Biosystems

Time compaction theory is a general framework explaining how a brain can efficiently deal with dynamic situations occurring in, e.g., sports games. It involves a geometric representation of the time dimension, which enables effective learning and strategic action planning. The theory has recently received experimental support in humans. However, its current computational model has an important limitation: it does not account for deliberate waiting and speed modulation, behaviors ubiquitous in natural environments. This work substantially extends the original model formulation by a dimensional lifting of an n-D workspace into (n + 1)-D mental space, where time remains geometrically embedded. The proposed biologically inspired computational model can generate adaptive behavior across increasingly complex situations, from navigation in everyday social environments to competitive sports. Furthermore, by actively conditioning the expected responses of other agents and stabilizing future predictions, we introduce the concept of uncertainty points in sequences of generalized cognitive maps to support the generation of adaptive strategies in interactive environments, where future prediction has a limited time horizon. Thus, we provide a mechanism for chaining short-term solutions into long-term strategies, which is illustrated by simulating the behavior of a player in a real football game. Author summaryHumans often anticipate future interactions in dynamic environments. Many behaviors, such as avoiding other pedestrians, letting someone pass through a narrow corridor, or reproducing the kind of dribbling maneuvers performed by elite football players, require deciding not only where to move but also when to move. Existing theories suggest that the brain simplifies such situations by representing future interactions as static spatial maps, making them easier to learn and recall. However, current computational models cannot naturally account for common behaviors such as waiting, slowing down, or modulating speed. Here we show that these behaviors readily emerge if the model space is extended by an additional virtual coordinate that encodes accumulated waiting rather than physical time. The proposed model simultaneously admits a wide variety of behaviors, including speed modulation, multigoal decisions, and compound actions, while preserving the principles of time compaction. We illustrate the model in everyday situations and by reproducing two real football plays, comparing the observed behaviors with model simulations. Our results suggest computational principles through which the human brain may efficiently represent, memorize, and exploit dynamic situations.

Based on our experimental observation of activation state oscillations of different frequencies used to communicate information by the master human stress response regulator protein p38 MAPK 1, we develop a simple graphical approach for understanding and predicting the behavior of complex biological networks acting upon signals carrying information as different frequency waves of chemicals. This approach uses the same techniques used for analyzing and understanding information transmission using waves of electrical currents and fields used in electrical alternating current (AC) circuits. We show how biological components can be organized to behave as standard components found in electronic telecommunications circuits. Finally, we demonstrate how such components can be organized into complex biological signaling cascades whose behavior can be qualitatively and quantitatively understood, and whose output resembles that experimentally observed in p38.

1. Plants produce a great chemodiversity, which is the diversity of specialized metabolites (SMs). These SMs are produced in complex metabolic pathways and play an important role in inter-species interactions. There are numerous hypotheses about the evolutionary processes which brought about and maintain chemodiversity. Some have been partially tested in lab and field studies. However, some of their assumptions and predictions are better tested by quantitative modeling, and so far no quantitative model has investigated the role of metabolic pathways. 2. To close this gap, we developed an individual-based model for metabolic pathway evolution. It models enzymes creating metabolites with various modifications. Enzymes undergo inheritance and mutation. We used the model to compare the screening and interaction diversity hypotheses. 3. The screening hypothesis predicts promiscuous enzymes, genetic drift, the presence of many non-beneficial metabolites, and high metabolite richness. The interaction diversity hypothesis predicts specialized enzymes, selection, the almost exclusive presence of beneficial metabolites, and situation- dependent metabolite richness. We found that the patterns predicted by the screening hypothesis did not occur, while those predicted by the interaction diversity hypothesis did. 4. This provides reason to favor the interaction diversity hypothesis over the screening hypothesis when connecting empirical results to their evolutionary context

Predicting patterns of behavioural coordination that emerge in small interacting groups is challenging because goal-directed social action is shaped by complex reciprocal and compensatory dynamics. In this study, we examined whether formal symmetry principles derived from group theory could explain coordination patterns among children performing a triadic jumping task. We investigated how geometric symmetries of the task environment and dispositional (a)symmetries associated with leader-follower tendencies jointly constrain collective behaviour. Forty-seven children were classified into symmetric or asymmetric triads based on teacher evaluations of leadership dispositions. Each triad completed multiple trials of a synchronized jumping game requiring movement between adjacent hoops arranged in triangular or square configurations. Results showed that temporal asymmetries in inter-child movement (first, second, or last to jump) were consistent with group-theoretic predictions. In triangular configurations, observed asymmetries corresponded to the highest-order subgroup defined by task and dispositional symmetries. These findings demonstrate that environmental symmetry exerts a hierarchically dominant constraint on collective coordination, within which actor dispositional (a)symmetries further modulate emerging patterns.

The dominant paradigm for temperature robustness in biomolecular circuits is for the parameters to be tuned to have matching temperature dependencies so that their overall effect cancels out. This contrasts with the robustness due to circuit structure, typically operative in circuits where robustness to a single input parameter is desired. The importance of the circuit structure in temperature robustness is generally unclear. We addressed this issue in a benchmark negative feedback circuit using a combination of theoretical modelling and experimental measurements. We found that the response to a temperature perturbation in a model of negative feedback was qualitatively different from the response in a model without feedback. We experimentally measured the response of the negative feedback circuit to a temperature perturbation and found that it was smaller than that of the circuit without feedback, in line with the theoretical finding. We confirmed this theoretical prediction experimentally. The initial response of the negative feedback circuit, paradoxically, was larger than the circuit without feedback. The resolution of this paradox was in accounting for the faster dynamics in the negative feedback circuit. These results show a simple design principle of temperature robustness that can operate in a widespread circuit motif and may also apply to other perturbations which, like temperature, affect multiple parameters simultaneously.

This paper presents and critically reviews the results of original and some literature based experimental studies conducted by the authors last years on the structural organization of DNA in dormant (starvation stress), anabiotic dormant (4 HR treatment) E. coli cells, as well as the K12 {Delta}dps strain, which lacks the Dps protein (Dps null E. coli). The experimental data includes small-angle synchrotron radiation diffraction (SAXS) and transmission electron microscopy (TEM) data. Synchrotron radiation diffraction experiments on K12{Delta}dps cells allowed us to conclude that peaks at 44.3, 22.1, and 14.8 angstrom resolutions are associated exclusively with ordered DNA organization. Peaks at 44.3, 22.1, and 14.8 angstrom resolutions are also observed for samples of dormant (starvation stress) cells and anabiotically dormant cells. Therefore, this ordered DNA organization also applies to samples of dormant and anabiotically dormant cells. A model is proposed that considers the ordered DNA organization in the cell as a cholesteric liquid crystal. The powder diffraction pattern calculated based on this model is compared with experimental small angle X ray scattering (SAXS) data obtained on Dps-null cell samples. The model completely reproduces the key features of the experimental diffraction pattern from Dps-null cell samples. Accordingly, the cholesteric liquid crystal model corresponds to DNA packaging in dormant and anabiotically dormant cells. Cholesteric liquid crystal ordering should be further considered in all models of cellular DNA packaging. To address the question of which structural organization of DNA predominates in the cell: the cholesteric liquid crystal or nanocrystalline or whether they coexist and fully manifest themselves under different external conditions, it is necessary to utilize the latest methodological advances in structural analysis.

Light simultaneously provides phototrophic organisms with energy and with information about environmental time. These two functions need not impose the same response to fluctuations in irradiance: photosynthetic outputs should remain amplitude-sensitive, whereas circadian phase should reject changes that do not alter dawn, dusk, or photoperiod. We formulate this problem for two spectral inputs by decomposing their logarithmic intensities into a common-irradiance coordinate a and a spectral-contrast coordinate r. The contribution of channel i to phase is Qi = ZiGi, where the non-negative gate Gi determines when the pathway is active and the signed phase-response projection Zi determines whether this activity advances or delays the oscillator. For a locked oscillator, robustness to common irradiance together with retained contrast sensitivity requires two non-zero cycle-averaged contributions of opposite sign, A1 [~=] -A2 = 0. Energetic responsiveness is preserved only when the physiological projection of the same inputs is not proportional to their phase projection. A canonical repressilator provides an explicit nonlinear realization of these conditions. Positive gates placed on opposite lobes of its infinitesimal phase-response curve strongly attenuate common-mode phase shifts while preserving contrast sensitivity. A minimal photosynthetic-capacity model then shows how this organization protects temporal alignment under day-to-day irradiance fluctuations. At the largest variability tested, differential routing reduced the mean phase displacement by more than one half and the associated alignment loss by approximately 82%, whereas the resulting production advantage remained small, approximately 0.1%. Thus, multichannel light sensing can stabilize circadian timing without suppressing the energetic response to irradiance. HighlightsO_LIAnalytical routing conditions separate common irradiance from spectral contrast. C_LIO_LIPositive temporal gates can generate opposite signed phase contributions. C_LIO_LIPhase robustness requires a projection distinct from the energetic projection. C_LIO_LIA canonical oscillator provides a constructive illustration of the mechanism. C_LIO_LIThe functional benefit is improved temporal alignment rather than a large growth gain. C_LI

A fundamental biological process of transcription occurs in the cell nucleus, which is a complex medium that also contains multiple heterogeneous structures known as biomolecular condensates. Interestingly, some of these condensates contain mRNA molecules in addition to proteins, suggesting an important cellular role in transcription that is not yet well understood. In this work, we develop a minimal theoretical framework for quantitative investigation of the role of reversible mRNA condensation in transcription. Our discrete-state stochastic approach accounts for the most relevant processes, allowing us to explicitly evaluate the properties of the system and clarify the effects of condensation. Analytical calculations supported by computer simulations suggest that reversible mRNA condensation influences the transcription processes by maintaining a constant level of free mRNA in the nucleoplasm while lowering the degree of stochastic noise and increasing the robustness against external perturbations. Physicochemical arguments are presented to explain these observations. The proposed theoretical framework elucidates important microscopic aspects of transcription, providing a convenient quantitative tool for investigating complex biological phenomena.

Network centrality is the workhorse of gene prioritisation, yet what a ranking omits is rarely audited. Scoring each selection against an annotation-count-matched maximum-entropy reference--asking whether a selected gene set covers the genomes functional space or collapses it-reveals that the criterion in standard use has a measurable blind spot in exactly the class it is meant to surface. Degree, the most widely used criterion, returns the cross-module bridges that are also locally dominant--connector hubs--and omits the non-hub connectors: where 26% of the genome occupies these coordinating roles, a degree-ranked list holds 18% and an EDVS-ranked list 55%, and degrees top-1% collapses functional coverage below the reference on all five networks tested. We repurpose EDVS (Entropy of Degree-Vector Sums), an information-theoretic diversity measure, as an annotation-free, partition-free centrality that recovers this omitted class. The coverage it preserves is carried by cross-module participation P, which cannot be computed without a community partition; EDVS matches P-level coverage on all five networks using none, and retains 0.84 of its selection under edge perturbation that leaves partition-based selections at 0.21-0.46. The deficit is general: the collapse holds in the same direction on the two networks built without functional annotation (0.5-1.1 bit; co-expression, physical interaction) as on the three supervised by it (1.6-3.3 bit; RiceNet, AraNet, STRING), so supervision amplifies it rather than creates it. The remedy is bounded: EDVS ceases to preserve coverage on the sparse physical-interaction network. And the class EDVS isolates is organizational, not an importance signal: pre-registered probes--essentiality, transcription-factor identity, tissue-specificity, date/party-hub character, phenotype co-localisation--return null or reversed throughout. The conclusive ones are equivalent to their degree-matched nulls within {+/-}5 percentage points (demonstrated, not merely undetected), and the classical coupling of centrality to importance itself holds only network-dependently. Author SummaryGenes rarely act alone: many diseases and agricultural traits are shaped by genes that coordinate several biological processes rather than specialising in one. The standard way to find such genes in a network of gene interactions is to count each genes connections--its "centrality"--and rank genes by that count. We show this standard approach has a blind spot: it favours genes that dominate one process over genes that quietly bridge several processes without dominating any, and this blind spot appears across rice, thale cress, and yeast gene networks. We repurpose a diversity measure from an unrelated field (originally used to compare citation patterns) as a new way to rank genes that finds these bridging genes from network structure alone, without needing gene-function annotations--which are themselves incomplete and biased toward well-studied genes--or a prior, unstable step of splitting the network into modules. We are careful to show where the new approach also falls short: on sparse, noisy networks it stops working, and the genes it recovers are not shown to be more biologically important than other genes, only differently positioned. What that position is for is a question this work leaves open.

Antibodies generally exhibit high specificity for their cognate epitopes, but structural and physicochemical similarities between distinct epitopes can enable an antibody to recognize different antigens, resulting in cross-reactivity. This property can be exploited for antibody repurposing. To identify epitopes that share such similarities, both sequence- and structure-based approaches can be employed. In this context, 3D Zernike descriptors provide a compact representation of protein surface geometry as numerical feature vectors, enabling quantitative comparisons independently of structural alignment and orientation. Thus, this study aimed to evaluate the application of 3D Zernike descriptors for the structural clustering of antibodies and epitopes and to explore their use in antibody repurposing for the recognition of new targets. To this end, antibody binding sites previously associated with recognition of similar epitopes were analyzed at different structural levels, considering the CDRs, CDRH3, and complete paratopes. Surface similarity was subsequently quantified by calculating the Euclidean distance between their corresponding 3D Zernike feature vectors. Performance was benchmarked against SPACE2. Additionally, different distance thresholds were evaluated based on their ability to recover antibody pairs recognizing the same epitope. The paratope-based approach provided the best balance between the number of identified pairs and precision at a distance threshold of 2.7, whereas epitope clustering showed robust performance up to a distance of 3.0. At these thresholds, the 3D Zernike descriptors identified a greater number of functional pairs than SPACE2 while maintaining comparable precision and identifying complementary sets of antibody pairs.. BTaken together, these findings support the use of 3D Zernike descriptors for structural clustering of antibodies and epitopes and for guiding antibody repurposing G, a highly lethal zoonotic pathogen. Structural screening identified three antibodies with epitopes similar to the NiV target that also showed a consistent binding preference for the target epitope in molecular docking assays. Notably, one candidate, originally directed against a SARS-CoV-2 epitope, formed a stable complex with the NiV epitope, remaining within the 5 [A] RMSD threshold during heated molecular dynamics simulations and emerging as a potential cross-reactive candidate.These results support the use of this computational framework for biopharmaceutical discovery against emerging targets. Taken together, these findings support the use of 3D Zernike descriptors for structural clustering of antibodies and epitopes and for guiding antibody repurposing.

Light history leaves persistent changes in circadian period, but where this history is stored remains unresolved. Suprachiasmatic nucleus (SCN) network models have often approached photoperiodic encoding through phase organization or coupling strength. We computationally tested slow adaptation of subregion-specific intrinsic periods as an alternative memory mechanism. The model asymmetrically couples dorsal (D) and ventral (V) SCN oscillators and adds a systemic oscillator (X) representing putative circadian feedback present in vivo but lost ex vivo. With a single parameter set, period adaptation captured the direction and approximate magnitude of behavioral aftereffects across photoperiod and T-cycle conditions. Adapting coupling strength instead of period failed to reproduce the V-leading-D phase order reported after T22. Removing systemic feedback preserved the photoperiod-dependent period ordering but inverted the T22 and T26 aftereffects, an inversion that matched SCN explant observations. The model also yielded distinct D-V phase organization for each of 18:6 LD, T23, and T25. These results suggest that subregion-specific period plasticity provides a parsimonious substrate for encoding light history, while the dependence on systemic feedback indicates that behavioral period may not be a readout of the SCN alone. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=160 SRC="FIGDIR/small/743784v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@d48266org.highwire.dtl.DTLVardef@1bd15bdorg.highwire.dtl.DTLVardef@de3d9forg.highwire.dtl.DTLVardef@9fbc39_HPS_FORMAT_FIGEXP M_FIG C_FIG A model with dorsal period adaptation and phenomenological systemic feedback accounts for behavioral and explanted SCN aftereffects. (A) During T22 entrainment, dorsal (D), ventral (V), and systemic (X) oscillators remain phase-locked. After release into constant darkness, systemic coupling maintains a unified in vivo rhythm, whereas removing X feedback in the explant simulation allows the D-V network to express a distinct ex vivo period aftereffect. (B) The SCN model is modeled as an asymmetrically coupled attractive-repulsive oscillator network with stronger photic input to V. Light history is encoded via plasticity of the intrinsic period in D, while X represents putative systemic circadian feedback available in vivo and lacking direct photic input. (C) The model reproduces concordant period changes in behavior and SCN explants across photoperiods, but opposing period changes following T-cycle entrainment.

Blue light using Flavin (BLUF) proteins are microbial photoreceptors that are involved in various physiological responses. Their occurrence and biochemical properties in fungi remain poorly understood. Here, we investigated a putative BLUF photoreceptor from the corn-smut fungus Mycosarcoma maydis (MmBLUF). Domain analysis, multiple sequence alignment of BLUF core regions, and structural modelling indicated conserved canonical BLUF fold and flavin-pocket residues. However, when heterologously expressed, UV-visible and fluorescence spectroscopy revealed different spectral behaviour than canonical BLUF protein. Further, we tested the role of extended N-terminus in modulation of chromophore binding by expressing N-terminus truncated protein variants. Our results suggest that the unusual spectral behaviour is not linked to the truncation construct (extended N-terminus), which also showed similar spectral features, indicating that the extended N-terminus is unlikely to account for an unusual photodynamics characteristics. Our findings support MmBLUF as a structurally conserved putative fungal BLUF-like photoreceptor with different photochemical properties. Further studies are required to establish its chromophore identity, photocycle and function of this unusual BLUF-like domain from fungal system.

How morphological complexity evolves is still enigmatic. While there is evidence in algae and plants as well as animals that diversification of the repertoire of transcription factors (TF) is causative for evolution of organismal complexity, there are many examples from lineages that follow their own way of complexity evolution, for example by expansion of particular families. For land plants, correlation of the size of the TF complement with number of cell types (as a proxy for morphological complexity) has been shown, and several families were identified as candidates to drive complexity evolution. Here, we expand a previously available dataset of cell type numbers from 12 to 82 proteomes and introduce a four class body plan scheme. We find that the total TF complement correlates with the number of cell types of Archaeplastida (primary plastid bearing plants and algae). We used TabPFN (Tabular Prior-data Fitted Network) for binary (uni- vs. multicellularity) as well as for four class Bauplan classification. TabPFN is able to predict the morphological complexity with high accuracy. This approach allows to determine organismal complexity based on the gene space of an organism. Based on our results, we can confirm that plant morphological evolution is driven by gain and expansion of TF families.

Tumors consist of heterogeneous phenotypic cells, such as normoxic cells, which are highly proliferative, and hypoxic cells, which are less proliferative. Their phenotypic switching depends on tumor microenvironmental factors, such as oxygen and nutrient concentrations supplied by local blood vessels. However, during ongoing angiogenesis, the process of sprouting new blood vessels at the tumor site from pre-existing blood vessels, and how this phenotypic switching affects and impacts tumor growth, remains poorly understood. In this article, we formulate a mathematical model to elucidate the crosstalk between vasculature and tumor cellular heterogeneity during tumor progression. The model results show a strong agreement with the experimental data. Our simulation results demonstrate that ongoing angiogenesis increases tumor growth rate. In addition, we observe that the influence of hypoxic cells on phenotypic switching from normoxic to hypoxic is more pronounced than their influence on the transition from hypoxic to normoxic. Furthermore, we perform a global sensitivity analysis using the Sobol's method to assess the importance of the model's parameters. It highlights that the volume at which blood vessels attain half-maximal rate has the maximum effect on the model.

In the active loop extrusion model, the cohesin protein complex creates chromatin loops in eukaryotic cells. Extrusion maintains topologically associated domains (TADs), which are contiguous segments of chromatin that preferentially colocalize in space and are typically bounded by CTCF proteins that pause cohesin translocation. Here, we model active loop extrusion with hybrid molecular dynamics - Monte Carlo simulations in entangled flexible linear polymer melts. Intra-chain contact probabilities of polymers with active loop extrusion are enhanced compared to their equilibrium, passive counterparts. Extrusion causes the size of chain segments to be much smaller than in passive melts. While the overlap parameter in passive melts without extrusion monotonically increases with segment length, it is nonmonotonic in active melts and on the order of unity within the parameters of this study. Active loop extrusion suppresses contacts between TADs in favor of intra-TAD contacts. Reduction of overlaps between chain segments dilutes entanglements in active melts. Depending on parameters, active extrusion without TADs may induce more compact conformations than with TADs, due in part to fractal loopy globule-like dynamics. This work suggests that active loop extrusion reduces overlaps between TADs, contributing to effective gene regulation by cis-regulatory elements.

Intertidal mudflats are low hydrodynamic energy environments hosting microphytobenthic communities that experience strong spatiotemporal variability in light regimes, including changes in spectral quality and light intensity that can lead to cellular photooxidative stress. To cope with these fluctuations, autotrophs exhibit diverse and highly plastic adaptations that are often species-dependent and shaped by their ecological niches. This study investigates photophysiological responses and metabolic remodeling in a diatom assemblage originating from a natural winter microphytobenthic biofilm under contrasting red and blue light intensities. To this end, photosynthetic parameters were monitored alongside changes in lipophilic metabolites, including untargeted lipids and lipophilic pigments. While few metabolites showed temporal remodeling, rapid and contrasting changes were observed within 30 minutes in response to both spectral quality and light intensity. Red light treatments induced broader remodeling of lipophilic metabolites than blue light, whereas blue light appeared to have a greater impact on photosynthetic parameters. Moreover, red light induced xanthophyll-cycle responses comparable to those observed under blue light at equivalent incident intensity. We discuss these metabolic responses in relation to diatom photoadaptive strategies, placing these findings within the intertidal environmental framework. This work further underlines the importance of understanding rapid metabolic plasticity in coping with light fluctuations, providing new insights into the photoregulatory strategies of natural microphytobenthic communities.

IIt has been suggested that courtship signals reflect a potential mate's learning ability or nervous system competence. However, there is no rigorous theory that explains how features of sexual signals represent a nervous system's "quality". Such a theory may provide a mechanism for mate assessment via sexual signals and offer an explanation for why courtship signals are rhythmic and stereotypic. In our paper, first we use a general model of optimal neural decision-making to show that variance in an organism's solution time for a given fitness problem lowers the fitness gain rate; more specifically, in well-supported "competing accumulator" models of decision making, we show that noise in the slope of spike rate increase in evidence accumulators increases both reaction time and the probability of a sub-optimal decision. In conclusion, higher timing regularity leads to quicker and better decisions. This finding accords with extensive human study data showing that variance in reaction times is negatively associated with various measures of motor and cognitive performance. Thus, selection should favor individuals that require potential mates to advertise courtship signal regularity to indicate their nervous system's general timing consistency (the timing-consistency signaling theory). The focus on signal consistency (rather than on signal duration or power) may account for why courtship signals are typically rhythmic, are often multi-modal, and why rhythmic signals are also employed in territorial contests. One of the model's several predictions is that individuals should favor potential mates with lower noise in courtship signal features such as inter-pulse intervals.

Spatial transcriptomics keeps measurement of gene expression while preserving spatial context, yet traditional analysis methods face challenges in computational efficiency, biological interpretability, and autonomous discovery. This project presents a framework solving these issues through three parts: (1) an ensemble clustering system achieving 66.7% improvement over baseline average and 23.9% over best single method with silhouette score of 0.540 and statistical significance (p = 0.0032, Cohens d = 1.82); (2) a knowledge-based clustering framework that annotates 88.6% of cells across 8 ovarian cell types using 428 marker genes; and (3) a GPT-4o-mini-powered autonomous agent that generated 3 biological hypotheses with validations.

Active teaching methodologies have been widely used to promote meaningful learning and student autonomy. In this context, quantitative approaches can help assess how students organize and integrate knowledge throughout the learning process. Among these approaches, semantic co-occurrence networks stand out, as they are capable of identifying relationships between words and revealing the conceptual structure of textual productions. The objective of this study was to investigate whether semantic network analyses can characterize differences in students conceptual organization in Microbiology during their participation in the active teaching methodology "Adopt a Bacterium." To this end, a case study was conducted in the Bacteriology course at the Institute of Biomedical Sciences of the University of Sao Paulo, analyzing the textual productions of two groups of students in the years 2024 and 2025 during their study of the bacterial genus Bacillus. The texts were evaluated using semantic co-occurrence networks, taking into account metrics of structure and conceptual integration. The results showed that both groups covered the microbiological content outlined in the course, though with different thematic focuses and approaches to integrating the concepts. Although both years featured modular structures (a statistical mode of 9 subgraphs), in 2025 the network exhibited greater discursive robustness (2 to 4 times more words with high Betweenness centrality) than in 2024. It is concluded that semantic network analysis allows for the characterization of differences in conceptual organization among students using active learning methodologies, serving as a complementary tool for assessing meaningful learning in Microbiology.

Recent advances in AI co-scientists have brought LLM agents into closed-loop experimental design. However, whether these agents use feedback from earlier rounds to revise subsequent experimental decisions remains unclear. We address this question with PerturbTrace, which evaluates each round-to-round transition through Feedback-to-State, State-to-Action, and Action-to-Outcome. These stages assess whether feedback is reflected in the agent's rationale and perturbation-selection strategy, whether the stated strategy guides the next perturbation batch, and whether that batch yields more hits than expected under random sampling. We evaluate four LLM agents on 17 screen-derived tasks and compare them with random selection, active learning, and LLM-guided Bayesian optimization baselines. Each agent outperforms the strongest non-agent method on at least 15 of the 17 tasks, yet controlled evaluations across six tasks show no consistent advantage from true feedback over random or no feedback. Among 576 transitions under true or random feedback, only 43 (7.5%) complete the full Feedback-State-Action-Outcome sequence, including 25 under random feedback. These findings show that high final recall does not necessarily indicate effective feedback use. They also highlight the need to evaluate closed-loop scientific agents by both their discovery performance and whether feedback changes their subsequent decisions.