A SystemC-based platform for assertion-based verification and mutation analysis in systems biology

Boolean models are gaining an increasing interest for reproducing dynamic behaviours, understanding processes, and predicting emerging properties of cellular signalling networks through in-silico experiments. They are emerging as a valid alternative to the quantitative approaches (i.e., based on ordinary differential equations) for exploratory modelling when little is known about reaction kinetics or equilibrium constants in the context of gene expression or signalling. Even though several approaches and software have been recently proposed for logic modelling of biological systems, they are limited to specific modelling contexts and they lack of automation in analysing biological properties such as complex attractors, molecule vulnerability, dose response. This paper presents a design and verification platform based on SystemC that applies methodologies and tools well established in the electronic-design automation (EDA) field such as assertion-based verification (ABV) and mutation analysis, which allow complex attractors (i.e., protein oscillations) and robustness/sensitivity of the signalling networks to be simulated and analysed. The paper reports the results obtained by applying such verification techniques for the analysis of the intracellular signalling network controlling integrin activation mediating leukocyte recruitment from the blood into the tissues.

[1]  Ilan Beer,et al.  FoCs: Automatic Generation of Simulation Checkers from Formal Specifications , 2000, CAV.

[2]  Mark A. Miller,et al.  A Dynamic Model of Guard Cell Abscisic Acid Signaling , 2006 .

[3]  Jim A. Rogers,et al.  ChemChains: a platform for simulation and analysis of biochemical networks aimed to laboratory scientists , 2009, BMC Systems Biology.

[4]  Michelle L. Wynn,et al.  Logic-based models in systems biology: a predictive and parameter-free network analysis method. , 2012, Integrative biology : quantitative biosciences from nano to macro.

[5]  Carlo Laudanna,et al.  A SystemC Platform for Signal Transduction Modelling and Simulation in Systems Biology , 2015, ACM Great Lakes Symposium on VLSI.

[6]  M Chaves,et al.  Methods of robustness analysis for Boolean models of gene control networks. , 2006, Systems biology.

[7]  Fabian J. Theis,et al.  Odefy -- From discrete to continuous models , 2010, BMC Bioinformatics.

[8]  Zeljko Zilic,et al.  Generating Hardware Assertion Checkers , 2008 .

[9]  Xin Liu,et al.  Dynamical and Structural Analysis of a T Cell Survival Network Identifies Novel Candidate Therapeutic Targets for Large Granular Lymphocyte Leukemia , 2011, PLoS Comput. Biol..

[10]  Franco Fummi,et al.  Testbench Qualification of SystemC TLM Protocols through Mutation Analysis , 2014, IEEE Transactions on Computers.

[11]  Song Li,et al.  Boolean network simulations for life scientists , 2008, Source Code for Biology and Medicine.

[12]  Giovanni De Micheli,et al.  Dynamic simulation of regulatory networks using SQUAD , 2007, BMC Bioinformatics.

[13]  Alan Veliz-Cuba,et al.  ADAM: Analysis of Discrete Models of Biological Systems Using Computer Algebra , 2010, BMC Bioinformatics.

[14]  Clare E. Giacomantonio,et al.  A Boolean Model of the Gene Regulatory Network Underlying Mammalian Cortical Area Development , 2010, PLoS Comput. Biol..

[15]  Hans A. Kestler,et al.  BoolNet - an R package for generation, reconstruction and analysis of Boolean networks , 2010, Bioinform..

[16]  Kwang-Ting Cheng,et al.  A single-state-transition fault model for sequential machines , 1990, 1990 IEEE International Conference on Computer-Aided Design. Digest of Technical Papers.

[17]  S. Leibler,et al.  Robustness in simple biochemical networks , 1997, Nature.

[18]  N LeNovère.  Quantitative and logic modelling of molecular and gene networks. , 2015 .

[19]  Réka Albert,et al.  A network model for plant–pollinator community assembly , 2010, Proceedings of the National Academy of Sciences.

[20]  Carlo Laudanna,et al.  JAK tyrosine kinases promote hierarchical activation of Rho and Rap modules of integrin activation , 2013, The Journal of cell biology.

[21]  R. Albert,et al.  Network model of survival signaling in large granular lymphocyte leukemia , 2008, Proceedings of the National Academy of Sciences.

[22]  D Thieffry,et al.  GINsim: a software suite for the qualitative modelling, simulation and analysis of regulatory networks. , 2006, Bio Systems.

[23]  Franco Fummi,et al.  Reusing RTL Assertion Checkers for Verification of SystemC TLM Models , 2015, J. Electron. Test..

[24]  H. D. Jong,et al.  Qualitative simulation of genetic regulatory networks using piecewise-linear models , 2004, Bulletin of mathematical biology.

[25]  Giovanni Scardoni,et al.  Dynamic Modeling and Simulation of Leukocyte Integrin Activation through an Electronic Design Automation Framework , 2014, CMSB.

[26]  David Zhang,et al.  SimBoolNet—a Cytoscape plugin for dynamic simulation of signaling networks , 2009, Bioinform..

[27]  M. Cybulsky,et al.  Getting to the site of inflammation: the leukocyte adhesion cascade updated , 2007, Nature Reviews Immunology.

[28]  Oliver Sawodny,et al.  ON/OFF and Beyond - A Boolean Model of Apoptosis , 2009, PLoS Comput. Biol..

[29]  S. Klamt,et al.  Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks , 2013, Cell Communication and Signaling.

[30]  Q. Ouyang,et al.  The yeast cell-cycle network is robustly designed. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Kwang-Ting Cheng,et al.  SCEMIT: A SystemC error and mutation injection tool , 2010, Design Automation Conference.