@article{21983, abstract = {Promoters and enhancers are cis-regulatory elements (CREs), DNA sequences that bind transcription factor (TF) proteins to up- or down-regulate target genes. Decades-long efforts yielded TF-DNA interaction models that predict how strongly an individual TF binds arbitrary DNA sequences and how individual binding events on the CRE combine to affect gene expression. These insights can be synthesized into a global, biophysically realistic, and quantitative genotype–phenotype map for gene regulation, a ‘holy grail’ for the application of evolutionary theory. A global map provides a rare opportunity to simulate the long-term evolution of regulatory sequences and pose several fundamental questions: How long does it take to evolve CREs de novo? How many non-trivial regulatory functions exist in sequence space? How connected are they? For which regulatory architecture is CRE evolution most rapid and evolvable? In this article, the first of a two-part series, we briefly review the pertinent modeling and simulation efforts for a unique system that enables close, quantitative, and mechanistic links between biophysics, as well as systems, synthetic, and evolutionary biology.}, author = {Mascolo, Elia and Körei, Reka E and Herrera-Álvarez, Santiago and Guet, Calin C and Crocker, Justin and Tkačik, Gašper}, issn = {1879-0380}, journal = {Current Opinion in Genetics & Development}, publisher = {Elsevier}, title = {{Long-term evolution of regulatory DNA sequences. Part 1: Simulations on global, biophysically-realistic genotype–phenotype maps}}, doi = {10.1016/j.gde.2026.102483}, volume = {99}, year = {2026}, } @article{21282, abstract = {Developmental patterning comprises processes that range from purely instructed, where external signals specify cell fates, to fully self-organized, where spatial patterns emerge autonomously through cellular interactions. We propose that both extremes—as well as the continuum of intermediate cases—can be conceptualized as information-processing systems, whose operation can be described using “Marr's three levels of analysis”: the computational problem being solved, the algorithms employed, and their molecular implementation. At the first level, we argue that normative theories, such as information-theoretic optimization principles, provide a formalization of the computational problem. At the second level, we show how simplified information-processing architectures provide a framework for developmental algorithms, which are formalized mathematically using dynamical systems theory. At the third level, the implementation of developmental algorithms is described by mechanistic biophysical and gene regulatory network models.}, author = {Brückner, David and Tkačik, Gašper}, issn = {2835-8279}, journal = {PRX Life}, publisher = {American Physical Society}, title = {{Marr's three levels for embryonic development: Information, dynamical systems, gene networks}}, doi = {10.1103/fdcf-dkws}, volume = {4}, year = {2026}, } @article{22326, abstract = {In many developmental systems, cells differentiate into a tissue by reading out morphogen concentration fields, a process fundamentally limited by noise. How much can the precision of this process be improved by nonlocal information, e.g., via cell-cell communication? Using a Bayes-optimal framework, we show that positional inference depends crucially on morphogen spatial correlations and on the "structural prior" that encodes the geometry of the cellular lattice performing the readout, thereby determining what a cell can reliably assume about the position of its neighbors when interpreting nonlocal morphogen signals. We derive upper bounds on positional information gain due to nonlocal readout and identify signal processing algorithms that approximate optimal positional inference, as well as simple chemical reaction schemes which implement such algorithms. Our theory suggests that correlational information can be exploited to significantly enhance developmental precision.}, author = {Zhang, Chen Y and Mateu Hoyos, Pablo and Brückner, David and Tkačik, Gašper}, issn = { 1079-7114}, journal = {Physical Review Letters}, publisher = {American Physical Society}, title = {{Nonlocal decoding of positional and correlational information during development}}, doi = {10.1103/mbjk-v4ym}, volume = {137}, year = {2026}, } @article{21759, abstract = {Promoters and enhancers are cis-regulatory elements (CREs), DNA sequences that bind transcription factor (TF) proteins to up- or down-regulate target genes. Decades-long efforts yielded TF-DNA interaction models that predict how strongly an individual TF binds arbitrary DNA sequences and how individual binding events on the CRE combine to affect gene expression. These insights can be synthesized into a global, biophysically realistic, and quantitative genotype-phenotype (GP) map for gene regulation, a ‘holy grail’ for the application of evolutionary theory. A global map provides a rare opportunity to simulate the long-term evolution of regulatory sequences and pose several fundamental questions: How long does it take to evolve CREs de novo? How many non-trivial regulatory functions exist in sequence space? How connected are they? For which regulatory architecture is CRE evolution most rapid and evolvable? In this article, the second of a two-part series, we review the application of evolutionary concepts — epistasis, robustness, evolvability, tunability, plasticity, and bet-hedging — to the evolution of gene regulatory sequences. We then evaluate the potential for a unifying theory for the evolution of regulatory sequences and identify key open challenges.}, author = {Mascolo, Elia and Körei, Reka E and Borst, Noa O. and Barton, Nicholas H and Crocker, Justin and Tkačik, Gašper}, issn = {1879-0380}, journal = {Current Opinion in Genetics and Development}, publisher = {Elsevier}, title = {{Long-term evolution of regulatory DNA sequences. Part 2: Theory and future challenges}}, doi = {10.1016/j.gde.2026.102472}, volume = {98}, year = {2026}, } @phdthesis{22399, abstract = {Gaining an understanding of how biological regulation evolves is a fundamental research question in both evolutionary biology and molecular genetics. In order to gain more insight into this topic, we focus on studying the simple gene regulatory system of the lac operon in E. coli. We show that simple population genetic models can shed light on evolution experiments and that this combined approach of modelling the experimental system gives insight to better understand the causes of evolutionary change in the experiment. We also study the natural diversity in the lac operon from 308 publicly available E. coli genomes that come from various host species and different regions of the world. Evidence that selection is generally maintaining the function of the lac operon across the sample regardless of host species is provided and we show that different protein coding genes in the operon are under different selective constraints on protein sequence preservation. A similar frameshift mutation found in experimental evolution studies is shown to be present in the sample we analyzed, indicating that selectively relevant variants in evolution experiments are also present in natural populations. We show that there is no simple phylogenetic relationship between host species, geographical location and the lac operon sequence. Finally, we argue that a combined approach of comparative genomics, experimental evolution and theoretical modelling contributes to a more complete understanding of molecular evolution. }, author = {Spasić, Aleksa}, issn = {2791-4585}, pages = {32}, publisher = {Institute of Science and Technology Austria}, title = {{Studying the evolutionary systems biology of the lac operon}}, doi = {10.15479/AT-ISTA-22399}, year = {2026}, } @article{22363, abstract = {Eukaryotic gene regulation relies on stochastic yet controlled promoter switching, in which genes transition between transcriptionally active and inactive states. Despite the molecular complexity of this process, recent studies have revealed a surprising invariance of the “switching correlation time” (TC)—the characteristic decay time of the autocorrelation function of promoter activity fluctuations—across gene expression levels in multiple genes and organisms. A biophysically plausible explanation for this invariance has so far been lacking. Here, we show that this empirical constraint imposes stringent requirements on minimal yet realistic models of transcriptional regulation. Specifically, reproducing TC–invariance requires regulatory architectures with at least four internal states and nonequilibrium dynamics that break detailed balance. Using Bayesian inference on Drosophila gap gene expression data, we demonstrate that such models i) quantitatively reproduce the observed TC–invariance, ii) remain robust to parameter perturbations, and iii) maximize information transmission from transcription factor concentration to gene expression. Remarkably, the TC-invariant modulation strategy we identify as optimal closely parallels contemporary control-theoretic results on the modulation of stochastic switching systems. Taken together, our results suggest that eukaryotic transcriptional regulation operates in a nonequilibrium regime to balance precision, reaction-rate limitations, and energy dissipation, thereby achieving near-optimal information transmission under fundamental physical constraints.}, author = {Zoller, Benjamin and Benichou, Alexis and Gregor, Thomas and Tkačik, Gašper}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, number = {28}, publisher = {National Academy of Sciences}, title = {{Invariant nonequilibrium dynamics in gene regulation optimize information flow}}, doi = {10.1073/pnas.2524855123}, volume = {123}, year = {2026}, } @article{21269, abstract = {The spatial organization of chromatin within the nucleus plays a crucial role in gene expression and genome function. However, the quantitative relationship between this organization and nuclear biochemical processes remains under debate. In this study, we present a graph-based generative model, bioSBM, designed to capture long-range chromatin interaction patterns from Hi-C data and, importantly, simultaneously link these patterns to biochemical features. Applying bioSBM to Hi-C maps of the GM12878 lymphoblastoid cell line, we identified a latent structure of chromatin interactions, revealing seven distinct communities that strongly align with known biological annotations. Additionally, we infer a linear transformation that maps biochemical observables, such as histone marks, to the parameters of the generative graph model, enabling accurate genome-wide predictions of chromatin contact maps on out-of-sample data, both within the same cell line and on the completely unseen HCT116 cell line under RAD21 depletion. These findings highlight bioSBM's potential as a powerful tool for elucidating the relationship between biochemistry and chromatin architecture and predicting long-range genome organization from independent biochemical data.}, author = {Zhang, Chen Y and Rosa, Angelo and Sanguinetti, Guido}, issn = {2835-8279}, journal = {PRX Life}, number = {4}, publisher = {American Physical Society}, title = {{bioSBM: A random graph model to integrate epigenomic data in chromatin structure prediction}}, doi = {10.1103/gy1p-4256}, volume = {3}, year = {2025}, } @article{18849, abstract = {Many biological systems operate near the physical limits to their performance, suggesting that aspects of their behavior and underlying mechanisms could be derived from optimization principles. However, such principles have often been applied only in simplified models. Here, we explore a detailed mechanistic model of the gap gene network in the Drosophila embryo, optimizing its 50+ parameters to maximize the information that gene expression levels provide about nuclear positions. This optimization is conducted under realistic constraints, such as limits on the number of available molecules. Remarkably, the optimal networks we derive closely match the architecture and spatial gene expression profiles observed in the real organism. Our framework quantifies the tradeoffs involved in maximizing functional performance and allows for the exploration of alternative network configurations, addressing the question of which features are necessary and which are contingent. Our results suggest that multiple solutions to the optimization problem might exist across closely related organisms, offering insights into the evolution of gene regulatory networks.}, author = {Sokolowski, Thomas R and Gregor, Thomas and Bialek, William and Tkačik, Gašper}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences}, number = {1}, publisher = {National Academy of Sciences}, title = {{Deriving a genetic regulatory network from an optimization principle}}, doi = {10.1073/pnas.2402925121}, volume = {122}, year = {2025}, } @article{18850, abstract = {Biophysical constraints limit the specificity with which transcription factors (TFs) can target regulatory DNA. While individual nontarget binding events may be low affinity, the sheer number of such interactions could present a challenge for gene regulation by degrading its precision or possibly leading to an erroneous induction state. Chromatin can prevent nontarget binding by rendering DNA physically inaccessible to TFs, at the cost of energy-consuming remodeling orchestrated by pioneer factors (PFs). Under what conditions and by how much can chromatin reduce regulatory errors on a global scale? We use a theoretical approach to compare two scenarios for gene regulation: one that relies on TF binding to free DNA alone and one that uses a combination of TFs and chromatin-regulating PFs to achieve desired gene expression patterns. We find, first, that chromatin effectively silences groups of genes that should be simultaneously OFF, thereby allowing more accurate graded control of expression for the remaining ON genes. Second, chromatin buffers the deleterious consequences of nontarget binding as the number of OFF genes grows, permitting a substantial expansion in regulatory complexity. Third, chromatin-based regulation productively co-opts nontarget TF binding for ON genes in order to establish a “leaky” baseline expression level, which targeted activator or repressor binding subsequently up- or down-modulates. Thus, on a global scale, using chromatin simultaneously alleviates pressure for high specificity of regulatory interactions and enables an increase in genome size with minimal impact on global expression error.}, author = {Perkins, Mindy Liu and Crocker, Justin and Tkačik, Gašper}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences}, number = {1}, publisher = {National Academy of Sciences}, title = {{Chromatin enables precise and scalable gene regulation with factors of limited specificity}}, doi = {10.1073/pnas.2411887121}, volume = {122}, year = {2025}, } @article{18936, abstract = {A major obstacle to predictive understanding of evolution stems from the complexity of biological systems, which prevents detailed characterization of key evolutionary properties. Here, we highlight some of the major sources of complexity that arise when relating molecular mechanisms to their evolutionary consequences and ask whether accounting for every mechanistic detail is important to accurately predict evolutionary outcomes. To do this, we developed a mechanistic model of a bacterial promoter regulated by 2 proteins, allowing us to connect any promoter genotype to 6 phenotypes that capture the dynamics of gene expression following an environmental switch. Accounting for the mechanisms that govern how this system works enabled us to provide an in-depth picture of how regulated bacterial promoters might evolve. More importantly, we used the model to explore which factors that contribute to the complexity of this system are essential for understanding its evolution, and which can be simplified without information loss. We found that several key evolutionary properties—the distribution of phenotypic and fitness effects of mutations, the evolutionary trajectories during selection for regulation—can be accurately captured without accounting for all, or even most, parameters of the system. Our findings point to the need for a mechanistic approach to studying evolution, as it enables tackling biological complexity and in doing so improves the ability to predict evolutionary outcomes.}, author = {Grah, Rok and Guet, Calin C and Tkačik, Gašper and Lagator, Mato}, issn = {1943-2631}, journal = {Genetics}, number = {2}, publisher = {Oxford University Press}, title = {{Linking molecular mechanisms to their evolutionary consequences: a primer}}, doi = {10.1093/genetics/iyae191}, volume = {229}, year = {2025}, } @misc{18991, abstract = {Research data for the article "Learning reshapes the hippocampal representation hierarchy" from Chiossi et al. (PNAS, 2025). The data includes hippocampal CA1 unit activity and behaviour tracking of 5 Long Evans rats during the learning of an associative memory task. Detailed information can be found in the 'readme.txt' file.}, author = {Chiossi, Heloisa}, keywords = {hippocampus, electrophysiology, behavior}, publisher = {Institute of Science and Technology Austria}, title = {{Research data for the publication "Learning reshapes the hippocampal representation hierarchy"}}, doi = {10.15479/AT:ISTA:18991}, year = {2025}, } @misc{19294, abstract = {Active regulation of gene expression, orchestrated by complex interactions of activators and repressors at promoters, controls the fate of organisms. In contrast, basal expression at uninduced promoters is considered to be a dynamically inert mode of non-functional “promoter leakiness”, merely a byproduct of transcriptional regulation. Here, we investigate the basal expression mode of the mar operon, the main regulator of intrinsic multiple antibiotic resistance in Escherichia coli, and link its dynamic properties to the non-canonical, yet highly conserved start codon of marR across Enterobacteriaceae. Real-time, single-cell measurements across tens of generations reveal that basal expression consists of rare stochastic gene expression pulses, which maximize variability in wildtype and, surprisingly, transiently accelerate cellular elongation rates. Competition experiments show that basal expression confers fitness advantages to wildtype across several transitions between exponential and stationary growth by shortening lag times. The dynamically rich basal expression of the mar operon has likely been evolutionarily maintained for its role in growth homeostasis of Enterobacteria within the gut environment, thereby allowing other ancillary gene regulatory roles to evolve, e.g. control of costly-to-induce multi-drug efflux pumps. Understanding the complex selection forces governing genetic systems involved in intrinsic multi-drug resistance is crucial for effective public health measures.}, author = {Jain, Kirti and Hauschild, Robert and Bochkareva, Olga and Römhild, Roderich and Tkačik, Gašper and Guet, Calin C}, publisher = {Institute of Science and Technology Austria}, title = {{Data for "Pulsatile basal gene expression as a fitness determinant in bacteria"}}, doi = {10.15479/AT:ISTA:19294}, year = {2025}, } @article{19453, abstract = {A key feature of biological and artificial neural networks is the progressive refinement of their neural representations with experience. In neuroscience, this fact has inspired several recent studies in sensory and motor systems. However, less is known about how higher associational cortical areas, such as the hippocampus, modify representations throughout the learning of complex tasks. Here, we focus on associative learning, a process that requires forming a connection between the representations of different variables for appropriate behavioral response. We trained rats in a space-context associative task and monitored hippocampal neural activity throughout the entire learning period, over several days. This allowed us to assess changes in the representations of context, movement direction, and position, as well as their relationship to behavior. We identified a hierarchical representational structure in the encoding of these three task variables that was preserved throughout learning. Nevertheless, we also observed changes at the lower levels of the hierarchy where context was encoded. These changes were local in neural activity space and restricted to physical positions where context identification was necessary for correct decision-making, supporting better context decoding and contextual code compression. Our results demonstrate that the hippocampal code not only accommodates hierarchical relationships between different variables but also enables efficient learning through minimal changes in neural activity space. Beyond the hippocampus, our work reveals a representation learning mechanism that might be implemented in other biological and artificial networks performing similar tasks.}, author = {Chiossi, Heloisa and Nardin, Michele and Tkačik, Gašper and Csicsvari, Jozsef L}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences}, number = {11}, publisher = {National Academy of Sciences}, title = {{Learning reshapes the hippocampal representation hierarchy}}, doi = {10.1073/pnas.2417025122}, volume = {122}, year = {2025}, } @article{19626, abstract = {Active regulation of gene expression, orchestrated by complex interactions of activators and repressors at promoters, controls the fate of organisms. In contrast, basal expression at uninduced promoters is considered to be a dynamically inert mode of nonfunctional “promoter leakiness,” merely a byproduct of transcriptional regulation. Here, we investigate the basal expression mode of the mar operon, the main regulator of intrinsic multiple antibiotic resistance in Escherichia coli, and link its dynamic properties to the noncanonical, yet highly conserved start codon of marR across Enterobacteriaceae. Real-time, single-cell measurements across tens of generations reveal that basal expression consists of rare stochastic gene expression pulses, which maximize variability in wildtype and, surprisingly, transiently accelerate cellular elongation rates. Competition experiments show that basal expression confers fitness advantages to wildtype across several transitions between exponential and stationary growth by shortening lag times. The dynamically rich basal expression of the mar operon has likely been evolutionarily maintained for its role in growth homeostasis of Enterobacteria within the gut environment, thereby allowing other ancillary gene regulatory roles to evolve, e.g., control of costly-to-induce multidrug efflux pumps. Understanding the complex selection forces governing genetic systems involved in intrinsic multidrug resistance is crucial for effective public health measures.}, author = {Jain, Kirti and Hauschild, Robert and Bochkareva, Olga and Römhild, Roderich and Tkačik, Gašper and Guet, Calin C}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences}, number = {15}, publisher = {National Academy of Sciences}, title = {{Pulsatile basal gene expression as a fitness determinant in bacteria}}, doi = {10.1073/pnas.2413709122}, volume = {122}, year = {2025}, } @misc{19658, abstract = {We consider a family of totally asymmetric simple exclusion processes (TASEPs), consisting of particles on a lattice that require binding by a "token" in various physical configurations to advance over the lattice. Using a combination of theory and simulations, we address the following questions: (i) How token binding kinetics affects the current-density relation on the lattice; (ii) How this current-density relation depends on the scarcity of tokens; (iii) How tokens propagate the effects of the locally-imposed disorder (such as a slow site) over the entire lattice; (iv) How a shared pool of tokens couples concurrent TASEPs running on multiple lattices; (v) How our results translate to TASEPs with open boundaries that exchange particles with the reservoir. Since real particle motion (including in biological systems that inspired the standard TASEP model, e.g., protein synthesis or movement of molecular motors) is often catalyzed, regulated, actuated, or otherwise mediated, the token-driven TASEP dynamics analyzed in this paper should allow for a better understanding of real systems and enable a closer match between TASEP theory and experimental observations.}, author = {Tkačik, Gašper}, publisher = {Institute of Science and Technology Austria}, title = {{Token-driven totally asymmetric simple exclusion processes}}, doi = {10.15479/AT:ISTA:19658}, year = {2025}, } @article{19701, abstract = {Living systems are characterized by controlled flows of matter, energy, and information. While the biophysics community has productively engaged with the first two, addressing information flows has been more challenging, with some scattered success in evolutionary theory and a more coherent track record in neuroscience. Nevertheless, interdisciplinary work of the past two decades at the interface of biophysics, quantitative biology, and engineering has led to an emerging mathematical language for describing information flows at the molecular scale. This is where the central processes of life unfold: from detection and transduction of environmental signals to the readout or copying of genetic information and the triggering of adaptive cellular responses. Such processes are coordinated by complex biochemical reaction networks that operate at room temperature, are out of equilibrium, and use low copy numbers of diverse molecular species with limited interaction specificity. Here we review how flows of information through biochemical networks can be formalized using information-theoretic quantities, quantified from data, and computed within various modeling frameworks. Optimization of information flows is presented as a candidate design principle that navigates the relevant time, energy, crosstalk, and metabolic constraints to predict reliable cellular signaling and gene regulation architectures built of individually noisy components.}, author = {Tkačik, Gašper and Wolde, Pieter Rein Ten}, issn = {1936-1238}, journal = {Annual review of biophysics}, pages = {249--274}, publisher = {Annual Reviews}, title = {{Information processing in biochemical networks}}, doi = {10.1146/annurev-biophys-060524-102720}, volume = {54}, year = {2025}, } @article{20664, abstract = {Conference travel contributes to the climate footprint of academic research. Here, we provide a quantitative estimate of the carbon emissions associated with conference attendance by analyzing travel data from participants of 10 international conferences in the field of magnetic resonance, namely EUROMAR, ENC and ICMRBS. We find that attending a EUROMAR conference produces, on average, more than 1 t CO2 eq.. For the analyzed conferences outside Europe, the corresponding value is about 2–3 times higher, on average, with intercontinental trips amounting to up to 5 t. We compare these conference-related emissions to other activities associated with research and show that conference travel is a substantial portion of the total climate footprint of a researcher in magnetic resonance. We explore several strategies to reduce these emissions, including the impact of selecting conference venues more strategically and the possibility of decentralized conferences. Through a detailed comparison of train versus air travel – accounting for both direct and infrastructure-related emissions – we demonstrate that train travel offers considerable carbon savings. These data may provide a basis for strategic choices of future conferences in the field and for individuals deciding on their conference attendance.}, author = {Kapoor, Lucky and Ruzickova, Natalia and Zivadinovic, Predrag and Leitner, Valentin and Sisak, Maria A and Mweka, Cecelia N and Dobbelaere, Jeroen A and Katsaros, Georgios and Schanda, Paul}, issn = {2699-0016}, journal = {Magnetic Resonance}, number = {2}, pages = {243--256}, publisher = {Copernicus Publications}, title = {{Quantifying the carbon footprint of conference travel: The case of NMR meetings}}, doi = {10.5194/mr-6-243-2025}, volume = {6}, year = {2025}, } @phdthesis{20811, abstract = { This thesis is organized into two parts, each comprising two chapters: Chapter 1 and 2 offer models for the evolution of vaccine resistance in response to diverse vaccination strategies. Chapter 3 and 4 review the statistics of records, their connection to models of innovation and an application to the cultural evolution of sports. In chapter 1 we present a modelling study from 2021 on the evolution of SARS-CoV-2. At that time the vaccine-resistant Omicron variant had not yet evolved. In our model we consider a population that is becoming vaccinated over time, while a pathogen is spreading in the population and eventually becoming resistant to the vaccine. We explore effective pharmaceutical and non-pharmaceutical interventions to prevent the emergence of vaccine resistance. In chapter 2 we model a particular set of complex vaccination strategies, mosaic and pyramid vaccination, where an immunologically diverse portfolio of vaccines is considered. We find that a bet-hatching strategy, in which vaccine types are distributed in the population, is effective at hindering the evolution of vaccine resistance if mutation rates are high. In chapter 3 we switch gears and present a review on the statistics of records. We highlight similarities and analogies to other models in the fields of statistical physics, evolution and innovation. This offers interesting complimentary perspectives on well-known models. In chapter 4 we apply models of record statistics and innovation to study cultural evolution in sport. We propose a model of sport evolution that combines deterministic improvements in performance and stochastic bursts of improvements due to innovation. }, author = {Rella, Simon}, issn = {2663-337X}, pages = {95}, publisher = {Institute of Science and Technology Austria}, title = {{Adaptive processes in biology and culture: Models of evolving vaccine resistance and the record statistics of innovation}}, doi = {10.15479/AT-ISTA-20811}, year = {2025}, } @article{19785, abstract = {We consider a family of totally asymmetric simple exclusion processes (TASEPs), consisting of particles on a lattice that require binding by a “token” in various physical configurations to advance over the lattice. Using a combination of theory and simulations, we address the following questions: (i) How does token binding kinetics affect the current-density relation on the lattice? (ii) How does this current-density relation depend on the scarcity of tokens? (iii) How do tokens propagate the effects of the locally imposed disorder (such as a slow site) over the entire lattice? (iv) How does a shared pool of tokens couple concurrent TASEPs running on multiple lattices? and (v) How do our results translate to TASEPs with open boundaries that exchange particles with the reservoir? Since real particle motion (including in biological systems that inspired the standard TASEP model, e.g., protein synthesis or movement of molecular motors) is often catalyzed, regulated, actuated, or otherwise mediated, the token-driven TASEP dynamics analyzed in this paper should allow for a better understanding of real systems and enable a closer match between TASEP theory and experimental observations.}, author = {Kavcic, Bor and Tkačik, Gašper}, issn = {2470-0053}, journal = {Physical Review E}, number = {5}, publisher = {American Physical Society}, title = {{Token-driven totally asymmetric simple exclusion processes}}, doi = {10.1103/physreve.111.054122}, volume = {111}, year = {2025}, } @phdthesis{20357, author = {Ruzickova, Natalia}, isbn = {978-3-99078-066-4}, issn = {2663-337X}, keywords = {gene regulation, networks, omnigenic model, pancreas, collective behaviour}, pages = {160}, publisher = {Institute of Science and Technology Austria}, title = {{Effect propagation in biological networks}}, doi = {10.15479/AT-ISTA-20357}, year = {2025}, }