@article{11638, abstract = {Statistical inference is central to many scientific endeavors, yet how it works remains unresolved. Answering this requires a quantitative understanding of the intrinsic interplay between statistical models, inference methods, and the structure in the data. To this end, we characterize the efficacy of direct coupling analysis (DCA)—a highly successful method for analyzing amino acid sequence data—in inferring pairwise interactions from samples of ferromagnetic Ising models on random graphs. Our approach allows for physically motivated exploration of qualitatively distinct data regimes separated by phase transitions. We show that inference quality depends strongly on the nature of data-generating distributions: optimal accuracy occurs at an intermediate temperature where the detrimental effects from macroscopic order and thermal noise are minimal. Importantly our results indicate that DCA does not always outperform its local-statistics-based predecessors; while DCA excels at low temperatures, it becomes inferior to simple correlation thresholding at virtually all temperatures when data are limited. Our findings offer insights into the regime in which DCA operates so successfully, and more broadly, how inference interacts with the structure in the data.}, author = {Ngampruetikorn, Vudtiwat and Sachdeva, Vedant and Torrence, Johanna and Humplik, Jan and Schwab, David J. and Palmer, Stephanie E.}, issn = {2643-1564}, journal = {Physical Review Research}, number = {2}, publisher = {American Physical Society}, title = {{Inferring couplings in networks across order-disorder phase transitions}}, doi = {10.1103/PhysRevResearch.4.023240}, volume = {4}, year = {2022}, } @article{12156, abstract = {Models of transcriptional regulation that assume equilibrium binding of transcription factors have been less successful at predicting gene expression from sequence in eukaryotes than in bacteria. This could be due to the non-equilibrium nature of eukaryotic regulation. Unfortunately, the space of possible non-equilibrium mechanisms is vast and predominantly uninteresting. The key question is therefore how this space can be navigated efficiently, to focus on mechanisms and models that are biologically relevant. In this review, we advocate for the normative role of theory—theory that prescribes rather than just describes—in providing such a focus. Theory should expand its remit beyond inferring mechanistic models from data, towards identifying non-equilibrium gene regulatory schemes that may have been evolutionarily selected, despite their energy consumption, because they are precise, reliable, fast, or otherwise outperform regulation at equilibrium. We illustrate our reasoning by toy examples for which we provide simulation code.}, author = {Zoller, Benjamin and Gregor, Thomas and Tkačik, Gašper}, issn = {2452-3100}, journal = {Current Opinion in Systems Biology}, keywords = {Applied Mathematics, Computer Science Applications, Drug Discovery, General Biochemistry, Genetics and Molecular Biology, Modeling and Simulation}, number = {9}, publisher = {Elsevier}, title = {{Eukaryotic gene regulation at equilibrium, or non?}}, doi = {10.1016/j.coisb.2022.100435}, volume = {31}, year = {2022}, } @article{10530, abstract = {Cell dispersion from a confined area is fundamental in a number of biological processes, including cancer metastasis. To date, a quantitative understanding of the interplay of single cell motility, cell proliferation, and intercellular contacts remains elusive. In particular, the role of E- and N-Cadherin junctions, central components of intercellular contacts, is still controversial. Combining theoretical modeling with in vitro observations, we investigate the collective spreading behavior of colonies of human cancer cells (T24). The spreading of these colonies is driven by stochastic single-cell migration with frequent transient cell-cell contacts. We find that inhibition of E- and N-Cadherin junctions decreases colony spreading and average spreading velocities, without affecting the strength of correlations in spreading velocities of neighboring cells. Based on a biophysical simulation model for cell migration, we show that the behavioral changes upon disruption of these junctions can be explained by reduced repulsive excluded volume interactions between cells. This suggests that in cancer cell migration, cadherin-based intercellular contacts sharpen cell boundaries leading to repulsive rather than cohesive interactions between cells, thereby promoting efficient cell spreading during collective migration. }, author = {Zisis, Themistoklis and Brückner, David and Brandstätter, Tom and Siow, Wei Xiong and d’Alessandro, Joseph and Vollmar, Angelika M. and Broedersz, Chase P. and Zahler, Stefan}, issn = {0006-3495}, journal = {Biophysical Journal}, keywords = {Biophysics}, number = {1}, pages = {P44--60}, publisher = {Elsevier}, title = {{Disentangling cadherin-mediated cell-cell interactions in collective cancer cell migration}}, doi = {10.1016/j.bpj.2021.12.006}, volume = {121}, year = {2022}, } @article{10736, abstract = {Predicting function from sequence is a central problem of biology. Currently, this is possible only locally in a narrow mutational neighborhood around a wildtype sequence rather than globally from any sequence. Using random mutant libraries, we developed a biophysical model that accounts for multiple features of σ70 binding bacterial promoters to predict constitutive gene expression levels from any sequence. We experimentally and theoretically estimated that 10–20% of random sequences lead to expression and ~80% of non-expressing sequences are one mutation away from a functional promoter. The potential for generating expression from random sequences is so pervasive that selection acts against σ70-RNA polymerase binding sites even within inter-genic, promoter-containing regions. This pervasiveness of σ70-binding sites implies that emergence of promoters is not the limiting step in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter function into a mechanistic model enabled not only more accurate predictions of gene expression levels, but also identified that promoters evolve more rapidly than previously thought.}, author = {Lagator, Mato and Sarikas, Srdjan and Steinrueck, Magdalena and Toledo-Aparicio, David and Bollback, Jonathan P and Guet, Calin C and Tkačik, Gašper}, issn = {2050-084X}, journal = {eLife}, publisher = {eLife Sciences Publications}, title = {{Predicting bacterial promoter function and evolution from random sequences}}, doi = {10.7554/eLife.64543}, volume = {11}, year = {2022}, } @article{12332, abstract = {Activity of sensory neurons is driven not only by external stimuli but also by feedback signals from higher brain areas. Attention is one particularly important internal signal whose presumed role is to modulate sensory representations such that they only encode information currently relevant to the organism at minimal cost. This hypothesis has, however, not yet been expressed in a normative computational framework. Here, by building on normative principles of probabilistic inference and efficient coding, we developed a model of dynamic population coding in the visual cortex. By continuously adapting the sensory code to changing demands of the perceptual observer, an attention-like modulation emerges. This modulation can dramatically reduce the amount of neural activity without deteriorating the accuracy of task-specific inferences. Our results suggest that a range of seemingly disparate cortical phenomena such as intrinsic gain modulation, attention-related tuning modulation, and response variability could be manifestations of the same underlying principles, which combine efficient sensory coding with optimal probabilistic inference in dynamic environments.}, author = {Mlynarski, Wiktor F and Tkačik, Gašper}, issn = {1545-7885}, journal = {PLoS Biology}, number = {12}, pages = {e3001889}, publisher = {Public Library of Science}, title = {{Efficient coding theory of dynamic attentional modulation}}, doi = {10.1371/journal.pbio.3001889}, volume = {20}, year = {2022}, } @article{12081, abstract = {Selection accumulates information in the genome—it guides stochastically evolving populations toward states (genotype frequencies) that would be unlikely under neutrality. This can be quantified as the Kullback–Leibler (KL) divergence between the actual distribution of genotype frequencies and the corresponding neutral distribution. First, we show that this population-level information sets an upper bound on the information at the level of genotype and phenotype, limiting how precisely they can be specified by selection. Next, we study how the accumulation and maintenance of information is limited by the cost of selection, measured as the genetic load or the relative fitness variance, both of which we connect to the control-theoretic KL cost of control. The information accumulation rate is upper bounded by the population size times the cost of selection. This bound is very general, and applies across models (Wright–Fisher, Moran, diffusion) and to arbitrary forms of selection, mutation, and recombination. Finally, the cost of maintaining information depends on how it is encoded: Specifying a single allele out of two is expensive, but one bit encoded among many weakly specified loci (as in a polygenic trait) is cheap.}, author = {Hledik, Michal and Barton, Nicholas H and Tkačik, Gašper}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences}, number = {36}, publisher = {Proceedings of the National Academy of Sciences}, title = {{Accumulation and maintenance of information in evolution}}, doi = {10.1073/pnas.2123152119}, volume = {119}, year = {2022}, } @article{10535, abstract = {Realistic models of biological processes typically involve interacting components on multiple scales, driven by changing environment and inherent stochasticity. Such models are often analytically and numerically intractable. We revisit a dynamic maximum entropy method that combines a static maximum entropy with a quasi-stationary approximation. This allows us to reduce stochastic non-equilibrium dynamics expressed by the Fokker-Planck equation to a simpler low-dimensional deterministic dynamics, without the need to track microscopic details. Although the method has been previously applied to a few (rather complicated) applications in population genetics, our main goal here is to explain and to better understand how the method works. We demonstrate the usefulness of the method for two widely studied stochastic problems, highlighting its accuracy in capturing important macroscopic quantities even in rapidly changing non-stationary conditions. For the Ornstein-Uhlenbeck process, the method recovers the exact dynamics whilst for a stochastic island model with migration from other habitats, the approximation retains high macroscopic accuracy under a wide range of scenarios in a dynamic environment.}, author = {Bod'ová, Katarína and Szep, Eniko and Barton, Nicholas H}, issn = {1553-7358}, journal = {PLoS Computational Biology}, number = {12}, publisher = {Public Library of Science}, title = {{Dynamic maximum entropy provides accurate approximation of structured population dynamics}}, doi = {10.1371/journal.pcbi.1009661}, volume = {17}, year = {2021}, } @unpublished{10912, abstract = {Brain dynamics display collective phenomena as diverse as neuronal oscillations and avalanches. Oscillations are rhythmic, with fluctuations occurring at a characteristic scale, whereas avalanches are scale-free cascades of neural activity. Here we show that such antithetic features can coexist in a very generic class of adaptive neural networks. In the most simple yet fully microscopic model from this class we make direct contact with human brain resting-state activity recordings via tractable inference of the model's two essential parameters. The inferred model quantitatively captures the dynamics over a broad range of scales, from single sensor fluctuations, collective behaviors of nearly-synchronous extreme events on multiple sensors, to neuronal avalanches unfolding over multiple sensors across multiple time-bins. Importantly, the inferred parameters correlate with model-independent signatures of "closeness to criticality", suggesting that the coexistence of scale-specific (neural oscillations) and scale-free (neuronal avalanches) dynamics in brain activity occurs close to a non-equilibrium critical point at the onset of self-sustained oscillations.}, author = {Lombardi, Fabrizio and Pepic, Selver and Shriki, Oren and Tkačik, Gašper and De Martino, Daniele}, pages = {37}, publisher = {arXiv}, title = {{Quantifying the coexistence of neuronal oscillations and avalanches}}, doi = {10.48550/ARXIV.2108.06686}, year = {2021}, } @unpublished{10579, abstract = {We consider a totally asymmetric simple exclusion process (TASEP) consisting of particles on a lattice that require binding by a "token" to move. Using a combination of theory and simulations, we address the following questions: (i) How token binding kinetics affects the current-density relation; (ii) How the current-density relation depends on the scarcity of tokens; (iii) How tokens propagate the effects of the locally-imposed disorder (such 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 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}, booktitle = {arXiv}, title = {{Token-driven totally asymmetric simple exclusion process}}, doi = {10.48550/arXiv.2112.13558}, year = {2021}, } @article{7463, abstract = {Resting-state brain activity is characterized by the presence of neuronal avalanches showing absence of characteristic size. Such evidence has been interpreted in the context of criticality and associated with the normal functioning of the brain. A distinctive attribute of systems at criticality is the presence of long-range correlations. Thus, to verify the hypothesis that the brain operates close to a critical point and consequently assess deviations from criticality for diagnostic purposes, it is of primary importance to robustly and reliably characterize correlations in resting-state brain activity. Recent works focused on the analysis of narrow-band electroencephalography (EEG) and magnetoencephalography (MEG) signal amplitude envelope, showing evidence of long-range temporal correlations (LRTC) in neural oscillations. However, brain activity is a broadband phenomenon, and a significant piece of information useful to precisely discriminate between normal (critical) and pathological behavior (non-critical), may be encoded in the broadband spatio-temporal cortical dynamics. Here we propose to characterize the temporal correlations in the broadband brain activity through the lens of neuronal avalanches. To this end, we consider resting-state EEG and long-term MEG recordings, extract the corresponding neuronal avalanche sequences, and study their temporal correlations. We demonstrate that the broadband resting-state brain activity consistently exhibits long-range power-law correlations in both EEG and MEG recordings, with similar values of the scaling exponents. Importantly, although we observe that the avalanche size distribution depends on scale parameters, scaling exponents characterizing long-range correlations are quite robust. In particular, they are independent of the temporal binning (scale of analysis), indicating that our analysis captures intrinsic characteristics of the underlying dynamics. Because neuronal avalanches constitute a fundamental feature of neural systems with universal characteristics, the proposed approach may serve as a general, systems- and experiment-independent procedure to infer the existence of underlying long-range correlations in extended neural systems, and identify pathological behaviors in the complex spatio-temporal interplay of cortical rhythms.}, author = {Lombardi, Fabrizio and Shriki, Oren and Herrmann, Hans J and de Arcangelis, Lucilla}, issn = {1872-8286}, journal = {Neurocomputing}, pages = {657--666}, publisher = {Elsevier}, title = {{Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches}}, doi = {10.1016/j.neucom.2020.05.126}, volume = {461}, year = {2021}, } @article{9226, abstract = {Half a century after Lewis Wolpert's seminal conceptual advance on how cellular fates distribute in space, we provide a brief historical perspective on how the concept of positional information emerged and influenced the field of developmental biology and beyond. We focus on a modern interpretation of this concept in terms of information theory, largely centered on its application to cell specification in the early Drosophila embryo. We argue that a true physical variable (position) is encoded in local concentrations of patterning molecules, that this mapping is stochastic, and that the processes by which positions and corresponding cell fates are determined based on these concentrations need to take such stochasticity into account. With this approach, we shift the focus from biological mechanisms, molecules, genes and pathways to quantitative systems-level questions: where does positional information reside, how it is transformed and accessed during development, and what fundamental limits it is subject to?}, author = {Tkačik, Gašper and Gregor, Thomas}, issn = {1477-9129}, journal = {Development}, number = {2}, publisher = {The Company of Biologists}, title = {{The many bits of positional information}}, doi = {10.1242/dev.176065}, volume = {148}, year = {2021}, } @article{9439, abstract = {The ability to adapt to changes in stimulus statistics is a hallmark of sensory systems. Here, we developed a theoretical framework that can account for the dynamics of adaptation from an information processing perspective. We use this framework to optimize and analyze adaptive sensory codes, and we show that codes optimized for stationary environments can suffer from prolonged periods of poor performance when the environment changes. To mitigate the adversarial effects of these environmental changes, sensory systems must navigate tradeoffs between the ability to accurately encode incoming stimuli and the ability to rapidly detect and adapt to changes in the distribution of these stimuli. We derive families of codes that balance these objectives, and we demonstrate their close match to experimentally observed neural dynamics during mean and variance adaptation. Our results provide a unifying perspective on adaptation across a range of sensory systems, environments, and sensory tasks.}, author = {Mlynarski, Wiktor F and Hermundstad, Ann M.}, issn = {1546-1726}, journal = {Nature Neuroscience}, pages = {998--1009}, publisher = {Springer Nature}, title = {{Efficient and adaptive sensory codes}}, doi = {10.1038/s41593-021-00846-0}, volume = {24}, year = {2021}, } @article{9822, abstract = {Attachment of adhesive molecules on cell culture surfaces to restrict cell adhesion to defined areas and shapes has been vital for the progress of in vitro research. In currently existing patterning methods, a combination of pattern properties such as stability, precision, specificity, high-throughput outcome, and spatiotemporal control is highly desirable but challenging to achieve. Here, we introduce a versatile and high-throughput covalent photoimmobilization technique, comprising a light-dose-dependent patterning step and a subsequent functionalization of the pattern via click chemistry. This two-step process is feasible on arbitrary surfaces and allows for generation of sustainable patterns and gradients. The method is validated in different biological systems by patterning adhesive ligands on cell-repellent surfaces, thereby constraining the growth and migration of cells to the designated areas. We then implement a sequential photopatterning approach by adding a second switchable patterning step, allowing for spatiotemporal control over two distinct surface patterns. As a proof of concept, we reconstruct the dynamics of the tip/stalk cell switch during angiogenesis. Our results show that the spatiotemporal control provided by our “sequential photopatterning” system is essential for mimicking dynamic biological processes and that our innovative approach has great potential for further applications in cell science.}, author = {Zisis, Themistoklis and Schwarz, Jan and Balles, Miriam and Kretschmer, Maibritt and Nemethova, Maria and Chait, Remy P and Hauschild, Robert and Lange, Janina and Guet, Calin C and Sixt, Michael K and Zahler, Stefan}, issn = {19448252}, journal = {ACS Applied Materials and Interfaces}, number = {30}, pages = {35545–35560}, publisher = {American Chemical Society}, title = {{Sequential and switchable patterning for studying cellular processes under spatiotemporal control}}, doi = {10.1021/acsami.1c09850}, volume = {13}, year = {2021}, } @article{9828, abstract = {Amplitude demodulation is a classical operation used in signal processing. For a long time, its effective applications in practice have been limited to narrowband signals. In this work, we generalize amplitude demodulation to wideband signals. We pose demodulation as a recovery problem of an oversampled corrupted signal and introduce special iterative schemes belonging to the family of alternating projection algorithms to solve it. Sensibly chosen structural assumptions on the demodulation outputs allow us to reveal the high inferential accuracy of the method over a rich set of relevant signals. This new approach surpasses current state-of-the-art demodulation techniques apt to wideband signals in computational efficiency by up to many orders of magnitude with no sacrifice in quality. Such performance opens the door for applications of the amplitude demodulation procedure in new contexts. In particular, the new method makes online and large-scale offline data processing feasible, including the calculation of modulator-carrier pairs in higher dimensions and poor sampling conditions, independent of the signal bandwidth. We illustrate the utility and specifics of applications of the new method in practice by using natural speech and synthetic signals.}, author = {Gabrielaitis, Mantas}, issn = {1941-0476}, journal = {IEEE Transactions on Signal Processing}, pages = {4039 -- 4054}, publisher = {Institute of Electrical and Electronics Engineers}, title = {{Fast and accurate amplitude demodulation of wideband signals}}, doi = {10.1109/TSP.2021.3087899}, volume = {69}, year = {2021}, } @article{9362, abstract = {A central goal in systems neuroscience is to understand the functions performed by neural circuits. Previous top-down models addressed this question by comparing the behaviour of an ideal model circuit, optimised to perform a given function, with neural recordings. However, this requires guessing in advance what function is being performed, which may not be possible for many neural systems. To address this, we propose an inverse reinforcement learning (RL) framework for inferring the function performed by a neural network from data. We assume that the responses of each neuron in a network are optimised so as to drive the network towards ‘rewarded’ states, that are desirable for performing a given function. We then show how one can use inverse RL to infer the reward function optimised by the network from observing its responses. This inferred reward function can be used to predict how the neural network should adapt its dynamics to perform the same function when the external environment or network structure changes. This could lead to theoretical predictions about how neural network dynamics adapt to deal with cell death and/or varying sensory stimulus statistics.}, author = {Chalk, Matthew J and Tkačik, Gašper and Marre, Olivier}, issn = {19326203}, journal = {PLoS ONE}, number = {4}, publisher = {Public Library of Science}, title = {{Inferring the function performed by a recurrent neural network}}, doi = {10.1371/journal.pone.0248940}, volume = {16}, year = {2021}, } @article{8997, abstract = {Phenomenological relations such as Ohm’s or Fourier’s law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial “growth laws,” which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems.}, author = {Kavcic, Bor and Tkačik, Gašper and Bollenbach, Tobias}, issn = {1553-7358}, journal = {PLOS Computational Biology}, keywords = {Modelling and Simulation, Genetics, Molecular Biology, Antibiotics, Drug interactions}, publisher = {Public Library of Science}, title = {{Minimal biophysical model of combined antibiotic action}}, doi = {10.1371/journal.pcbi.1008529}, volume = {17}, year = {2021}, } @article{9283, abstract = {Gene expression levels are influenced by multiple coexisting molecular mechanisms. Some of these interactions such as those of transcription factors and promoters have been studied extensively. However, predicting phenotypes of gene regulatory networks (GRNs) remains a major challenge. Here, we use a well-defined synthetic GRN to study in Escherichia coli how network phenotypes depend on local genetic context, i.e. the genetic neighborhood of a transcription factor and its relative position. We show that one GRN with fixed topology can display not only quantitatively but also qualitatively different phenotypes, depending solely on the local genetic context of its components. Transcriptional read-through is the main molecular mechanism that places one transcriptional unit (TU) within two separate regulons without the need for complex regulatory sequences. We propose that relative order of individual TUs, with its potential for combinatorial complexity, plays an important role in shaping phenotypes of GRNs.}, author = {Nagy-Staron, Anna A and Tomasek, Kathrin and Caruso Carter, Caroline and Sonnleitner, Elisabeth and Kavcic, Bor and Paixão, Tiago and Guet, Calin C}, issn = {2050-084X}, journal = {eLife}, keywords = {Genetics and Molecular Biology}, publisher = {eLife Sciences Publications}, title = {{Local genetic context shapes the function of a gene regulatory network}}, doi = {10.7554/elife.65993}, volume = {10}, year = {2021}, } @article{7553, abstract = {Normative theories and statistical inference provide complementary approaches for the study of biological systems. A normative theory postulates that organisms have adapted to efficiently solve essential tasks, and proceeds to mathematically work out testable consequences of such optimality; parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function, utility, or fitness. Traditionally, these two approaches were developed independently and applied separately. Here we unify them in a coherent Bayesian framework that embeds a normative theory into a family of maximum-entropy “optimization priors.” This family defines a smooth interpolation between a data-rich inference regime (characteristic of “bottom-up” statistical models), and a data-limited ab inito prediction regime (characteristic of “top-down” normative theory). We demonstrate the applicability of our framework using data from the visual cortex, and argue that the flexibility it affords is essential to address a number of fundamental challenges relating to inference and prediction in complex, high-dimensional biological problems.}, author = {Mlynarski, Wiktor F and Hledik, Michal and Sokolowski, Thomas R and Tkačik, Gašper}, journal = {Neuron}, number = {7}, pages = {1227--1241.e5}, publisher = {Cell Press}, title = {{Statistical analysis and optimality of neural systems}}, doi = {10.1016/j.neuron.2021.01.020}, volume = {109}, year = {2021}, } @unpublished{10077, abstract = {Although much is known about how single neurons in the hippocampus represent an animal’s position, how cell-cell interactions contribute to spatial coding remains poorly understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured cell-to-cell interactions whose statistics depend on familiar vs. novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the signal-to-noise ratio of their spatial inputs. Moreover, the topology of the interactions facilitates linear decodability, making the information easy to read out by downstream circuits. These findings suggest that the efficient coding hypothesis is not applicable only to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain.}, author = {Nardin, Michele and Csicsvari, Jozsef L and Tkačik, Gašper and Savin, Cristina}, booktitle = {bioRxiv}, publisher = {Cold Spring Harbor Laboratory}, title = {{The structure of hippocampal CA1 interactions optimizes spatial coding across experience}}, doi = {10.1101/2021.09.28.460602}, year = {2021}, } @article{8105, abstract = {Physical and biological systems often exhibit intermittent dynamics with bursts or avalanches (active states) characterized by power-law size and duration distributions. These emergent features are typical of systems at the critical point of continuous phase transitions, and have led to the hypothesis that such systems may self-organize at criticality, i.e. without any fine tuning of parameters. Since the introduction of the Bak-Tang-Wiesenfeld (BTW) model, the paradigm of self-organized criticality (SOC) has been very fruitful for the analysis of emergent collective behaviors in a number of systems, including the brain. Although considerable effort has been devoted in identifying and modeling scaling features of burst and avalanche statistics, dynamical aspects related to the temporal organization of bursts remain often poorly understood or controversial. Of crucial importance to understand the mechanisms responsible for emergent behaviors is the relationship between active and quiet periods, and the nature of the correlations. Here we investigate the dynamics of active (θ-bursts) and quiet states (δ-bursts) in brain activity during the sleep-wake cycle. We show the duality of power-law (θ, active phase) and exponential-like (δ, quiescent phase) duration distributions, typical of SOC, jointly emerge with power-law temporal correlations and anti-correlated coupling between active and quiet states. Importantly, we demonstrate that such temporal organization shares important similarities with earthquake dynamics, and propose that specific power-law correlations and coupling between active and quiet states are distinctive characteristics of a class of systems with self-organization at criticality.}, author = {Lombardi, Fabrizio and Wang, Jilin W.J.L. and Zhang, Xiyun and Ivanov, Plamen Ch}, issn = {2100-014X}, journal = {EPJ Web of Conferences}, publisher = {EDP Sciences}, title = {{Power-law correlations and coupling of active and quiet states underlie a class of complex systems with self-organization at criticality}}, doi = {10.1051/epjconf/202023000005}, volume = {230}, year = {2020}, } @article{7490, abstract = {In plants, clathrin mediated endocytosis (CME) represents the major route for cargo internalisation from the cell surface. It has been assumed to operate in an evolutionary conserved manner as in yeast and animals. Here we report characterisation of ultrastructure, dynamics and mechanisms of plant CME as allowed by our advancement in electron microscopy and quantitative live imaging techniques. Arabidopsis CME appears to follow the constant curvature model and the bona fide CME population generates vesicles of a predominantly hexagonal-basket type; larger and with faster kinetics than in other models. Contrary to the existing paradigm, actin is dispensable for CME events at the plasma membrane but plays a unique role in collecting endocytic vesicles, sorting of internalised cargos and directional endosome movement that itself actively promote CME events. Internalized vesicles display a strongly delayed and sequential uncoating. These unique features highlight the independent evolution of the plant CME mechanism during the autonomous rise of multicellularity in eukaryotes.}, author = {Narasimhan, Madhumitha and Johnson, Alexander J and Prizak, Roshan and Kaufmann, Walter and Tan, Shutang and Casillas Perez, Barbara E and Friml, Jiří}, issn = {2050-084X}, journal = {eLife}, publisher = {eLife Sciences Publications}, title = {{Evolutionarily unique mechanistic framework of clathrin-mediated endocytosis in plants}}, doi = {10.7554/eLife.52067}, volume = {9}, year = {2020}, } @misc{9779, author = {Grah, Rok and Friedlander, Tamar}, publisher = {Public Library of Science}, title = {{Distribution of crosstalk values}}, doi = {10.1371/journal.pcbi.1007642.s003}, year = {2020}, } @misc{9776, author = {Grah, Rok and Friedlander, Tamar}, publisher = {Public Library of Science}, title = {{Supporting information}}, doi = {10.1371/journal.pcbi.1007642.s001}, year = {2020}, } @article{7656, abstract = {We propose that correlations among neurons are generically strong enough to organize neural activity patterns into a discrete set of clusters, which can each be viewed as a population codeword. Our reasoning starts with the analysis of retinal ganglion cell data using maximum entropy models, showing that the population is robustly in a frustrated, marginally sub-critical, or glassy, state. This leads to an argument that neural populations in many other brain areas might share this structure. Next, we use latent variable models to show that this glassy state possesses well-defined clusters of neural activity. Clusters have three appealing properties: (i) clusters exhibit error correction, i.e., they are reproducibly elicited by the same stimulus despite variability at the level of constituent neurons; (ii) clusters encode qualitatively different visual features than their constituent neurons; and (iii) clusters can be learned by downstream neural circuits in an unsupervised fashion. We hypothesize that these properties give rise to a “learnable” neural code which the cortical hierarchy uses to extract increasingly complex features without supervision or reinforcement.}, author = {Berry, Michael J. and Tkačik, Gašper}, issn = {16625188}, journal = {Frontiers in Computational Neuroscience}, publisher = {Frontiers}, title = {{Clustering of neural activity: A design principle for population codes}}, doi = {10.3389/fncom.2020.00020}, volume = {14}, year = {2020}, } @article{8698, abstract = {The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a model for neural codes that accurately estimates the likelihood of individual spiking patterns and has a straightforward, scalable, efficient, learnable, and realistic neural implementation. This model’s performance on simultaneously recorded spiking activity of >100 neurons in the monkey visual and prefrontal cortices is comparable with or better than that of state-of-the-art models. Importantly, the model can be learned using a small number of samples and using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes, further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation.}, author = {Maoz, Ori and Tkačik, Gašper and Esteki, Mohamad Saleh and Kiani, Roozbeh and Schneidman, Elad}, issn = {10916490}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, number = {40}, pages = {25066--25073}, publisher = {National Academy of Sciences}, title = {{Learning probabilistic neural representations with randomly connected circuits}}, doi = {10.1073/pnas.1912804117}, volume = {117}, year = {2020}, } @article{8955, abstract = {Skeletal muscle activity is continuously modulated across physiologic states to provide coordination, flexibility and responsiveness to body tasks and external inputs. Despite the central role the muscular system plays in facilitating vital body functions, the network of brain-muscle interactions required to control hundreds of muscles and synchronize their activation in relation to distinct physiologic states has not been investigated. Recent approaches have focused on general associations between individual brain rhythms and muscle activation during movement tasks. However, the specific forms of coupling, the functional network of cortico-muscular coordination, and how network structure and dynamics are modulated by autonomic regulation across physiologic states remains unknown. To identify and quantify the cortico-muscular interaction network and uncover basic features of neuro-autonomic control of muscle function, we investigate the coupling between synchronous bursts in cortical rhythms and peripheral muscle activation during sleep and wake. Utilizing the concept of time delay stability and a novel network physiology approach, we find that the brain-muscle network exhibits complex dynamic patterns of communication involving multiple brain rhythms across cortical locations and different electromyographic frequency bands. Moreover, our results show that during each physiologic state the cortico-muscular network is characterized by a specific profile of network links strength, where particular brain rhythms play role of main mediators of interaction and control. Further, we discover a hierarchical reorganization in network structure across physiologic states, with high connectivity and network link strength during wake, intermediate during REM and light sleep, and low during deep sleep, a sleep-stage stratification that demonstrates a unique association between physiologic states and cortico-muscular network structure. The reported empirical observations are consistent across individual subjects, indicating universal behavior in network structure and dynamics, and high sensitivity of cortico-muscular control to changes in autonomic regulation, even at low levels of physical activity and muscle tone during sleep. Our findings demonstrate previously unrecognized basic principles of brain-muscle network communication and control, and provide new perspectives on the regulatory mechanisms of brain dynamics and locomotor activation, with potential clinical implications for neurodegenerative, movement and sleep disorders, and for developing efficient treatment strategies.}, author = {Rizzo, Rossella and Zhang, Xiyun and Wang, Jilin W.J.L. and Lombardi, Fabrizio and Ivanov, Plamen Ch}, issn = {1664042X}, journal = {Frontiers in Physiology}, publisher = {Frontiers}, title = {{Network physiology of cortico–muscular interactions}}, doi = {10.3389/fphys.2020.558070}, volume = {11}, year = {2020}, } @article{9000, abstract = {In prokaryotes, thermodynamic models of gene regulation provide a highly quantitative mapping from promoter sequences to gene-expression levels that is compatible with in vivo and in vitro biophysical measurements. Such concordance has not been achieved for models of enhancer function in eukaryotes. In equilibrium models, it is difficult to reconcile the reported short transcription factor (TF) residence times on the DNA with the high specificity of regulation. In nonequilibrium models, progress is difficult due to an explosion in the number of parameters. Here, we navigate this complexity by looking for minimal nonequilibrium enhancer models that yield desired regulatory phenotypes: low TF residence time, high specificity, and tunable cooperativity. We find that a single extra parameter, interpretable as the “linking rate,” by which bound TFs interact with Mediator components, enables our models to escape equilibrium bounds and access optimal regulatory phenotypes, while remaining consistent with the reported phenomenology and simple enough to be inferred from upcoming experiments. We further find that high specificity in nonequilibrium models is in a trade-off with gene-expression noise, predicting bursty dynamics—an experimentally observed hallmark of eukaryotic transcription. By drastically reducing the vast parameter space of nonequilibrium enhancer models to a much smaller subspace that optimally realizes biological function, we deliver a rich class of models that could be tractably inferred from data in the near future.}, author = {Grah, Rok and Zoller, Benjamin and Tkačik, Gašper}, issn = {10916490}, journal = {PNAS}, number = {50}, pages = {31614--31622}, publisher = {National Academy of Sciences}, title = {{Nonequilibrium models of optimal enhancer function}}, doi = {10.1073/pnas.2006731117}, volume = {117}, year = {2020}, } @article{8084, abstract = {Origin and functions of intermittent transitions among sleep stages, including brief awakenings and arousals, constitute a challenge to the current homeostatic framework for sleep regulation, focusing on factors modulating sleep over large time scales. Here we propose that the complex micro-architecture characterizing sleep on scales of seconds and minutes results from intrinsic non-equilibrium critical dynamics. We investigate θ- and δ-wave dynamics in control rats and in rats where the sleep-promoting ventrolateral preoptic nucleus (VLPO) is lesioned (male Sprague-Dawley rats). We demonstrate that bursts in θ and δ cortical rhythms exhibit complex temporal organization, with long-range correlations and robust duality of power-law (θ-bursts, active phase) and exponential-like (δ-bursts, quiescent phase) duration distributions, features typical of non-equilibrium systems self-organizing at criticality. We show that such non-equilibrium behavior relates to anti-correlated coupling between θ- and δ-bursts, persists across a range of time scales, and is independent of the dominant physiologic state; indications of a basic principle in sleep regulation. Further, we find that VLPO lesions lead to a modulation of cortical dynamics resulting in altered dynamical parameters of θ- and δ-bursts and significant reduction in θ–δ coupling. Our empirical findings and model simulations demonstrate that θ–δ coupling is essential for the emerging non-equilibrium critical dynamics observed across the sleep–wake cycle, and indicate that VLPO neurons may have dual role for both sleep and arousal/brief wake activation. The uncovered critical behavior in sleep- and wake-related cortical rhythms indicates a mechanism essential for the micro-architecture of spontaneous sleep-stage and arousal transitions within a novel, non-homeostatic paradigm of sleep regulation.}, author = {Lombardi, Fabrizio and Gómez-Extremera, Manuel and Bernaola-Galván, Pedro and Vetrivelan, Ramalingam and Saper, Clifford B. and Scammell, Thomas E. and Ivanov, Plamen Ch.}, issn = {1529-2401}, journal = {Journal of Neuroscience}, number = {1}, pages = {171--190}, publisher = {Society for Neuroscience}, title = {{Critical dynamics and coupling in bursts of cortical rhythms indicate non-homeostatic mechanism for sleep-stage transitions and dual role of VLPO neurons in both sleep and wake}}, doi = {10.1523/jneurosci.1278-19.2019}, volume = {40}, year = {2020}, } @phdthesis{8155, abstract = {In the thesis we focus on the interplay of the biophysics and evolution of gene regulation. We start by addressing how the type of prokaryotic gene regulation – activation and repression – affects spurious binding to DNA, also known as transcriptional crosstalk. We propose that regulatory interference caused by excess regulatory proteins in the dense cellular medium – global crosstalk – could be a factor in determining which type of gene regulatory network is evolutionarily preferred. Next,we use a normative approach in eukaryotic gene regulation to describe minimal non-equilibrium enhancer models that optimize so-called regulatory phenotypes. We find a class of models that differ from standard thermodynamic equilibrium models by a single parameter that notably increases the regulatory performance. Next chapter addresses the question of genotype-phenotype-fitness maps of higher dimensional phenotypes. We show that our biophysically realistic approach allows us to understand how the mechanisms of promoter function constrain genotypephenotype maps, and how they affect the evolutionary trajectories of promoters. In the last chapter we ask whether the intrinsic instability of gene duplication and amplification provides a generic alternative to canonical gene regulation. Using mathematical modeling, we show that amplifications can tune gene expression in many environments, including those where transcription factor-based schemes are hard to evolve or maintain. }, author = {Grah, Rok}, issn = {2663-337X}, pages = {310}, publisher = {Institute of Science and Technology Austria}, title = {{Gene regulation across scales – how biophysical constraints shape evolution}}, doi = {10.15479/AT:ISTA:8155}, year = {2020}, } @unpublished{7675, abstract = {In prokaryotes, thermodynamic models of gene regulation provide a highly quantitative mapping from promoter sequences to gene expression levels that is compatible with in vivo and in vitro bio-physical measurements. Such concordance has not been achieved for models of enhancer function in eukaryotes. In equilibrium models, it is difficult to reconcile the reported short transcription factor (TF) residence times on the DNA with the high specificity of regulation. In non-equilibrium models, progress is difficult due to an explosion in the number of parameters. Here, we navigate this complexity by looking for minimal non-equilibrium enhancer models that yield desired regulatory phenotypes: low TF residence time, high specificity and tunable cooperativity. We find that a single extra parameter, interpretable as the “linking rate” by which bound TFs interact with Mediator components, enables our models to escape equilibrium bounds and access optimal regulatory phenotypes, while remaining consistent with the reported phenomenology and simple enough to be inferred from upcoming experiments. We further find that high specificity in non-equilibrium models is in a tradeoff with gene expression noise, predicting bursty dynamics — an experimentally-observed hallmark of eukaryotic transcription. By drastically reducing the vast parameter space to a much smaller subspace that optimally realizes biological function prior to inference from data, our normative approach holds promise for mathematical models in systems biology.}, author = {Grah, Rok and Zoller, Benjamin and Tkačik, Gašper}, booktitle = {bioRxiv}, publisher = {Cold Spring Harbor Laboratory}, title = {{Normative models of enhancer function}}, doi = {10.1101/2020.04.08.029405}, year = {2020}, } @article{7569, abstract = {Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. In particular, positive or negative regulation can lead to activation of a gene in response to an external signal. Previous works proposed that the form of regulation of a gene correlates with its frequency of usage: positive regulation when the gene is frequently expressed and negative regulation when infrequently expressed. Such network design means that, in the absence of their regulators, the genes are found in their least required activity state, hence regulatory intervention is often necessary. Due to the multitude of genes and regulators, spurious binding and unbinding events, called “crosstalk”, could occur. To determine how the form of regulation affects the global crosstalk in the network, we used a mathematical model that includes multiple regulators and multiple target genes. We found that crosstalk depends non-monotonically on the availability of regulators. Our analysis showed that excess use of regulation entailed by the formerly suggested network design caused high crosstalk levels in a large part of the parameter space. We therefore considered the opposite ‘idle’ design, where the default unregulated state of genes is their frequently required activity state. We found, that ‘idle’ design minimized the use of regulation and thus minimized crosstalk. In addition, we estimated global crosstalk of S. cerevisiae using transcription factors binding data. We demonstrated that even partial network data could suffice to estimate its global crosstalk, suggesting its applicability to additional organisms. We found that S. cerevisiae estimated crosstalk is lower than that of a random network, suggesting that natural selection reduces crosstalk. In summary, our study highlights a new type of protein production cost which is typically overlooked: that of regulatory interference caused by the presence of excess regulators in the cell. It demonstrates the importance of whole-network descriptions, which could show effects missed by single-gene models.}, author = {Grah, Rok and Friedlander, Tamar}, issn = {1553-7358}, journal = {PLOS Computational Biology}, number = {2}, publisher = {Public Library of Science}, title = {{The relation between crosstalk and gene regulation form revisited}}, doi = {10.1371/journal.pcbi.1007642}, volume = {16}, year = {2020}, } @misc{9777, author = {Grah, Rok and Friedlander, Tamar}, publisher = {Public Library of Science}, title = {{Maximizing crosstalk}}, doi = {10.1371/journal.pcbi.1007642.s002}, year = {2020}, } @misc{8097, abstract = {Antibiotics that interfere with translation, when combined, interact in diverse and difficult-to-predict ways. Here, we explain these interactions by "translation bottlenecks": points in the translation cycle where antibiotics block ribosomal progression. To elucidate the underlying mechanisms of drug interactions between translation inhibitors, we generate translation bottlenecks genetically using inducible control of translation factors that regulate well-defined translation cycle steps. These perturbations accurately mimic antibiotic action and drug interactions, supporting that the interplay of different translation bottlenecks causes these interactions. We further show that growth laws, combined with drug uptake and binding kinetics, enable the direct prediction of a large fraction of observed interactions, yet fail to predict suppression. However, varying two translation bottlenecks simultaneously supports that dense traffic of ribosomes and competition for translation factors account for the previously unexplained suppression. These results highlight the importance of "continuous epistasis" in bacterial physiology.}, author = {Kavcic, Bor}, keywords = {Escherichia coli, antibiotic combinations, translation, growth laws, drug interactions, bacterial physiology, translation inhibitors}, publisher = {Institute of Science and Technology Austria}, title = {{Analysis scripts and research data for the paper "Mechanisms of drug interactions between translation-inhibiting antibiotics"}}, doi = {10.15479/AT:ISTA:8097}, year = {2020}, } @misc{8930, abstract = {Phenomenological relations such as Ohm’s or Fourier’s law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial “growth laws,” which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems.}, author = {Kavcic, Bor}, keywords = {Escherichia coli, antibiotic combinations, translation, growth laws, drug interactions, bacterial physiology, translation inhibitors}, publisher = {Institute of Science and Technology Austria}, title = {{Analysis scripts and research data for the paper "Minimal biophysical model of combined antibiotic action"}}, doi = {10.15479/AT:ISTA:8930}, year = {2020}, } @misc{7383, abstract = {Organisms cope with change by employing transcriptional regulators. However, when faced with rare environments, the evolution of transcriptional regulators and their promoters may be too slow. We ask whether the intrinsic instability of gene duplication and amplification provides a generic alternative to canonical gene regulation. By real-time monitoring of gene copy number mutations in E. coli, we show that gene duplications and amplifications enable adaptation to fluctuating environments by rapidly generating copy number, and hence expression level, polymorphism. This ‘amplification-mediated gene expression tuning’ occurs on timescales similar to canonical gene regulation and can deal with rapid environmental changes. Mathematical modeling shows that amplifications also tune gene expression in stochastic environments where transcription factor-based schemes are hard to evolve or maintain. The fleeting nature of gene amplifications gives rise to a generic population-level mechanism that relies on genetic heterogeneity to rapidly tune expression of any gene, without leaving any genomic signature.}, author = {Grah, Rok}, keywords = {Matlab scripts, analysis of microfluidics, mathematical model}, publisher = {Institute of Science and Technology Austria}, title = {{Matlab scripts for the Paper: Gene Amplification as a Form of Population-Level Gene Expression regulation}}, doi = {10.15479/AT:ISTA:7383}, year = {2020}, } @phdthesis{8657, abstract = {Synthesis of proteins – translation – is a fundamental process of life. Quantitative studies anchor translation into the context of bacterial physiology and reveal several mathematical relationships, called “growth laws,” which capture physiological feedbacks between protein synthesis and cell growth. Growth laws describe the dependency of the ribosome abundance as a function of growth rate, which can change depending on the growth conditions. Perturbations of translation reveal that bacteria employ a compensatory strategy in which the reduced translation capability results in increased expression of the translation machinery. Perturbations of translation are achieved in various ways; clinically interesting is the application of translation-targeting antibiotics – translation inhibitors. The antibiotic effects on bacterial physiology are often poorly understood. Bacterial responses to two or more simultaneously applied antibiotics are even more puzzling. The combined antibiotic effect determines the type of drug interaction, which ranges from synergy (the effect is stronger than expected) to antagonism (the effect is weaker) and suppression (one of the drugs loses its potency). In the first part of this work, we systematically measure the pairwise interaction network for translation inhibitors that interfere with different steps in translation. We find that the interactions are surprisingly diverse and tend to be more antagonistic. To explore the underlying mechanisms, we begin with a minimal biophysical model of combined antibiotic action. We base this model on the kinetics of antibiotic uptake and binding together with the physiological response described by the growth laws. The biophysical model explains some drug interactions, but not all; it specifically fails to predict suppression. In the second part of this work, we hypothesize that elusive suppressive drug interactions result from the interplay between ribosomes halted in different stages of translation. To elucidate this putative mechanism of drug interactions between translation inhibitors, we generate translation bottlenecks genetically using in- ducible control of translation factors that regulate well-defined translation cycle steps. These perturbations accurately mimic antibiotic action and drug interactions, supporting that the interplay of different translation bottlenecks partially causes these interactions. We extend this approach by varying two translation bottlenecks simultaneously. This approach reveals the suppression of translocation inhibition by inhibited translation. We rationalize this effect by modeling dense traffic of ribosomes that move on transcripts in a translation factor-mediated manner. This model predicts a dissolution of traffic jams caused by inhibited translocation when the density of ribosome traffic is reduced by lowered initiation. We base this model on the growth laws and quantitative relationships between different translation and growth parameters. In the final part of this work, we describe a set of tools aimed at quantification of physiological and translation parameters. We further develop a simple model that directly connects the abundance of a translation factor with the growth rate, which allows us to extract physiological parameters describing initiation. We demonstrate the development of tools for measuring translation rate. This thesis showcases how a combination of high-throughput growth rate mea- surements, genetics, and modeling can reveal mechanisms of drug interactions. Furthermore, by a gradual transition from combinations of antibiotics to precise genetic interventions, we demonstrated the equivalency between genetic and chemi- cal perturbations of translation. These findings tile the path for quantitative studies of antibiotic combinations and illustrate future approaches towards the quantitative description of translation.}, author = {Kavcic, Bor}, isbn = {978-3-99078-011-4}, issn = {2663-337X}, pages = {271}, publisher = {Institute of Science and Technology Austria}, title = {{Perturbations of protein synthesis: from antibiotics to genetics and physiology}}, doi = {10.15479/AT:ISTA:8657}, year = {2020}, } @article{8250, abstract = {Antibiotics that interfere with translation, when combined, interact in diverse and difficult-to-predict ways. Here, we explain these interactions by “translation bottlenecks”: points in the translation cycle where antibiotics block ribosomal progression. To elucidate the underlying mechanisms of drug interactions between translation inhibitors, we generate translation bottlenecks genetically using inducible control of translation factors that regulate well-defined translation cycle steps. These perturbations accurately mimic antibiotic action and drug interactions, supporting that the interplay of different translation bottlenecks causes these interactions. We further show that growth laws, combined with drug uptake and binding kinetics, enable the direct prediction of a large fraction of observed interactions, yet fail to predict suppression. However, varying two translation bottlenecks simultaneously supports that dense traffic of ribosomes and competition for translation factors account for the previously unexplained suppression. These results highlight the importance of “continuous epistasis” in bacterial physiology.}, author = {Kavcic, Bor and Tkačik, Gašper and Bollenbach, Tobias}, issn = {2041-1723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{Mechanisms of drug interactions between translation-inhibiting antibiotics}}, doi = {10.1038/s41467-020-17734-z}, volume = {11}, year = {2020}, } @unpublished{7673, abstract = {Combining drugs can improve the efficacy of treatments. However, predicting the effect of drug combinations is still challenging. The combined potency of drugs determines the drug interaction, which is classified as synergistic, additive, antagonistic, or suppressive. While probabilistic, non-mechanistic models exist, there is currently no biophysical model that can predict antibiotic interactions. Here, we present a physiologically relevant model of the combined action of antibiotics that inhibit protein synthesis by targeting the ribosome. This model captures the kinetics of antibiotic binding and transport, and uses bacterial growth laws to predict growth in the presence of antibiotic combinations. We find that this biophysical model can produce all drug interaction types except suppression. We show analytically that antibiotics which cannot bind to the ribosome simultaneously generally act as substitutes for one another, leading to additive drug interactions. Previously proposed null expectations for higher-order drug interactions follow as a limiting case of our model. We further extend the model to include the effects of direct physical or allosteric interactions between individual drugs on the ribosome. Notably, such direct interactions profoundly change the combined drug effect, depending on the kinetic parameters of the drugs used. The model makes additional predictions for the effects of resistance genes on drug interactions and for interactions between ribosome-targeting antibiotics and antibiotics with other targets. These findings enhance our understanding of the interplay between drug action and cell physiology and are a key step toward a general framework for predicting drug interactions.}, author = {Kavcic, Bor and Tkačik, Gašper and Bollenbach, Tobias}, booktitle = {bioRxiv}, publisher = {Cold Spring Harbor Laboratory}, title = {{A minimal biophysical model of combined antibiotic action}}, doi = {10.1101/2020.04.18.047886}, year = {2020}, } @article{7652, abstract = {Organisms cope with change by taking advantage of transcriptional regulators. However, when faced with rare environments, the evolution of transcriptional regulators and their promoters may be too slow. Here, we investigate whether the intrinsic instability of gene duplication and amplification provides a generic alternative to canonical gene regulation. Using real-time monitoring of gene-copy-number mutations in Escherichia coli, we show that gene duplications and amplifications enable adaptation to fluctuating environments by rapidly generating copy-number and, therefore, expression-level polymorphisms. This amplification-mediated gene expression tuning (AMGET) occurs on timescales that are similar to canonical gene regulation and can respond to rapid environmental changes. Mathematical modelling shows that amplifications also tune gene expression in stochastic environments in which transcription-factor-based schemes are hard to evolve or maintain. The fleeting nature of gene amplifications gives rise to a generic population-level mechanism that relies on genetic heterogeneity to rapidly tune the expression of any gene, without leaving any genomic signature.}, author = {Tomanek, Isabella and Grah, Rok and Lagator, M. and Andersson, A. M. C. and Bollback, Jonathan P and Tkačik, Gašper and Guet, Calin C}, issn = {2397-334X}, journal = {Nature Ecology & Evolution}, number = {4}, pages = {612--625}, publisher = {Springer Nature}, title = {{Gene amplification as a form of population-level gene expression regulation}}, doi = {10.1038/s41559-020-1132-7}, volume = {4}, year = {2020}, } @unpublished{7552, abstract = {There is increasing evidence that protein binding to specific sites along DNA can activate the reading out of genetic information without coming into direct physical contact with the gene. There also is evidence that these distant but interacting sites are embedded in a liquid droplet of proteins which condenses out of the surrounding solution. We argue that droplet-mediated interactions can account for crucial features of gene regulation only if the droplet is poised at a non-generic point in its phase diagram. We explore a minimal model that embodies this idea, show that this model has a natural mechanism for self-tuning, and suggest direct experimental tests. }, author = {Bialek, William and Gregor, Thomas and Tkačik, Gašper}, booktitle = {arXiv:1912.08579}, pages = {5}, publisher = {ArXiv}, title = {{Action at a distance in transcriptional regulation}}, year = {2019}, } @article{5945, abstract = {In developing organisms, spatially prescribed cell identities are thought to be determined by the expression levels of multiple genes. Quantitative tests of this idea, however, require a theoretical framework capable of exposing the rules and precision of cell specification over developmental time. We use the gap gene network in the early fly embryo as an example to show how expression levels of the four gap genes can be jointly decoded into an optimal specification of position with 1% accuracy. The decoder correctly predicts, with no free parameters, the dynamics of pair-rule expression patterns at different developmental time points and in various mutant backgrounds. Precise cellular identities are thus available at the earliest stages of development, contrasting the prevailing view of positional information being slowly refined across successive layers of the patterning network. Our results suggest that developmental enhancers closely approximate a mathematically optimal decoding strategy.}, author = {Petkova, Mariela D. and Tkacik, Gasper and Bialek, William and Wieschaus, Eric F. and Gregor, Thomas}, journal = {Cell}, number = {4}, pages = {844--855.e15}, publisher = {Cell Press}, title = {{Optimal decoding of cellular identities in a genetic network}}, doi = {10.1016/j.cell.2019.01.007}, volume = {176}, year = {2019}, } @article{6049, abstract = {In this article it is shown that large systems with many interacting units endowing multiple phases display self-oscillations in the presence of linear feedback between the control and order parameters, where an Andronov–Hopf bifurcation takes over the phase transition. This is simply illustrated through the mean field Landau theory whose feedback dynamics turn out to be described by the Van der Pol equation and it is then validated for the fully connected Ising model following heat bath dynamics. Despite its simplicity, this theory accounts potentially for a rich range of phenomena: here it is applied to describe in a stylized way (i) excess demand-price cycles due to strong herding in a simple agent-based market model; (ii) congestion waves in queuing networks triggered by user feedback to delays in overloaded conditions; and (iii) metabolic network oscillations resulting from cell growth control in a bistable phenotypic landscape.}, author = {De Martino, Daniele}, journal = {Journal of Physics A: Mathematical and Theoretical}, number = {4}, publisher = {IOP Publishing}, title = {{Feedback-induced self-oscillations in large interacting systems subjected to phase transitions}}, doi = {10.1088/1751-8121/aaf2dd}, volume = {52}, year = {2019}, } @article{6046, abstract = {Sudden stress often triggers diverse, temporally structured gene expression responses in microbes, but it is largely unknown how variable in time such responses are and if genes respond in the same temporal order in every single cell. Here, we quantified timing variability of individual promoters responding to sublethal antibiotic stress using fluorescent reporters, microfluidics, and time‐lapse microscopy. We identified lower and upper bounds that put definite constraints on timing variability, which varies strongly among promoters and conditions. Timing variability can be interpreted using results from statistical kinetics, which enable us to estimate the number of rate‐limiting molecular steps underlying different responses. We found that just a few critical steps control some responses while others rely on dozens of steps. To probe connections between different stress responses, we then tracked the temporal order and response time correlations of promoter pairs in individual cells. Our results support that, when bacteria are exposed to the antibiotic nitrofurantoin, the ensuing oxidative stress and SOS responses are part of the same causal chain of molecular events. In contrast, under trimethoprim, the acid stress response and the SOS response are part of different chains of events running in parallel. Our approach reveals fundamental constraints on gene expression timing and provides new insights into the molecular events that underlie the timing of stress responses.}, author = {Mitosch, Karin and Rieckh, Georg and Bollenbach, Mark Tobias}, journal = {Molecular systems biology}, number = {2}, publisher = {Embo Press}, title = {{Temporal order and precision of complex stress responses in individual bacteria}}, doi = {10.15252/msb.20188470}, volume = {15}, year = {2019}, } @article{6784, abstract = {Mathematical models have been used successfully at diverse scales of biological organization, ranging from ecology and population dynamics to stochastic reaction events occurring between individual molecules in single cells. Generally, many biological processes unfold across multiple scales, with mutations being the best studied example of how stochasticity at the molecular scale can influence outcomes at the population scale. In many other contexts, however, an analogous link between micro- and macro-scale remains elusive, primarily due to the challenges involved in setting up and analyzing multi-scale models. Here, we employ such a model to investigate how stochasticity propagates from individual biochemical reaction events in the bacterial innate immune system to the ecology of bacteria and bacterial viruses. We show analytically how the dynamics of bacterial populations are shaped by the activities of immunity-conferring enzymes in single cells and how the ecological consequences imply optimal bacterial defense strategies against viruses. Our results suggest that bacterial populations in the presence of viruses can either optimize their initial growth rate or their population size, with the first strategy favoring simple immunity featuring a single restriction modification system and the second strategy favoring complex bacterial innate immunity featuring several simultaneously active restriction modification systems.}, author = {Ruess, Jakob and Pleska, Maros and Guet, Calin C and Tkačik, Gašper}, issn = {1553-7358}, journal = {PLoS Computational Biology}, number = {7}, publisher = {Public Library of Science}, title = {{Molecular noise of innate immunity shapes bacteria-phage ecologies}}, doi = {10.1371/journal.pcbi.1007168}, volume = {15}, year = {2019}, } @misc{9786, author = {Ruess, Jakob and Pleska, Maros and Guet, Calin C and Tkačik, Gašper}, publisher = {Public Library of Science}, title = {{Supporting text and results}}, doi = {10.1371/journal.pcbi.1007168.s001}, year = {2019}, } @article{7422, abstract = {Biochemical reactions often occur at low copy numbers but at once in crowded and diverse environments. Space and stochasticity therefore play an essential role in biochemical networks. Spatial-stochastic simulations have become a prominent tool for understanding how stochasticity at the microscopic level influences the macroscopic behavior of such systems. While particle-based models guarantee the level of detail necessary to accurately describe the microscopic dynamics at very low copy numbers, the algorithms used to simulate them typically imply trade-offs between computational efficiency and biochemical accuracy. eGFRD (enhanced Green’s Function Reaction Dynamics) is an exact algorithm that evades such trade-offs by partitioning the N-particle system into M ≤ N analytically tractable one- and two-particle systems; the analytical solutions (Green’s functions) then are used to implement an event-driven particle-based scheme that allows particles to make large jumps in time and space while retaining access to their state variables at arbitrary simulation times. Here we present “eGFRD2,” a new eGFRD version that implements the principle of eGFRD in all dimensions, thus enabling efficient particle-based simulation of biochemical reaction-diffusion processes in the 3D cytoplasm, on 2D planes representing membranes, and on 1D elongated cylinders representative of, e.g., cytoskeletal tracks or DNA; in 1D, it also incorporates convective motion used to model active transport. We find that, for low particle densities, eGFRD2 is up to 6 orders of magnitude faster than conventional Brownian dynamics. We exemplify the capabilities of eGFRD2 by simulating an idealized model of Pom1 gradient formation, which involves 3D diffusion, active transport on microtubules, and autophosphorylation on the membrane, confirming recent experimental and theoretical results on this system to hold under genuinely stochastic conditions.}, author = {Sokolowski, Thomas R and Paijmans, Joris and Bossen, Laurens and Miedema, Thomas and Wehrens, Martijn and Becker, Nils B. and Kaizu, Kazunari and Takahashi, Koichi and Dogterom, Marileen and ten Wolde, Pieter Rein}, issn = {1089-7690}, journal = {The Journal of Chemical Physics}, number = {5}, publisher = {AIP Publishing}, title = {{eGFRD in all dimensions}}, doi = {10.1063/1.5064867}, volume = {150}, year = {2019}, } @article{6900, abstract = {Across diverse biological systems—ranging from neural networks to intracellular signaling and genetic regulatory networks—the information about changes in the environment is frequently encoded in the full temporal dynamics of the network nodes. A pressing data-analysis challenge has thus been to efficiently estimate the amount of information that these dynamics convey from experimental data. Here we develop and evaluate decoding-based estimation methods to lower bound the mutual information about a finite set of inputs, encoded in single-cell high-dimensional time series data. For biological reaction networks governed by the chemical Master equation, we derive model-based information approximations and analytical upper bounds, against which we benchmark our proposed model-free decoding estimators. In contrast to the frequently-used k-nearest-neighbor estimator, decoding-based estimators robustly extract a large fraction of the available information from high-dimensional trajectories with a realistic number of data samples. We apply these estimators to previously published data on Erk and Ca2+ signaling in mammalian cells and to yeast stress-response, and find that substantial amount of information about environmental state can be encoded by non-trivial response statistics even in stationary signals. We argue that these single-cell, decoding-based information estimates, rather than the commonly-used tests for significant differences between selected population response statistics, provide a proper and unbiased measure for the performance of biological signaling networks.}, author = {Cepeda Humerez, Sarah A and Ruess, Jakob and Tkačik, Gašper}, issn = {15537358}, journal = {PLoS computational biology}, number = {9}, pages = {e1007290}, publisher = {Public Library of Science}, title = {{Estimating information in time-varying signals}}, doi = {10.1371/journal.pcbi.1007290}, volume = {15}, year = {2019}, } @article{196, abstract = {The abelian sandpile serves as a model to study self-organized criticality, a phenomenon occurring in biological, physical and social processes. The identity of the abelian group is a fractal composed of self-similar patches, and its limit is subject of extensive collaborative research. Here, we analyze the evolution of the sandpile identity under harmonic fields of different orders. We show that this evolution corresponds to periodic cycles through the abelian group characterized by the smooth transformation and apparent conservation of the patches constituting the identity. The dynamics induced by second and third order harmonics resemble smooth stretchings, respectively translations, of the identity, while the ones induced by fourth order harmonics resemble magnifications and rotations. Starting with order three, the dynamics pass through extended regions of seemingly random configurations which spontaneously reassemble into accentuated patterns. We show that the space of harmonic functions projects to the extended analogue of the sandpile group, thus providing a set of universal coordinates identifying configurations between different domains. Since the original sandpile group is a subgroup of the extended one, this directly implies that it admits a natural renormalization. Furthermore, we show that the harmonic fields can be induced by simple Markov processes, and that the corresponding stochastic dynamics show remarkable robustness over hundreds of periods. Finally, we encode information into seemingly random configurations, and decode this information with an algorithm requiring minimal prior knowledge. Our results suggest that harmonic fields might split the sandpile group into sub-sets showing different critical coefficients, and that it might be possible to extend the fractal structure of the identity beyond the boundaries of its domain. }, author = {Lang, Moritz and Shkolnikov, Mikhail}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences}, number = {8}, pages = {2821--2830}, publisher = {National Academy of Sciences}, title = {{Harmonic dynamics of the Abelian sandpile}}, doi = {10.1073/pnas.1812015116}, volume = {116}, year = {2019}, } @article{5817, abstract = {We theoretically study the shapes of lipid vesicles confined to a spherical cavity, elaborating a framework based on the so-called limiting shapes constructed from geometrically simple structural elements such as double-membrane walls and edges. Partly inspired by numerical results, the proposed non-compartmentalized and compartmentalized limiting shapes are arranged in the bilayer-couple phase diagram which is then compared to its free-vesicle counterpart. We also compute the area-difference-elasticity phase diagram of the limiting shapes and we use it to interpret shape transitions experimentally observed in vesicles confined within another vesicle. The limiting-shape framework may be generalized to theoretically investigate the structure of certain cell organelles such as the mitochondrion.}, author = {Kavcic, Bor and Sakashita, A. and Noguchi, H. and Ziherl, P.}, issn = {1744-6848}, journal = {Soft Matter}, number = {4}, pages = {602--614}, publisher = {Royal Society of Chemistry}, title = {{Limiting shapes of confined lipid vesicles}}, doi = {10.1039/c8sm01956h}, volume = {15}, year = {2019}, } @phdthesis{6473, abstract = {Single cells are constantly interacting with their environment and each other, more importantly, the accurate perception of environmental cues is crucial for growth, survival, and reproduction. This communication between cells and their environment can be formalized in mathematical terms and be quantified as the information flow between them, as prescribed by information theory. The recent availability of real–time dynamical patterns of signaling molecules in single cells has allowed us to identify encoding about the identity of the environment in the time–series. However, efficient estimation of the information transmitted by these signals has been a data–analysis challenge due to the high dimensionality of the trajectories and the limited number of samples. In the first part of this thesis, we develop and evaluate decoding–based estimation methods to lower bound the mutual information and derive model–based precise information estimates for biological reaction networks governed by the chemical master equation. This is followed by applying the decoding-based methods to study the intracellular representation of extracellular changes in budding yeast, by observing the transient dynamics of nuclear translocation of 10 transcription factors in response to 3 stress conditions. Additionally, we apply these estimators to previously published data on ERK and Ca2+ signaling and yeast stress response. We argue that this single cell decoding-based measure of information provides an unbiased, quantitative and interpretable measure for the fidelity of biological signaling processes. Finally, in the last section, we deal with gene regulation which is primarily controlled by transcription factors (TFs) that bind to the DNA to activate gene expression. The possibility that non-cognate TFs activate transcription diminishes the accuracy of regulation with potentially disastrous effects for the cell. This ’crosstalk’ acts as a previously unexplored source of noise in biochemical networks and puts a strong constraint on their performance. To mitigate erroneous initiation we propose an out of equilibrium scheme that implements kinetic proofreading. We show that such architectures are favored over their equilibrium counterparts for complex organisms despite introducing noise in gene expression. }, author = {Cepeda Humerez, Sarah A}, issn = {2663-337X}, keywords = {Information estimation, Time-series, data analysis}, pages = {135}, publisher = {Institute of Science and Technology Austria}, title = {{Estimating information flow in single cells}}, doi = {10.15479/AT:ISTA:6473}, year = {2019}, }