Agent-based Computational Model Research Articles

Towards a Reconciliation Between Simulational, Ludic, and Historiographical Practices in Research

Background Simulations, ludic or otherwise, have so far struggled to gain a foothold in mainstream historiography. Some authors suggest there may be fundamental incompatibilities between history and the language of simulations and scholarly games. Others believe that designing, employing, and validating historical simulations may be simply too costly and/or labor-intensive to justify their widespread adoption. Intervention This paper intends to identify points of friction between historiography and simulation-based research and suggest practical solutions to these issues. Methods My discussion is based on the description and analysis of a case study, the ThomondSim/ The Triumphs of Turlough research project. The initiative consisted of the development and application of an agent-based computational model (ABM) and a scholarly board game to investigate the possible associations between economic, environmental, and military hazards in 13th and 14th centuries Ireland. Results and Discussion Ensuring the simulations matched historical evidence to a standard deemed acceptable by the historiographical community limited their phase space, compromising their capacity to explore emergent phenomena. The intricacy of the underlying conceptual model suited the ABM better than the board game, which struggled to reconcile complexity with good game design practices. The board game, however, proved to be an effective validating tool for the ABM. Limitations and Suggestions for Further Research The project espoused an overt simulation- and game-centric approach, paying little attention to unguided play. Recent literature suggests that fostering, rather than hindering, playful exploration could address some of the pitfalls identified by this project. Conclusion Play could be a means of reconciliation between simulational, ludic, and historiographical practices. However, to ensure that projects adhere to epistemic standards, it is recommended that a methodology is developed to integrate it into research in ways that can be tested and evaluated.

When are females dominant over males in rats (Rattus norvegicus)?

In group-living animals, males are assumed to be dominant over females when they are larger than females. Despite this, females have sometimes been proven to be dominant over some males via the winner-loser effect, which becomes stronger when the intensity of aggression in the group is higher. To test whether the winner-loser effect leads to (partial) female dominance in a species with a pronounced sexual dimorphism, we studied the hierarchy in 12 rat colonies (Rattus norvegicus) in which the rats could freely interact with their group members within a spacious area. To investigate the underlying mechanisms, we compared the empirical data to hypotheses generated by the agent-based model ‘DomWorld’. We show that females dominated on average 55% of the males, and occupied the alpha position in four colonies, in three of them they shared it with one or several males. Moreover, in line with the predictions of the computational model, females dominated a higher percentage of males when the intensity of aggression of the colony was higher. This shows that although females are only half as heavy as males, they dominate part of the males probably through the winner-loser effect. We suggest that this effect may be widespread in many other species and can be tested experimentally.Significance statementIt is often assumed that males automatically dominate females because males are bigger and stronger than females in many species. However, the present study shows that females can dominate males due to the winner-loser effect. We used an agent-based computational model to generate specific hypotheses that we empirically tested in a large sample of rat colonies. Despite this species having a pronounced male-biased sex dimorphism, some females dominated males – with one female even occupying an unshared alpha position. Such partial female dominance was stronger in colonies with higher intensity of aggression. Here, defeated males may suffer a drastic decrease in their fighting capability and consequently give females more opportunities to surpass them in the hierarchy.

Optimization of periodic treatment strategies for bacterial biofilms using an agent-based in silico approach.

Biofilms are responsible for most chronic infections and are highly resistant to antibiotic treatments. Previous studies have demonstrated that periodic dosing of antibiotics can help sensitize persistent subpopulations and reduce the overall dosage required for treatment. Because the dynamics and mechanisms of biofilm growth and the formation of persister cells are diverse and are affected by environmental conditions, it remains a challenge to design optimal periodic dosing regimens. Here, we develop a computational agent-based model to streamline this process and determine key parameters for effective treatment. We used our model to test a broad range of persistence switching dynamics and found that if periodic antibiotic dosing was tuned to biofilm dynamics, the dose required for effective treatment could be reduced by nearly 77%. The biofilm architecture and its response to antibiotics were found to depend on the dynamics of persister cells. Despite some differences in the response of biofilm governed by different persister switching rates, we found that a general optimized periodic treatment was still effective in significantly reducing the required antibiotic dose. As persistence becomes better quantified and understood, our model has the potential to act as a foundation for more effective strategies to target bacterial infections.

A Generalizable Theory-Driven Agent-Based Framework to Study Conflict-Induced Forced Migration

Large-scale population displacements arising from conflict-induced forced migration generate uncertainty and introduce several policy challenges. Addressing these concerns requires an interdisciplinary approach that integrates knowledge from both computational modeling and social sciences. We propose a generalized computational agent-based modeling framework grounded by Theory of Planned Behavior to model conflict-induced migration outflows within Ukraine during the start of that conflict in 2022. Existing migration modeling frameworks that attempt to address policy implications primarily focus on destination while leaving absent a generalized computational framework grounded by social theory focused on the conflict-induced region. We propose an agent-based framework utilizing a spatiotemporal gravity model and a Bi-threshold model over a Graph Dynamical System to update migration status of agents in conflict-induced regions at fine temporal and spatial granularity. This approach significantly outperforms previous work when examining the case of Russian invasion in Ukraine. Policy implications of the proposed framework are demonstrated by modeling the migration behavior of Ukrainian civilians attempting to flee from regions encircled by Russian forces. We also showcase the generalizability of the model by simulating a past conflict in Burundi, an alternative conflict setting. Results demonstrate the utility of the framework for assessing conflict-induced migration in varied settings as well as identifying vulnerable civilian populations.

Myosin-induced F-actin fragmentation facilitates contraction of actin networks.

Mechanical forces play a crucial role in diverse physiological processes, such as cell migration, cytokinesis, and morphogenesis. The actin cytoskeleton generates a large fraction of the mechanical forces via molecular interactions between actin filaments (F-actins) and myosin motors. Recent studies have shown that the common tendency of actomyosin networks to contract into a smaller structure deeply involves F-actin buckling induced by motor activities, fragmentation of F-actins, and the force-dependent unbinding of cross-linkers that inter-connect F-actins. The fragmentation of F-actins was shown to originate from either buckling or tensile force from previous single-molecule experiments. While the role of buckling in network contraction has been studied extensively, to date, the role of tension-induced F-actin fragmentation in network contraction has not been investigated. In this study, we employed in vitro experiments and an agent-based computational model to illuminate when and how the tension-induced F-actin fragmentation facilitates network contraction. Our experiments demonstrated that F-actins can be fragmented due to tensile forces, immediately followed by catastrophic rupture and contraction of networks. Using the agent-based model, we showed that F-actin fragmentation by tension results in distinct rupture dynamics different from that observed in networks only with cross-linker unbinding. Moreover, we found that tension-induced F-actin fragmentation is particularly important for the contraction of networks with high connectivity. Results from our study shed light on an important regulator of the contraction of actomyosin networks which has been neglected. In addition, our results provide insights into the rupture mechanisms of polymeric network structures and bio-inspired materials.

Balancing Affordances and Constraints: Designing Enterprise Social Media for Organizational Knowledge Work

Enterprise social media (ESM) is changing how knowledge workers interact and share information; however, a debate persists as to whether ESM is an adequate knowledge management system. ESM provides a rich set of affordances for organizational knowledge work, such as improved organizational memory, but also constrains knowledge work performance because of digital interruptions. Extending and complementing existing scholarship, this study asks the following research question: How can organizations design ESM to realize its knowledge work benefits? Using a computational agent-based model that incorporates the design features of ESM, workers’ attitudes, and resulting ESM-use affordances and constraints, this study shows how ESM-use outcomes are contingent both on the design of and users’ attitudes toward ESM. Specifically, the negative effects of ESM interactivity are mitigated when employees have a low transparency preference and access ESM without posting as much. The study further unpacks asymmetric engagement as the mechanism that leads low transparency configurations to be more resilient to the negative effects of interruptions driven by ESM interactivity. Asymmetric engagement—learning from posted content without interacting often—enables the gradual creation of organizational memory while maintaining a broad user base by minimizing interruptions. These results ultimately contribute a multilevel model of ESM use and knowledge work outcomes, enhancing the theoretical understanding of previously studied mechanisms such as communication visibility and providing implications for organizations designing ESM.

AN AGENT-BASED COMPUTATIONAL MODEL ON HOUSEHOLD ELECTRICITY CONSUMPTION IN INDIAN CITIES

ABSTRACT Traditional methods of planning and managing cities often adopt a simplified linear approach to understanding their composition and functioning. These approaches have led to the creation of many undesirable and non-functional cities. However, over the last few decades, there has been a radical shift in the approach to investigating cities through the adoption of complexity science and its various concepts. Unfortunately, this paradigm shift has been limited to only a handful of developed Western nations. This study proposes an extendable agent-based computational model for assessing household electricity consumption patterns in cities of developing nations. The model is demonstrated using an urban precinct in India as a representative case. It simulates the monthly electricity consumption of an urban precinct in Nagpur by combining the significant factors impacting household electricity consumption in developing nations, with the household's electricity consumption process in relation to outdoor weather conditions, and the heterogeneity in occupants’ behavior. The predictive validation of the model using the “BehaviorSpace” tool yielded promising results, with the model consistently and accurately predicting the growth of variables and household electricity consumption patterns. The model also successfully generated multiple future scenarios, showcasing its capacity to forecast the future implications of policy interventions. The simulation results indicated that household electricity consumption in Indian cities is driven by population growth and income levels. Additionally, it suggests that widely practiced rooftop solar panel installation policies may not be a viable approach to achieving sustainability in this sector. This study finds that the “emergent urbanism” approach has the potential to revitalize the urban energy planning process through an effective framework for comprehensively understanding the phenomena and addressing its various complexities.

Understanding students’ residential dynamics around university campuses: A computational agent-based modelling approach

Understanding students’ residential dynamics around university campuses: A computational agent-based modelling approach

Seven design principles for teaching complex socioscientific issues: the design of a complex systems agent-based disease epidemic model and the application of epistemic practices in high school biology

Historic challenges in the biological sciences, such as the spread of disease and climate change, have created an unprecedented need for humans to engage with scientific information to address societal problems. However, understanding these socioscientific issues (SSI) can be hard due to the difficulty of comprehending their complex structures and behaviors, the intentional propagation of misinformation, and an insufficient understanding of the epistemic practices that scientists use to develop relevant knowledge. Education researchers have highlighted additional problems in the way science is taught with a focus mainly on concepts rather than practices, competing curricular mandates, and professional development activities that do not provide usable knowledge. The research reported here follows more than a decade of work using agent-based computational models to support the comprehension and analysis of complex biological systems. Our recent work has aimed to build tools and strategies to support students in decision making about complex SSIs. In this paper, we discuss 7 design challenges and principles that underpin this recent focus. Specifically, we combine agent-based modeling with strategies to develop students’ epistemic performance in high school biology curricula. We then provide a detailed case study of how the 7 design principles were used to create a disease epidemic model and unit anchored in the biology topic of the nature of science. Our goal is to offer a comprehensive set of research-derived design principles that can bridge classroom experiences in biology to applications of SSIs.

Vicarious Learning Without Knowledge Differentials

The benefits of vicarious learning are usually conceptualized in terms of a mechanism for learners to utilize the superior knowledge of others. Building on the fact that vicarious learning typically co-occurs and interacts with individual learning-by-doing, we propose an alternative mechanism—one in which vicarious learning is useful because it corrects for certain well-known limitations of individual learning-by-doing. Using computational agent-based models, we show that, under this mechanism, vicarious learning can be beneficial, even without any ex ante differential knowledge to exploit. Our analysis contributes to a deeper understanding of the microfoundations of vicarious learning, which is a vital component of organizational learning. We draw implications for empirical analysis and managerial practice. This paper was accepted by Lamar Pierce, organizations. Funding: Financial support from The Desmarais Fund at INSEAD is gratefully acknowledged. Supplemental Material: The data file and online appendix are available at https://doi.org/10.1287/mnsc.2023.4842 .

Multiple Myeloma Cell Simulation Using an Agent-Based Framework Coupled with a Continuous Fluid Model

Bone marrow mechanical conditions play a key role in multiple myeloma cancer. The complex mechanical and chemical conditions, as well as the interactions with other resident cells, hinder the development of effective treatments. Agent-based computational models, capable of defining the specific conditions for every single cell, can be a useful tool to identify the specific tumor microenvironment. In this sense, we have developed a novel hybrid 3D agent-based model with coupled fluid and particle dynamics to study multiple myeloma cells’ growth. The model, which considers cell–cell interactions, cell maturation, and cell proliferation, has been implemented by employing user-defined functions in the commercial software Fluent. To validate and calibrate the model, cell sedimentation velocity and cell proliferation rates have been compared with in vitro results, as well as with another previously in-house developed model. The results show that cell proliferation increased as cell–cell, and cell–extracellular matrix interactions increased, as a result of the reduction n maturation time. Cells in contact form cell aggregates, increasing cell–cell interactions and thus cell proliferation. Saturation in cell proliferation was observed when cell aggregates increased in size and the lack of space inhibited internal cells’ proliferation. Compared with the previous model, a huge reduction in computational costs was obtained, allowing for an increase in the number of simulated cells.

Predicting 1-year in-stent restenosis in superficial femoral arteries through multiscale computational modelling.

In-stent restenosis in superficial femoral arteries (SFAs) is a complex, multi-factorial and multiscale vascular adaptation process whose thorough understanding is still lacking. Multiscale computational agent-based modelling has recently emerged as a promising approach to decipher mechanobiological mechanisms driving the arterial response to the endovascular intervention. However, the long-term arterial response has never been investigated with this approach, although being of fundamental relevance. In this context, this study investigates the 1-year post-operative arterial wall remodelling in three patient-specific stented SFA lesions through a fully coupled multiscale agent-based modelling framework. The framework integrates the effects of local haemodynamics and monocyte gene expression data on cellular dynamics through a bi-directional coupling of computational fluid dynamics simulations with an agent-based model of cellular activities. The framework was calibrated on the follow-up data at 1 month and 6 months of one stented SFA lesion and then applied to the other two lesions. The calibrated framework successfully captured (i) the high lumen area reduction occurring within the first post-operative month and (ii) the stabilization of the median lumen area from 1-month to 1-year follow-ups in all the stented lesions, demonstrating the potentialities of the proposed approach for investigating patient-specific short- and long-term responses to endovascular interventions.

Corrected and Republished from: "Understanding Lactobacillus paracasei and Streptococcus oralis Biofilm Interactions through Agent-Based Modeling".

As common commensals residing on mucosal tissues, Lactobacillus species are known to promote health, while some Streptococcus species act to enhance the pathogenicity of other organisms in those environments. In this study we used a combination of in vitro imaging of live biofilms and computational modeling to explore biofilm interactions between Streptococcus oralis, an accessory pathogen in oral candidiasis, and Lactobacillus paracasei, an organism with known probiotic properties. A computational agent-based model was created where the two species interact only by competing for space, oxygen, and glucose. Quantification of bacterial growth in live biofilms indicated that S. oralis biomass and cell numbers were much lower than predicted by the model. Two subsequent models were then created to examine more complex interactions between these species, one where L. paracasei secretes a surfactant and another where L. paracasei secretes an inhibitor of S. oralis growth. We observed that the growth of S. oralis could be affected by both mechanisms. Further biofilm experiments support the hypothesis that L. paracasei may secrete an inhibitor of S. oralis growth, although they do not exclude that a surfactant could also be involved. This contribution shows how agent-based modeling and experiments can be used in synergy to address multiple-species biofilm interactions, with important roles in mucosal health and disease. IMPORTANCE We previously discovered a role of the oral commensal Streptococcus oralis as an accessory pathogen. S. oralis increases the virulence of Candida albicans infections in murine oral candidiasis and epithelial cell models through mechanisms which promote the formation of tissue-damaging biofilms. Lactobacillus species have known inhibitory effects on biofilm formation of many microbes, including Streptococcus species. Agent-based modeling has great advantages as a means of exploring multifaceted relationships between organisms in complex environments such as biofilms. Here, we used an iterative collaborative process between experimentation and modeling to reveal aspects of the mostly unexplored relationship between S. oralis and L. paracasei in biofilm growth. The inhibitory nature of L. paracasei on S. oralis in biofilms may be exploited as a means of preventing or alleviating mucosal fungal infections.

Computational Modeling of the Chlamydial Developmental Cycle Reveals a Potential Role for Asymmetric Division.

Chlamydia trachomatis is an obligate intracellular bacterium that progresses through an essential multicell form developmental cycle. Infection of the host is initiated by the elementary body (EB). Once in the host, the EB cell differentiates into the noninfectious, but replication-competent, reticulate body, or RB. After multiple rounds of replication, RBs undergo secondary differentiation, eventually producing newly infectious EBs. Here, we generated paired cell-type promoter reporter constructs and determined the kinetics of the activities of the euo, hctA, and hctB promoters. The paired constructs revealed that the developmental cycle produces at least three phenotypically distinct cell types, the RB (euoprom+), intermediate body (IB; hctAprom+), and EB (hctBprom+). The kinetic data from the three dual-promoter constructs were used to generate two computational agent-based models to reproduce the chlamydial developmental cycle. Both models simulated EB germination, RB amplification, IB formation, and EB production but differed in the mechanism that generated the IB. The direct conversion and the asymmetric production models predicted different behaviors for the RB population, which were experimentally testable. In agreement with the asymmetric production model, RBs acted as stem cells after the initial amplification stage, producing one IB and self-renewing after every division. We also demonstrated that IBs are a transient cell population, maturing directly into EBs after formation without the need for cell division. The culmination of these results suggests that the developmental cycle can be described by a four-stage model, EB germination, RB amplification/maturation, IB production, and EB formation. IMPORTANCE Chlamydia trachomatis is an obligate intracellular bacterial pathogen responsible for both ocular and sexually transmitted infections. All Chlamydiae are reliant on a complex developmental cycle, consisting of both infectious and noninfectious cell forms. The EB cell form initiates infection, whereas the RB cell replicates. The infectious cycle requires both cell types, as RB replication increases the cell population while EB formation disseminates the infection to new hosts. The mechanisms of RB-to-EB development are largely unknown. Here, we developed unique dual promoter reporters and used live-cell imaging and confocal microscopy to visualize the cycle at the single-cell and kinetic levels. These data were used to develop and test two agent-based models, simulating either direct conversion of RBs to EBs or production of EBs via asymmetric RB division. Our results suggest that RBs mature into a stem cell-like population producing intermediate cell forms through asymmetric division, followed by maturation of the intermediate cell type into the infectious EB. Ultimately, a more complete mechanistic understanding of the developmental cycle will lead to novel therapeutics targeting cell type development to eliminate chlamydial dissemination.

Complex network effects on price setting: an agent-based computational approach

Abstract We present an adaptation of a new Keynesian model into an agent-based computational model, accounting for the importance of heterogeneity, interaction and bounded rationality in problems such as price setting and expectations formation. We evaluate the evolution of the distribution of price setting strategy frequencies, which agents might choose on each period between inflationary, neutral or deflationary, a process based on private and social features of agents’ utility functions. In addition, we consider the network’s topological structure and find that the regular network model fails in replicating exactly the new Keynesian model’s original results, while the complex network model presents results in accordance with the originals.

A Scientometric Research on Applications and Advances of Fire Safety Evacuation in Buildings

Fire safety evacuation has been used in numerous different kinds of buildings. This research conducts a scientometric review of fire safety evacuation applications and advances in the buildings to clarify the research trends of fire evacuation in the future and provide guidance for relevant research. A total of 3312 journals and conference proceedings were analyzed through different dimensions. The result proves that evacuation environments concentrate mainly on residential building, commercial building, school, and railway station. The characteristics of the evacuee have been gradually refined in recent years, including children, the elderly, patients, and vulnerable groups. The main experimental approaches of fire safety evacuation are evacuation drills, site records, and VR/AR experiments. The crowd behavior models mainly consist of six types: a cellular automata model, a social force model, a lattice gas model, a game-theoretic model, an animal agent-based model, and a computer agent-based model. The analysis results in the theoretical method are becoming gradually closer to the behavioral characteristics and movement data of the crowd during the actual evacuation with improvements of practical considerations. The study of evacuation drills, disaster rescue, emergencies, and other external environmental factors will become the forefront of future research, and subway stations, airports, high-rise building, and other personnel places will be the focus of the study of crowd evacuation.

Scaling of agent-based models to evaluate transmission risks of infectious diseases

The scaling behaviour of agent-based computational models, to evaluate transmission risks of infectious diseases, is addressed. To this end we use an existing computational code, made available in the public domain by its author, to analyse the system dynamics from a general perspective. The goal being to obtain deeper insight into the system behaviour than can be obtained from considering raw data alone. The data analysis collapses the output data for infection numbers and leads to closed-form expressions for the results. It is found that two parameters are sufficient to summarize the system development and the scaling of the data. One of the parameters characterizes the overall system dynamics. It represents a scaling factor for time when expressed in iteration steps of the computational code. The other parameter identifies the instant when the system adopts its maximum infection rate. The data analysis methodology presented constitutes a means for a quantitative intercomparison of predictions for infection numbers, and infection dynamics, for data produced by different models and can enable a quantitative comparison to real-world data.

On the Epidemiological Evolution of Colistin-Resistant Acinetobacter Baumannii in the City of Valencia: An Agent-Based Modelling Approach

Antibiotic resistance is one of the greatest public health threats today, mainly due to the non-rational use of antibiotics. Acinetobacter baumannii is an example of a microorganism with high antibiotic resistance that has developed rapidly in recent years. Consequently, only a few lastresort antibiotics, such as colistin, are currently effective against it. In this work, we propose a random agent-based computational model to describe the evolution of colistin-resistant A. baumannii in the population of Valencia (Spain) and to predict its impact both on the whole population and by age groups. The agent- based model uses a synthetic population of individuals with a vector of characteristics or state variables. These variables change over time based on a series of random events with certain conditional probabilities. The synthetic population statistical features and the probabilities have been found in demographic and hospital databases. One of these probabilities, the probability of infection by a resistant strain, has been modeled using random differential equations. The model takes into account antibiotic consumption as the primary driving force of variation and assumes non-rewersibility of resistance as the worst-case scenario. The agent-based model calibration and the selection of a real-world representative set of solutions have been carried out using the Partide Swarm Optimization evolutionary algorithm. This approach takes into account the inherent stochasticity of the model and the uncertainty of the data. Finally, projections of the incidence and absolute cases of colistin-resistant A. baumannii have been performed. Our results suggest that, if the same consumption pattern continues, the ervolution of the colistin-resistant strain proportion will be exponential, exceeding 50% in 2025. Additionally, the results reveal that, despite the low incidence in Valencian hospitals, the impact on people over 60 years old will be more significant in terms of the number of cases. Based on these findings, it can be deduced that colistin will cense to be an effective antibiotic in the coming years, negatively impacting the human population, especially the most advanced age groups.

Cellular-automaton model for tumor growth dynamics: Virtualization of different scenarios

Mathematical Oncology has emerged as a research field that applies either continuous or discrete models to mathematically describe cancer-related phenomena. Such methods are usually expressed in terms of differential equations, however tumor composition involves specific cellular structure and can demonstrate probabilistic nature, often requiring tailor-made approaches. In this context, cell-based models allow monitoring independent single parameters, which might vary in both time and space. By relying on extant tumor growth models in the literature, this study introduces cellular-automata simulation strategies that admit heterogeneous cell population while capturing both single-cell and cluster-cell behaviors. In this agent-based computational model, tumor cells are limited to follow four possible courses of action, namely: proliferation, migration, apoptosis or quiescence. Despite the apparent simplicity of those actions, the model can represent different complex tumor features depending on parameter settings. This study virtualized five different scenarios, showcasing model capabilities of representing tumor dynamics including alternate dormancy periods, cell death instability and cluster formation. Implementation techniques are also explored together with prospective model expansion towards deterministic features. The proposed stochastic cellular automaton model is able to effectively simulate different scenarios regarding tumor growth effectively, figuring as an interesting tool for in silico modeling, with promising capabilities of expansion to support research in mathematical oncology, thus improving diagnosis tools and/or personalized treatment.

Agent-based modeling predicts RAC1 is critical for ovarian cancer metastasis.

Experimental and computational studies pinpoint rate-limiting step(s) in metastasis governed by Rac1. Using ovarian cancer cell and animal models, Rac1 expression was manipulated, and quantitative measurements of cell-cell and cell-substrate adhesion, cell invasion, mesothelial clearance, and peritoneal tumor growth discriminated the tumor behaviors most highly influenced by Rac1. The experimental data were used to parameterize an agent-based computational model simulating peritoneal niche colonization, intravasation, and hematogenous metastasis to distant organs. Increased ovarian cancer cell survival afforded by the more rapid adhesion and intravasation upon Rac1 overexpression is predicted to increase the numbers of and the rates at which tumor cells are disseminated to distant sites. Surprisingly, crowding of cancer cells along the blood vessel was found to decrease the numbers of cells reaching a distant niche irrespective of Rac1 overexpression or knockdown, suggesting that sites for tumor cell intravasation are rate limiting and become accessible if cells intravasate rapidly or are displaced due to diminished viability. Modeling predictions were confirmed through animal studies of Rac1-dependent metastasis to the lung. Collectively, the experimental and modeling approaches identify cell adhesion, rapid intravasation, and survival in the blood as parameters in the ovarian metastatic cascade that are most critically dependent on Rac1.