Qinsi Wang

Formal analysis for logical models of pancreatic cancer

We apply formal verification techniques for studying the behavior of signaling pathways important in cancer. In particular, we use Model Checking for verifying behavioral properties of a single-cell, in silico model of pancreatic cancer. We are interested in properties associated with apoptosis (programmed cell death), cell cycle arrest and proliferation. The properties are specified in temporal logics and include, for example, whether there are checkpoints that the cancer cell should go through before it reaches a given state. Our model includes several major signaling pathways, including the Hedgehog, WNT, KRAS, RB-E2F, NFkB, p53, TGFβ, and apoptosis pathways, which have been recently found to be mutated frequently in pancreatic cancer. The model is formally analyzed via symbolic Model Checking, and shown to agree well qualitatively with experiments. We conclude that Model Checking offers a powerful approach for studying logical models of relevant biological processes.

\(2^5\) Years of Model Checking

Model Checking is an automatic verification technique for large state transition systems. It was originally developed for reasoning about finite-state concurrent systems. The technique has been used successfully to debug complex computer hardware, communication protocols, and software. It is beginning to be used for analyzing cyber-physical, biological, and financial systems as well. The major challenge for the technique is a phenomenon called the State Explosion Problem. This issue is impossible to avoid in the worst case; but, by using sophisticated data structures and clever search algorithms, it is now possible to verify state transition systems with an astronomical number of states. In this paper, we will briefly review the development of Model Checking over the past 32 years, with an emphasis on model checking stochastic hybrid systems.

SReach: A Probabilistic Bounded Delta-Reachability Analyzer for Stochastic Hybrid Systems

In this paper, we present a new tool SReach, which solves probabilistic bounded reachability problems for two classes of models of stochastic hybrid systems. The first one is (nonlinear) hybrid automata with parametric uncertainty. The second one is probabilistic hybrid automata with additional randomness for both transition probabilities and variable resets. Standard approaches to reachability problems for linear hybrid systems require numerical solutions for large optimization problems, and become infeasible for systems involving both nonlinear dynamics over the reals and stochasticity. SReach encodes stochastic information by using a set of introduced random variables, and combines \(\delta \)-complete decision procedures and statistical tests to solve \(\delta \)-reachability problems in a sound manner. Compared to standard simulation-based methods, it supports non-deterministic branching, increases the coverage of simulation, and avoids the zero-crossing problem. We demonstrate SReach’s applicability by discussing three representative biological models and additional benchmarks for nonlinear hybrid systems with multiple probabilistic system parameters.

Formal Analysis Provides Parameters for Guiding Hyperoxidation in Bacteria using Phototoxic Proteins

In this work, we developed a methodology to analyze a bacteria model that mimics the stages through which bacteria change when phage therapy is applied. Due to the widespread misuse and overuse of antibiotics, drug resistant bacteria now pose significant risks to health, agriculture and the environment. Therefore, we were interested in an alternative to conventional antibiotics, a phage therapy. Our model was designed according to an experimental procedure to engineer a temperate phage, Lambda (λ), and then kill bacteria via light-activated production of superoxide. We applied formal analysis to our model and the results show that such an approach can speed up evaluation of the system, which would be impractical or possibly not even feasible to study in a wet lab.

Formal Modeling and Analysis of Pancreatic Cancer Microenvironment

The focus of pancreatic cancer research has been shifted from pancreatic cancer cells towards their microenvironment, involving pancreatic stellate cells that interact with cancer cells and influence tumor progression. To quantitatively understand the pancreatic cancer microenvironment, we construct a computational model for intracellular signaling networks of cancer cells and stellate cells as well as their intercellular communication. We extend the rule-based BioNetGen language to depict intra- and inter-cellular dynamics using discrete and continuous variables respectively. Our framework also enables a statistical model checking procedure for analyzing the system behavior in response to various perturbations. The results demonstrate the predictive power of our model by identifying important system properties that are consistent with existing experimental observations. We also obtain interesting insights into the development of novel therapeutic strategies for pancreatic cancer.

Probabilistic reachability analysis of the tap withdrawal circuit in caenorhabditis elegans

We present a probabilistic reachability analysis of a (nonlinear ODE) model of a neural circuit in Caeorhabditis elegans (C. elegans), the common roundworm. In particular, we consider Tap Withdrawal (TW), a reflexive behavior exhibited by a C. elegans worm in response to vibrating the surface on which it is moving. The neural circuit underlying this response is the subject of this investigation. Specially, we perform bounded-time reachability analysis on the TW circuit model of Wicks et al. (1996) to estimate the probability of various TW responses. The Wicks et al. model has a number of parameters, and we demonstrate that the various TW responses and their probability of occurrence in a population of worms can be viewed as a problem of parameter uncertainty. Our approach to this problem rests on encoding each TW response as a hybrid automaton with parametric uncertainty. We then perform probabilistic reachability analysis on these automata using a technique that combines a δ-decision procedure with statistical tests. The results we obtain are a significant extension of those of Wicks et al. (1996), who equip their model with fixed parameter values that reproduce a single TW response. In contrast, our technique allow us to more thoroughly explore the models parameter space using statistical sampling theory, identifying in the process the distribution of TW responses. Wicks et al. conducted a number of ablation experiments on a population of worms in which one or more of the neurons in the TW circuit are surgically ablated (removed). We show that our technique can be used to correctly estimate TW response-probabilities for four of these ablation groups. We also use our technique to predict TW response behavior for two ablation groups not previously considered by Wicks et al.

Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking

Biomedical research results are being published at a high rate, and with existing search engines, the vast amount of published work is usually easily accessible. However, reproducing published results, either experimental data or observations is often not viable. In this work, we propose a framework to overcome some of the issues of reproducing previous research, and to ensure re-usability of published information. We present here a framework that utilizes the results from state-of-the-art biomedical literature mining, biological system modeling and analysis techniques, and provides means to scientists to assemble and reason about information from voluminous, fragmented and sometimes inconsistent literature. The overall process of automated reading, assembly and reasoning can speed up discoveries from the order of decades to the order of hours or days. Our framework described here allows for rapidly conducting thousands of in silico experiments that are designed as part of this process.

High-level modeling and verification of cellular signaling

We use computational modeling and formal analysis techniques to study temporal behavior of a discrete logical model of the naïve T cell differentiation. The model is analyzed formally and automatically by performing temporal logic queries via statistical model checking. While the model can be verified and then further explored using Monte Carlo simulations, model checking allows for much more efficient analysis by testing a large set of system properties, with much smaller runtime than the one required by simulations. The results obtained using model checking provide details about relative timing of events in the system, which would otherwise be very cumbersome and time consuming to obtain through simulations only. We efficiently test a large number of properties, and confirm or reject hypotheses that were drawn from previous analysis of experimental and simulation data.

CyberCardia project: Modeling, verification and validation of implantable cardiac devices

In this paper, we survey recent progress in CyberCardia project, a CPS Frontier project funded by the National Science Foundation. The CyberCardia project will lead to significant advances in the state of the art for system verification and cardiac therapies based on the use of formal methods and closed-loop control and verification. The animating vision for the work is to enable the development of a true in silico design methodology for medical devices that can be used to speed the development of new devices and to provide greater assurance that their behavior matches designer intentions, and to pass regulatory muster more quickly so that they can be used on patients needing their care. The acceleration in medical-device innovation achievable as a result of the CyberCardia research will also have long-term and sustained societal benefits, as better diagnostic and therapeutic technologies enter into the practice of medicine more quickly.