TY - GEN A1 - Liu, Shengheng A1 - Mao, Zihuan A1 - Liu, Yiran A1 - Fei, Tai A1 - Gardill, Markus A1 - Huang, Yongming T1 - TRANM: Decoherenced DoA Estimation for Automotive Radar Using Generalized Sparse Arrays T2 - Signal Processing N2 - This paper tackles the challenge of coherent single-snapshot direction-of-arrival estimation in automotive linear frequency modulated continuous wave (LFMCW) radar using a generalized sparse array. By leveraging atomic-norm minimization (ANM)-based interpolation and Toeplitz rearrangement, a TRANM framework is proposed to address the rank-deficiency issue in the range-Doppler domain. To further enhance computational efficiency, we re-formulate the TRANM problem into an equivalent optimization with reduced dimensionality. The problem is then solved using the alternating direction method of multipliers, which provides an optimal solution via an iterative process. Numerical simulations validate that the proposed approach can accurately resolve coherent signals with improved degrees of freedom and achieve super-resolution, all while maintaining a low computational cost. Y1 - 2025 U6 - https://doi.org/10.1016/j.sigpro.2025.109895 VL - 231 PB - Elsevier ER - TY - GEN A1 - Heiner, Monika A1 - Yang, Ming A1 - Liu, Fei T1 - Modeling and analyzing biological systems using colored hierarchical Petri nets illustrated by C. elegans vulval development T2 - Journal of Biological Systems Y1 - 2014 U6 - https://doi.org/10.1142/S0218339014500181 SN - 1793-6470 VL - 22 IS - 3 SP - 463 EP - 493 ER - TY - GEN A1 - Liu, Fei A1 - Heiner, Monika T1 - Modeling membrane systems using colored stochastic Petri nets T2 - Natural Computing Y1 - 2013 U6 - https://doi.org/10.1007/s11047-013-9367-8 SN - 1572-9796 VL - 12 IS - 4 SP - 617 EP - 629 ER - TY - GEN A1 - Liu, Fei A1 - Heiner, Monika T1 - Multiscale modelling of coupled Ca2+ channels using coloured stochastic Petri nets T2 - IET Systems Biology Y1 - 2013 SN - 1751-8849 VL - 7 IS - 4 SP - 106 EP - 113 ER - TY - CHAP A1 - Herajy, Mostafa A1 - Liu, Fei A1 - Rohr, Christian ED - Heiner, Monika T1 - Coloured hybrid Petri nets for systems biology T2 - Proceedings of the 5th International Workshop on Biological Processes & Petri Nets (BioPPN) Y1 - 2014 UR - http://ceur-ws.org/Vol-1159/ SP - 60 EP - 76 ER - TY - CHAP A1 - Liu, Fei A1 - Heiner, Monika T1 - Coloured Petri Nets to Model and simulate Biological Systems T2 - Proceedings of the Workshops of the 31st International Conference on Application and Theory of Petri Nets and Other Models of Concurrency (PETRI NETS 2010) Y1 - 2010 UR - http://ceur-ws.org/Vol-827 SP - 70 EP - 84 ER - TY - CHAP A1 - Liu, Fei A1 - Heiner, Monika ED - Chen, Ming ED - Hofestädt, Ralf T1 - Petri Nets for Modeling and Analyzing Biochemical Reaction Networks T2 - Approaches in Integrative Bioinformatics KW - Petri nets KW - Biochemical reaction networks KW - Unifying Petri net frame- work KW - Qualitative KW - Stochastic KW - Conti nuous and hybrid Petri nets KW - Colored Petri nets KW - Repressilator Y1 - 2014 SN - 978-3-642-41280-6 SN - 978-3-642-41281-3 U6 - https://doi.org/10.1007/978-3-642-41281-3_9 SP - 245 EP - 272 PB - Springer CY - Heidelberg ER - TY - GEN A1 - Liu, Fei A1 - Heiner, Monika A1 - Yang, Ming T1 - Representing Network Reconstruction Solutions with Colored Petri Nets T2 - Neurocomputing Y1 - 2016 UR - http://www.sciencedirect.com/science/article/pii/S0925231215012473 U6 - https://doi.org/10.1016/j.neucom.2015.04.112 SN - 0925-2312 VL - 174 IS - A SP - 483 EP - 493 ER - TY - GEN A1 - Liu, Fei A1 - Heiner, Monika A1 - Yang, Ming T1 - Fuzzy stochastic Petri nets for modeling biological systems with uncertain kinetic parameters T2 - PLoS ONE Y1 - 2016 UR - http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149674 U6 - https://doi.org/10.1371/journal.pone.0149674 SN - 1932-6203 VL - 11 IS - 2 SP - 0149674 ER - TY - GEN A1 - Pârvu, Ovidiu A1 - Gilbert, David A1 - Heiner, Monika A1 - Liu, Fei A1 - Saunders, Nigel A1 - Shaw, Simon T1 - Spatial-temporal modelling and analysis of bacterial colonies with phase variable genes T2 - ACM Transactions on Modeling and Computer Simulation (TOMACS) Y1 - 2015 UR - http://dl.acm.org/citation.cfm?id=2742546 U6 - https://doi.org/10.1145/2742546 SN - 1558-1195 SN - 1049-3301 VL - 25 IS - 2 SP - 13 ER - TY - GEN A1 - Liu, Fei A1 - Blätke, Mary-Ann A1 - Heiner, Monika A1 - Yang, Ming T1 - Modelling and simulating reaction–diffusion systems using coloured Petri nets T2 - Computers in Biology and Medicine Y1 - 2014 UR - http://www.sciencedirect.com/science/article/pii/S0010482514001693 U6 - https://doi.org/10.1016/j.compbiomed.2014.07.004 SN - 0010-4825 VL - 53 SP - 297 EP - 308 ER - TY - GEN A1 - Gao, Quian A1 - Gilbert, David A1 - Heiner, Monika A1 - Liu, Fei A1 - Maccagnola, Daniele A1 - Tree, David T1 - Multiscale Modelling and Analysis of Planar Cell Polarity in the Drosophila Wing T2 - IEEE/ACM Transactions on Computational Biology and Bioinformatics Y1 - 2013 U6 - https://doi.org/10.1109/TCBB.2012.101 SN - 1545-5963 VL - 10 IS - 2 SP - 337 EP - 351 ER - TY - CHAP A1 - Parvu, Ovidiu A1 - Gilbert, David A1 - Heiner, Monika A1 - Liu, Fei A1 - Saunders, Nigel ED - Gupta, Ashutosh ED - Henzinger, Thomas A. T1 - Modelling and Analysis of Phase Variation in Bacterial Colony Growth T2 - Computational Methods in Systems Biology, 11th International Conference, CMSB 2013, Klosterneuburg, Austria, September 22-24, 2013 Y1 - 2013 SN - 978-3-642-40707-9 U6 - https://doi.org/10.1007/978-3-642-40708-6_7 N1 - referiert SP - 78 EP - 91 PB - Springer CY - Berlin [u.a.] ER - TY - CHAP A1 - Liu, Fei A1 - Heiner, Monika A1 - Yang, Ming T1 - Colored Petri Nets for Multiscale Systems Biology – Current Modeling and Analysis Capabilities in Snoopy T2 - 7th International Conference on Systems Biology (ISB), 2013, 23 - 25 Aug. 2013, Huangshan, China Y1 - 2013 SN - 978-1-4799-1387-9 U6 - https://doi.org/10.1109/ISB.2013.6623788 N1 - referiert SP - 24 EP - 30 PB - IEEE CY - Piscataway, NJ ER - TY - CHAP A1 - Gilbert, David A1 - Heiner, Monika A1 - Liu, Fei A1 - Saunders, Nigel T1 - Colouring Space - A Coloured Framework for Spatial Modelling in Systems Biology T2 - Application and Theory of Petri Nets and Concurrency, 34th International Conference, PETRI NETS 2013, Milan, Italy, June 24-28, 2013 Y1 - 2013 UR - http://link.springer.com/chapter/10.1007%2F978-3-642-38697-8_13 N1 - referiert SP - 230 EP - 249 PB - Springer CY - Berlin [u.a.] ER - TY - GEN A1 - Herajy, Mostafa A1 - Liu, Fei A1 - Heiner, Monika T1 - Efficient modelling of yeast cell cycles based on multisite phosphorylation using coloured hybrid Petri nets with marking-dependent arc weights T2 - Nonlinear analysis: Hybrid systems Y1 - 2017 UR - https://www.sciencedirect.com/science/article/pii/S1751570X17300808 U6 - https://doi.org/10.1016/j.nahs.2017.09.002 SN - 1751-570X VL - 27 SP - 191 EP - 212 ER - TY - GEN A1 - Liu, Fei A1 - Heiner, Monika A1 - Gilbert, David T1 - Coloured Petri nets for multilevel, multiscale and multidimensional modelling of biological systems T2 - Briefings in Bioinformatics Y1 - 2019 U6 - https://doi.org/10.1093/bib/bbx150 SN - 1477-4054 SN - 1467-5463 VL - 20 IS - 3 SP - 877 EP - 886 ER - TY - GEN A1 - Herajy, Mostafa A1 - Liu, Fei A1 - Rohr, Christian A1 - Heiner, Monika T1 - Snoopy’s hybrid simulator: a tool to construct and simulate hybrid biological models T2 - BMC Systems Biology Y1 - 2017 UR - https://link.springer.com/epdf/10.1186/s12918-017-0449-6?author_access_token=54a9mC7bl_t-6TjlSqdqNG_BpE1tBhCbnbw3BuzI2RMKIgJAZ9nr6Hm0LME2XT_scd04D6IjdxN2SajBh1-bYG6TDIkKQYJrtczMPmCV290rFO3LRZ0YPUqCBRlezXRq1AJVJo-7YPWMt0G3LF52OA%3D%3D U6 - https://doi.org/10.1186/s12918-017-0449-6 SN - 1752-0509 VL - 11 ER - TY - THES A1 - Liu, Fei T1 - Colored Petri Nets for Systems Biology Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus-23656 UR - http://opus.kobv.de/btu/volltexte/2012/2365/pdf/LiuFei_Thesis_final.pdf ER - TY - GEN A1 - Liu, Fei A1 - Chen, Siyuan A1 - Heiner, Monika A1 - Song, Hengjie T1 - Modeling Biological Systems with Uncertain Kinetic Data Using Fuzzy Continuous Petri Nets T2 - BMC Systems Biology Y1 - 2018 U6 - https://doi.org/10.1186/s12918-018-0568-8 SN - 1752-0509 VL - 12 IS - Supplement 4 SP - 64 EP - 74 ER - TY - GEN A1 - Herajy, Mostafa A1 - Liu, Fei A1 - Rohr, Christian A1 - Heiner, Monika T1 - Coloured Hybrid Petri Nets: an Adaptable Modelling Approach for Multi-scale Biological Networks T2 - Computational Biology and Chemistry KW - Coloured Petri nets KW - Hybrid Petri Nets KW - Multi-scale modelling KW - Bistable switch KW - Dendritic spines Y1 - 2018 U6 - https://doi.org/10.1016/j.compbiolchem.2018.05.023 SN - 1476-9271 VL - 76 SP - 87 EP - 100 ER - TY - GEN A1 - Liu, Fei A1 - Heiner, Monika A1 - Gilbert, David T1 - Fuzzy Petri nets for modelling of uncertain biological systems T2 - Briefings in Bioinformatics Y1 - 2018 U6 - https://doi.org/10.1093/bib/bby118 SN - 1477-4054 SN - 1467-5463 VL - 2018 ER - TY - GEN A1 - Assaf, George A1 - Heiner, Monika A1 - Liu, Fei ED - Bortolussi, Luca ED - Sanguinetti, Guido T1 - Biochemical reaction networks with fuzzy kinetic parameters in Snoopy T2 - Computational Methods in Systems Biology : 17th International Conference, CMSB 2019, Trieste, Italy, September 18–20, 2019, Proceedings Y1 - 2019 SN - 978-3-030-31303-6 SN - 978-3-030-31304-3 U6 - https://doi.org/http://dx.doi.org/10.1007/978-3-030-31304-3_17 SP - 302 EP - 307 PB - Springer ER - TY - GEN A1 - Liu, Fei A1 - Sun, Wujie A1 - Heiner, Monika A1 - Gilbert, David T1 - Hybrid modelling of biological systems using fuzzy continuous Petri nets T2 - Briefings in Bioinformatics N2 - 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. KW - systems biology KW - integrated modelling KW - fuzzy continuous Petri nets KW - uncertainties KW - hybrid simulation Y1 - 2019 U6 - https://doi.org/10.1093/bib/bbz114 VL - 22(2021) SP - 438 EP - 450 ER - TY - GEN A1 - Schwarick, Martin A1 - Rohr, Christian A1 - Liu, Fei A1 - Assaf, George A1 - Chodak, Jacek A1 - Heiner, Monika T1 - Efficient Unfolding of Coloured Petri Nets using Interval Decision Diagrams T2 - Application and Theory of Petri Nets and Concurrency : 41st International Conference, PETRI NETS 2020, Paris, France, June 24–25, 2020, Proceedings N2 - 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. KW - Coloured Petri KW - Unfolding KW - Symbolic data structures KW - Interval decision diagrams Y1 - 2020 SN - 978-3-030-51830-1 SN - 978-3-030-51831-8 U6 - https://doi.org/10.1007/978-3-030-51831-8_16 SP - 324 EP - 344 PB - Springer CY - Cham ER - TY - RPRT A1 - Assaf, George A1 - Heiner, Monika A1 - Liu, Fei T1 - Fuzzy Petri nets N2 - 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. KW - Fuzzy Petri Nets coloured fuzzy Petri nets Snoopy Y1 - 2021 UR - https://www-dssz.informatik.tu-cottbus.de/publications/btu-reports/fpn_manual.pdf ER - TY - GEN A1 - Liu, Fei A1 - Heiner, Monika A1 - Gilbert, David T1 - Hybrid modelling of biological systems: current progress and future prospects T2 - Briefings in Bioinformatics N2 - 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. KW - biological modelling KW - hybrid modelling KW - modelling formalisms Y1 - 2022 UR - https://academic.oup.com/bib/article/23/3/bbac081/6555400 U6 - https://doi.org/10.1093/bib/bbac081 SN - 1477-4054 SN - 1467-5463 VL - 23 IS - 3 SP - 1 EP - 15 ER - TY - GEN A1 - Assaf, George A1 - Heiner, Monika A1 - Liu, Fei T1 - Coloured fuzzy Petri nets for modelling and analysing membrane systems T2 - Biosystems N2 - 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. KW - Fuzzy membrane systems KW - Parameter uncertainties KW - Coloured fuzzy stochastic Petri nets KW - Coloured fuzzy continuous Petri net KW - Coloured fuzzy hybrid Petri net KW - Modelling KW - Fuzzy simulation Y1 - 2022 UR - https://www.sciencedirect.com/science/article/pii/S0303264721002318?via%3Dihub U6 - https://doi.org/10.1016/j.biosystems.2021.104592 SN - 0303-2647 VL - 212 SP - 1 EP - 10 ER - TY - GEN A1 - Assaf, George A1 - Heiner, Monika A1 - Liu, Fei T1 - Colouring Fuzziness for Systems Biology T2 - Theoretical Computer Science N2 - 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. KW - fuzzy logic KW - Fuzzy kinetic parameters KW - Coloured fuzzy continuous, stochastic and hybrid Petri nets KW - Modelling and simulation KW - Modelling uncertainty Y1 - 2021 UR - https://www.sciencedirect.com/science/article/abs/pii/S0304397521002152?via%3Dihub U6 - https://doi.org/10.1016/j.tcs.2021.04.011 SN - 1879-2294 SN - 0304-3975 VL - 875 SP - 52 EP - 64 ER - TY - GEN A1 - Liu, Fei A1 - Assaf, George A1 - Chen, Ming A1 - Heiner, Monika T1 - A Petri nets-based framework for whole-cell modeling T2 - Biosystems N2 - 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. KW - Whole-cell modeling KW - Systems biology KW - Petri nets KW - Modeling framework Y1 - 2021 UR - https://www.sciencedirect.com/science/article/abs/pii/S0303264721001738?via%3Dihub U6 - https://doi.org/10.1016/j.biosystems.2021.104533 SN - 0303-2647 SN - 1872-8324 VL - 210 ER - TY - GEN A1 - Fei, Tai A1 - Mukhopadhyay, Subhas A1 - Da Costa, João Paulo Javidi A1 - Gardill, Markus A1 - Liu, Shengheng A1 - Roychaudhuri, Chirasree A1 - Lan, Lan A1 - Demitri, Nevine T1 - Guest editorial special issue on smartness and robustness of spatial environment perception in automated systems T2 - IEEE Sensors Journal N2 - 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. Y1 - 2024 U6 - https://doi.org/10.1109/JSEN.2024.3409137 SN - 1558-1748 VL - 24 IS - 14 SP - 21800 EP - 21800 ER - TY - GEN A1 - Berto, Federico A1 - Hua, Chuanbo A1 - Park, Junyoung A1 - Luttmann, Laurin A1 - Ma, Yining A1 - Bu, Fanchen A1 - Wang, Jiarui A1 - Ye, Haoran A1 - Kim, Minsu A1 - Choi, Sanghyeok A1 - Zepeda, Nayeli Gast A1 - Hottung, André A1 - Zhou, Jianan A1 - Bi, Jieyi A1 - Hu, Yu A1 - Liu, Fei A1 - Kim, Hyeonah A1 - Son, Jiwoo A1 - Kim, Haeyeon A1 - Angioni, Davide A1 - Kool, Wouter A1 - Cao, Zhiguang A1 - Zhang, Qingfu A1 - Kim, Joungho A1 - Zhang, Jie A1 - Shin, Kijung A1 - Wu, Cathy A1 - Ahn, Sungsoo A1 - Song, Guojie A1 - Kwon, Changhyun A1 - Tierney, Kevin A1 - Xie, Lin A1 - Park, Jinkyoo T1 - RL4CO : an extensive reinforcement learning for combinatorial optimization benchmark T2 - KDD '25 : proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 N2 - 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. Y1 - 2025 SN - 979-8-4007-1454-2 U6 - https://doi.org/10.1145/3711896.3737433 SP - 5278 EP - 5289 PB - ACM CY - New York, NY, USA ER -