@misc{HerajyLiuRohretal., author = {Herajy, Mostafa and Liu, Fei and Rohr, Christian and Heiner, Monika}, title = {Coloured Hybrid Petri Nets: an Adaptable Modelling Approach for Multi-scale Biological Networks}, series = {Computational Biology and Chemistry}, volume = {76}, journal = {Computational Biology and Chemistry}, issn = {1476-9271}, doi = {10.1016/j.compbiolchem.2018.05.023}, pages = {87 -- 100}, language = {en} } @misc{LiuHeinerGilbert, author = {Liu, Fei and Heiner, Monika and Gilbert, David}, title = {Fuzzy Petri nets for modelling of uncertain biological systems}, series = {Briefings in Bioinformatics}, volume = {2018}, journal = {Briefings in Bioinformatics}, issn = {1477-4054}, doi = {10.1093/bib/bby118}, pages = {13}, language = {en} } @misc{AssafHeinerLiu, author = {Assaf, George and Heiner, Monika and Liu, Fei}, title = {Biochemical reaction networks with fuzzy kinetic parameters in Snoopy}, series = {Computational Methods in Systems Biology : 17th International Conference, CMSB 2019, Trieste, Italy, September 18-20, 2019, Proceedings}, journal = {Computational Methods in Systems Biology : 17th International Conference, CMSB 2019, Trieste, Italy, September 18-20, 2019, Proceedings}, editor = {Bortolussi, Luca and Sanguinetti, Guido}, publisher = {Springer}, isbn = {978-3-030-31303-6}, doi = {http://dx.doi.org/10.1007/978-3-030-31304-3_17}, pages = {302 -- 307}, language = {en} } @misc{LiuSunHeineretal., author = {Liu, Fei and Sun, Wujie and Heiner, Monika and Gilbert, David}, title = {Hybrid modelling of biological systems using fuzzy continuous Petri nets}, series = {Briefings in Bioinformatics}, volume = {22(2021)}, journal = {Briefings in Bioinformatics}, doi = {10.1093/bib/bbz114}, pages = {438 -- 450}, abstract = {Integrated modelling of biological systems is challenged by composing components with sufficient kinetic data and components with insufficient kinetic data or components built only using experts' experience and knowledge. Fuzzy continuous Petri nets (FCPNs) combine continuous Petri nets with fuzzy inference systems, and thus offer an hybrid uncertain/certain approach to integrated modelling of such biological systems with uncertainties. In this paper, we give a formal definition and a corresponding simulation algorithm of FCPNs, and briefly introduce the FCPN tool that we have developed for implementing FCPNs. We then present a methodology and workflow utilizing FCPNs to achieve hybrid (uncertain/certain) modelling of biological systems illustrated with a case study of the Mercaptopurine metabolic pathway. We hope this research will promote the wider application of FCPNs and address the uncertain/certain integrated modelling challenge in the systems biology area.}, language = {en} } @misc{SchwarickRohrLiuetal., author = {Schwarick, Martin and Rohr, Christian and Liu, Fei and Assaf, George and Chodak, Jacek and Heiner, Monika}, title = {Efficient Unfolding of Coloured Petri Nets using Interval Decision Diagrams}, series = {Application and Theory of Petri Nets and Concurrency : 41st International Conference, PETRI NETS 2020, Paris, France, June 24-25, 2020, Proceedings}, journal = {Application and Theory of Petri Nets and Concurrency : 41st International Conference, PETRI NETS 2020, Paris, France, June 24-25, 2020, Proceedings}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-51830-1}, doi = {10.1007/978-3-030-51831-8_16}, pages = {324 -- 344}, abstract = {We consider coloured Petri nets, qualitative and quantitative ones alike, as supported by our PetriNuts tool family, comprising, among others, Snoopy, Marcie and Spike. Currently, most analysis and simulation techniques require to unfold the given coloured Petri net into its corresponding plain, uncoloured Petri net representation. This unfolding step is rather straightforward for finite discrete colour sets, but tends to be time-consuming due to the potentially huge number of possible transition bindings. We present an unfolding approach building on a special type of symbolic data structures, called Interval Decision Diagram, and compare its runtime performance with an unfolding engine employing an off-the-shelf library to solve constraint satisfaction problems. For this comparison we use the 22 scalable coloured models from the MCC benchmark suite, complemented by a few from our own collection.}, language = {en} } @techreport{AssafHeinerLiu, author = {Assaf, George and Heiner, Monika and Liu, Fei}, title = {Fuzzy Petri nets}, pages = {34}, abstract = {This document explains the procedure of modelling and simulating FPN and FPNC in Snoopy; please compare Figure 1 . Please note that the same steps for one net class can be equally applied to the other classes, just differentiate between uncoloured Petri nets (PN) and coloured Petri nets (PNC). Furthermore, we give more details about Latin Hybercube Sampling strategies supported by Snoopy's FPN.}, language = {en} } @misc{LiuHeinerGilbert, author = {Liu, Fei and Heiner, Monika and Gilbert, David}, title = {Hybrid modelling of biological systems: current progress and future prospects}, series = {Briefings in Bioinformatics}, volume = {23}, journal = {Briefings in Bioinformatics}, number = {3}, issn = {1477-4054}, doi = {10.1093/bib/bbac081}, pages = {1 -- 15}, abstract = {Integrated modelling of biological systems is becoming a necessity for constructing models containing the major biochemical processes of such systems in order to obtain a holistic understanding of their dynamics and to elucidate emergent behaviours. Hybrid modelling methods are crucial to achieve integrated modelling of biological systems. This paper reviews currently popular hybrid modelling methods, developed for systems biology, mainly revealing why they are proposed, how they are formed from single modelling formalisms and how to simulate them. By doing this, we identify future research requirements regarding hybrid approaches for further promoting integrated modelling of biological systems.}, language = {en} } @misc{AssafHeinerLiu, author = {Assaf, George and Heiner, Monika and Liu, Fei}, title = {Coloured fuzzy Petri nets for modelling and analysing membrane systems}, series = {Biosystems}, volume = {212}, journal = {Biosystems}, issn = {0303-2647}, doi = {10.1016/j.biosystems.2021.104592}, pages = {1 -- 10}, abstract = {Membrane systems are a very powerful computational modelling formalism inspired by the internal organisation of living cells. Modelling of membrane systems is challenged by composing many structurally similar components, which may result in very large models. Furthermore, some components may suffer from a lack of precise kinetic parameters. Coloured fuzzy Petri nets combine coloured Petri nets with fuzzy kinetic parameters, and thus offer an approach to address these challenges. In this paper, we use coloured fuzzy Petri nets to model and simulate membrane systems which are enriched by fuzzy kinetic parameters. We also introduce a methodology and workflow utilising coloured fuzzy Petri nets for modelling and simulating general biological systems which have to cope with incomplete knowledge of their kinetic data.}, language = {en} } @misc{AssafHeinerLiu, author = {Assaf, George and Heiner, Monika and Liu, Fei}, title = {Colouring Fuzziness for Systems Biology}, series = {Theoretical Computer Science}, volume = {875}, journal = {Theoretical Computer Science}, issn = {1879-2294}, doi = {10.1016/j.tcs.2021.04.011}, pages = {52 -- 64}, abstract = {Snoopy is a powerful modelling and simulation tool for various types of Petri nets, which have been applied to a wide range of biochemical reaction networks. We present an enhanced version of Snoopy, now supporting coloured and uncoloured stochastic, continuous and hybrid Petri Nets with fuzzy kinetic parameters. Colour helps to cope with modelling challenges imposed by larger and more complex networks. Fuzzy parameters are specifically useful when kinetic parameter values can not be precisely measured or estimated. By running fuzzy simulation we obtain output bands of the variables of interest induced by the effect of the fuzzy kinetic parameters. Simulation is always done on the uncoloured level. For this purpose, coloured fuzzy Petri nets are automatically unfolded to their corresponding uncoloured counterparts. Combining the power of fuzzy kinetic parameters with the modelling convenience of coloured Petri nets provides a new quality in user support with sophisticated modelling and analysis features.}, language = {en} } @misc{LiuAssafChenetal., author = {Liu, Fei and Assaf, George and Chen, Ming and Heiner, Monika}, title = {A Petri nets-based framework for whole-cell modeling}, series = {Biosystems}, volume = {210}, journal = {Biosystems}, issn = {0303-2647}, doi = {10.1016/j.biosystems.2021.104533}, abstract = {Whole-cell modeling aims to incorporate all main genes and processes, and their interactions of a cell in one model. Whole-cell modeling has been regarded as the central aim of systems biology but also as a grand challenge, which plays essential roles in current and future systems biology. In this paper, we analyze whole-cell modeling challenges and requirements and classify them into three aspects (or dimensions): heterogeneous biochemical networks, uncertainties in components, and representation of cell structure. We then explore how to use different Petri net classes to address different aspects of whole-cell modeling requirements. Based on these analyses, we present a Petri nets-based framework for whole-cell modeling, which not only addresses many whole-cell modeling requirements, but also offers a graphical, modular, and hierarchical modeling tool. We think this framework can offer a feasible modeling approach for whole-cell model construction.}, language = {en} } @misc{FeiMukhopadhyayDaCostaetal., author = {Fei, Tai and Mukhopadhyay, Subhas and Da Costa, Jo{\~a}o Paulo Javidi and Gardill, Markus and Liu, Shengheng and Roychaudhuri, Chirasree and Lan, Lan and Demitri, Nevine}, title = {Guest editorial special issue on smartness and robustness of spatial environment perception in automated systems}, series = {IEEE Sensors Journal}, volume = {24}, journal = {IEEE Sensors Journal}, number = {14}, issn = {1558-1748}, doi = {10.1109/JSEN.2024.3409137}, pages = {21800 -- 21800}, abstract = {As the curtains close on this special issue dedicated to advanced sensor research, we reflect on the critical role sensor technology plays in the future of automation. Throughout this issue, we have explored significant advancements and ongoing challenges in developing intelligent, resilient automated systems. A key theme is the vital need for smarter, more robust sensing systems. Modern sensors, now more adaptable, adjust their operations based on external changes, crucial for maximizing automation in varied real-world situations. Efforts to enhance sensor robustness have produced improvements in performance in extreme conditions, cybersecurity, cost-effective solutions, and flexible system requirements while preserving performance. The addition of self-monitoring and calibration features allows for continuous refinement of sensor accuracy and rapid problem detection, boosting system reliability. The capacity for seamlessly integrating alternative technologies when specific sensors fail further strengthens system resilience. This issue includes 33 manuscripts selected from 65 submissions, exploring these diverse aspects.}, language = {en} } @misc{BertoHuaParketal., author = {Berto, Federico and Hua, Chuanbo and Park, Junyoung and Luttmann, Laurin and Ma, Yining and Bu, Fanchen and Wang, Jiarui and Ye, Haoran and Kim, Minsu and Choi, Sanghyeok and Zepeda, Nayeli Gast and Hottung, Andr{\´e} and Zhou, Jianan and Bi, Jieyi and Hu, Yu and Liu, Fei and Kim, Hyeonah and Son, Jiwoo and Kim, Haeyeon and Angioni, Davide and Kool, Wouter and Cao, Zhiguang and Zhang, Qingfu and Kim, Joungho and Zhang, Jie and Shin, Kijung and Wu, Cathy and Ahn, Sungsoo and Song, Guojie and Kwon, Changhyun and Tierney, Kevin and Xie, Lin and Park, Jinkyoo}, title = {RL4CO : an extensive reinforcement learning for combinatorial optimization benchmark}, series = {KDD '25 : proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2}, journal = {KDD '25 : proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2}, publisher = {ACM}, address = {New York, NY, USA}, isbn = {979-8-4007-1454-2}, doi = {10.1145/3711896.3737433}, pages = {5278 -- 5289}, abstract = {Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation. Deep reinforcement learning (RL) has recently shown significant benefits in solving CO problems, reducing reliance on domain expertise and improving computational efficiency. However, the absence of a unified benchmarking framework leads to inconsistent evaluations, limits reproducibility, and increases engineering overhead, raising barriers to adoption for new researchers. To address these challenges, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 27 CO problem environments and 23 state-of-the-art baselines. Built on efficient software libraries and best practices in implementation, RL4CO features modularized implementation and flexible configurations of diverse environments, policy architectures, RL algorithms, and utilities with extensive documentation. RL4CO helps researchers build on existing successes while exploring and developing their own designs, facilitating the entire research process by decoupling science from heavy engineering. We finally provide extensive benchmark studies to inspire new insights and future work. RL4CO has already attracted numerous researchers in the community and is open-sourced at https://github.com/ai4co/rl4co.}, language = {en} }