You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: https://www.cecam.org/workshop-details/multi-scale-and-multi-purpose-simulations-of-dna-the-importance-of-data-1484.
\r\n
\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the CECAM Event Manager if you have any question.
\r\n
\r\nDescription
\r\n
\r\nDNA is a dramatic example of a multiscale system, where Å-scale details impact the global properties of a meter-long fiber and where femtosecond processes can impact on the entire genome years later. This implies that any theoretical study on DNA should take into consideration the vast variety of space and time scales, making it necessary the adoption of multi-physics approaches, covering the entire range of theoretical methods from quantum chemistry to rough mesoscopic models. Within this scenario the importance of data to bias simulations and as a reference to calibrate low resolution methods (Dans et al. 2017; Neguembor et al. 2022; Schultz et al. 2025).
\r\nLarge efforts have been made to develop accurate low level DFT and semiempirical methods that can be data-providers for a new generation of force-field, as well as integrated in QM/MM packages for an efficient representation of DNA reactivity (Aranda et al. 2019). Atomistic force-field have gained accuracy, showing good ability to reproduce unusual forms of DNA and long segments of DNA in the context of chromatin (Collepardo-Guevara et al. 2015; Genna et al. 2025) and providing very useful data for the calibration of lower level coarse-grained or mesoscopic methods(De Pablo 2011; Farré-Gil et al. 2024) ,which have gained sequence specificity, scalability and computational efficiency, allowing to simulate kilo-to-megabase fragments of DNA. Very remarkable efforts have been made to move up these methods to represent chromatin, which requires the introduction of biases derived from experimental data (MNAseq, chromosome conformation capture, and even static or dynamic pictures obtained by ultra-resolution microscopy, and others (Buitrago et al. 2019; Neguembor et al. 2022; Li and Schlick 2024)). This has opened the possibility to recover dynamic “base-pair” resolution pictures of chromatin and study aspects from local and global chromatin rearrangements to inter-play between effector proteins and nucleosomes, the impact of lesions in chromatin structure, and even the role of phase separation in defining local chromatin arrangements (Joseph et al. 2021; Liu et al. 2025; Park et al. 2025).
\r\nAs the target systems move from the small atomistic detail to the entire chromatin fiber, the community is broken into different sub-communities. This generates a risk of disconnection, which would lead to a waste of effort reformulating solutions to already solved problems, or ignoring the characteristic that a method should have to maintain coherence with more accurate models, or to scale to represent systems of real biological interest. This will be the main objective of this meeting, which will join a variety of sub-communities with a common interest: the DNA.
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\r\nReferences
\r\n
\r\n[1] J. Aranda, M. Terrazas, H. Gómez, N. Villegas, M. Orozco, Nat. Catal., 2, 544-552 (2019)
\r\n[2] D. Buitrago, L. Codó, R. Illa, P. de Jorge, F. Battistini, O. Flores, G. Bayarri, R. Royo, M. Del Pino, S. Heath, A. Hospital, J. Gelpí, I. Heath, M. Orozco, Nucleic Acids Research, 47, 9511-9523 (2019)
\r\n[3] R. Collepardo-Guevara, G. Portella, M. Vendruscolo, D. Frenkel, T. Schlick, M. Orozco, J. Am. Chem. Soc., 137, 10205-10215 (2015)
\r\n[4] P. Dans, I. Ivani, A. Hospital, G. Portella, C. González, M. Orozco, Nucleic. Acids. Res., gkw1355 (2017)
\r\n[5] J. de Pablo, Annu. Rev. Phys. Chem., 62, 555-574 (2011)
\r\n[6] D. Farré-Gil, J. Arcon, C. Laughton, M. Orozco, Nucleic Acids Research, 52, 6791-6801 (2024)
\r\n[7] V. Genna, G. Portella, A. Sala, M. Terrazas, I. Serrano-Chacón, J. González, N. Villegas, L. Mateo, C. Castellazzi, M. Labrador, A. Aviño, A. Hospital, A. Gandioso, P. Aloy, I. Brun-Heath, C. Gonzalez, R. Eritja, M. Orozco, Nucleic Acids Research, 53, (2025)
\r\n[8] J. Joseph, A. Reinhardt, A. Aguirre, P. Chew, K. Russell, J. Espinosa, A. Garaizar, R. Collepardo-Guevara, Nat. Comput. Sci., 1, 732-743 (2021)
\r\n[9] Z. Li, T. Schlick, Nucleic Acids Research, 52, 583-599 (2023)
\r\n[10] S. Liu, C. Wang, B. Zhang, Biochemistry, 64, 1750-1761 (2025)
\r\n[11] M. Neguembor, J. Arcon, D. Buitrago, R. Lema, J. Walther, X. Garate, L. Martin, P. Romero, J. AlHaj Abed, M. Gut, J. Blanc, M. Lakadamyali, C. Wu, I. Brun Heath, M. Orozco, P. Dans, M. Cosma, Nat. Struct. Mol. Biol., 29, 1011-1023 (2022)
\r\n[12] S. Park, R. Merino-Urteaga, V. Karwacki-Neisius, G. Carrizo, A. Athreya, A. Marin-Gonzalez, N. Benning, J. Park, M. Mitchener, N. Bhanu, B. Garcia, B. Zhang, T. Muir, E. Pearce, T. Ha, Nature, (2025)
\r\n[13] E. Schultz, J. Kaplan, Y. Wu, S. Kyhl, R. Willett, J. de Pablo, WIREs. Comput. Mol. Sci., 15, (2025)
You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: https://www.cecam.org/workshop-details/bridging-biomolecular-simulations-and-experiments-across-time-and-length-scales-from-single-molecules-to-entire-organelles-1493.
\r\n
\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the CECAM Event Manager if you have any question.
\r\n
\r\nDescription
\r\n
\r\nMolecular simulations are firmly established as a central tool in the life sciences over the last few decades. This is evident from the now-standard use of molecular dynamics simulations by molecular biologists and biophysicists, and the remarkable success of AlphaFold, which has convinced even the most skeptical of the critical role of these methods in contemporary biological research.
\r\nHowever, new challenges are emerging. It is increasingly apparent that to understand biomolecular function, we must move beyond studying isolated molecules. The focus is now shifting towards examining large, dynamic complexes of biomolecules within their complex native environments, complete with post-translational modifications. Embracing this complexity is crucial for understanding how biological functions and cellular structures emerge and adapt.
\r\nThis workshop will address existing and emerging frontiers, discussing both current challenges and the future of molecular simulations needed to meet them. It will gather simulation experts that have been actively developing methods that can increase simulation accuracy and extend their applicability range across multiple scales, as well as experimentalists performing advanced studies that can address outstanding challenges occurring at computationally accessible time and length scales. A main aim will be to discuss how to improve the accuracy of simulations, integrate simulations and cutting-edge experiments, and how to best take advantage of innovative enhanced sampling and machine learning-based approaches.
\r\nThe workshop will seize the opportunity to celebrate the outstanding scientific achievements of Gerhard Hummer, a prominent leader in the field, on his sixty’s birthday. Many of the participants that have already expressed their intention to attend and support the workshop or past or current theoretical and experimental scientist that have been either collaborators and co-authors, mentored by, or inspired by Gerhard’s ideas and expertise.
You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: https://www.cecam.org/workshop-details/theoretical-realisation-of-quantum-phenomena-in-computational-materials-discovery-1485.
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\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the CECAM Event Manager if you have any question.
\r\n
\r\nDescription
\r\n
\r\nQuantum phenomena in materials underpin a range of emerging technologies, including spin-based quantum technologies, efficient energy transport materials and ultra-narrow bandwidth lasers.1,2,3 Emergent behaviour such as quantum magnetism, superconductivity and superradiance4 arise from the complex interplay between electronic and structural properties; electronic features including strong electron correlation, spin-orbit coupling and reduced dimensionality can lead to phenomena such as unconventional superconductivity and room-temperature spin coherences, whilst structural factors such as crystal symmetry, doping concentrations and Moiré twist patterns are pivotal in shaping these quantum characteristics.5,6 Computational quantum materials discovery requires both highly advanced theoretical models of the electronic structure and high-throughput approaches for identifying stable crystal structures and predicting their properties.3,7
\r\nStrongly correlated electrons, ubiquitous in quantum materials, challenge conventional density functional theory (DFT). Quantum embedding methods, such as Density Matrix Embedding Theory (DMET) and Quantum Defect Embedding Theory (QDET), are powerful tools for describing strongly correlated electronic states in materials. QDET solves an effective Hamiltonian for a strongly-correlated subset of DFT orbitals using full configuration interaction, parameterized via a Green's function approach.8 DMET, however, maps the solid-state problem onto a self-consistent quantum impurity coupled to a mean-field bath, with the impurity solved by high-level methods.9 The application of these advanced techniques is rapidly growing, from analysing superconducting cuprates to describing quantum spin defects in semiconductors.8,9
\r\nModel Hamiltonians, such as the multi-band Hubbard model, are increasingly used to describe the low-energy physics of quantum materials.10 While the constrained random phase approximation is the traditional choice for parametrising these models,11 the newly developed moment-conserved RPA may offer superior accuracy by conserving instantaneous two-point correlation functions.12,13 Powerful numerical techniques like Determinant Quantum Monte Carlo have recently been pioneered for solving the model Hamiltonian and predicting quantum phenomena such as pairing susceptibilities.14
\r\nSuch theoretical methods are also essential for computational discovery of spin defects in semiconductors, a promising platform for room-temperature qubits.3,15 Advanced theoretical treatments are essential to predict defect electronic, magnetic, and optical properties, incorporating effects like spin-orbit and spin-phonon coupling which determine spin coherence and optical manipulation characteristics. The current state-of-the-art combines DFT studies of semiconductor bulk properties with ab initio treatments of the defect; quantum embedding methods are emerging as a promising alternative.16,17
\r\nGiven the immense diversity of materials, high-throughput screening is a cornerstone of modern materials discovery. DFT, particularly with state-of-the-art approximations like r2SCAN+rVV10, remains the workhorse for reliably determining material structures; such calculations often offer critical insight into both a systems stability and electronic structure.7,18,19,20 Machine learning (ML) is transforming materials discovery by slashing the computational cost of such calculations, allowing a wider exploration of composition space.21,22
\r\nComputational quantum materials modelling is advancing rapidly, however reconciling methods treating strongly correlated electrons with computational workflows employed in modern materials discovery remains relatively unexploited. The synergy of advanced theory, high-performance computing and ML has the potential to drive breakthroughs in quantum materials discovery and accelerate development of emerging technologies, from novel qubit platforms to room-temperature superconductors.
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\r\nReferences
\r\n
\r\n[1] C. Scott, G. Booth, Phys. Rev. Lett., 132, 076401 (2024)
\r\n[2] X. Jiang, W. Wang, S. Tian, H. Wang, T. Lookman, Y. Su, npj. Comput. Mater., 11, 79 (2025)
\r\n[3] S. Giaremis, M. Righi, Tribology International, 204, 110438 (2025)
\r\n[4] Z. Zhu, J. Park, H. Sahasrabuddhe, A. Ganose, R. Chang, J. Lawson, A. Jain, npj. Comput. Mater., 10, 258 (2024)
\r\n[5] R. Nelson, C. Ertural, J. George, V. Deringer, G. Hautier, R. Dronskowski, J. Comput. Chem., 41, 1931-1940 (2020)
\r\n[6] M. Kothakonda, A. Kaplan, E. Isaacs, C. Bartel, J. Furness, J. Ning, C. Wolverton, J. Perdew, J. Sun, ACS Mater. Au, 3, 102-111 (2022)
\r\n[7] V. Briganti, A. Lunghi, npj. Comput. Mater., 11, 62 (2025)
\r\n[8] A. Kundu, F. Martinelli, G. Galli, J. Phys. Chem. Lett., 16, 1973-1979 (2025)
\r\n[9] A. Gali, A. Schleife, A. Heinrich, A. Laucht, B. Schuler, C. Chakraborty, C. Anderson, C. Déprez, J. McCallum, L. Bassett, M. Friesen, M. Flatté, P. Maurer, S. Coppersmith, T. Zhong, V. Begum-Hudde, Y. Ping, MRS Bulletin, 49, 256-276 (2024)
\r\n[10] P. Mai, B. Cohen-Stead, T. Maier, S. Johnston, Proc. Natl. Acad. Sci. U.S.A., 121, (2024)
\r\n[11] C. Pellegrini, C. Kukkonen, A. Sanna, Phys. Rev. B, 108, 064511 (2023)
\r\n[12] R. Goyal, S. Maharaj, P. Kumar, M. Chandrasekhar, J Mater. Sci: Mater Eng., 20, 4 (2025)
\r\n[13] Y. Chang, E. van Loon, B. Eskridge, B. Busemeyer, M. Morales, C. Dreyer, A. Millis, S. Zhang, T. Wehling, L. Wagner, M. Rösner, npj. Comput. Mater., 10, 129 (2024)
\r\n[14] H. Padma, J. Thomas, S. TenHuisen, W. He, Z. Guan, J. Li, B. Lee, Y. Wang, S. Lee, Z. Mao, H. Jang, V. Bisogni, J. Pelliciari, M. Dean, S. Johnston, M. Mitrano, Phys. Rev. X, 15, 021049 (2025)
\r\n[15] Z. Cui, J. Yang, J. Tölle, H. Ye, S. Yuan, H. Zhai, G. Park, R. Kim, X. Zhang, L. Lin, T. Berkelbach, G. Chan, Nat. Commun., 16, 1845 (2025)
\r\n[16] L. Otis, Y. Jin, V. Yu, S. Chen, L. Gagliardi, G. Galli, J. Phys. Chem. Lett., 16, 3092-3099 (2025)
\r\n[17] A. Ganose, H. Sahasrabuddhe, M. Asta, K. Beck, T. Biswas, A. Bonkowski, J. Bustamante, X. Chen, Y. Chiang, D. Chrzan, J. Clary, O. Cohen, C. Ertural, M. Gallant, J. George, S. Gerits, R. Goodall, R. Guha, G. Hautier, M. Horton, T. Inizan, A. Kaplan, R. Kingsbury, M. Kuner, B. Li, X. Linn, M. McDermott, R. Mohanakrishnan, A. Naik, J. Neaton, S. Parmar, K. Persson, G. Petretto, T. Purcell, F. Ricci, B. Rich, J. Riebesell, G. Rignanese, A. Rosen, M. Scheffler, J. Schmidt, J. Shen, A. Sobolev, R. Sundararaman, C. Tezak, V. Trinquet, J. Varley, D. Vigil-Fowler, D. Wang, D. Waroquiers, M. Wen, H. Yang, H. Zheng, J. Zheng, Z. Zhu, A. Jain, Digital Discovery, (2025)
\r\n[18] W. Ko, Z. Gai, A. Puretzky, L. Liang, T. Berlijn, J. Hachtel, K. Xiao, P. Ganesh, M. Yoon, A. Li, Advanced Materials, 35, (2022)
\r\n[19] L. Du, M. Molas, Z. Huang, G. Zhang, F. Wang, Z. Sun, Science, 379, (2023)
\r\n[20] C. Zhu, S. Boehme, L. Feld, A. Moskalenko, D. Dirin, R. Mahrt, T. Stöferle, M. Bodnarchuk, A. Efros, P. Sercel, M. Kovalenko, G. Rainò, Nature, 626, 535-541 (2024)
\r\n[21] Á. Gali, Nanophotonics, 12, 359-397 (2023)
\r\n[22] V. Harris, P. Andalib, Front. Mater., 11, (2024)
You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: https://www.cecam.org/workshop-details/emergent-dynamics-of-active-colloids-chirality-non-reciprocity-and-memory-1496.
\r\n
\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the CECAM Event Manager if you have any question.
\r\n
\r\nDescription
\r\n
\r\nBiological systems in Nature are intrinsically out-of-equilibrium to maintain their structural complexity and functional diversity. Similarly, out-of-equilibrium dissipative colloidal systems subjected to an external energy injection often develop nontrivial collective dynamics and self-organize into large scale structures, which are far more complex than their equilibrium counterparts [1-17]. The main sources of such emergent behavior are the many-body dissipative interactions between colloids (e. g. steric, electrostatic, magnetic), the external energy injection, and the coupling of particles dynamics through the fluid flow around them. Collective dynamics and self-organization in out-of-equilibrium colloidal systems (often termed as active colloids) is a rapidly growing area of research which led to the discovery of novel dynamic architectures and functionalities that are not generally available at equilibrium.
\r\n Colloidal systems have been the subject of intense research for a long time due to their ubiquitous technological applications. Colloidal particles display Brownian motion, size in the visible wavelength and dynamics in experimentally accessible timeframes (milliseconds to seconds) making them an attractive platform for the experiments and the computational modeling. The pair interactions between particles can be easily adjusted in strength and range by applying relatively small external fields. When driven by external forces or an internal energy source, colloids can mimic motile biological entities and can serve as a testbed for exploring the rich and complex physics of out-of-equilibrium systems. These dissipative colloidal structures utilize energy to generate and maintain structural complexity. Experiments and numerical simulations along this line of research have often revealed nontrivial collective dynamics and emergent large-scale structures [1-17]. With the proposed workshop we would like to provide a platform for discussing several new and important trends in this field of active colloidal materials, that is, chirality, non-reciprocity, and memory.
\r\nA recent hot trend in the field of active colloids explores the emergence of coherent motion and self-organization in systems with chirality [5-11]. Chirality is an intrinsic fundamental property of many natural and synthetic systems. Colloidal particles driven by external torques [12-18] constitute an ideal model system to investigate these phenomena since they avoid the inherent complexity of biological active matter. Spinning particles dispersed in a fluid represent a special class of artificial active systems that inject vorticity at the microscopic level [19-25]. Dense collections of interacting spinning particles represent a chiral fluid [26], which breaks parity and time-reversal symmetries, and displays a novel viscosity feature called the odd viscosity and elasticity [27, 28]. The odd viscosity has been identified in interacting chiral spinners [29], and it led to remarkable effects such as production of flow perpendicular to the pressure [27], topological waves [30], or the emergence of edge currents [29]. Magnetic rollers dynamically assemble into a vortex under harmonic confinement, that spontaneously selects a sense of rotation and is capable of chirality switching [31,32]. Multiple motile vortices unbound from any confinement have been revealed in ensembles of magnetic rollers powered by a uniaxial field [33]. Oscillating chiral flows were generated when a roller liquid was coupled to fixed obstacles [34]. There has been an increasing effort to investigate collective phenomena in systems composed of chiral active units [11, 35-40]. Synchronized self-assembled magnetic spinners at the liquid interface revealed structural transitions from liquid to nearly crystalline states and demonstrated reconfigurability coupled to a self-healing behavior [41]. Activity-induced synchronization leading to a mutual flocking, and chiral self- sorting has been observed in modeled ensembles of self-propelled circle swimmers [42]. Shape anisotropic particles powered by the Quincke phenomenon led to the realization of chiral rollers (similar to circle swimmers) with spontaneously selected handedness of their motion and activity-dependent curvature of trajectories [43].
\r\nAnother fast-developing direction in the field of non-equilibrium active and driven colloids is the realization of systems characterized by non-reciprocity of interactions or memory effects and how they can lead to emerging collective phenomena. Due to the intrinsic nonequilibrium nature of active systems, the couplings between particles often deviate from the standard form derivable from a Hamiltonian. One intriguing example is a time-delayed coupling involving a discrete delay time (or a distribution of such times). Such a situation arises, for example, through a delay in communication or sensing, and can be artificially created via a feedback loop [44]. Another topic attracting a lot of attention in the community is based on active systems with nonreciprocal couplings that can arise, for example, through chemotaxis or phoretic interactions between self-propelling colloids [45], or through predator-prey or vision-cone interactions [46,47] in macroscopic active systems. On the collective level, is now well established that non-reciprocity can induce new types of phase transitions [48] and patterns with broken time- and parity symmetry, including travelling patterns [49,50] and globally chiral motion without chirality of the individual constituents [51]. While many of these studies have been pursued only at a mean field-theoretical level, there is also an increasing interest in understanding corresponding particle-scale effects, that can only be accessed by numerical simulations [52] or corresponding experiments. For example, non-reciprocal interactions may generate new types of self-assembled systems able to learn and to produce transition between different shapes [53]. Establishing the precise connection between the different length and time scales is still an important challenge. Here, computer simulations are an indispensable tool.
\r\nMany standard models of active motion implicitly assume an inert (equilibrium) environment yielding instantaneous friction and noise. In contrast, several recent studies [54,19] explore the impact of retarded friction as it arises in viscoelastic environments made, e.g., of polymers, liquid crystals, or biological tissues [55-57]. An extreme case is time-delay [44]. From a theoretical and computational perspective, retarded friction or, more generally, non-Markovian dynamics, still provides a severe challenge. This concerns, e.g., the extraction (or modelling) of memory kernels, but also the actual solution of the coupled equations of motion, each being subject to history effects. As a consequence, only few studies on the emerging collective behavior of active particles with memory are currently available, including collective effects in systems of feedback-driven colloids [58] and pattern formation in a non-Newtonian active system [59]. Advancing numerical methods capable of treating memory effects will become more and more important in view of the recent experimental progress in this field. Experimentally, the memory effects in the system can be induced, e.g., by temporal activity modulations at intermediate timescales of the interactions in the colloidal ensemble [60]. Such modulations generate active particles with partial memory (at the particle level) of their motion from the previous activity cycles (either through partial depolarization or remnant hydrodynamic flows induced by the particle motion). Novel dynamic patterns (such as localized multiple vortices, flocks, pulsating lattices) has been revealed in ensembles of Quinke rollers [60,61]. When coupled to the fluid flows, active particle with memory can produce activity shockwaves [62]. Also, it has been recently demonstrated that active colloidal ensembles realized by Quinke rollers can effectively develop “ensemble memory”, where the information about the dynamic state of the system is distributed over the whole ensemble [63]. This information can be effectively exploited to command subsequent collective polar states of the active colloidal ensemble through activity cycling [63] and can pave the way toward direct applications in different technological fields related to microfluidics and microrobotics.
\r\nDeveloping fundamental understanding of the complex colloidal dynamics in systems driven out-of-equilibrium by external fields represents a significant theoretical and computational challenge as it involves multi-body interactions, the overlapping of length- and timescales, and the coupling of particle interactions with the fluid flow. Some of the features may be understood using phenomenological using continuum descriptions [21-23] Nevertheless, the microscopic mechanisms leading to the dynamic self-assembly and their relations to the emergent behavior in active colloidal fluids with chirality, non-reciprocal interactions, and memory often remain unclear. Computer simulations are practically the only method to theoretically investigate such questions. However, modeling of the nonequilibrium dynamics presents a formidable computational challenge due to the complex many- body interactions and collective dynamics at different time and lengths scales. One of the main challenges is to properly account for the particle-fluid coupling. On a coarse-grained level, the fluid flow around colloids is modeled by molecular dynamics methods like Lattice-Boltzmann [64] and Multi Particle Collision Dynamics [65,66]. An alternative approach is to describe the colloidal dynamics by molecular dynamics simulation, or an amplitude equation (Ginzburg-Landau type equation) coupled to the Navier-Stokes equations describing large-scale time- averaged hydrodynamic flows induced by the colloids [67,68].
\r\n
\r\nReference
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\r\n[1] B. A. Grzybowski and G. M. Whitesides, “Dynamic Aggregation of Chiral Spinners” Science 296, 718-721 (2002).
\r\n[2] Y. Sumino, K. H. Nagai, Y. Shitaka, D. Tanaka, K. Yoshikawa, H. Chaté, K. Oiwa “Large-scale vortex lattice emerging from collectively moving microtubules”, Nature 483, 448-452 (2012).
\r\n[3] A Snezhko, I. Aranson, “Magnetic manipulation of self-assembled colloidal asters”, Nature Materials 10, 698-703 (2011).
\r\n[4] A. P. Petrov, X.-L. Wu, and A. Libchaber, “Fast-Moving Bacteria Self-Organize into Active Two- Dimensional Crystals of Rotating Cells”, Phys. Rev. Lett. 114, 158102 (2015).
\r\n[5] Bowick, M. J., Fakhri, N., Marchetti, M. C., & Ramaswamy, S. “Symmetry, thermodynamics, and topology in active matter”, Phys. Rev. X, 12(1), 010501 (2022).
\r\n[6] C. Scholz, A. Ldov, T. Pöschel, M. Engel, H. Löwen “Surfactants and rotelles in active chiral fluids” Science Advances 7 (16), eabf8998 (2021).
\r\n[7] G. Kokot, S. Das, R. Winkler, G. Gompper, I. Aranson, and A. Snezhko, “Active turbulence in a gas of self- assembled spinners”, Proc. Nat. Acad. Sci. U.S.A. 114, 12870 (2017).
\r\n[8] B. C. van Zuiden, J. Paulose, W. T. M. Irvine, D. Bartolo, and V. Vitelli, “Spatiotemporal order and emergent edge currents in active spinner materials” Proc. Natl Acad. Sci. USA 113, 12919 (2016).
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\r\n[13] F. Ma, S. Wang, D. T. Wu and N. Wu, \"Electric-field–induced assembly and propulsion of chiral colloidal clusters\" Proc. Natl. Acad. Sci. U. S. A. 112, 6307–6312 (2015).
\r\n[14] Z. Shen, A. Würger and J. S. Lintuvuori “Hydrodynamic self-assembly of active colloids: chiral spinners and dynamic crystals” Soft Matter, 15, 1508-1521 (2019).
\r\n[15] P. Tierno, R. Muruganathan, and T. M. Fischer, “Viscoelasticity of Dynamically Self-Assembled Paramagnetic Colloidal Clusters”, Phys. Rev. Lett. 98, 028301 (2007).
\r\n[16] Driscoll, M., Delmotte, B., Youssef, M., Sacanna, S., Donev, A., Chaikin, P., 2017, “Unstable fronts and motile structures formed by microrollers”, Nature Physics, 13, 375 (2017).
\r\n[17] J. E. Martin, A. Snezhko, “Driving self-assembly and emergent dynamics in colloidal suspensions by time- dependent magnetic fields”, Rep. Prog. Phys. 76, 126601 (2013).
\r\n[18] R. Di Leonardo, A. Buzas, L. Kelemen, G. Vizsnyiczai, L. Oroszi, and P. Ormos, “Hydrodynamic Synchronization of Light Driven Microrotors” Phys. Rev. Lett. 109, 034104 (2012).
\r\n[19] N. Narinder, C. Bechinger and J. R. Gomez-Solano “Memory-Induced Transition from a Persistent Random Walk to Circular Motion for Achiral Microswimmers”, Phys. Rev. Lett. 121, 078003 (2018).
\r\n[20] C. Lozano, J. Ruben Gomez-Solano and C. Bechinger “Active particles sense micromechanical properties of glasses” Nat. Materials, 18, 1118–1123 (2019).
\r\n[21] M. C. Marchetti, J. F. Joanny, S. Ramaswamy, T. B. Liverpool, J. Prost, M. Rao, and R. Aditi Simha “Hydrodynamics of soft active matter” Reviews of Modern Physics 85 (3), 1143.
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\r\n[23] M. Leoni and T. B. Liverpool, “Dynamics and interactions of active rotors” Europhys. Lett. 92, 64004 (2010).
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\r\n[35] S. Farhadi, S. Machaca, J. Aird, B. O. Torres Maldonado, S. Davis, P. E. Arratia, D. J. Durian, Dynamics and thermodynamics of air-driven active spinners. Soft Matter 14, 5588–5594 (2018).
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\r\n
You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: https://www.cecam.org/workshop-details/challenges-in-modelling-ion-channels-simulations-meet-experiments-1369.
\r\n
\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the CECAM Event Manager if you have any question.
\r\n
\r\nDescription
\r\n
\r\nThe human genome includes more than 300 genes coding for ion channel proteins, representing approximately 2% of the total number of genes. This abundance of ion channels highlights their critical role in numerous biological processes and their involvement in diseases, underscoring their importance as potential drug targets. Ion channels exert their biological roles through three main functional characteristics: the highly efficient selective conduction of ions; the capacity to open and close in response to chemical/physical stimuli (gating); and the decrease in conductance upon sustained stimuli (inactivation). In the last 20 years, the number of experimental atomic structures of ion channels has increased from a few units to hundreds, now including representative structures for most of the ion channel families. Simulations based on these experimental structures have significantly contributed to the current understanding of conduction, selectivity, gating, and inactivation [1-2]. Strengthening the quantitative agreement between simulations and experiments is now essential for advancing in this field. This effort is currently hampered by common issues in biomolecular simulations, such as the limited timescales for observing biologically relevant events and the sub-optimal accuracy of the underlying physical models. Both of these shortcomings are expected to be mitigated by recent methodological developments. For instance, atomic simulations of ion channels with polarizable force fields have been recently reported [3]. Lack of polarization is a well-known limitation of classical force fields, especially when describing ion-protein and ion-water interactions in a crowded environment like the pore cavity. Consequently, the usage of polarizable force fields is considered a promising strategy for improving the agreement with experimental data about ion conduction and selectivity. An alternative strategy for enhancing the model accuracy in critical channel regions is to combine molecular mechanics with quantum approaches. Thanks to the ever-increasing computational resources, now combined with advancements in codes for hybrid QM/MM models, this approach is becoming feasible for ion channel research [4]. Increasing computational resources, coupled with improved algorithms for accelerating rare events and potentially harnessing machine learning, are also opening new possibilities in the study of state transitions. Gating and inactivation events of ion channels are finally becoming accessible to atomic simulations, offering important insights into the mechanistic functioning of this important protein superfamily [5]. The proposed workshop will foster further developments in the field by bringing together leading scientists in the experimental methodologies and computational techniques used in ion channel research in a stimulating and collaborative environment.
\r\n
\r\nReferences
\r\n
\r\n[1] E. Flood, C. Boiteux, B. Lev, I. Vorobyov, T. Allen, Chem. Rev., 119, 7737-7832 (2019)
\r\n[2] C. Guardiani, F. Cecconi, L. Chiodo, G. Cottone, P. Malgaretti, L. Maragliano, M. Barabash, G. Camisasca, M. Ceccarelli, B. Corry, R. Roth, A. Giacomello, B. Roux, Advances in Physics: X, 7, (2022)
\r\n[3] V. Ngo, H. Li, A. MacKerell, T. Allen, B. Roux, S. Noskov, J. Chem. Theory Comput., 17, 1726-1741 (2021)
\r\n[4] F. Schackert, J. Biedermann, S. Abdolvand, S. Minniberger, C. Song, A. Plested, P. Carloni, H. Sun, J. Chem. Inf. Model., 63, 1293-1300 (2023)
\r\n[5] S. Pérez-Conesa, L. Delemotte, Free energy landscapes of KcsA inactivation, 2023
You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: https://www.cecam.org/workshop-details/g-protein-coupled-receptors-functional-dynamics-revealed-by-experimental-and-computational-structural-data-1488.
\r\n
\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the CECAM Event Manager if you have any question.
\r\n
\r\nDescription
\r\n
\r\nG protein-coupled receptors (GPCRs) represent a vast and diverse class of transmembrane proteins that orchestrate a wide range of physiological processes by responding to both endogenous and exogenous ligands [1,2]. These receptors are essential to critical functions such as metabolism, immune regulation, neuronal signaling, and sensory perception - including vision and olfaction. Due to their physiological relevance and membrane accessibility, GPCRs are the targets of approximately 34% of all prescribed medications, accounting for nearly 27% of the global pharmaceutical market [3].
\r\nDespite their pharmaceutical importance, key aspects of GPCR function remain elusive. The canonical activation model posits that agonist binding to the extracellular orthosteric site triggers allosteric changes - most notably, the outward displacement of transmembrane helices 5 (TM5) and 6 (TM6) on the intracellular side - ultimately leading to receptor activation [2-4]. However, recent evidence suggests a more nuanced mechanism. In several GPCRs, activation appears to involve cooperative engagement between the agonist and the G protein. For example, the G protein may disrupt an \"inactivating ionic lock\" - a salt bridge between TM3 and TM6 - while the agonist stabilizes the active conformation. In some receptors, this is complemented by the formation of an “activating ionic lock” between TM5 and TM6 [5-8]. These dual contributions are considered thermodynamically essential for full activation [7].
\r\nAdding further complexity, GPCR activity is regulated by conformational microswitches and finely tuned intra-protein interaction networks. These dynamic rearrangements are difficult to capture and often elude direct correlation with functional outcomes. Moreover, allosteric ligands - which bind sites distinct from the orthosteric pocket - are being increasingly identified [9-12], along with small molecules capable of biased signaling, i.e., preferential activation of specific intracellular pathways [11-13, 16, 17]. These findings reveal a rich and underexplored conformational landscape that governs GPCR signaling. In addition, native membrane components—such as lipids and interacting proteins, including GPCR oligomers—are known to significantly modulate receptor function [11, 18-22].
\r\nTo disentangle these intricacies, computational modeling has become indispensable, offering atomistic insight into GPCR conformational dynamics and mechanistic understanding [1-2, 7, 11, 14, 16–21, 23]. Nevertheless, key questions remain - particularly regarding the structural basis of biased signaling, strategies for leveraging allosteric networks in pharmacology, and the modulatory role of the lipid environment. Addressing these gaps is crucial for both fundamental biology and the rational design of next-generation GPCR-targeting drugs with improved selectivity and safety profiles.
\r\nThese scientific challenges form the foundation of our upcoming workshop, which will focus on the latest experimental and computational approaches for studying the functional dynamics of GPCRs. Given the profound health, economic, and societal implications of modulating these receptors with precision, we aim to strengthen the interdisciplinary nature of the event by increasing the representation of experimental research and integrating cutting-edge artificial intelligence applications into the program.
\r\nBuilding upon the success of the 2022 and 2024 editions - which led to new collaborations and a landmark publication in Nature Reviews Drug Discovery [24] - our goal is to further enhance communication and collaboration between experimentalists and theoreticians. The workshop will serve as a reference point for young scientists and students, offering a platform to interact with leading international experts. We are confident that this initiative will foster insightful discussions and contribute meaningfully to advancing the field of GPCR pharmacology.
\r\n
\r\nReferences
\r\n
\r\n[1] J. Smith, R. Lefkowitz, S. Rajagopal, Nat. Rev. Drug. Discov., 17, 243-260 (2018)
\r\n[2] P. Conflitti, E. Lyman, M. Sansom, P. Hildebrand, H. Gutiérrez-de-Terán, P. Carloni, T. Ansell, S. Yuan, P. Barth, A. Robinson, C. Tate, D. Gloriam, S. Grzesiek, M. Eddy, S. Prosser, V. Limongelli, Nat. Rev. Drug. Discov., 24, 251-275 (2025)
\r\n[3] L. Picard, A. Orazietti, D. Tran, A. Tucs, S. Hagimoto, Z. Qi, S. Huang, K. Tsuda, A. Kitao, A. Sljoka, R. Prosser, Nat. Chem. Biol., 21, 71-79 (2024)
\r\n[4] B. Huang, C. St. Onge, H. Ma, Y. Zhang, Drug Discovery Today, 26, 189-199 (2021)
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\r\n[7] S. Huang, O. Almurad, R. Pejana, Z. Morrison, A. Pandey, L. Picard, M. Nitz, A. Sljoka, R. Prosser, eLife, 11, (2022)
\r\n[8] A. Duncan, W. Song, M. Sansom, Annu. Rev. Pharmacol. Toxicol., 60, 31-50 (2020)
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\r\n[12] D. Wootten, A. Christopoulos, M. Marti-Solano, M. Babu, P. Sexton, Nat. Rev. Mol. Cell. Biol., 19, 638-653 (2018)
\r\n[13] D. Hilger, M. Masureel, B. Kobilka, Nat. Struct. Mol. Biol., 25, 4-12 (2018)
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\r\n[15] D. Thal, A. Glukhova, P. Sexton, A. Christopoulos, Nature, 559, 45-53 (2018)
\r\n[16] L. Slosky, M. Caron, L. Barak, Trends in Pharmacological Sciences, 42, 283-299 (2021)
\r\n[17] C. Zhu, X. Lan, Z. Wei, J. Yu, J. Zhang, Acta Pharmaceutica Sinica B, 14, 67-86 (2024)
\r\n[18] V. D’Amore, P. Conflitti, L. Marinelli, V. Limongelli, Chem, 10, 3678-3698 (2024)
\r\n[19] A. Mafi, S. Kim, W. Goddard, Nat. Chem., 15, 1127-1137 (2023)
\r\n[20] H. Batebi, G. Pérez-Hernández, S. Rahman, B. Lan, A. Kamprad, M. Shi, D. Speck, J. Tiemann, R. Guixà-González, F. Reinhardt, P. Stadler, M. Papasergi-Scott, G. Skiniotis, P. Scheerer, B. Kobilka, J. Mathiesen, X. Liu, P. Hildebrand, Nat. Struct. Mol. Biol., 31, 1692-1701 (2024)
\r\n[21] A. Manglik, T. Kim, M. Masureel, C. Altenbach, Z. Yang, D. Hilger, M. Lerch, T. Kobilka, F. Thian, W. Hubbell, R. Prosser, B. Kobilka, Cell, 161, 1101-1111 (2015)
\r\n[22] M. Congreve, C. de Graaf, N. Swain, C. Tate, Cell, 181, 81-91 (2020)
\r\n[23] J. Lorente, A. Sokolov, G. Ferguson, H. Schiöth, A. Hauser, D. Gloriam, Nat. Rev. Drug. Discov., 24, 458-479 (2025)
\r\n[24] M. Lopez‐Balastegui, T. Stepniewski, M. Kogut‐Günthel, A. Di Pizio, M. Rosenkilde, J. Mao, J. Selent, British. J. Pharmacology., 182, 3211-3224 (2024)\r\n
You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: https://www.cecam.org/workshop-details/toward-intelligent-behavior-in-macroscopic-active-matter-1481.
\r\n
\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the CECAM Event Manager if you have any question.
\r\n
\r\nDescription
\r\n
\r\nActive matter has emerged as a central framework for understanding systems composed of self-driven units across scales, ranging from molecular motors and cytoskeletal filaments to animal groups and robotic swarms. Initially, many foundational models focused on macroscopic agents – such as flocks, swarms, and driven granular particles – where simple interaction rules give rise to rich collective phenomena. However, over the past two decades, much of the focus has shifted toward microscopic and mesoscopic active systems, especially in soft and biological matter, supported by the technological development of high-resolution imaging, force measurement, and microfabrication. These advances have driven a more refined theoretical understanding, connecting microscopic dynamics with hydrodynamic and continuum-scale descriptions, and have found applications in biophysics, material science, and cellular biology.
\r\nIn parallel, yet often semi-independently, active matter concepts have flourished in ecological and robotic systems. In these domains, the agents – be they insects, birds, autonomous vehicles, or soft robots – not only self-propel and interact, but also sense their environments, make decisions, and adapt their behavior. These systems extend the classical framework of active matter by incorporating elements of intelligence, information processing, and environmental feedback. Notably, such systems can operate far from equilibrium and exhibit coordinated behavior that seems tuned for functional outcomes – navigation, foraging, or collective decision-making.
\r\nThese trends point toward a convergence: macroscopic active matter systems capable of intelligent, adaptive, or programmable behavior. This includes both natural systems (e.g., flocking insects, social insects, animal herds) and artificial systems (e.g., modular robots, programmable matter, active granular agents). The interplay of self-propulsion, interaction rules, information exchange, learning or memory, and system-level feedback opens exciting new directions for both fundamental science and applications. Recent efforts in this space combine techniques from statistical physics, nonlinear dynamics, robotics, and machine learning.
\r\nHowever, the communities working on these different aspects of active matter – soft matter physicists, ecologists, roboticists, and complexity scientists – remain fragmented, with limited opportunity for sustained dialogue. Bridging these communities is essential to develop a shared language, identify unifying principles, and guide the development of new experimental platforms and theoretical frameworks.
\r\n
\r\nReferences
\r\n
\r\n[1] F. Gu, B. Guiselin, N. Bain, I. Zuriguel, D. Bartolo, Nature, 638, 112-119 (2025)
\r\n[2] A. Rafsanjani, K. Bertoldi, A. Studart, Sci. Robot., 4, (2019)
\r\n[3] J. Tirado, A. Parvaresh, B. Seyidoğlu, D. Bedford, J. Jørgensen, A. Rafsanjani, Cyborg. Bionic. Syst., 6, (2025)
\r\n[4] J. O’Byrne, Y. Kafri, J. Tailleur, F. van Wijland, Nat. Rev. Phys., 4, 167-183 (2022)
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