Biology subjects

Tkacik, G.

Publications and source records attributed to Tkacik, G..

At least 19 recordsLinked to original sources

bioRxiv2026

Stable and robust differentiation programs are predominantly irreversible

Multicellular differentiation programs are almost always irreversible, with cells rarely revisiting states observed during earlier developmental stages. An important factor contributing to such irreversibility may be the cellular context: the dependence of each cells fate decisions on inductive signals from other, simultaneously present cell types. Using a class of rule-based models for context-dependent cell fate decisions we examine the evolutionary basis for the observed prevalence of irreversibility. Our study suggests that irreversibility, instead of being directly selected for, could be widespread due to its association with two other key traits, stability and robustness, which are essential for reproducible, cancer-free organismal formation. We identify further structural properties of desirable fate-decision programs and illustrate how explicit knowledge of cellular context is required for predicting developmental outcomes.

evolutionary biology↗

bioRxiv2026

Cells specify fate within an optimal window of positional information determined by morphogenesis

How cells sense position to adopt appropriate fates is a central problem in development. In a classic paradigm, cells read morphogen gradients encoding positional information (PI), with prolonged signal integration improving fate precision. However, how cells adapt this strategy in morphogenetic tissues remains unclear. Here, we reconstructed complete positional and signalling histories for individual cells during zebrafish neurulation, where the Sonic hedgehog (Shh) gradient patterns ventral progenitors as the neural plate folds into a tube. Despite steadily increasing Shh activity, Shh-encoded PI peaked early and then declined. Morphogenesis set this early readout window and imposed a [~]1.2-bit ceiling on Shh-encoded information about final position and fate. Fate mapping, transcriptomic analyses, and timed Shh inhibition showed that fate specification is temporally and functionally aligned with this early readout window. Thus, when morphogenesis decouples signal quality from signal strength, cells specify fate when signalling is most informative, not when signalling is strongest.

Developmental Biology↗

bioRxiv2026

Intrinsic Population Dynamics are a Neuronal Substrate for Visual Attention

Perception results from a dynamic interplay between the feedforward processing of sensory stimuli and intrinsic neural activity 1, which is often dismissed as noise 2. To tailor perceptual processes to the organisms current needs on a continuous, moment-to-moment basis, intrinsic dynamics - rather than just being noise - have been suggested to reflect prior expectations 3,4, task demands 5, and attentional focus 6. Here, we identify a novel signature of attentive state in which intrinsic, collective neural activity modulates visuospatial attention by dynamically interacting with sensory input within the superior colliculus (SC), a midbrain hub that integrates bottom-up visual input with top-down signals 7. We show that these intrinsic dynamics organize cell activity into structured, blob-like features that are temporally and topographically localized and that rival sensory-evoked responses in strength. These features emerge as animals learn to engage in the visual detection task, and predict behavioral outcomes on a trial-by-trial basis. Although independent of sensory input or overt behavior, the features can be recruited to enhance visual responses four-fold in the attentive state, generating a "dynamic saliency map" that aligns with reaction times and behavioral outcomes. A computational model indicates that tunable blob-like features can arise from local excitatory-inhibitory interactions within the SC, and enhance sensory-evoked responses in line with observations. Together, our results identify state-dependent intrinsic population activity that interacts with sensory input to give rise to a saliency-map-like representation linked to flexible, goal-directed behavior.

neuroscience↗

bioRxiv2026

The scaling behavior of hippocampal activity in sleep/rest predicts spatial memory performance

In the hippocampus, reactivation of spatial memories during sleep enhances consolidation and recall. However, network dynamics may independently influence memory retention. We applied the Phenomenological Renormalization Group to CA1 neuronal activity in rats during sleep/rest epochs surrounding a spatial learning task. The scaling exponent of activity variance (), assessed either before or after learning, predicted subsequent recall performance independently of reactivation. The prediction model was transferable across subjects, suggesting that it is a robust biomarker of learning ability. Single-cell features, such as burst propensity and intrinsic timescales, correlated with and memory, predicted retention even when controlling for these factors. Our results identified a link between scale-free circuit dynamics and memory stabilization, suggesting that the tuning of the underlying dynamical regime near criticality is a key determinant of memory longevity. Targeted modulation of these collective states could offer new avenues for memory enhancement.

neuroscience↗

bioRxiv2025

Evolution and information content of optimal gene regulatory architectures

Evolution of gene regulatory sequences is shaped by their genotype-phenotype (GP) map, but this map itself can evolve via changes in the molecular machinery that reads out the regulatory sequences. Using population genetics theory, we derive an optimality principle, implying that the evolved GP maps will associate fit phenotypes with larger numbers of possible genotypes. Mathematically, this builds on analogies between population genetics and statistical physics, as well as optimal coding in information theory. The results are particularly interesting in the context of transcriptional regulation, where optimal values of certain parameters that determine the gene regulatory architecture can be derived even without a complete knowledge of the molecular mechanisms involved. We illustrate the theory using a simplified model of transcription factor (TF) binding to cis-regulatory elements (CREs): the fraction of possible CREs across the entire genotype space that bind a given TF evolves to match the fraction of CREs under selection to bind that TF. Similar "frequency matching" results are expected to emerge generically, because selection implicitly optimizes the entire regulatory architecture that determines the "replicated GP map" for all CREs, while it simultaneously adapts towards individually fit CREs on that GP map. We discuss the subtleties and limitations of the theory related to biophysical constraints and the need for mutational robustness. Lastly, our theory suggests a possible mathematical definition for evolvability with predictive power that can be tested in evolutionary simulations and, ultimately, in comparative genetics studies.

evolutionary biology↗

bioRxiv2025

Regulatory architectures optimized for rapid evolution of gene expression

Cis-regulatory elements (CREs), such as enhancers and promoters, control gene expression by binding regulatory proteins. In contrast to their bacterial counterparts, eukaryotic CREs typically bind transcription factors with short recognition motifs across multiple functional yet often weak binding sites. The evolutionary origin of this genetic architecture remains unclear. Here we study adaptive evolution of entire CREs under selection for controllable gene expression. In a biophysical toy model that recapitulates the essential non-linearities of eukaryotic regulation, a regulatory phenotype requires a gene to be active when its CRE binds cognate TFs, yet inactive in the presence of noncognate TFs that can cause deleterious crosstalk. We explore CRE evolutionary outcomes utilizing "optimize-to-adapt" approach suggested by the theory presented in a companion paper. In this approach, CRE evolution is simulated explicitly at the sequence level, while the parameters of the biophysical model itself - i.e., the properties of the replicated genotype-phenotype map - are numerically optimized for evolvability of CREs. In the optimal regime, selection navigates the tradeoff between slowly evolving strong and long binding sites (which guarantee low crosstalk), and rapidly evolving multiple short and weak binding sites (which necessitate diffuse selection against too much noncognate binding across the entire CRE). When we further explore various scenarios for cooperative regulation, we find that the optimal regime predicts a diversity of strong and weak, yet always short, binding sites and favors "synergistic activation" of transcription, as reported empirically for eukaryotes. These results showcase how information theory can link evolutionary dynamics with biophysical constraints to rationalize - and possibly even predict - optimal regulatory architectures.

evolutionary biology↗

bioRxiv2024

Pulsatile basal gene expression as a fitness determinant in bacteria

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.

systems biology↗

bioRxiv2024

Learning reshapes the hippocampal representation hierarchy

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 spatial-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.

neuroscience↗

bioRxiv2024

Linking Molecular Mechanisms to their Evolutionary Consequences: a primer

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 two proteins, allowing us to connect any promoter genotype to six 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.

evolutionary biology↗

bioRxiv2024

Chromatin enables precise and scalable gene regulation with factors of limited specificity

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. Significance StatementReliably keeping a gene off is as important as controlling its expression level when the gene is on. Yet both tasks become challenging in the packed nuclear environment of a eukaryotic cell, where the numerous and diverse regulatory proteins that are present cannot bind enhancers for target genes with perfect specificity. While regulatory schemes based on prokaryotic models would be overwhelmed by errors in such conditions, we show that chromatin-based regulation, an evolutionary innovation of eukaryotic cells, successfully rescues precise gene expression control by reliably keeping desired genes off. Our systems-level computational analysis demonstrates that this result is nontrivial, because chromatin opening must itself be correctly regulated. We furthermore identify when and how chromatin-based regulation outperforms alternative schemes.

biophysics↗

bioRxiv2024

Quantitative omnigenic model discovers interpretable genome-wide associations

As their statistical power grows, genome-wide association studies (GWAS) have identified an increasing number of loci underlying quantitative traits of interest. These loci are scattered throughout the genome and are individually responsible only for small fractions of the total heritable trait variance. The recently proposed omnigenic model provides a conceptual framework to explain these observations by postulating that numerous distant loci contribute to each complex trait via effect propagation through intracellular regulatory networks. We formalize this conceptual framework by proposing the "quantitative omnigenic model" (QOM), a statistical model that combines prior knowledge of the regulatory network topology with genomic data. By applying our model to gene expression traits in yeast, we demonstrate that QOM achieves similar gene expression prediction performance to traditional GWAS with hundreds of times less parameters, while simultaneously extracting candidate causal and quantitative chains of effect propagation through the regulatory network for every individual gene. We estimate the fraction of heritable trait variance in cis- and in trans-, break the latter down by effect propagation order, assess the trans- variance not attributable to transcriptional regulation, and show that QOM correctly accounts for the low-dimensional structure of gene expression covariance. We furthermore demonstrate the relevance of QOM for systems biology, by employing it as a statistical test for the quality of regulatory network reconstructions, and linking it to the propagation of non-transcriptional (including environmental) effects. Significance statementGenetic variation leads to differences in traits implicated in health and disease. Identifying genetic variants associated with these traits is spearheaded by "genome-wide association studies" (GWAS) - statistically rigorous procedures whose power has grown with the number of genotyped samples. Nevertheless, GWAS have a substantial shortcoming: they are ill-equipped to detect the causal basis and reveal the complex systemic mechanisms of polygenic traits. Even a single genetic change can propagate throughout the entire genetic regulatory network causing a myriad of spurious detections, thereby significantly limiting GWAS usefulness. To this end, we propose a novel statistical approach that incorporates known regulatory network information with the potential to boost interpretability of state-of-the-art genomic analyses while simultaneously extracting systems biology insights.

genomics↗

bioRxiv2021

Accumulation and maintenance of information in evolution

Selection accumulates information in the genome -- it guides stochastically evolving populations towards states (geno-type 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.

evolutionary biology↗

bioRxiv2021

Limited specificity of molecular interactions incurs an environment-dependent fitness cost in bacteria

Reliable operation of cellular programs depends crucially on the specificity of biomolecular interactions. In gene regulatory networks, the appropriate expression of genes is determined through the specific binding of transcription factors (TFs) to their cognate DNA sequences. However, the large genomic background likely contains many DNA sequences showing similarity to TF target motifs, potentially allowing for substantial non-cognate TF binding with low specificity. Whether and how non-cognate TF binding impacts cellular function and fitness remains unclear. We show that increased expression of different transcriptional regulators in Escherichia coli and Salmonella enterica can significantly inhibit population growth across multiple environments. This effect depends upon (i) TF binding to a large number of DNA sequences with low specificity, (ii) TF cooperativity, and (iii) the ratio of TF to DNA. DNA binding due to the limited specificity of promiscuous or non-native TFs can thus severely impact fitness, giving rise to a fundamental biophysical constraint on gene regulatory design and evolution.

molecular biology↗

bioRxiv2021

The structure of hippocampal CA1 interactions optimizes spatial coding across experience

Although much is known about how single neurons in the hippocampus represent an animals 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.

neuroscience↗

bioRxiv2021

Attentional dynamics of efficient codes

Top-down attention is hypothesized to dynamically allocate limited neural resources to task-relevant computations. According to this view, sensory neurons are driven not only by stimuli but also by feedback signals from higher brain areas that adapt the sensory code to the goals of the organism and its belief about the state of the environment. Here we formalize this view by optimizing a model of population coding in the visual cortex for maximally accurate perceptual inference at minimal activity cost. The resulting optimality predictions reproduce measured properties of attentional modulation in the visual system and generate novel hypotheses about the functional role of top-down feedback, response variability, and noise correlations. Our results suggest that a range of seemingly disparate attentional phenomena can be derived from a general theory combining probabilistic inference with efficient coding in a dynamic environment.

neuroscience↗

bioRxiv2020

Structure and Evolution of Constitutive Bacterial Promoters

Predicting gene expression levels from any DNA sequence is a major challenge in biology. Using libraries with >25,000 random mutants, we developed a biophysical model that accounts for major features of {sigma}70-binding bacterial promoters to accurately predict constitutive gene expression levels of any sequence. We experimentally and theoretically estimated that 10-20% of random sequences lead to expression and 82% of non-expressing sequences are one point mutation away from a functional promoter. Generating expression from random sequences is pervasive, such that selection acts against {sigma}70-RNA polymerase binding sites even within inter-genic, promoter-containing regions. The pervasiveness of {sigma}70- binding sites, which arises from the structural features of promoters captured by our biophysical model, implies that their emergence is unlikely the limiting step in gene regulatory evolution.

evolutionary biology↗

bioRxiv2020

A Minimal Biophysical Model of Combined Antibiotic Action

Phenomenological relations such as Ohms or Fouriers 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 systematically refine the model by including direct physical interactions of different drugs on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions derived using entropy maximization. It further makes parameter-free predictions for combined drug effects on cells carrying resistance genes and for drugs that mimic poor nutrient environments. We show experimentally that resistance genes can drastically alter drug interactions in notable agreement with our theoretical predictions. 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.

biophysics↗

bioRxiv2020

Normative models of enhancer function

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.

biophysics↗