAdamage repair network, and irinotecan metabolism, with respect to the biological properties of this anticancer drug. 1
Approximate probabilistic analysis of biopathway dynamics
 Bioinformatics
, 2012
"... Motivation: Biopathways are often modeled as systems of ordinary differential equations (ODEs). Such systems will usually have many unknown parameters and hence will be difficult to calibrate. Since the data available for calibration will have limited precision, an approximate representation of the ..."
Abstract

Cited by 7 (3 self)
 Add to MetaCart
(Show Context)
Motivation: Biopathways are often modeled as systems of ordinary differential equations (ODEs). Such systems will usually have many unknown parameters and hence will be difficult to calibrate. Since the data available for calibration will have limited precision, an approximate representation of the ODEs dynamics should suffice. One must however be able to efficiently construct such approximations for large models and perform model calibration and subsequent analysis. Results: We present a GPUbased scheme by which a system of ODEs is approximated as a dynamic Bayesian network (DBN). We then construct a model checking procedure for DBNs based on a simple probabilistic linear time temporal logic. The GPU implementation considerably extends the reach of our previous PCcluster based implementation (Liu et al., 2011b). Further, the key components of our algorithm can serve as the GPU kernel for other Monte Carlo simulations based analysis of biopathway dynamics. Similarly, our model checking framework is a generic one and can be applied in other systems biology settings. We have tested our methods on three ODE models of biopathways: the EGFNGF pathway, the segmentation clock network and the MLCphosphorylation pathway models. The GPU implementation shows significant gains in performance and scalability while the model checking framework turns out to be convenient and efficient for specifying and verifying interesting pathways properties. Availability: The source code is freely available at
Verification of an afdx infrastructure using simulations and probabilities. volume 6418 of LNCS
, 2010
"... Abstract. Until recently, there was not a strong need for networking inside aircrafts. Indeed, the communications were mainly cabled and handled by Ethernet protocols. The evolution of avionics embedded systems and the number of integrated functions in civilian aircrafts has changed the situation. I ..."
Abstract

Cited by 5 (3 self)
 Add to MetaCart
(Show Context)
Abstract. Until recently, there was not a strong need for networking inside aircrafts. Indeed, the communications were mainly cabled and handled by Ethernet protocols. The evolution of avionics embedded systems and the number of integrated functions in civilian aircrafts has changed the situation. Indeed, those functionalities implies a huge increase in the quantity of data exchanged and thus in the number of connections between functions. Among the available mechanisms provided to handle this new complexity, one find Avionics Full Duplex Switched Ethernet (AFDX), a protocol that allows to simulate a pointtopoint network between a source and one or more destinations. The core idea in AFDX is the one of Virtual Links (VL) that are used to simulate pointtopoint communication between devices. One of the main challenge is to show that the total delivery time for packets on VL is bounded by some predefined value. This is a difficult problem that also requires to provide a formal, but quite evolutive, model of the AFDX network. In this paper, we propose to use a componentbased design methodology to describe the behavior of the model. We then propose a stochastic abstraction that allows not only to simplify the complexity of the verification process but also to provide quantitative information on the protocol. 1
Statistical Model Checking for Distributed ProbabilisticControl Hybrid Automata with Smart Grid Applications
, 2011
"... This technical report is a more detailed version of a published paper [12]. The power industry is currently moving towards a more dynamical, intelligent power grid. This Smart Grid is still in its infancy and a formal evaluation of the expensive technologies and ideas on the table is necessary befor ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
(Show Context)
This technical report is a more detailed version of a published paper [12]. The power industry is currently moving towards a more dynamical, intelligent power grid. This Smart Grid is still in its infancy and a formal evaluation of the expensive technologies and ideas on the table is necessary before committing to a full investment. In this paper, we argue that a good model for the Smart Grid must match its basic properties: it must be hybrid (both evolve over time, and perform control/computation), distributed (multiple concurrently executing entities), and allow for asynchronous communication and stochastic behaviour (to accurately model realworld power consumption). We propose Distributed ProbabilisticControl Hybrid Automata (DPCHA) as a model for this purpose, and extend Bounded LTL to Quantified Bounded LTL in order to adapt and apply existing statistical modelchecking techniques. We provide an implementation of a framework for developing and verifying DPCHAs. Finally, we conduct a case study for Smart Grid communications analysis. Keywords: statistical model checking, hybrid automata, hybrid systems, power
Quantitative Analysis of AODV and its Variants on Dynamic Topologies using Statistical Model Checking
"... Abstract. Wireless Mesh Networks (WMNs) are selforganising adhoc networks that support broadband communication. Due to changes in the topology, route discovery and maintenance play a crucial role in the reliability and the performance of such networks. Formal analysis of WMNs using exhaustive mode ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
Abstract. Wireless Mesh Networks (WMNs) are selforganising adhoc networks that support broadband communication. Due to changes in the topology, route discovery and maintenance play a crucial role in the reliability and the performance of such networks. Formal analysis of WMNs using exhaustive model checking techniques is often not feasible: network size (up to hundreds of nodes) and topology changes yield statespace explosion. Statistical Model Checking, however, can overcome this problem and allows a quantitative analysis. In this paper we illustrate this by a careful analysis of the Ad hoc Ondemand Distance Vector (AODV) protocol. We show that some optional features of AODV are not useful, and that AODV shows unexpected behaviour—yielding a high probability of route discovery failure. 1