Approximate probabilistic analysis of biopathway dynamics

Liu, Bing; Hagiescu, Andrei; Palaniappan, Sucheendra K.; Chattopadhyay, Bipasa; Cui, Zheng; Wong, Weng-Fai; Thiagarajan, P. S.

doi:10.1093/bioinformatics/bts166

Cited by 25 publications

(13 citation statements)

References 20 publications

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“…In comparison, a 10-PC cluster failed to deal with this large model. 113 We are also beginning to study the means for implementing the parameter estimation and sensitivity analysis tasks on the GPU platform. Finally, work is underway for doing probabilistic model checking on the DBN approximations.…”

Section: Resultsmentioning

confidence: 99%

Modeling and Analysis of Biopathways Dynamics

Liu

Thiagarajan

2012

J. Bioinform. Comput. Biol.

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Cellular processes are governed and coordinated by a multitude of biopathways. A pathway can be viewed as a complex network of biochemical reactions. The dynamics of this network largely determines the functioning of the pathway. Hence the modeling and analysis of biochemical networks dynamics is an important problem and is an active area of research. Here we review quantitative models of biochemical networks based on ordinary differential equations (ODEs). We mainly focus on the parameter estimation and sensitivity analysis problems and survey the current methods for tackling them. In this context we also highlight a recently developed probabilistic approximation technique using which these two problems can be considerably simplified.

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Section: Resultsmentioning

confidence: 99%

Modeling and Analysis of Biopathways Dynamics

Liu

Thiagarajan

2012

J. Bioinform. Comput. Biol.

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“…More formally, the PLTLs format would be P ≥ 0.45 G ([ gcy 5] > [ gcy 6]) {[ time ] > 300} AND P ≤ 0.55 G ([ gcy 5] > [ gcy 6]) {[ time ] > 300}. Readers unfamiliar with temporal logics and model checking in systems biology can find the relevant background materials in [19-21]. …”

Section: Resultsmentioning

confidence: 99%

Improved statistical model checking methods for pathway analysis

et al. 2012

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Statistical model checking techniques have been shown to be effective for approximate model checking on large stochastic systems, where explicit representation of the state space is impractical. Importantly, these techniques ensure the validity of results with statistical guarantees on errors. There is an increasing interest in these classes of algorithms in computational systems biology since analysis using traditional model checking techniques does not scale well. In this context, we present two improvements to existing statistical model checking algorithms. Firstly, we construct an algorithm which removes the need of the user to define the indifference region, a critical parameter in previous sequential hypothesis testing algorithms. Secondly, we extend the algorithm to account for the case when there may be a limit on the computational resources that can be spent on verifying a property; i.e, if the original algorithm is not able to make a decision even after consuming the available amount of resources, we resort to a p-value based approach to make a decision. We demonstrate the improvements achieved by our algorithms in comparison to current algorithms first with a straightforward yet representative example, followed by a real biological model on cell fate of gustatory neurons with microRNAs.

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“…They are used to then fill the Conditional Probability Tables (CPTs) of the DBN by a simple counting strategy. This can be made efficiently using the power of GPUs [15]. More details of this abstraction are described in Section 2.2.…”

Section: Introductionmentioning

confidence: 99%

“…This significantly reduces the computational burden of repetitive analysis tasks on the original system as shown in [17,18]. Probabilistic verification methods can also be applied [11,15] to further augment the framework.…”

Section: Introductionmentioning

confidence: 99%

A Look-Ahead Simulation Algorithm for DBN Models of Biochemical Pathways

Palaniappan

Pichené

Batt

et al. 2016

Hybrid Systems Biology

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Abstract. Dynamic Bayesian Networks (DBNs) have been proposed [16] as an efficient abstraction formalism of biochemical models. They have been shown to approximate well the dynamics of biochemical models, while offering improved efficiency for their analysis [17,18]. In this paper, we compare different representations and simulation schemes on these DBNs, testing their efficiency and accuracy as abstractions of biological pathways. When generating these DBNs, many configurations are never explored by the underlying dynamics of the biological systems. This can be used to obtain sparse representations to store and analyze DBNs in a compact way. On the other hand, when simulating these DBNs, singular configurations may be encountered, that is configurations from where no transition probability is defined. This makes simulation more complex. We initially evaluate two simple strategies for dealing with singularities: First, re-sampling simulations visiting singular configurations; second filling up uniformly these singular transition probabilities. We show that both these approaches are error prone. Next, we propose a new algorithm which samples only those configurations that avoid singularities by using a look-ahead strategy. Experiments show that this approach is the most accurate while having a reasonable run time.

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Approximate probabilistic analysis of biopathway dynamics

Abstract: The source code is freely available at http://www.comp.nus.edu.sg/~rpsysbio/pada-gpu/

Cited by 25 publications

References 20 publications

Modeling and Analysis of Biopathways Dynamics

Modeling and Analysis of Biopathways Dynamics

Improved statistical model checking methods for pathway analysis

A Look-Ahead Simulation Algorithm for DBN Models of Biochemical Pathways

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