Should We Learn Probabilistic Models for Model Checking? A New Approach and An Empirical Study

Many automated system analysis techniques (e.g., model checking, model-based testing) rely on first obtaining a model of the system under analysis. System modeling is often done manually, which is often considered as a hindrance to adopt model-based system analysis and development techniques. To overcome this problem, researchers have proposed to automatically "learn" models based on sample system executions and shown that the learned models can be useful sometimes. There are however many questions to be answered. For instance, how much shall we generalize from the observed samples and how fast would learning converge? Or, would the analysis result based on the learned model be more accurate than the estimation we could have obtained by sampling many system executions within the same amount of time? In this work, we investigate existing algorithms for learning probabilistic models for model checking, propose an evolution-based approach for better controlling the degree of generalization and conduct an empirical study in order to answer the questions. One of our findings is that the effectiveness of learning may sometimes be limited.Comment: 15 pages, plus 2 reference pages, accepted by FASE 2017 in ETAP

Modulation of Glucose Transporter 1 (GLUT1) Expression Levels Alters Mouse Mammary Tumor Cell Growth In Vitro and In Vivo

Tumor cells exhibit an altered metabolism characterized by elevated aerobic glycolysis and lactate secretion which is supported by an increase in glucose transport and consumption. We hypothesized that reducing or eliminating the expression of the most prominently expressed glucose transporter(s) would decrease the amount of glucose available to breast cancer cells thereby decreasing their metabolic capacity and proliferative potential

Fractional Sobolev Metrics on Spaces of Immersed Curves

Motivated by applications in the field of shape analysis, we study reparametrization invariant, fractional order Sobolev-type metrics on the space of smooth regular curves Imm(S1 , R ) and on its Sobolev completions ℐ (S1 , R ). We prove local well-posedness of the geodesic equations both on the Banach manifold ℐ (S1 , R ) and on the Fr´echetmanifold Imm(S1 , R ) provided the order of the metric is greater or equal to one. In addition we show that the -metric induces a strong Riemannian metric on the Banach manifold ℐ (S1 , R ) of the same order , provided > 3 2 . These investigations can be also interpreted as a generalization of the analysis for right invariant metrics on the diffeomorphism group

Unfavourable expression of pharmacologic markers in mucinous colorectal cancer

Patients with mucinous colorectal cancer generally have worse prognoses than those with the nonmucinous variety. The reason for this disparity is unclear, but may result from a differential response to adjuvant chemotherapy. We examined known molecular markers for response to common chemotherapy in these two histological subtypes. In all, 21 patients with mucinous and 30 with nonmucinous Dukes C colorectal cancer were reviewed for demographic data and outcome. Total RNA from the tumours and adjacent normal mucosa was isolated and reverse transcribed. Quantitative expression levels of drug pathway genes were determined using TaqMan RT–PCR (5-fluorouracil (5-FU): TYMS, DPYD, ECGF1; oxaliplatin: GSTP1 (glutathione S-transferase pi), ERCC1 and 2; irinotecan: ABCB1, ABCG2, CYP3A4, UGT1A1, CES2, TOP1). Mucinous tumours significantly overexpressed both TYMS and GSTP1 relative to nonmucinous tumours and patient-matched normal mucosa. No significant differences in expression of the remaining markers were found. Mean follow-up was 20 months; 17 patients had recurrent disease. Among patients receiving 5-FU, those with mucinous tumours experienced shorter disease-free survival (DFS) than those with nonmucinous tumours (median DFS 13.8 vs 46.5 months, P=0.053). Mucinous colorectal cancer overexpresses markers of resistance to 5-FU and oxaliplatin. Likewise, DFS may be decreased in patients with mucinous tumours who receive 5-FU. The presence of mucin should be carefully evaluated in developmental trials of new agents for treating colorectal cancer

Bayesian statistical parameter synthesis for linear temporal properties of stochastic models

Parameterized verification of temporal properties is an active research area, being extremely relevant for model-based design of complex systems. In this paper, we focus on parameter synthesis for stochastic models, looking for regions of the parameter space where the model satisfies a linear time specification with probability greater (or less) than a given threshold. We propose a statistical approach relying on simulation and leveraging a machine learning method based on Gaussian Processes for statistical parametric verification, namely Smoothed Model Checking. By injecting active learning ideas, we obtain an efficient synthesis routine which is able to identify the target regions with statistical guarantees. Our approach, which is implemented in Python, scales better than existing ones with respect to state space of the model and number of parameters. It is applicable to linear time specifications with time constraints and to more complex stochastic models than Markov Chains

Immunogenic Profiling in Mice of a HIV/AIDS Vaccine Candidate (MVA-B) Expressing Four HIV-1 Antigens and Potentiation by Specific Gene Deletions

BACKGROUND: The immune parameters of HIV/AIDS vaccine candidates that might be relevant in protection against HIV-1 infection are still undefined. The highly attenuated poxvirus strain MVA is one of the most promising vectors to be use as HIV-1 vaccine. We have previously described a recombinant MVA expressing HIV-1 Env, Gag, Pol and Nef antigens from clade B (referred as MVA-B), that induced HIV-1-specific immune responses in different animal models and gene signatures in human dendritic cells (DCs) with immunoregulatory function. METHODOLOGY/PRINCIPAL FINDINGS: In an effort to characterize in more detail the immunogenic profile of MVA-B and to improve its immunogenicity we have generated a new vector lacking two genes (A41L and B16R), known to counteract host immune responses by blocking the action of CC-chemokines and of interleukin 1beta, respectively (referred as MVA-B DeltaA41L/DeltaB16R). A DNA prime/MVA boost immunization protocol was used to compare the adaptive and memory HIV-1 specific immune responses induced in mice by the parental MVA-B and by the double deletion mutant MVA-B DeltaA41L/DeltaB16R. Flow cytometry analysis revealed that both vectors triggered HIV-1-specific CD4(+) and CD8(+) T cells, with the CD8(+) T-cell compartment responsible for >91.9% of the total HIV-1 responses in both immunization groups. However, MVA-B DeltaA41L/DeltaB16R enhanced the magnitude and polyfunctionality of the HIV-1-specific CD4(+) and CD8(+) T-cell immune responses. HIV-1-specific CD4(+) T-cell responses were polyfunctional and preferentially Env-specific in both immunization groups. Significantly, while MVA-B induced preferentially Env-specific CD8(+) T-cell responses, MVA-B DeltaA41L/DeltaB16R induced more GPN-specific CD8(+) T-cell responses, with an enhanced polyfunctional pattern. Both vectors were capable of producing similar levels of antibodies against Env. CONCLUSIONS/SIGNIFICANCE: These findings revealed that MVA-B and MVA-B DeltaA41L/DeltaB16R induced in mice robust, polyfunctional and durable T-cell responses to HIV-1 antigens, but the double deletion mutant showed enhanced magnitude and quality of HIV-1 adaptive and memory responses. Our observations are relevant in the immune evaluation of MVA-B and on improvements of MVA vectors as HIV-1 vaccines

Probabilistic Model Checking for Continuous-Time Markov Chains via Sequential Bayesian Inference

Probabilistic model checking for systems with large or unbounded state space is a challenging computational problem in formal modelling and its applications. Numerical algorithms require an explicit representation of the state space, while statistical approaches require a large number of samples to estimate the desired properties with high confidence. Here, we show how model checking of time-bounded path properties can be recast exactly as a Bayesian inference problem. In this novel formulation the problem can be efficiently approximated using techniques from machine learning. Our approach is inspired by a recent result in statistical physics which derived closed form differential equations for the first-passage time distribution of stochastic processes. We show on a number of non-trivial case studies that our method achieves both high accuracy and significant computational gains compared to statistical model checking

A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking

Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems