An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing

The authors would like to thank the support on this research by the CRISP Project (Combinatorial Responses In Stress Pathways) funded by the BBSRC (BB/F00513X/1) under the Systems Approaches to Biological Research (SABR) Initiative.Peer reviewedPublisher PD

EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System

Emergency vehicles (EMVs) play a crucial role in responding to time-critical calls such as medical emergencies and fire outbreaks in urban areas. Existing methods for EMV dispatch typically optimize routes based on historical traffic-flow data and design traffic signal pre-emption accordingly; however, we still lack a systematic methodology to address the coupling between EMV routing and traffic signal control. In this paper, we propose EMVLight, a decentralized reinforcement learning (RL) framework for joint dynamic EMV routing and traffic signal pre-emption. We adopt the multi-agent advantage actor-critic method with policy sharing and spatial discounted factor. This framework addresses the coupling between EMV navigation and traffic signal control via an innovative design of multi-class RL agents and a novel pressure-based reward function. The proposed methodology enables EMVLight to learn network-level cooperative traffic signal phasing strategies that not only reduce EMV travel time but also shortens the travel time of non-EMVs. Simulation-based experiments indicate that EMVLight enables up to a $42.6\%$ reduction in EMV travel time as well as an $23.5\%$ shorter average travel time compared with existing approaches.Comment: 19 figures, 10 tables. Manuscript extended on previous work arXiv:2109.05429, arXiv:2111.0027

Biomodelkit - a framework for modular biomodel-engineering

Otto-von-Guericke-Universität Magdeburg, Fakultät für Naturwissenschaften, Dissertation, 2017von Dipl.-Ing. Mary-Ann BlätkeLiteraturverzeichnis: Seite [177]-18

Tools and techniques for multi-valued networks using rewriting logic

PhD ThesisMulti-valued networks (MVNs) are an important, widely used qualitative modelling technique where time and states are discrete. MVNs extend the well-known Boolean networks by providing a more powerful qualitative modelling approach for biological systems by allowing an entity’s state to be within a range of discrete set of values instead of just 0 and 1. They provide a logical framework for qualitatively modelling and analysing control systems and have been successfully applied to biological systems and circuit design. While a range of support tools for developing and analysing MVNs exist, more work is needed to develop tools to support the practical applications of those techniques. One of the frameworks that have been successfully applied to biological systems is Rewriting Logic (RL), an algebraic specification framework that is capable of modelling and analysing the behaviour of dynamic, concurrent systems. The flexibility of RL techniques such as implementation of strategies has allowed it to be successfully used to model a wide range of different formalisms and systems, such as process algebras, Petri nets, and biological systems. RL specification, programming and computation is supported by a range of powerful analysis tools which was one of the motivations for choosing to use RL. We choose Maude as a tool in our work here which is a high-performance reflective language supporting both equational and RL specification. Maude is going to be used through this thesis to model and analyse a range of MVNs using RL. In this thesis we aim to investigate the application of RL to modelling and analysing both synchronous and asynchronous MVNs, thus enabling the application of support tools available for RL. We start by constructing an RL model for MVNs using a translation approach that translates an MVNs set of equations into rewrite rules. We formally show that our translation approach is correct by proving its soundness and completeness. We illustrate the techniques and the developed RL framework for MVNs by presenting a range of case studies which provides a good illustration of the practical application of the developed RL framework. We then introduce an artificial, scalable MVN model in order to allow a range of model sizes to be considered and we investigate the performance of our RL framework. We analyse a larger regulatory network from the literature using our RL framework to give some insights into how it coped with a larger case studyMinistry of Higher Education in Saudi Arabi

Qualitatively modelling genetic regulatory networks : Petri net techniques and tools

The development of post-genomic technologies has led to a paradigm shift in the way we study genetic regulatory networks (GRNs) - the underlying systems which mediate cell function. To complement this, the focus is on devising scalable, unambiguous and automated formal techniques for holistically modelling and analysing these complex systems. Quantitative approaches offer one possible solution, but do not appear to be commensurate with currently available data. This motivates qualitative approaches such as Boolean networks (BNs) , which abstractly model the system without requiring such a high level of data completeness. Qualitative approaches enable fundamental dynamical properties to be studied, and are well-suited to initial investigations. However, strengthened formal techniques and tool support are required if they are to meet the demands of the biological community. This thesis aims to investigate, develop and evaluate the application of Petri nets (PNs) for qualitatively modelling and analysing GRNs. PNs are well-established in the field of computer science, and enjoy a number of attractive benefits, such a wide range of techniques and tools, which make them ideal for studying biological systems. We take an existing qualitative PN approach for modelling GRNs based on BNs, and extend it to more general models based on multi-valued networks (MVNs). Importantly, we develop tool support to automate model construction. We illustrate our approach with two detailed case studies on Boolean models for carbon stress in Escherichia coli and sporulation in Bacillus subtilis, and then consider a multi-valued model of the former. These case studies explore the analysis power of PN s by exploiting a range of techniques and tools. A number of behavioural differences are identified between the two E. coli models which lead us to question their formal relationship. We investigate this by proposing a framework for reasoning about the behaviour of MVNs at different levels of abstraction. We develop tool support for practical models, and show a number of important results which motivate the need for multi-valued modelling. Asynchronous BN s can be seen to be more biologically realistic than their synchronous counterparts. However, they have the drawback of capturing behaviour which is unrealisable in practice. We propose a novel approach for refining such behaviour using signal transition graphs, a PN formalism from asynchronous circuit design. We automate our approach, and demonstrate it using a BN of the lysis-lysogeny switch in phage A. Our results show that a more realistic asynchronous model can be derived which preserves the stochastic switch.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Computational methods and tools for protein phosphorylation analysis

Signaling pathways represent a central regulatory mechanism of biological systems where a key event in their correct functioning is the reversible phosphorylation of proteins. Protein phosphorylation affects at least one-third of all proteins and is the most widely studied posttranslational modification. Phosphorylation analysis is still perceived, in general, as difficult or cumbersome and not readily attempted by many, despite the high value of such information. Specifically, determining the exact location of a phosphorylation site is currently considered a major hurdle, thus reliable approaches are necessary for the detection and localization of protein phosphorylation. The goal of this PhD thesis was to develop computation methods and tools for mass spectrometry-based protein phosphorylation analysis, particularly validation of phosphorylation sites. In the first two studies, we developed methods for improved identification of phosphorylation sites in MALDI-MS. In the first study it was achieved through the automatic combination of spectra from multiple matrices, while in the second study, an optimized protocol for sample loading and washing conditions was suggested. In the third study, we proposed and evaluated the hypothesis that in ESI-MS, tandem CID and HCD spectra of phosphopeptides can be accurately predicted and used in spectral library searching. This novel strategy for phosphosite validation and identification offered accuracy that outperformed the other currently existing popular methods and proved applicable to complex biological samples. And finally, we significantly improved the performance of our command-line prototype tool, added graphical user interface, and options for customizable simulation parameters and filtering of selected spectra, peptides or proteins. The new software, SimPhospho, is open-source and can be easily integrated in a phosphoproteomics data analysis workflow. Together, these bioinformatics methods and tools enable confident phosphosite assignment and improve reliable phosphoproteome identification and reportin

Toward Accessible Multilevel Modeling in Systems Biology: A Rule-based Language Concept

Promoted by advanced experimental techniques for obtaining high-quality data and the steadily accumulating knowledge about the complexity of life, modeling biological systems at multiple interrelated levels of organization attracts more and more attention recently. Current approaches for modeling multilevel systems typically lack an accessible formal modeling language or have major limitations with respect to expressiveness. The aim of this thesis is to provide a comprehensive discussion on associated problems and needs and to propose a concrete solution addressing them