Andrea Patane

Pareto Optimal Design for Synthetic Biology

Recent advances in synthetic biology call for robust, flexible and efficient in silico optimization methodologies. We present a Pareto design approach for the bi-level optimization problem associated to the overproduction of specific metabolites in Escherichia coli. Our method efficiently explores the high dimensional genetic manipulation space, finding a number of trade-offs between synthetic and biological objectives, hence furnishing a deeper biological insight to the addressed problem and important results for industrial purposes. We demonstrate the computational capabilities of our Pareto-oriented approach comparing it with state-of-the-art heuristics in the overproduction problems of i) 1,4-butanediol, ii) myristoyl-CoA, i ii) malonyl-CoA , iv) acetate and v) succinate. We show that our algorithms are able to gracefully adapt and scale to more complex models and more biologically-relevant simulations of the genetic manipulations allowed. The Results obtained for 1,4-butanediol overproduction significantly outperform results previously obtained, in terms of 1,4-butanediol to biomass formation ratio and knock-out costs. In particular overproduction percentage is of +662.7%, from 1.425 mmolh-1gDW-1 (wild type) to 10.869 mmolh-1gDW-1, with a knockout cost of 6. Whereas, Pareto-optimal designs we have found in fatty acid optimizations strictly dominate the ones obtained by the other methodologies, e.g., biomass and myristoyl-CoA exportation improvement of +21.43% (0.17 h-1) and +5.19% (1.62 mmolh-1gDW-1), respectively. Furthermore CPU time required by our heuristic approach is more than halved. Finally we implement pathway oriented sensitivity analysis, epsilon-dominance analysis and robustness analysis to enhance our biological understanding of the problem and to improve the optimization algorithm capabilities.

Synthesising Robust and Optimal Parameters for Cardiac Pacemakers Using Symbolic and Evolutionary Computation Techniques

We consider the problem of automatically finding safe and robust values of timing parameters of cardiac pacemaker models so that a quantitative objective, such as the pacemaker energy consumption or its cardiac output (a heamodynamic indicator of the human heart), is optimised in a finite path. The models are given as parametric networks of timed I/O automata with data, which extend timed I/O automata with priorities, real variables and real-valued functions, and specifications as Counting Metric Temporal Logic (CMTL) formulas. We formulate the parameter synthesis as a bilevel optimisation problem, where the quantitative objective (the outer problem) is optimised in the solution space obtained from optimising an inner problem that yields the maximal robustness for any parameter of the model. We develop an SMT-based method for solving the inner problem through a discrete encoding, and combine it with evolutionary algorithms and simulations to solve the outer optimisation task. We apply our approach to the composition of a (non-linear) multi-component heart model with the parametric dual chamber pacemaker model in order to find the values of multiple timing parameters of the pacemaker for different heart diseases.

Hardware-in-the-loop simulation and energy optimization of cardiac pacemakers.

Implantable cardiac pacemakers are medical devices that can monitor and correct abnormal heart rhythms. To provide the necessary safety assurance for pacemaker software, both testing and verification of the code, as well as testing the entire pacemaker hardware in the loop, is necessary. In this paper, we present a hardware testbed that enables detailed hardware-in-the-loop simulation and energy optimisation of pacemaker algorithms with respect to a heart model. Both the heart and the pacemaker models are encoded in Simulink/Stateflow™ and translated into executable code, with the pacemaker executed directly on the microcontroller. We evaluate the usefulness of the testbed by developing a parameter synthesis algorithm which optimises the timing parameters based on power measurements acquired in real-time. The experiments performed on real measurements successfully demonstrate that the testbed is capable of energy minimisation in real-time and obtains safe pacemaker timing parameters.

Closed-Loop Quantitative Verification of Rate-Adaptive Pacemakers

Rate-adaptive pacemakers are cardiac devices able to automatically adjust the pacing rate in patients with chronotropic incompetence, i.e., whose heart is unable to provide an adequate rate at increasing levels of physical, mental, or emotional activity. These devices work by processing data from physiological sensors in order to detect the patient’s activity and update the pacing rate accordingly. Rate adaptation parameters depend on many patient-specific factors, and effective personalization of such treatments can only be achieved through extensive exercise testing, which is normally intolerable for a cardiac patient. In this work, we introduce a data-driven and model-based approach for the automated verification of rate-adaptive pacemakers and formal analysis of personalized treatments. To this purpose, we develop a novel dual-sensor pacemaker model where the adaptive rate is computed by blending information from an accelerometer, and a metabolic sensor based on the QT interval. Our approach enables personalization through the estimation of heart model parameters from patient data (electrocardiogram), and closed-loop analysis through the online generation of synthetic, model-based QT intervals and acceleration signals. In addition to personalization, we also support the derivation of models able to account for the varied characteristics of a virtual patient population, thus enabling safety verification of the device. To capture the probabilistic and nonlinear dynamics of the heart, we define a probabilistic extension of timed I/O automata with data and employ statistical model checking for quantitative verification of rate modulation. We evaluate our rate-adaptive pacemaker design on three subjects and a pool of virtual patients, demonstrating the potential of our approach to provide rigorous, quantitative insights into the closed-loop behavior of the device under different exercise levels and heart conditions.

Metabolic Circuit Design Automation by Multi-objective BioCAD

We present a thorough in silico analysis and optimization of the genome-scale metabolic model of the mycolic acid pathway in M. tuberculosis. We apply and further extend meGDMO to account for finer sensitivity analysis and post-processing analysis, thanks to the combination of statistical evaluation of strains robustness, and clustering analysis to map the phenotype-genotype relationship among Pareto optimal strains. In the first analysis scenario, we find 12 Pareto-optimal single gene set knockout, which completely shut down the pathway, hence critically reducing the pathogenicity of M. tuberculosis; as well as 34 genotypically different strains in which the production of mycolic acid is severely reduced.

CommonSense: Collaborative learning of scene semantics by robots and humans

The recent introduction of robots to everyday scenarios has revealed new opportunities for collaboration and social interaction between robots and people. However, high level interaction will require semantic understanding of the environment. In this paper, we advocate that co-existence of assistive robots and humans can be leveraged to enhance the semantic understanding of the shared environment, and improve situation awareness. We propose a probabilistic framework that combines human activity sensor data generated by smart wearables with low level localisation data generated by robots. Based on this low level information and leveraging colocation events between a user and a robot, it can reason about semantic information and track humans and robots across different rooms. The proposed system relies on two-way sharing of information between the robot and the user. In the first phase, user activities indicative of room utility are inferred from consumer wearable devices and shared with the robot, enabling it to gradually build a semantic map of the environment. This will enable natural language interaction and high-level tasks for both assistive and co-working robots. In a second phase, via colocation events, the robot is able to share semantic information with the user, by labelling raw user data with semantic information about room type. Over time, the labelled data is used for training an Hidden Markov Model for room-level localisation, effectively making the user independent from the robot.

A multi-objective clonal selection algorithm for analog circuit and solar cell design

We present PareDA (ParetoDesignAutomation), a composite automated methodology for the simulation-based multi-scenario multi-objective optimization of analog circuits and thin-film cell devices, relying on randomized algorithms, both domain and constraints sensitivity analysis, epsilon-dominance and global robustness analysis. We test PareDA algorithm on the designing problem of a three stage operational amplifier, a yield-aware optimization of a folded-cascode operational amplifier (requiring multiple operating conditions) and an optical model for tandem thin-film silicon solar cells. In these scenarios, comparisons with state-of-the-art techniques (as NSGA-II and YdIRCO) undoubtedly demonstrate PareDA effectiveness, in terms of Pareto optimality of the design found and convergence time. The latter obtains, in fact, a significant average performance improvement (from 35% to 49%), finding Pareto-optimal designs dominating the ones found by state-of-the-art algorithms. Moreover CPU time required by PareDA to converge is reduced of at least 75% compared to the other methodologies here analysed (e.g. optimal folded- cascode operational amplifier are found in just 320 s). Finally, PareDA algorithm thanks to parallel computations gains a 5.62x speed-up with 70% efficiency, compared to the non-parallel version.