Efficient Parallel Statistical Model Checking of Biochemical Networks

We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property P holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by on-the-fly verification of P which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of P to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture

Nanoparticles exhibiting self-regulating temperature as innovative agents for Magnetic Fluid Hyperthermia

During the last few years, for therapeutic purposes in oncology, considerable attention has been focused on a method called magnetic fluid hyperthermia (MFH) based on local heating of tumor cells. In this paper, an innovative, promising nanomaterial, M48 composed of iron oxide-based phases has been tested. M48 shows self-regulating temperature due to the observable second order magnetic phase transition from ferromagnetic to paramagnetic state. A specific hydrophilic coating based on both citrate ions and glucose molecules allows high biocompatibility of the nanomaterial in biological matrices and its use in vivo. MFH mediator efficiency is demonstrated in vitro and in vivo in breast cancer cells and tumors, confirming excellent features for biomedical application. The temperature increase, up to the Curie temperature, gives rise to a phase transition from ferromagnetic to paramagnetic state, promoting a shortage of the r2 transversal relaxivity that allows a switch in the contrast in Magnetic Resonance Imaging (MRI). Combining this feature with a competitive high transversal (spin-spin) relaxivity, M48 paves the way for a new class of temperature sensitive T2 relaxing contrast agents. Overall, the results obtained in this study prepare for a more affordable and tunable heating mechanism preventing the damages of the surrounding healthy tissues and, at the same time, allowing monitoring of the temperature reached

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Integrating artificial with natural cells to translate chemical messages that direct E. coli behaviour

Previous efforts to control cellular behaviour have largely relied upon various forms of genetic engineering. Once the genetic content of a living cell is modified, the behaviour of that cell typically changes as well. However, other methods of cellular control are possible. All cells sense and respond to their environment. Therefore, artificial, non-living cellular mimics could be engineered to activate or repress already existing natural sensory pathways of living cells through chemical communication. Here we describe the construction of such a system. The artificial cells expand the senses of Escherichia coli by translating a chemical message that E. coli cannot sense on its own to a molecule that activates a natural cellular response. This methodology could open new opportunities in engineering cellular behaviour without exploiting genetically modified organisms

Independent Component Analysis for the Aggregation of Stochastic Simulation Output

Computational models and simulation algorithms are commonly applied tools in biological sciences. Among those, discrete stochastic models and stochastic simulation proved to be able to effectively capture the effects of intrinsic noise at molecular level, improving over deterministic approaches when system dynamics is driven by a limited amount of molecules. A challenging task that is offered to researchers is then the analysis and ultimately the inference of knowledge from a set of multiple, noisy, simulated trajectories. We propose in this paper a method, based on Independent Component Analysis (ICA), to automatically analyze multiple output traces of stochastic simulation runs. ICA is a statistical technique for revealing hidden factors that underlie sets of signals. Its applications span from digital image processing, to audio signal reconstruction and economic indicators analysis. Here we propose the application of ICA to identify and describe the noise in time-dependent evolution of biochemical species and to extract aggregate knowledge on simulated biological systems. We present the results obtained with the application of the proposed methodology on the well-known MAPK cascade system, which demonstrate the ability of the proposed methodology to decompose and identify the noisy components of the evolution. Quantitative descriptions of the noise component can be further analytically characterized by a simple first order autoregressive model

Knowledge discovery for stochastic models of biological systems

Biology is the science of life and living organisms. Empowered by the deployment of several automated experimental frameworks, this discipline has seen a tremendous growth during the last decades. Recently, the focus towards studying biological systems holistically, has lead to biology converging with other disciplines. In particular, computer science is playing an increasingly important role in biology, because of its ability to disentangle complex system level issues. This increasing interplay between computer science and biology has lead to great progress in both fields and to the opening of new important areas for research. In this thesis we present methods and approaches to tackle the problem of knowledge discovery in computational biology from a stochastic perspective. Major bottlenecks in adopting a stochastic representation can be overcome with the use of proper methodologies by integrating statistics and computer science. In particular we focus on parameter inference for stochastic models and efficient model analysis. We show the application of these approaches on real biological case studies aiming at inferring new knowledge even when a priori (and/or experimental) information is limited

Predicting the Effects of Parameters Changes in Stochastic Models through Parallel Synthetic Experiments and Multivariate Analysis

Usually researchers require many experiments to verify how biological systems respond to stimuli. However, the high cost of reagents and facilities as well as the time required to carry out experiments are sometimes the main cause of failure. In this regards, Information Technology offers a valuable help: modeling and simulation are mathematical tools to execute virtual experiments on computing devices. Through synthetic experimentation, researchers can sample the parameters space of a biological system and obtain hundreds of potential results, ready to be reused to design and conduct more targeted wet-lab experiments. A non negligible achievement of this is the enormous saving of resources and time. In this paper, we present a plug-in-based software prototype that combines high performance computing and statistics. Our framework relies on parallel computing to run large numbers of synthetic experiments. Multivariate analysis is then used to interpret and validate results. The software is tested on two well-known oscillatory models: Predator-Prey (Lotka-Volterra) and Repressilator

Cellular imitations

Synthetic biologists typically construct new pathways within existing cells. While useful, this approach in many ways ignores the undefined but necessary components of life. A growing number of laboratories have begun to try to remove some of the mysteries of cellular life by building life-like systems from non-living component parts. Some of these attempts rely on purely chemical and physical forces alone without the aid of biological molecules, while others try to build artificial cells from the parts of life, such as nucleic acids, proteins, and lipids. Both bottom-up strategies suffer from the complication of trying to build something that remains undefined. The result has been the development of research programs that try to build systems that mimic in some way recognized living systems. Since it is difficult to quantify the mimicry of life, success often times is evaluated with a degree of subjectivity. Herein we highlight recent advances in mimicking the organization and behavior of cellular life from the bottom-up