Minimal Set Of Rules Research Articles

Automatic evolutionary design of quantum rule-based systems and applications to quantum reinforcement learning

Explainable artificial intelligence is a research topic whose relevance has increased in recent years, especially with the advent of large machine learning models. However, very few attempts have been proposed to improve interpretability in the case of quantum artificial intelligence, and many existing quantum machine learning models in the literature can be considered almost as black boxes. In this article, we argue that an appropriate semantic interpretation of a given quantum circuit that solves a problem can be of interest to the user not only to certify the correct behavior of the learned model, but also to obtain a deeper insight into the problem at hand and its solution. We focus on decision-making problems that can be formulated as classification tasks and propose a method for learning quantum rule-based systems to solve them using evolutionary optimization algorithms. The approach is tested to learn rules that solve control and decision-making tasks in reinforcement learning environments, to provide interpretable agent policies that help to understand the internal dynamics of an unknown environment. Our results conclude that the learned policies are not only highly explainable, but can also help detect non-relevant features of problems and produce a minimal set of rules.

How to Create and Foster Sustainable Smart Cities? Insights on Ethics, Trust, Privacy, Transparency, Incentives, and Success

This paper describes the motivation, framework, concepts, method, implementation, and results of the HCII2023 Design Café, a dedicated participatory highly interactive design workshop held in Copenhagen in July 2023. Motivated by the ubiquitous challenges our world is facing, this initiative had the goal to explore six main issues from an interdisciplinary perspective with a focus on the UN SDG 11: “Sustainable Cities and Communities” and the HCI Grand Challenge 2 “Human-Environment Interactions.” The six issues were formulated as questions and presented to the participants working in small groups. (1) How to create inclusive and ethical smart cities? (2) How to establish trust between people and smart environments? (3) How to address privacy concerns in smart environments that adopt the “disappearing computer” paradigm? (4) How to promote explainability and transparency of policies and measures to citizens of smart cities or in smart environments in general? (5) How to design incentives and rewards for engagement and sustainable behavior in smart cities at a personal as well as collective/corporate level? (6) How to measure success and impact in sustainable smart city projects? The method and approach of the Design Café is a tailored composition of a guided, structured format combined with and inspired by processes of informal communication and exchange of knowledge and ideas. Aligned moderation, a minimum set of rules, a set of relevant topics and an interdisciplinary group of motivated participants working in rotating formations provides the structure for achieving results. The results show that the six issues are not independent of each other, but require a holistic view, considering the various dependencies as well as synergies when exploring solutions. Nevertheless, the importance of first establishing higher-level goals based on ethical and inclusive approaches fostering human dignity and human rights were key. Acceptance of overall goals, processes, rules, and regulations was considered as the fundamental pre-requisite for sustainable change towards a declared goal. Acceptance needs trust and privacy as well as explainability and transparency of policies and measures. The role of incentives and rewards for engagement and sustainable behavior was twofold. Offering incentives must include planning on how to measure their impact. Effective measurement of success and impact depends heavily on how the institutions address privacy. Explainability and transparency should become one of the ethical guidelines that steer and control concepts, decision making, and implementations of all activities. The scale of these societal challenges still needs to be recognized by those responsible. It is essential to educate decision makers in the psychological needs and effects of sense making, comprehensibility, and finally acceptance and well-being moving towards a humanity-centered design.

Neurologically Motivated Simulation of Ensemble Performance

A “rhythmic agent” is simulated based on the foundation of a previously published behavioral sensorimotor synchronization (SMS) model. The model is adjustable to control the auditory and tactile modalities of the tap's feedback. In addition to the conventional mechanisms of phase and period error correction, as well as their activation conditions, the period is estimated by modeling a central timekeeper impacted by a novel short-term memory. Inspired by The ADaptation and Anticipation Model (ADAM), a mechanism for linearly extrapolating anticipation is also tested. To better match the perceptual and motor cognitive functions, the model's parameters have been tuned to observations from experimental neurosensory literature with an emphasis on transduction delays. The agent is programmed to synchronize with various external rhythmic input signals while accounting for both adaptive and predictive mechanisms. The definition of the agent is based on a minimal set of rules yet has successfully replicated results of real-world observations: against a metronome; it produces the well-known negative mean asynchrony. In a rhythmic joint action, the simulation of joint delayed coordination shows a behavior previously observed in human subjects: in a rhythmic collaboration, a moderate amount of delay is necessary to keep the tempo steady, and below that threshold, the rhythm tends to speed up. It is also shown that giving more weight to the tactile afferent feedback than the auditory intensifies this effect. Moreover, it is observed that including anticipation in addition to the reactive mechanism will decrease the effect. The proposed model as a rhythmic engine, combined with other standard modules such as a beat detection algorithm, can be used to implement musical co-performers that could improvise with a human rhythmically or perform a given score in a way that feels human-like.

The Code of Administrative Justice of Ukraine in the system of regulation of alternative methods of solving public legal disputes and conflicts

The article states that the sphere of resolving public-law disputes and conflicts is insufficiently regulated and indicates that it requires further adoption of relevant legislative acts or amendments to current acts in this area. At the same time, it is noted that the legislative principles for the application of alternative methods of resolving public legal disputes and conflicts are contained in the Code of Administrative Justice of Ukraine. It is noted that the Code of Administrative Justice of Ukraine contains the following legislative provisions regarding the possibility of using alternative methods of resolving public legal disputes and conflicts. The specified Code was adopted on July 6, 2005, but part of the norms related to the above issues and, in particular, mediation in the Code appeared much later. In particular, the Code was supplemented with such norms on the basis of the third clause of Chapter IV "Final and Transitional Provisions" of the Law of Ukraine "On Mediation" dated November 16, 2021. In view of the analysis of the provisions of the Code of Administrative Procedure of Ukraine, this legal act orients the parties to the possibility of resolving the dispute in the following ways: 1) by conciliation; 2) through mediation as an out-of-court method of dispute settlement; 3) by applying to the court to settle the dispute with the participation of a judge. At the same time, the Code regulates only conciliation and going to court to settle the dispute with the participation of a judge. Mediation as an alternative way of resolving public legal disputes and conflicts is taken into account by the Code in different ways, but the detailed regulation of this procedure, as is known, is carried out by a separate Law. The article states that the proceduralization of such a form as settlement of a dispute with the participation of a judge is of a rather limited nature and is connected with the introduction at the level of procedural law of a minimal set of rules without detailed regulation of the specifics of their application. Taking into account the professional consciousness of judges and their constant activity within the limits of the maximum procedural regulation of such activity, the stated approach of the legislator, in our opinion, needs clarification and development at the level of other acts. In particular, within the limits of such acts, it would be possible to approve a scientifically based and practically tested method of dispute settlement with the participation of a judge.

Management of links of interest in European Union expertise authorities dealing with plant protection products: comparative analysis and recommendations

BackgroundTo ensure a high level of public health and environmental protection, authorities that deliver scientific expertise to inform decision-makers and the public at large need to be independent from external stakeholders and free of conflicts of interest. This independence requires effective rules for managing links of interest (LoIs) and a high level of transparency, including publication of declarations of interest (DoIs) where appropriate. In the particular case of plant protection products (PPPs) within the European Union, these requirements should apply to all Competent Authorities contributing to the marketing authorization processes.MethodsA comparative analysis of LoIs management procedures was performed on a selection of ten National Competent Authorities from different member states (NCAs). This analysis was based on (i) the identification of 17 criteria aiming at characterizing good practices for LoIs management; (ii) a survey of ten NCAs, based on an analysis of their institutional websites and their responses to official mail requests.ResultsThe comparative analysis showed: (i) a frequent lack of transparency of NCAs regarding their procedures for managing LoIs; (ii) a significant heterogeneity between the NCAs’ LoIs management rules, even though they are in charge of comparable missions regarding the marketing of PPPs; (iii) substantial gaps between the LoIs management procedures adopted by several NCAs and the good practices that are promoted by EFSA.Current limits on their practices regarding LoIs management might open ways for undue external influences on scientific expertise, and ultimately impact negatively the risk management options adopted by national or European authorities. Limitations of this study and its extension for a more thorough overview of the current LoIs management practices are also discussed.ConclusionsLoIs management and transparency rules need to be improved across NCAs, given their contribution as (co-)rapporteurs or peer reviews participants to the health and environmental risk assessment steps of the EFSA processes. To this end, a common minimum set of rules should be defined by EFSA; recommendations are proposed, based on the best practices implemented by the investigated NCAs. Such progress would contribute promoting high-quality unbiased scientific expertise and enhance EU citizens’ trust.

Chapter 10 - Finding the falsification threshold of the toxic proteinopathy hypothesis in neurodegeneration

A biomedical hypothesis is a theoretical assumption amenable to being tested in a randomized clinical trial. The main hypotheses in neurodegenerative disorders are based on the concept that proteins accumulate in an aggregated fashion and trigger toxicity. The toxic proteinopathy hypothesis posits that neurodegeneration is caused by toxicity of aggregated amyloid in Alzheimer's disease (toxic amyloid hypothesis), aggregated α-synuclein in Parkinson's disease (toxic synuclein hypothesis), and aggregated tau in progressive supranuclear palsy (toxic tau hypothesis). To date, we have accumulated 40 negative anti-amyloid randomized clinical, 2 anti-synuclein trials, and 4 anti-tau trials. These results have not prompted a major reconsideration of the toxic proteinopathy hypothesis of causality. Imperfections in trial design and execution (incorrect dosage, insensitive endpoints, too-advanced population) but not in the underlying hypotheses have prevailed as explaining the failures. We review here the evidence suggesting that the threshold of hypothesis falsifiability may be too high and advocate in favor of a minimal set of rules that facilitate the interpretation of negative clinical trials as falsifying the driving hypotheses, in particular if the desirable change in surrogate endpoints has been achieved. We propose four steps to refute a hypothesis in future-negative surrogate-backed trials and argue that for the actual rejection to take place, refutation must be accompanied by the proposal of an alternative hypothesis. The absence of alternative hypotheses may be the single greatest reason why there remains hesitancy in rejecting the toxic proteinopathy hypothesis: in the absence of alternatives, we have no clear guidance as to where to redirect or focus.

Dynamical modeling of the H3K27 epigenetic landscape in mouse embryonic stem cells.

The Polycomb system via the methylation of the lysine 27 of histone H3 (H3K27) plays central roles in the silencing of many lineage-specific genes during development. Recent experimental evidence suggested that the recruitment of histone modifying enzymes like the Polycomb repressive complex 2 (PRC2) at specific sites and their spreading capacities from these sites are key to the establishment and maintenance of a proper epigenomic landscape around Polycomb-target genes. Here, to test whether such mechanisms, as a minimal set of qualitative rules, are quantitatively compatible with data, we developed a mathematical model that can predict the locus-specific distributions of H3K27 modifications based on previous biochemical knowledge. Within the biological context of mouse embryonic stem cells, our model showed quantitative agreement with experimental profiles of H3K27 acetylation and methylation around Polycomb-target genes in wild-type and mutants. In particular, we demonstrated the key role of the reader-writer module of PRC2 and of the competition between the binding of activating and repressing enzymes in shaping the H3K27 landscape around transcriptional start sites. The predicted dynamics of establishment and maintenance of the repressive trimethylated H3K27 state suggest a slow accumulation, in perfect agreement with experiments. Our approach represents a first step towards a quantitative description of PcG regulation in various cellular contexts and provides a generic framework to better characterize epigenetic regulation in normal or disease situations.

The Dominance-based Rough Set Approach for analysing patterns of flexibility allocation and design-cost criteria in large-scale irrigation systems

Water planners must provide end-users with reliable and high-quality access to fresh water while complying with financial, institutional, and water availability constraints. In the pursuit of these goals, an over-investment in design can result in stranded assets of significant value and often unwanted environmental implications. Under-investment can lead to supply restrictions affecting human health, the economy, and the environment. The present study uses the Dominance-based Rough Set Approach (DRSA) to develop a balancing strategy concerning complexities encountered in water resource planning for irrigation systems. The methodology relies on the Dominance-based Learning from Examples Module (DOMLEM) algorithm, which extracts minimal set of rules regarding relevant combinations between flexibility allocation and design-cost criteria. The algorithm delineates outcomes in the form of “if., then.” rules that translate decision possibilities facing water planners into: “if (the design is more flexible by this amount), then (we expect this range of cost increment”). Then, a confusion matrix is computed for each irrigation system in order to exclude the rules generating incorrect and ambiguous classification results. The outcome reveals that cost is more subject to elasticity at the hydrant (eh) increment than the network’s coefficient (r). Furthermore, the analysis reveals that the parameter P(q) has only a minor impact on the cost and, as a result, the final decision. Any elasticity (eh) less than 3 assigned to any given coefficient (r) becomes a low-cost increment. For any given value of (eh), the cost increases as the coefficient (r) decrease. Elasticity from 4 to 5 with a network's coefficient (r) equal to or greater than 18/24 becomes a medium-cost increment. Elasticity (eh) from 5 to 6 associated with an (r) equal or less than 16/24 becomes a very high-cost increment. Finally, rather than identifying one solution that seems better than others, this approach provides an interactive schematic that helps identify the appropriate range of flexibility justified by the expense criterion, which allows for debate and supports decision-making.

Nanoeconomics of Households in Lockdown Using Agent Models during COVID-19

The world is experiencing a global pandemic with COVID-19, for which few measures have proven their efficiency. Prevention through lockdown belongs to the portfolio of Non-Pharmaceutical Intervention (NPI). The implementation of a lockdown comes with a potential health care benefit balanced with an economic and human cost: people are constrained to stay in their homes. Households hence have to live together in what we call “zero-space”, which means within the walls of their flat or house. The loss of “space-domain” freedom, preventing them to move in “free” space is accompanied by a continued “time-domain” freedom with the possibility to allocate their time, and what they do with it, within the location they are not permitted to leave (with very defined exceptions). We study the microeconomics framework in such a setting, starting from the rules shaping such a “nano-market” with very few agents (the members of the household), and its consequence for nano-economic interaction. Since the behaviour of the agents is hyperconstrained in the space domain and relatively free in the time domain, behavioral economics is used to describe decisions made in the home, for the actions remaining possible during lockdown. A minimal set of rules is introduced and illustrated to describe efficiently the agents at play in this new and particular context, which has been replicated worldwide during the pandemic. Hypotheses for this model are presented and discussed, so as to allow future variations and adaptations for other specific cases with different options chosen. Such hypotheses concern agents, their interests, behaviours, and the equivalent of non-financial “nano-market transactions and contracts”.

The role of spatial competitive interactions between trees in shaping forest patterns

General patterns that are observed in forests irrespective of their ecological biome or species composition are indicators of general biological processes that drive forest dynamics. Modelling can be used to infer these processes by identifying the minimum set of rules that generate the observed patterns. The regular spatial pattern of the largest trees, the decreasing skewness of the tree diameter distribution in young developing stands, and the size bimodality that may result from the tail of dominated trees are three general patterns observed in natural forests. Using a simple individual-based space-dependent growth model based on asymmetric competition between trees and space-independent approximations of this model, the objectives were (1) to identify a single mechanism that may explain these three patterns, and (2) to clarify the role of space in the emergence of these patterns. The space-dependent model was able to qualitatively generate the three patterns. Through cascading spatial interactions, competition resulted in the establishment of a spatially structured competitive hierarchy among trees. A second-order approximation of the space-dependent model was derived by structuring the population of trees according to diameter and competitive status. This second-order approximation succeeded in predicting left-skewed diameter distributions but did not predict diameter bimodality. Asymmetric competition thus seems to be an important driver of tree growth and the cause rather than the consequence of bimodality. The second-order approximation of the space-dependent model may be useful to generalize the stationary diameter distributions developed in the demographic equilibrium theory.

Promoting the adoption of agent-based modelling for synergistic interventions and decision-making during pandemic outbreaks

Abstract. Geography has long sought to explain spatial relationships between social and physical processes, including the spread of infectious diseases, within the context of modelling human-environment interactions. The spread of the recent COVID-19 pandemic, and its devastating effects on human activity and welfare, represent but examples of such complex human-environment interactions. In this paper, we discuss the value of agent-based models for simulating the spread of the COVID-19 virus to support decision-making with regards to non-pharmaceutical interventions, e.g., lock-down. We also develop a prototype agent-based model using a minimal set of rules regarding patterns of human mobility within a hypothetical town, and couple that with an epidemiological model of infectious disease spread. The coupled model is used to: (a) create synthetic trajectories corresponding to daily and weekly activities postulated between a set of predefined points of interest (e.g., home, work), and (b) simulate new infections at contact points and their subsequent effects on the spread of the disease. We finally use the model simulations as a means of evaluating decisions regarding the number and type of activities to be limited during a planned lockdown in a COVID-19 pandemic context.

Sequence-encoded and composition-dependent protein-RNA interactions control multiphasic condensate morphologies

Multivalent protein-protein and protein-RNA interactions are the drivers of biological phase separation. Biomolecular condensates typically contain a dense network of multiple proteins and RNAs, and their competing molecular interactions play key roles in regulating the condensate composition and structure. Employing a ternary system comprising of a prion-like polypeptide (PLP), arginine-rich polypeptide (RRP), and RNA, we show that competition between the PLP and RNA for a single shared partner, the RRP, leads to RNA-induced demixing of PLP-RRP condensates into stable coexisting phases—homotypic PLP condensates and heterotypic RRP-RNA condensates. The morphology of these biphasic condensates (non-engulfing/ partial engulfing/ complete engulfing) is determined by the RNA-to-RRP stoichiometry and the hierarchy of intermolecular interactions, providing a glimpse of the broad range of multiphasic patterns that are accessible to these condensates. Our findings provide a minimal set of physical rules that govern the composition and spatial organization of multicomponent and multiphasic biomolecular condensates.

Policy-Engineering Optimization with Visual Representation and Separation-of-Duty Constraints in Attribute-Based Access Control

Recently, attribute-based access control (ABAC) has received increasingly more attention and has emerged as the desired access control mechanism for many organizations because of its flexibility and scalability for authorization management, as well as its security policies, such as separation-of-duty constraints and mutually exclusive constraints. Policy-engineering technology is an effective approach for the construction of ABAC systems. However, most conventional methods lack interpretability, and their constructing processes are complex. Furthermore, they do not consider the separation-of-duty constraints. To address these issues in ABAC, this paper proposes a novel method called policy engineering optimization with visual representation and separation of duty constraints (PEO_VR&SOD). First, to enhance interpretability while mining a minimal set of rules, we use the visual technique with Hamming distance to reduce the policy mining scale and present a policy mining algorithm. Second, to verify whether the separation of duty constraints can be satisfied in a constructed policy engineering system, we use the method of SAT-based model counting to reduce the constraints and construct mutually exclusive constraints to implicitly enforce the given separation of duty constraints. The experiments demonstrate the efficiency and effectiveness of the proposed method and show encouraging results.

Design and Characterization of Track Routing Architecture for RSFQ and AQFP Circuits in a Multilayer Process

Place and route tools for synthesized superconductor logic circuits, either for dc-biased rapid single flux quantum (RSFQ) or ac-biased adiabatic quantum flux parametron (AQFP), are required to automate the design of complex logic circuits. For hand-crafted circuit layout, logic cells, clock, and bias distribution, and signal interconnect can be optimized for tight fit and the adherence to design rules. For complex systems with thousands of logic gates, a hand-crafted approach is not efficient and automated place and route tools are a necessity. Such tools require logic cell layout for placement with a minimum set of rules, followed by an interconnect design that allows maximum routability and strict adherence to layer fill requirements. In this article, we present the design and characterization of a routing architecture that allows rule-based automated place and route for both RSFQ and AQFP logic families. We show that a layout tile size of 10 × 10 μm can be designed to accommodate all design rules for layout fill densities, passive transmission line routing, bias current distribution, and the vias needed to stitch multiple ground planes and provide shielded signal and bias tracks. We also characterize the performance of the layout architecture in terms of transmission line parameters and bias current coupling with powerful simulation tools developed for the SuperTools project. The result is a track layout that doubles as chip fill, is now used for integrated circuit layout under SuperTools and is also applicable to any similar fabrication process with at least eight superconductor metal layers.

An agent-based approach for modelling collective dynamics in animal groups distinguishing individual speed and orientation.

Collective dynamics in animal groups is a challenging theme for the modelling community, being treated with a wide range of approaches. This topic is here tackled by a discrete model. Entering in more details, each agent, represented by a material point, is assumed to move following a first-order Newtonian law, which distinguishes speed and orientation. In particular, the latter results from the balance of a given set of behavioural stimuli, each of them defined by a direction and a weight, that quantifies its relative importance. A constraint on the sum of the weights then avoids implausible simultaneous maximization/minimization of all movement traits. Our framework is based on a minimal set of rules and parameters and is able to capture and classify a number of collective group dynamics emerging from different individual preferred behaviour, which possibly includes attractive, repulsive and alignment stimuli. In the case of a system of animals subjected only to the first two behavioural inputs, we also show how analytical arguments allow us to a priori relate the equilibrium interparticle spacing to critical model coefficients. Our approach is then extended to account for the presence of predators with different hunting strategies, which impact on the behaviour of a prey population. Hints for model refinement and applications are finally given in the conclusive part of the article. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.

A situation refinement model for complex event processing

Complex Event Processing (CEP) systems aim at processing large flows of events to discover situations of interest (SOI). CEP uses predefined pattern templates to detect occurrences of complex events in an event stream. CEP systems rely on domain experts to define complex patterns rules to recognize SOI. The task of identifying complex patterns faces with several challenges, such as the complexity of writing the pattern rules, and the need to acquire and process background information considering event stream’s real-time constraints. Developing an efficient rule mining algorithm to fine-tune the CEP pattern to recognize SOI requires the tackling of three main obstacles. First, the CEP pattern rules must be inferred by utilizing the user’s preferred context and the history of the event stream. Second, to avoid the issue of pattern complexity, the minimum number of rules must be used in the refinement process. Finally, to respond to emerging situations, the refinement task must be fulfilled in near real-time. In this work, we present a rule mining model to refine the CEP pattern rules by considering these obstacles while providing the ability to adjust the level of refinement to fit the applied scenario. This paper aims to: (1) Review the challenges associated with incorporating domain knowledge in CEP systems to improve awareness of real-world situations; (2) Present a Situation Refinement model to extract the minimal set of rules from external knowledge required to identify Situations Of Interest; (3) Demonstrate the summary update process of the event stream; and (4) Evaluate the derived rules with respect to their coverage and complexity.

Rough set theory in the classification of loan applications

Abstract The article presents the results of the study, which is a fragment of the work carried out under the project entitled “Hybrid system for intelligent diagnostics of prognostic models”, co-financed through the National Centre for Research and Development from the European Regional Development Fund. The aim of the study was to analyse the effectiveness of methods based on rough set theory in binary classification tasks, on the example of cash loans granted. In accordance with the assumptions for the construction of the hybrid system, it was assumed that an important criterion for the selection of methods included in various scenarios of implemented research procedures is their automation, starting from machine selection of features, through machine discretization and model construction until obtaining classification results. Based on the results of preliminary tests, the research procedure was established, which included the use of: 1) the method of random undersampling in order to balance training data set, 2) heuristic MD algorithm (Maximum Discernibility) for discretization and reduction of variables, and 3) LEM 2 algorithm for generating the minimal set of rules for decision-making. In subsequent scenarios, the basic research procedure was extended by the stage of initial reduction of features using two filtration methods, CFS and SU-FCBF, and using the results of the expert approach commonly used in the construction of logistic regression models. By adding the number of sub-scenarios, a total of 242 different classification results were obtained. These results showed that the use of filtration methods resulted in a significant improvement in the results of rule classification.

Robust minimal matching rules for quasicrystals.

A unified framework is proposed for dealing with matching rules of quasiperiodic patterns, relevant for both tiling models and real-world quasicrystals. The approach is intended for extraction and validation of a minimal set of matching rules, directly from the phased diffraction data. The construction yields precise values for the spatial density of distinct atomic positions and tolerates the presence of defects in a robust way.

A P system model of swarming and aggregation in a Myxobacterial colony

Bacterial communities provide an interesting subject for the study of emergence and complexity as the consequence of many local interactions. In particular, the soil-dwelling social bacterium Myxobacteria demonstrates two distinct types of motility, social motility via the sensing of bacterial slime deposits and adventurous motility. Both modes of motility are governed by local interactions. Using P systems, a membrane computing methodology based on compartmental rewrite rules for modelling computational processes; this work demonstrates how minimal set of rules can model swarming and aggregating behaviour in Myxobacteria bacterial populations. Our model uses a multi-environment P system similar a 2D cellular automaton to represent the substrate environment whilst stochastic rule selection dictates Myxobacterial motion according to behaviour observed in vitro. The rules account for both mechanisms of motility, the deposit and detection of slime, a change in direction due to C-signal induction and the mixing of population numbers. Simulations demonstrate an extensible computational framework for the modelling of bacterial behaviour, with the potential for extension into additional emergent behaviours.

Dynamics of PAR Proteins Explain the Oscillation and Ratcheting Mechanisms in Dorsal Closure

We present a vertex-based model for Drosophila dorsal closure that predicts the mechanics of cell oscillation and contraction from the dynamics of the PAR proteins. Based on experimental observations of how aPKC, Par-6, and Bazooka translocate from the circumference of the apical surface to the medial domain, and how they interact with each other and ultimately regulate the apicomedial actomyosin, we formulate a system of differential equations that captures the key features of dorsal closure, including distinctive behaviors in its early, slow, and fast phases. The oscillation in cell area in the early phase of dorsal closure results from an intracellular negative feedback loop that involves myosin, an actomyosin regulator, aPKC, and Bazooka. In the slow phase, gradual sequestration of apicomedial aPKC by Bazooka clusters causes incomplete disassembly of the actomyosin network over each cycle of oscillation, thus producing a so-called ratchet. The fast phase of rapid cell and tissue contraction arises when medial myosin, no longer antagonized by aPKC, builds up in time and produces sustained contraction. Thus, a minimal set of rules governing the dynamics of the PAR proteins, extracted from experimental observations, can account for all major mechanical outcomes of dorsal closure, including the transitions between its three distinct phases.