Consistency and Sensitivity Analysis of Multi-level Petri Net Models of Biological Systems

The recent developments in biological experiments have awarded the research community with valuable information, which describe finely regulated systems that govern the cell dynamics. One of the greatest challenges, however, remains to represent this extensive amount of knowledge in a proper way that can be used in simulations, and validated automatically, in order to understand the dynamics and ultimately achieve a desired behaviour for the system (cell) under control. Many tools and techniques have been proposed in the literature to address this important problem. In this research, the use of Petri nets for knowledge representation is investigated. The initial focus of this research is then to introduce a concept of consistency between Petri nets obtained from various knowledge sources. Two algorithms are provided to construct Petri net models for cell dynamics using data available in public domain biological database. The first algorithm generates a low-level model capturing protein- protein interactions and the second, produces a high-level model which describes pathway sequences and is considerably easier to analyze. Appropriate tests are developed to study consistency of such models. In the context of biological systems, diseases that alter cell dynamics, such as cancer, can be regarded as faults in the system, and disease diagnosis and treatment will correspond to fault detection and control. In this research a framework has been proposed for sensitivity analysis in Petri net representation of biological systems. Efficient tools and procedures are developed to achieve sensitivity analysis. It is demonstrated using actual biological system models, that the results of such analysis can be used as a basis of drug discovery

Switching control using generalized sampled-data hold functions

In this thesis, switching control of linear time-invariant systems using generalized sampled-data hold functions (GSHF) is investigated. It is assumed that the plant model belongs to a finite set of known plants. The output of the system is periodically sampled and a control signal is being generated by using a suitable hold function from a set of GSHFs which solve the robust servomechanism problem for the family of plant models and a set of simultaneous stabilizing GSHFs for certain subsets of plant models. It is shown that by using the above sets of hold functions and choosing a proper switching sequence, one can minimize the number of switchings to destabilizing GSHFs. This can significantly improve the transient response of the system, which is one of the common weak points in most switching control schemes. Simulation results show the effectiveness of the proposed method in improving the transient response. It is also desirable to achieve a digital control law that reduces the complexity of online computation

Machine Learning at Microsoft with ML .NET

Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for developers to author. This presents a significant engineering challenge, since currently data science and modeling are largely decoupled from standard software development processes. This separation makes incorporating machine learning capabilities inside applications unnecessarily costly and difficult, and furthermore discourage developers from embracing ML in first place. In this paper we present ML .NET, a framework developed at Microsoft over the last decade in response to the challenge of making it easy to ship machine learning models in large software applications. We present its architecture, and illuminate the application demands that shaped it. Specifically, we introduce DataView, the core data abstraction of ML .NET which allows it to capture full predictive pipelines efficiently and consistently across training and inference lifecycles. We close the paper with a surprisingly favorable performance study of ML .NET compared to more recent entrants, and a discussion of some lessons learned