An overview of existing modeling tools making use of model checking in the analysis of biochemical networks

Carrillo, Miguel; Góngora, Pedro Arturo; Rosenblueth, David A.

doi:10.3389/fpls.2012.00155

Cited by 26 publications

(24 citation statements)

References 95 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Model checking is a formal method from computer science to determine whether a transition system satisfies a temporal specification. See Carrillo et al ( 2012 ) for a review of its application to computational biology.…”

Section: Introductionmentioning

confidence: 99%

Approximating Attractors of Boolean Networks by Iterative CTL Model Checking

Klarner

Siebert

2015

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

This paper introduces the notion of approximating asynchronous attractors of Boolean networks by minimal trap spaces. We define three criteria for determining the quality of an approximation: “faithfulness” which requires that the oscillating variables of all attractors in a trap space correspond to their dimensions, “univocality” which requires that there is a unique attractor in each trap space, and “completeness” which requires that there are no attractors outside of a given set of trap spaces. Each is a reachability property for which we give equivalent model checking queries. Whereas faithfulness and univocality can be decided by model checking the corresponding subnetworks, the naive query for completeness must be evaluated on the full state space. Our main result is an alternative approach which is based on the iterative refinement of an initially poor approximation. The algorithm detects so-called autonomous sets in the interaction graph, variables that contain all their regulators, and considers their intersection and extension in order to perform model checking on the smallest possible state spaces. A benchmark, in which we apply the algorithm to 18 published Boolean networks, is given. In each case, the minimal trap spaces are faithful, univocal, and complete, which suggests that they are in general good approximations for the asymptotics of Boolean networks.

show abstract

Section: Introductionmentioning

confidence: 99%

Approximating Attractors of Boolean Networks by Iterative CTL Model Checking

Klarner

Siebert

2015

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…Model checking ( Clarke & Emerson, 1982 ) is an exhaustive technique used to verify the existence or absence of different properties in a given system ( Carrillo, Góngora & Rosenblueth, 2012 ). The system is represented as a state graph and different properties test for their prevalence either throughout the state graph ( Carrillo, Góngora & Rosenblueth, 2012 ).…”

Section: Methodsmentioning

confidence: 99%

Formal modeling and analysis of ER-αassociated Biological Regulatory Network in breast cancer

Khalid¹,

Hanif²,

Tareen

et al. 2016

PeerJ

View full text Add to dashboard Cite

BackgroundBreast cancer (BC) is one of the leading cause of death among females worldwide. The increasing incidence of BC is due to various genetic and environmental changes which lead to the disruption of cellular signaling network(s). It is a complex disease in which several interlinking signaling cascades play a crucial role in establishing a complex regulatory network. The logical modeling approach of René Thomas has been applied to analyze the behavior of estrogen receptor-alpha (ER-α) associated Biological Regulatory Network (BRN) for a small part of complex events that leads to BC metastasis.MethodsA discrete model was constructed using the kinetic logic formalism and its set of logical parameters were obtained using the model checking technique implemented in the SMBioNet software which is consistent with biological observations. The discrete model was further enriched with continuous dynamics by converting it into an equivalent Petri Net (PN) to analyze the logical parameters of the involved entities.ResultsIn-silico based discrete and continuous modeling of ER-α associated signaling network involved in BC provides information about behaviors and gene-gene interaction in detail. The dynamics of discrete model revealed, imperative behaviors represented as cyclic paths and trajectories leading to pathogenic states such as metastasis. Results suggest that the increased expressions of receptors ER-α, IGF-1R and EGFR slow down the activity of tumor suppressor genes (TSGs) such as BRCA1, p53 and Mdm2 which can lead to metastasis. Therefore, IGF-1R and EGFR are considered as important inhibitory targets to control the metastasis in BC.ConclusionThe in-silico approaches allow us to increase our understanding of the functional properties of living organisms. It opens new avenues of investigations of multiple inhibitory targets (ER-α, IGF-1R and EGFR) for wet lab experiments as well as provided valuable insights in the treatment of cancers such as BC.

show abstract

“…Such models comprise nonlinear systems of ordinary differential equations (ODEs), where each variable denotes the concentration of a different gene product [31]. …”

Section: Modeling Algorithmsmentioning

confidence: 99%

Genome Scale Modeling in Systems Biology: Algorithms and Resources

Najafi

Bidkhori

Bozorgmehr

et al. 2014

View full text Add to dashboard Cite

In recent years, in silico studies and trial simulations have complemented experimental procedures. A model is a description of a system, and a system is any collection of interrelated objects; an object, moreover, is some elemental unit upon which observations can be made but whose internal structure either does not exist or is ignored. Therefore, any network analysis approach is critical for successful quantitative modeling of biological systems. This review highlights some of most popular and important modeling algorithms, tools, and emerging standards for representing, simulating and analyzing cellular networks in five sections. Also, we try to show these concepts by means of simple example and proper images and graphs. Overall, systems biology aims for a holistic description and understanding of biological processes by an integration of analytical experimental approaches along with synthetic computational models. In fact, biological networks have been developed as a platform for integrating information from high to low-throughput experiments for the analysis of biological systems. We provide an overview of all processes used in modeling and simulating biological networks in such a way that they can become easily understandable for researchers with both biological and mathematical backgrounds. Consequently, given the complexity of generated experimental data and cellular networks, it is no surprise that researchers have turned to computer simulation and the development of more theory-based approaches to augment and assist in the development of a fully quantitative understanding of cellular dynamics.

show abstract

An overview of existing modeling tools making use of model checking in the analysis of biochemical networks

Cited by 26 publications

References 95 publications

Approximating Attractors of Boolean Networks by Iterative CTL Model Checking

Approximating Attractors of Boolean Networks by Iterative CTL Model Checking

Formal modeling and analysis of ER-αassociated Biological Regulatory Network in breast cancer

Genome Scale Modeling in Systems Biology: Algorithms and Resources

Contact Info

Product

Resources

About