Biosystems

When three cyanobacterial proteins--KaiA, KaiB, and KaiC--are incubated with ATP in vitro, the phosphorylation level of KaiC exhibits stable circadian oscillations. Biochemical and structural analyses have shown that KaiCs ATPase activity is crucial for these oscillations, leading to the hypothesis that ATP-consuming dynamics function as a molecular clock, determining the oscillation period of individual molecules. Moreover, these molecular clocks synchronize with one another, resulting in collective oscillations at the ensemble level. In this study, we develop a theoretical model to test this molecular clockwork hypothesis. Our model clarifies the relationship between the oscillation period and ATPase activity, explaining the significant changes in the period induced by amino-acid substitutions near the CI-CII domain boundary of the KaiC hexamer. Furthermore, the model addresses the physical basis for temperature compensation concerning both the oscillation period and ATPase activity. Thus, the molecular clockwork perspective provides a framework for understanding the atomic design behind collective oscillations.

Cells adjust their internal circuits in response to changes in their environment. Hence, exposing cells to changing conditions provides a way to probe the intrinsic dynamics of cellular internal circuits. Metabolic networks are examples of such circuits since metabolic fluxes dynamically adjust when environmental conditions are transiently altered. Most existing theoretical frameworks focus on cellular metabolic steady states and do not consider the dynamics of changes in metabolic fluxes. In this work, we applied transfer function analysis from control theory to analyze the changes of NADH oxidative fluxes in the mitochondria and cytoplasm in mouse oocytes in response to dynamical perturbations of oxygen depletion and recovery. We observed an overshoot of NADH oxidative flux in the cytoplasm upon oxygen recovery which is absent in the mitochondrial NADH oxidative flux. Metabolic perturbation experiments and transfer function analysis indicate that this cytoplasmic NADH overshoot results from the coupling of the mitochondrial and cytoplasmic NADH cycles. The degree of overshoot is determined by competing timescales associated with the exchange rates of lactate and pyruvate with the media and their interconversion rates catalyzed by lactate dehydrogenase. Applying control theory to the data enables the inference of the exchange and conversion rates of pyruvate and lactate, allowing predictions of the contribution of lactate to mitochondrial respiration. Our work indicates that the oocytes maintain a homeostatic respiration rate across nutrient conditions by modulating the contribution of lactate to mitochondrial respiration.

KaiC, a clock protein in cyanobacteria, cycles between dephosphorylated and phosphorylated states in a 24-hour period in the presence of KaiA and KaiB. We identified the 322nd residue of KaiC as a third example of period-tuning sites. 322nd-site-directed saturation mutagenesis resulted in a variety of KaiC mutants exhibiting either shortened or lengthened cycles. The tunable range of the periods was from approximately 11 to 78 h without significantly compromising temperature compensation. We conducted biochemical analyses of the 322nd variants and examined their predicted structural models. In contrast to another known period-tuning site, where the period decreases sharply as the side-chain volume increases due to mutations, the cycle lengths correlate only modestly with bulkiness at the 322nd residues. The 322nd residue is located in a C-terminal domain of KaiC and influences ATPase cycles in both the C-terminal domain and an N-terminal domain through its interaction with a flexible loop connecting the two domains. The structural models predict that placing less bulky but polar side chains, such as serine and threonine, at the 322nd position leads to the formation of a hydrogen-bonding network between that site and the loop. This reduces the mobility of the loop, resulting in the longer cycles due to decreases in the ATPase activity of the N-terminal domain. Conversely, placing bulky residues such as phenylalanine at the 322nd position appears to alter the loop structure, shortening the periods by enhancing the ATP activities of both the domains. The third period-tuning mechanism is distinct from other known mechanisms. Significance StatementA Kai-protein clock system serves as a model for studying how long circadian rhythms are achieved. We identified the 322nd residue of KaiC as a third example of period-tuning sites that allow tuning of the period in either long- and short-period directions. The third period-tuning mechanism differs from the two previously known types in several respects. Previous studies have suggested that the ATPase activity in an N-terminal domain of KaiC is the primary regulator of the period. On the other hand, the 322nd residues of KaiC can affect the period by activating the ATPase cycle in its C-terminal domain. Our findings will stimulate future studies on the period-tuning mechanism mediated by the ATPase activity in the C-terminal domain of KaiC.

AO_SCPLOWBSTRACTC_SCPLOWCell fate decisions are driven by gene regulatory networks (GRNs). While the mutually inhibitory toggle switch effectively models binary fate decisions, fully connected inhibitory networks with more than two nodes fail to capture multi-fate decisions due to the low prevalence of "single high states", where only a single master regulator is highly expressed. The goal of this study is to find network structures that support all single high states. We find that the only network that attains the highest possible prevalence of all single high states within the set of monotone Boolean (MB) models is completely disconnected. Since biological networks typically require connectivity, we investigate network structures that support equipotency, where all single high states have equal prevalence within MB models. Finally, we characterize the networks that support multistability between all single high states, finding that it is possible only in networks in which each node either has self-activations or is inhibited by every other network node. Our findings provide a theoretical framework for understanding the network design principles that can support simultaneous differentiation into multiple distinct cell types.

Numerous advances in quantum and computational chemistry over the last decades, well as the development of computer science, allowed utilisation of more precise and complex models, which can be now applied to much bigger systems than in the past. The authors used Gaussian, coupled with theoretical methods, to predict a new way of peptide bond formation, which could have taken place in prebiotic conditions. To better tackle this difficult task, the properties of substrates (glycine-derived radicals) were extensively analysed, using the aforementioned tool - Gaussian, paired with taking resonance and hybridisation into account, to better understand the stereochemistry and the very nature of processes taking place. The result is a series of reactions, which without any sophisticated catalysts and with relatively low energy thresholds ({inverted exclamation}20 kcal/mol) can lead to formation of dipeptides (and further, oligopeptides). The authors also hope, the other predicted properties of the investigated molecules can be of use to any researcher, who would like to utilise them in their experiments. Author summaryOur goal was to investigate a way first peptide bonds in prebiotic conditions could have been formed. This is an extremely important step in research into the beginning of life on Earth. We found a very promising series of reactions, which uses atomic hydrogen as its only catalyst and confirmed our expectations with theoretical calculations, using Gaussian. There are two radicals derived from glycine, which perform major roles in the process, so we investigated their properties with Gaussian and verified that the results are in agreement with our own theoretical considerations. This involved checking for possible geometric isomers and conformers and creating models which could explain their properties. We are well aware that such calculations have limitations and there is no model, which is 100% accurate, so our results should be further confirmed by empirical data in the future. However, we still to be as thorough as possible in how we approached the subject.

A hexameric clock protein KaiC exhibits a 24-hour phosphorylation cycle with a unique property termed temperature compensation. The period is kept constant over physiological temperatures through compensatory coordination of underlying elementary reactions. The temperature-compensated ATPase activity of KaiC is one such key reactions that potentially contribute to maintaining a constant circadian period. We identified four amino acid residues responsible for the temperature compensation in an N-terminal ATPase domain of KaiC. D82 and K172 were located in a primary site, and the ATPase activity of each alanine mutant showed a positive correlation with rising temperature. N62 and E69 constituted a secondary site, where each alanine replacement resulted in a negative correlation with the temperature. The primary site exerts a compensatory regulation over the ATPase cycle locally within the N-terminal domain. The secondary site prevents the ATPase activity from becoming over-compensated by suppressing another compensatory regulation mediated through a non-local interaction with a C-terminal domain of KaiC. Therefore, any imbalance between the local and non-local compensatory regulations in KaiC affects the temperature dependence of its phosphorylation rhythm.

1Endogenous circadian clocks control circadian rhythms in physiology and behavior. The predominant hypothesis of biological timing suggests that the responsible master clock for all endogenous circadian rhythms is constituted by an evolutionary conserved transcriptional-translational feedback loop (TTFL) clock consisting of positive feedforward and negative feedback elements. Unexpectedly, in contrast to the evolutionary derived insect Drosophila, RNAi-dependent knockdown of any of the negative feedback elements of the core TTFL clock in the basal Madeira cockroach Rhyparobia maderae does not delete circadian rhythms in locomotor activity. Shown here, neither RNAi-dependent triple knockdowns of all three negative feedback elements Period, Timeless 1, and Cryptochrome 2, nor single and double knockdown of the positive elements Clock and Cycle did directly delete circadian locomotor rhythms as mRNA levels declined. Thus, our experimental data do not support the predominant hierarchical hypothesis of circadian timing. To explore alternative mechanisms, we constructed a computational model of a neuronal circadian pacemaker network using planar switching affine systems (PSAS). The PSAS model comprises plasma membrane-associated posttranslational feedback loop (PTFL) clocks that are coupled to the TTFL nuclear clocks. Modeling results aligned with our experimental results. Therefore, both our experimental and modeling data support a systemic hypothesis of biological timing. 3 Significance statementBased mostly upon genetic studies in derived taxa like Drosophila it is hypothesized that circadian timing of behavior is strictly controlled by specific circadian clock neurons in the brain, realized through a transcriptional-translational feedback loop (TTFL) clock. In contrast to this common hierarchical model that requires transcription, we provide evidence in a basal taxon - the Madeira cockroach - for a systemic explanation of circadian timing of behavior that is based on coupled TTFL and posttranslational feedback loop (PTFL) clocks in adaptive neuronal networks.

1.Partial Rank Correlation Coefficient (PRCC), usually performed following Latin Hyper-cube Sampling (LHS), is a global sensitivity analysis that quantifies the monotonic relationship between model parameters and the desired output. To carry out this analysis, a range of acceptable parameter values must be known or estimated. However, within a biological context, approximating these values may be difficult. Parameter values and ranges can be taken from different organisms or systems or be estimated to produce qualitative phenomena in the model. Using a mathematical model of the epithelial mesenchymal transition (EMT) as a test case, this work examines how the parameter ranges chosen prior to analysis can influence LHS-PRCC results and shape subsequent analysis interpretations. Previous LHS-PRCC analysis of this model restricted parameters to {+/-}10% of their original value, which limits the scope and interpretability of parameter influence. Such a small range assumes, in the biological sense, that parameters are well-measured with little variability. Here, this work extends the previous analysis and explores several parameter ranges ({+/-}25%, {+/-}50% of the original value). This work also tests whether, within the {+/-}10%, {+/-}25% and {+/-}50% parameter ranges, the bistable switch present in the original model are maintained. Ultimately, this work showcases how a choice made prior to analysis, such as the accepted parameter ranges for biological rates and values in complex dynamical systems can influence sensitivity analysis results and interpretability. Additionally, these choices can have hidden consequences, such as the loss of phenomenological behavior. Thus, explicit prior knowledge about the appropriate parameter values is needed before using analysis to guide future experiments and model development.

Predicting protein-ligand interactions in the modern drug discovery has revolved from the involvement of artificial intelligence and structural bioinformatics using Graph Neural Networks (GNNs). The limited explainability of GNN models presents an important encumbrance in biomedical research, but it has achieved a high degree of accuracy in determining and identifying binding affinity and active compounds, as evidenced by [1] [2] [3] [4]. Here this research focuses on the interpretation of protein-ligand interactions at a molecular level, a rapidly developing area within Graph Neural Networks (GNNs). Now days modern study handling techniques such as visualization techniques, attention mechanism and model-based feature ascription by model to boost, and make robust and decrease false predictions on binding. Along with some approaches include like graph pooling strategies, message-passing optimization, self-supervised learning, transfer learning and contrastive learning are rapidly utilized to enhance the representative learnings. Furthermore, integration of molecular docking simulations, hybrid deep learning architectures and protein language model gives more reliable & biological predictions of protein-ligand interactions. That focuses on given process that identifies key ligand atoms and binding residues, as well as physicochemical factors influencing affinity, through chemical thought processes. Here this research work identified the challenges of developing biologically significant explanations, transparency, and the corollary dataset biases on interpretability. The research work conducted an in-depth investigation into the consolidation of protein language models to establish more reliable pathways for future research, examining hybrid architectures, transparent and energy-efficient GNNs, and scientifically grounded AI models for drug discovery. My research work highlights that XGNNs establishes a connection between Deep Learning and Biochemical expertise with increased confidence, which will enhance the accuracy of predictive models and computational models.

AnnotationMitochondrial heteroplasmy --the coexistence of both wild-type and mutant copies of mitochondrial DNA (mtDNA) within a cell--is a key factor in the pathogenesis of mitochondrial diseases. Classical approaches, which rely solely on the scalar fraction of mutant DNA, fail to fully account for threshold effects, the stochastic nature of heteroplasmy dynamics, and tissue specificity. The aim of the work is to construct a complex stochastic model of heteroplasmy dynamics, which for the first time combines the effects of selection, genetic drift, migration of mitochondrial genomes between tissues and threshold mechanisms of pathology development, for a quantitative assessment of the risk of mitochondrial diseases. In this paper, we propose a complex-phase formalism in which the state of a cells mitochondrial genome is described by a complex number Z = a + ib, where a and b are the absolute numbers of normal and mutant mtDNA copies, respectively. This approach naturally combines information on copy number and heteroplasmy level, and the argument{phi} = arctan (b / a) is interpreted as a phase characterizing the mutant load. Based on this formalism, we developed a stochastic model of tissue dynamics that includes the processes of selection, genetic drift, and intertissue migration of mitochondrial genomes. Using Monte Carlo methods (1000 simulations), we demonstrated that neuronal tissues are characterized by high heteroplasmy variability and a significant probability of reaching a pathological threshold even with a relatively low systemic mutant load. Kaplan-Meier survival analysis demonstrates that the development of pathology is probabilistic and can be described as a time -to-event process . The proposed approach enables quantitative assessment of the individual risk of developing mitochondrial diseases and opens the door to personalized prognosis.

Protein homeostasis - i.e., proteostasis - is the biological process by which the qualitative and quantitative balance of the proteome is conducted, either by preserving functionally relevant proteins or by degrading unnecessary ones. Stress conditions can modulate cellular proteostasis in order to promote cytoprotection and preserve the viability of living organisms. Among the cellular pathways already described that can play an important role in preserving biological functions by modulating proteostasis are the heat shock response and the ribosome quality control pathways. In this work, we show that the Rqc1p protein is necessary for the thermoadaptation of S. cerevisiae to heat shock, as RQC1-deficient yeast is sensitive to elevated temperatures. In silico approaches - such as multiple sequence alignment, structural analysis, and molecular dynamics simulations - confirmed earlier predictions that Rqc1p shares characteristics with the bHLH family of proteins. We also verified, through computational prediction of sub-cellular localization, that S. cerevisiae Rqc1p contains nuclear localization signals, suggesting that this protein can potentially be translocated toward the nucleus, thereby broadening its current range of recognized biological functions in this organism. Also, analysis of yeast transcriptomes subjected to heat shock showed that Rqc1p mRNA levels do not fluctuate in response to heat shock, suggesting that cellular concentrations of Rqc1p are already at optimal levels to elicit a rapid and effective response during thermal stress in S. cerevisiae.

Small molecule modifiers whence bound, allosterically, will alter the binding of a macromolecule to one- or more-cognate substrates/partners via conformational and non-conformational changes. Although allostery is inferred directly from empirical data, the mathematical basis of these models, constraints deployed and choice of parameter(s) are not clear. Here, we present and characterize a discrete-to-continuous mathematical model for ensemble distributions of a ligand-interacting macromolecular species across milieux-dependent conformational states and examine its role in the genesis and progression of cooperative binding. The premise, of our model, is a set of occupancy matrices (sparse, binary, strictly delocalized) which can be partitioned by a probability-based hyperparameter into mutually exclusive proper subsets of occupancy matrices with identical multinomial probabilities. Since each subset is canonical with a constituent occupancy matrix, it is characterized by a unique multinomial probability. The inner product of combinatorial pairs of all mutually exclusive subsets of occupancy matrices, with an expression for the summed transitional probabilities (finite differences between unique multinomial probabilities), is the differentiable matrix of strictly positive real-valued numbers for the system of ensemble distributions. Whilst the harmonic mean is presented as a generic solution for a system of ensemble distributions, the row-wise definite integral for each column is the finite union of open intervals (contiguous, strictly monotone) which in tandem with a set of interval-specific and bounded transitional probabilities constitutes a piecewise smooth curve (path-connected-, closed- and compact-set). Our discrete-to-continuous model is phenomenological and able to recapitulate the basic tenets of cooperative binding whilst offering insights into the genesis and progression of the same.

Integrins are adhesion proteins that diffuse along the cell membrane, bind to ligands, and cluster with each other in the early stage of cell adhesion. Integrin clustering and its specific spatial distribution play important roles in subsequent biological processes; however, the mechanisms that give rise to their characteristic spatial distribution remain poorly understood. To address this issue, we developed a cell adhesion model that incorporates cell membrane deformation and integrin dynamics. A hybrid continuous/discrete model was applied to represent membrane deformation, whereas Brownian dynamics combined with a transition state model was used to describe integrin dynamics and binding kinetics. Comparison of numerical simulations of cell adhesion to a substrate with experimental observations at the early stage of adhesion successfully reproduced the characteristic spatial distribution of integrin clusters, in which high-density clusters formed at the periphery of the region adhering to the substrate. These results suggest that the cellular-scale distribution of integrin clusters can be reproduced using only minimal elements, such as adhesion-driven membrane deformation and integrin-ligand binding. In addition, we found that the strength of integrin-ligand binding regulates the degree of clustering by changing the size of the part of the membrane that is deformed, thereby mechanically supporting the mechanical involvement of the actin cytoskeleton in integrin clustering. Furthermore, the formation and spatial distribution of integrin clusters were shown to be determined not only by the static mechanical equilibrium of membrane deformation and physical adsorption, but also by membrane spreading/deformation and the dynamic behavior of integrins. This suggests that the size and spatial distribution of integrin clusters may be controllable by modulating the speed of membrane spreading.

Progress in quantum computation offers new opportunities for addressing longstanding combinatorial challenges. One such challenge is the Eternity II edge-matching puzzle, consisting of 256 tiles, which has resisted solution despite extensive community effort. The computational complexity of this NP-complete problem exceeds the capacity of current quantum annealing processors but lies within reach of hybrid quantum-classical solvers. Testing a quadratic unconstrained binary optimization (QUBO) model of a puzzle on a D-Wave hybrid solver demonstrates a complete solution only for puzzle instances up to 64 tiles. Simulated quantum annealing fails on this benchmark, whereas an original classical heuristic, "nucleation with deduction", succeeds. To approach the full Eternity II puzzle, I developed a MATLAB package that integrates multiple quantum and classical approaches, including neural-network transformers and gradient-based refinement. A multistage computation pipeline is demonstrated successfully on a puzzle comparable in complexity to Eternity II and with a known solution, based on multiple hybrid optimization steps with both "hard" and "soft" constraint formulations, identification of persistent substructures, and a final classical refinement stage. The resulting optimization problem involves [~]100,000 logical variables and requires partial initialization. Intriguingly, solving this puzzle mirrors the "end game" of protein folding, a process that nature completes in mere fractions of a second, seemingly defying expectations set by the Levinthal paradox. The prospect of predicting protein structure by quantum annealing is reviewed in light of these results.

The development and physiology of neurons rely on their microtubule organization, which is characterized by plus-end-out oriented microtubules in the axon and a mix of plus-end-out and plus-end-in oriented microtubules in dendrites. This orientational pattern is established early in neuronal development and is tightly linked to axon-dendrite differentiation. Even though multiple potentially relevant mechanisms have been proposed, fundamental questions remain: How does the microtubule organization in neurons emerge, and how does a neuron develop a single axon and multiple dendrites? Here, we address these questions at two distinct, complementary levels: at a higher level by proposing a conceptual framework, in which we classify mechanisms into three categories based on how they contribute to the microtubule organization: orientational bias, parallel amplification, and polarization; at a lower level we build a biophysical model that incorporates multiple mechanisms of microtubule dynamics in a neuron, from which, using analytical calculations and simulations, we derive insights into the emergence of microtubule organization in developing neurons. We show that geometrical effects alone can confer a bias in microtubule orientation. Parallel amplification then enhances the resulting polarity. Coupling multiple neurites to a common cell body that serves as a shared reservoir of resources allows for a polarization mechanism that ensures that the microtubule organization of one neurite becomes axonal while all others are dendritic. This framework unifies diverse molecular observations and yields experimentally testable predictions about microtubule self-organization in early neuronal development. Author summaryNeurons communicate through long protrusions called neurites, which are of two types: dendrites, which receive signals, and axons, which send signals. Their development relies primarily on microtubules, which are polar filaments with two distinct ends, known as the plus and minus ends. Microtubules self-organize into functional architectures that are significantly different between axons and dendrites. In axons, all microtubules point their plus end away from the cell body, whereas in dendrites, they point either towards the cell body or have mixed orientations depending on the species. This orientation guides intracellular transport by motors and is closely linked to whether a neurite develops into an axon or a dendrite. Despite decades of research identifying individual mechanisms, the bigger picture behind the emergence of microtubule orientation in neurons remains unclear. Here, we construct a conceptual framework and a biophysical model to identify the principles underlying the emergence of microtubule orientation in developing neurons. Our conceptual framework provides a high-level perspective on how individual mechanisms influence microtubule organization in neurites. In our concrete biophysical model, we study a selection of mechanisms to gain specific, quantitative insight into the organizational process. We propose a minimal model of a neuron that exhibits neuronal polarization, giving rise to a single axon-like neurite and multiple dendrite-like ones, consistent with experimental observations. This in silico neuron helps to explain how neurons break symmetry during development and provides a systematic way to generate and test new hypotheses about neuronal polarity.

The light absorption of the protein cryptochrome and its chromophore FAD is important for the regulation of circadian rhythms and in some species for sensing magnetic fields. To compute the absorption spectrum of chromophore, typically only a small region is treated quantum-mechanically due the high computational cost of spectroscopic calculations. We present a formalism that allows a quantum-mechanical treatment of not only the chromophore but also the neighbouring amino acids which differ from species to species. This is achieved by using the real-time time-dependent Hartree-Fock method. This method allows extending the quantum domain from typically only a few dozen atoms up to around 1,200 atoms for the largest calculations. The presented framework allows the treatment of neighbouring tryptophan residues or the cofactor molecule MTHF in the same calculation and allows to extract information of which regions absorb light depending on wavelength. The presented results also show that the environment around the chromophore FAD amplifies the light absorption in cryptochrome.

Cancer, one of the worlds leading causes of death, is characterized by a complex metabolic reprogramming that features the Warburg effect as one of its hallmarks. The Warburg effect involves increased glucose and amino acid metabolism, which promotes tumor proliferation and progression. Although cancer has historically been attributed to genetic mutations, recent studies suggest a possible metabolic origin. However, a key characteristic of cancer cells is their greater adaptability than normal cells, as evidenced by their resistance to chemotherapy, which stems from their high mutability. This underscores the need to examine the relationship between metabolic reprogramming and cancer development from both metabolic and evolutionary perspectives. In this context, Saccharomyces cerevisiae snf1{Delta} strain has emerged as an ideal cellular model for studying the Warburg effect. This study aimed to determine whether deletion of the SNF1 gene in S. cerevisiae affects its chronological aging and competitiveness in a glucose and amino acid-dependent manner. Herein, we provide evidence that the snf1{Delta} strain changes the chronological aging depending on nutrimental condition, under low-nutrient levels shortens (0.1% glucose + 0.1x amino acids), and increases under high-nutrient levels (5% glucose + 3x amino acids). Competitiveness of the snf1{Delta} strain in co-cultivation with wild-type was also improved in 5% glucose + 3x amino acids, by approximately 2 Log10. These results indicate that snf1{Delta} strain aging and competitiveness are also sensitive to nutrimental status, as was observed in cancer cells.

In this study, resource-constrained learning methods were developed as a model for the learning behavior of the fly brain, specifically the mushroom body. Recent research on the mushroom bodies of flies shows that unfamiliar odors activate certain output neurons (MBONs); however, these effects are rapidly suppressed upon repeated exposure to the same odor. Such MBON behaviors appear to reflect odor learning. We investigated how flies continue learning about odors throughout their lives despite their small brains. Researchers have suggested that learning about new odors can help flies forget existing memories. Therefore, we hypothesized that the main reason for continual learning is that it serves as a strategy for forgetting. To test the validity of this hypothesis, we designed three models using a kernel perceptron. This approach is suitable for estimating ongoing learning capacity within a budget. According to the results of computer simulations and theoretical analysis, the model demonstrated the importance of forgetting mechanisms for two reasons: first, to prepare for subsequent learning sessions, and second, to reduce the negative effects of deleting memories. Author summaryDrosophila mushroom body output neurons (MBONs) in the 3 compartment of the fruit fly brain are highly activated by novel odors, and their activation triggers alerting behavior. Interestingly, these specific neurons react only to unfamiliar odor information, suggesting they constantly undergo incremental learning of new odors. This study was aimed at constructing three incremental learning models of the MBON 3 neurons. Although there have been numerous studies on complex circuit designs to reproduce activation waveforms, herein we constructed a fundamental learning model based on a kernelized learning method. Since kernelized learning models interpret Hebbian learning as the addition or subtraction of kernel functions, the model is easy to analyze theoretically. Consequently, we conclude that the forgetting property observed in the MBON 3 neurons is essential for reducing error when learning occurs within a brain of limited capacity.

Microtubules are cytoskeletal structures composed of polymers of /{beta}-tubulin heterodimers. They play a central role in cell division and motility by a stochastic process of alternating polymerization and depolymerization episodes (dynamic instability) that can be modulated by phosphorylation. Protein kinase C and cyclin-dependent kinase 1 are known to phosphorylate Ser165 of -tubulin (:Ser165) and Ser172 of {beta}-tubulin, ({beta}:Ser172), respectively. Using all-atom molecular dynamics simulations of 6-mer {beta}-tubulin systems modeled on the cryo-EM structure of a microtubule (PDB 3J6E), the impact of phosphorylation at each site is explored in terms of secondary structures (:helix H8/loop T7 segment and {beta}:loops T3/T5) that lie at the inter-dimer cleft near the E-site {beta}:GTP. If properly aligned, :Glu254 (helix H8) hydrolyzes {beta}:GTP to GDP thereby triggering the transition from a polymerizing to a depolymerizing microtubule. -Tubulin phosphorylated at :Ser165 displaces helix H8 (:Glu254/:Gln256) and loop T5 towards the {gamma}-phosphate of {beta}:GTP. This movement coincides with a shift of the {beta}:GTP nucleotide by 4.5-5.5 [A], stabilization of the {gamma}P of {beta}:GTP by additional H-bonding and weakened inter-dimer interactions. In a phosphorylated {beta}:Ser172 system, loop T5 is displaced toward {beta}:GTP and coincides with stabilization of inter-dimer interactions. Therefore, phosphorylation of either - or {beta}-tubulin generates a distinct profile of intramolecular rearrangements that remodel the inter-dimer cleft and modulate dynamic instability. These profiles may provide a useful reference for screening mutations identified in tumor genomes.

In mammalian cells, under normal circumstances, the p53 protein exhibits oscillatory dynamics in response to DNA damage and maintains the cells in a cell-cycle-arrested state. Intriguingly, some cells can escape this cell-cycle-arrested state even after prolonged DNA damage, and often undergo mitotic catastrophe. In this context, the precise role of p53 dynamics and its complex interplay with cell-cycle regulation remain poorly understood. Herein, by constructing a comprehensive network model, we have identified crucial crosstalk regulations between the p53 protein and key cell-cycle regulators that enable some cells to escape cell-cycle arrest during prolonged DNA damage. The model further illustrates a probable cellular mechanism underlying mitotic catastrophe and predicts ways to induce it in a therapeutically relevant manner.