In Silico Clinical Trials through AI and Statistical Model Checking

A Virtual Patient (VP) is a computational model accounting for individualised (patho-) physiology and Pharmaco-Kinetics/Dynamics of relevant drugs. Availability of VPs is among the enabling technology for In Silico Clinical Trials. Here we shortly outline the state of the art as for VP generation and summarise our recent work on Artificial Intelligence (AI) and Statistical Model Checking based generation of VPs.

[1]  Toni Mancini,et al.  SyLVaaS: System Level Formal Verification as a Service , 2015, 2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing.

[2]  Toni Mancini,et al.  An Efficient Algorithm for Network Vulnerability Analysis Under Malicious Attacks , 2018, ISMIS.

[3]  Enrico Tronci,et al.  Linearizing Discrete-Time Hybrid Systems , 2017, IEEE Transactions on Automatic Control.

[4]  Gonzalo Guillén-Gosálbez,et al.  Deterministic global optimization algorithm based on outer approximation for the parameter estimation of nonlinear dynamic biological systems , 2012, BMC Bioinformatics.

[5]  François Fages,et al.  Symbolic Model Checking of Biochemical Networks , 2003, CMSB.

[6]  Wei Wu,et al.  A Parallel Attractor Finding Algorithm Based on Boolean Satisfiability for Genetic Regulatory Networks , 2014, PloS one.

[7]  E. Tronci,et al.  Lack of Associations between Female Hormone Levels and Visuospatial Working Memory, Divided Attention and Cognitive Bias across Two Consecutive Menstrual Cycles , 2017, Front. Behav. Neurosci..

[8]  Toni Mancini,et al.  Optimal Fault-Tolerant Placement of Relay Nodes in a Mission Critical Wireless Network , 2018, RCRA@FLoC.

[9]  Toni Mancini,et al.  On minimising the maximum expected verification time , 2017, Inf. Process. Lett..

[10]  J. Geoffrey Chase,et al.  Characterisation of the iterative integral parameter identification method , 2011, Medical & Biological Engineering & Computing.

[11]  Dirk Husmeier,et al.  A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thaliana , 2013, BMC Bioinformatics.

[12]  Toni Mancini,et al.  Residential demand management using individualised demand aware price policies , 2017, 2017 IEEE Power & Energy Society General Meeting.

[13]  Lubos Brim,et al.  Parameter Scanning by Parallel Model Checking with Applications in Systems Biology , 2010, 2010 Ninth International Workshop on Parallel and Distributed Methods in Verification, and Second International Workshop on High Performance Computational Systems Biology.

[14]  Souhei Ito,et al.  Qualitative analysis of gene regulatory networks by temporal logic , 2015, Theor. Comput. Sci..

[15]  Maw-Shang Chang,et al.  Dynamic sensitivity analysis of biological systems , 2008, BMC Bioinformatics.

[16]  Toni Mancini,et al.  Computing Biological Model Parameters by Parallel Statistical Model Checking , 2015, IWBBIO.

[17]  Toni Mancini,et al.  Parallel Statistical Model Checking for Safety Verification in Smart Grids , 2018, 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm).

[18]  Edmund M. Clarke,et al.  Formal analysis for logical models of pancreatic cancer , 2011, IEEE Conference on Decision and Control and European Control Conference.

[19]  E. Tronci,et al.  Associations Between Natural Physiological and Supraphysiological Estradiol Levels and Stress Perception , 2019, Front. Psychol..

[20]  Christian C. Zuluaga-Bedoya,et al.  Identifiability Analysis of Three Control-Oriented Models for Use in Artificial Pancreas Systems , 2018, Journal of diabetes science and technology.

[21]  John G. Albeck,et al.  Systematic calibration of a cell signaling network model , 2010, BMC Bioinformatics.

[22]  Toni Mancini,et al.  System Level Formal Verification via Model Checking Driven Simulation , 2013, CAV.

[23]  Toni Mancini,et al.  Combinatorial problem solving over relational databases: view synthesis through constraint-based local search , 2012, SAC '12.

[24]  Toni Mancini,et al.  Patient-specific models from inter-patient biological models and clinical records , 2014, 2014 Formal Methods in Computer-Aided Design (FMCAD).

[25]  Aurélien Naldi,et al.  Model Checking to Assess T-Helper Cell Plasticity , 2014, bioRxiv.

[26]  Toni Mancini,et al.  User Flexibility Aware Price Policy Synthesis for Smart Grids , 2015, 2015 Euromicro Conference on Digital System Design.

[27]  J. Banga,et al.  Structural Identifiability of Systems Biology Models: A Critical Comparison of Methods , 2011, PloS one.

[28]  Toni Mancini,et al.  Computing Personalised Treatments through In Silico Clinical Trials , 2018, RCRA@FLoC.

[29]  N. Novère.  Quantitative and logic modelling of molecular and gene networks , 2015, Nature Reviews Genetics.

[30]  Jonathan M. Garibaldi,et al.  Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[31]  Nathalie Lassau,et al.  Virtual Patients and Sensitivity Analysis of the Guyton Model of Blood Pressure Regulation: Towards Individualized Models of Whole-Body Physiology , 2012, PLoS Comput. Biol..

[32]  Jasper A. Vrugt,et al.  PyDREAM: high-dimensional parameter inference for biological models in python , 2017, Bioinform..

[33]  James Lu,et al.  An In-Silico Model of Lipoprotein Metabolism and Kinetics for the Evaluation of Targets and Biomarkers in the Reverse Cholesterol Transport Pathway , 2014, PLoS Comput. Biol..

[34]  Edmund M. Clarke,et al.  Studies of biological networks with statistical model checking: application to immune system cells , 2013, BCB.

[35]  Eva Balsa-Canto,et al.  Optimality and identification of dynamic models in systems biology: an inverse optimal control framework , 2018, Bioinform..

[36]  U. Abdulla,et al.  Identification of parameters in systems biology. , 2018, Mathematical biosciences.

[37]  Jan Hasenauer,et al.  PESTO: Parameter EStimation TOolbox , 2017, Bioinform..

[38]  Radu Mateescu,et al.  Validation of qualitative models of genetic regulatory networks by model checking: analysis of the nutritional stress response in Escherichia coli , 2005, ISMB.

[39]  Nicole Radde,et al.  Hamiltonian Monte Carlo methods for efficient parameter estimation in steady state dynamical systems , 2014, BMC Bioinformatics.

[40]  T R Pieber,et al.  A Generic Integrated Physiologically based Whole-body Model of the Glucose-Insulin-Glucagon Regulatory System , 2013, CPT: pharmacometrics & systems pharmacology.

[41]  Marta Z. Kwiatkowska,et al.  Probabilistic model checking of complex biological pathways , 2008, Theor. Comput. Sci..

[42]  Radu Grosu,et al.  Monte Carlo Model Checking , 2005, TACAS.

[43]  David A. Rosenblueth,et al.  "Antelope": a hybrid-logic model checker for branching-time Boolean GRN analysis , 2011, BMC Bioinformatics.

[44]  Stefan Willmann,et al.  Using Bayesian-PBPK modeling for assessment of inter-individual variability and subgroup stratification , 2013, In Silico Pharmacology.

[45]  Dionisios G. Vlachos,et al.  Parametric sensitivity analysis for biochemical reaction networks based on pathwise information theory , 2013, BMC Bioinformatics.

[46]  Toni Mancini,et al.  Evaluating ASP and Commercial Solvers on the CSPLib , 2006, Constraints.

[47]  J. M. Pastor,et al.  Advanced Boolean modeling of biological networks applied to systems pharmacology , 2017, Bioinform..

[48]  Frank A. Quinn,et al.  Establishment of detailed reference values for luteinizing hormone, follicle stimulating hormone, estradiol, and progesterone during different phases of the menstrual cycle on the Abbott ARCHITECT® analyzer , 2006, Clinical chemistry and laboratory medicine.

[49]  Enrico Tronci,et al.  Model-based synthesis of control software from system-level formal specifications , 2011, TSEM.

[50]  Peter Deuflhard,et al.  A mathematical model of the human menstrual cycle for the administration of GnRH analogues. , 2013, Journal of theoretical biology.

[51]  Julio R. Banga,et al.  Reverse engineering and identification in systems biology: strategies, perspectives and challenges , 2014, Journal of The Royal Society Interface.

[52]  E. Tronci,et al.  Negative affect is unrelated to fluctuations in hormone levels across the menstrual cycle: Evidence from a multisite observational study across two successive cycles. , 2017, Journal of psychosomatic research.

[53]  Choujun Zhan,et al.  Parameter estimation in systems biology models using spline approximation , 2011, BMC Systems Biology.

[54]  Giancarlo Mauri,et al.  Computational Intelligence for Parameter Estimation of Biochemical Systems , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[55]  Toni Mancini,et al.  Anytime system level verification via parallel random exhaustive hardware in the loop simulation , 2016, Microprocess. Microsystems.

[56]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .