[{"content":" Cells constantly navigate complex environments where they are exposed to multiple, often overlapping and contradictory chemical gradients. Understanding how cells integrate and prioritize these competing cues, deciding when to follow local versus distant signals, is critical for biological processes such as immune responses and tissue repair. Experimental observations reveal diverse behaviors, including cells prioritizing distant sources, exhibiting multistep navigation, or showing oscillatory motion and confinement between opposing gradients.\nOur research proposes that many of these complex chemotactic behaviors can be explained by considering the precision with which cells sense each chemical gradient. This precision, quantified as the signal-to-noise ratio (SNR), is non-monotonic with chemoattractant concentrations; cells sense gradients most accurately at intermediate concentrations and less effectively at very low or very high ones.\nOur model treats cells as active random walkers that possess independent receptors for different chemoattractants. They independently estimate the direction of each local gradient, and these individual estimates are then integrated through a vector sum to determine the overall movement direction. Crucially, cells effectively weight their movement bias towards the gradient they sense more accurately (i.e., the one with a higher SNR). This simple principle can explain a wide range of observed phenomena without needing to invoke more complex mechanisms like memory or receptor desensitization.\nKey Findings Explained by Gradient Sensing Accuracy: # Diverse Spatial Patterns and Confinement: The model predicts that depending on the decay length (λ) of the gradients, cells can exhibit different behaviors.\nFor small λ, SNR peaks are close to the sources, leading cells to migrate toward the nearest source. For larger λ, SNR peaks shift and can cross, causing cells to oscillate or become confined to a region between the sources, referred to as an \u0026ldquo;equipotential region\u0026rdquo; where chemotactic responses balance out. An intermediate λ* results in SNR peaks overlapping centrally, leading to no directional bias and broader cell dispersion. Hierarchical Chemotactic Response and Multistep Navigation: This model explains why cells might prioritize certain \u0026ldquo;end-target\u0026rdquo; chemoattractants (like fMLP) over \u0026ldquo;intermediate\u0026rdquo; ones (like IL-8 or LTB4). This hierarchy emerges from differences in cellular sensitivity to each chemoattractant, such as varying receptor numbers. If a cell has more receptors for one chemoattractant, it creates an asymmetric chemotactic response map, where larger regions favor recruitment towards that more sensitively sensed source. This naturally leads to multistep navigation, where a cell might initially move toward a weaker source before redirecting its trajectory to the preferred, more accurately sensed source. Impact of Source Strength (S0): Variations in the concentration magnitude at the source can alter the spatial distribution of cells, changing from elongated along the source axis to increased dispersion perpendicular to it for large S0 values.\nIn essence, our research highlights that the fundamental accuracy of a cell\u0026rsquo;s gradient sensing is a primary driver of how it navigates and organizes itself in complex, multi-signal environments. The model provides testable hypotheses for future experiments by predicting how changes in chemoattractant concentration, source distance, or receptor numbers could alter cell migration and distribution. This principle could be exploited to strategically organize cell distributions, for example, to concentrate immune defenses in specific areas.\nThink of it like a navigator trying to find their way using multiple, potentially conflicting maps, each with varying levels of detail and accuracy. Instead of blindly following any single map, the navigator intuitively trusts the maps that provide the clearest, most reliable information, even if it means taking a longer or seemingly indirect path to their destination.\nPerez Ipiña, E., \u0026amp; Camley, B. A. (2025). Competing chemical gradients change chemotactic dynamics and cell distribution. arXiv preprint arXiv:2507.19341 (2025) link. ","externalUrl":null,"permalink":"/research/multiple_gradients/","section":"Research","summary":"How do cells decide which chemical signal to follow in complex environments? By weighting their movement toward the gradient they can sense most accurately, cells navigate competing cues and find effective paths even when signals conflict or overlap.","title":"Navigating multiple gradients","type":"research"},{"content":" Cells modify their environment as they move # Cells don’t just move across their environment—they reshape it as they go, leaving footprints that can strongly influence their future motion. A central goal of my research is to understand how these feedbacks between cell motility and the environment generate new migration strategies in both single cells and collectives.\nHT-1080 fibrosarcoma epithelial cell moving on the extracellular matrix. Developmental cell, 56(6), 826-841 Cells use their footprints to change their migration strategies # In my recent research, I explored how cells can change their migration strategies by interacting with molecular footprints they leave behind. My work builds on studies showing that migrating cells leave molecular footprints on the ECM, which can induce oscillatory motion.\nWe developed a mechanistic framework that couples cell shape, a deposited footprint, and intracellular polarity signaling. In the model, a phase‑field description of the cell is linked to a polarity module in which local contact with the footprint activates Rac1, biasing protrusion toward previously explored regions. This creates a positive feedback loop: the cell moves, deposits more footprint, and becomes more likely to move along that path again.\nGeometry plays a central role. On 1D micropatterned stripes, our model reproduces oscillations whose amplitude grows as cells repeatedly revisit their own tracks.\nTwo motility modes. Left: the cell oscillates, turning at the edge of its footprint, with the oscillation amplitude increasing over time. Right: the cell advances persistently in one direction into areas it has not previously visited. By varying two key factors— such as the rate cells deposit footprint—we find sharp transitions among confined motion, oscillatory back‑and‑forth motion, and persistent exploration. Small parameter changes can therefore produce large behavioral shifts, suggesting that modest biochemical or mechanical regulation may toggle a cell between being trapped and being exploratory.\nGromit frantically lays down train tracks just ahead of a speeding locomotive. From Wallace \u0026amp; Gromit: The Wrong Trousers (1993). In 2D, the same basic mechanism yields two distinct outcomes: circularly confined trajectories that slowly expand, and fully exploratory paths that escape confinement. We revisited experiments on 2D substrates and observed both expanding circular motion and exploratory migration, consistent with the model’s predictions.\nCells on a 2D substrate display different exploratory behaviors depending on how rapidly they modify their environment. Overall, our results support a simple principle with broad implications: cells can use their own footprints to control their migration strategy.\nPerez Ipiña, E., d\u0026rsquo;Alessandro, J., Ladoux, B., Camley, B. A. Deposited footprints let cells switch between confined, oscillatory, and exploratory migration. Proceedings of the National Academy of Sciences 121.22 (2024): e2318248121 link. ","externalUrl":null,"permalink":"/research/footprints/","section":"Research","summary":"Migrating cells actively remodel their environment by leaving molecular footprints, which shape both individual and collective cell movement through dynamic feedback.","title":"How Cellular Footprints Guide Migration","type":"research"},{"content":" How do bacteria actually find host cells when moving close to a surface? # Motility is one of the key tools bacteria use to explore their environment. My research focuses on what happens when bacteria, like E. coli and Salmonella, move near surfaces such as host tissues—the critical step for starting an infection.\nIs infection just random, or do bacteria really chase host cells? What about chemotaxis? # Bacteria are well known for chemotaxis—following chemical signals to move towards food or targets. In bulk liquid, this strategy works well. But near surfaces, it gets complicated. At the surface, bacteria like E. coli don’t always move straight towards host cells. Their movement becomes circular due to physical effects, and classic chemotaxis doesn’t function in the same way.\nWhat is really interesting—and a main focus of my research—is that E. coli and other bacteria show a special kind of movement when close to surfaces: the stop and go dynamics. They swim for a while, then stop and briefly adhere to the surface before starting again. Our results suggest that the bacteria regulate how often they stop, and that there is an optimal stopping frequency which maximizes how fast they explore the surface (their surface diffusivity). This “stop and go” strategy is not random: it seems to help bacteria scan the surface more efficiently and could be a way to recover some ability to bias their search towards host cells, even when classical chemotaxis is suppressed.\nWhy does this matter? # Understanding this stop and go motion is important because it reveals a possible mechanism bacteria use to increase their chances of successfully finding and infecting host cells. It also explains why infection in these conditions often appears random, but is actually driven by clever search strategies evolved by the bacteria. These dynamics are a big piece of the puzzle for knowing when and how infection starts.\nWhat does this tell us about microorganism motility and the environment? # My broader research tries to connect motility of microorganisms with the physical and chemical environment. Chemotaxis is only one aspect—physical structures, surfaces, and the ability to stop and stick also play a huge role. I am interested in how all these mechanisms combine in real infection scenarios, and how bacteria can adapt their strategies depending on where they are.\nKey questions I am working on include:\nHow do bacteria optimize their surface movement and search strategies? What is the real impact of stop-and-go dynamics for infection and for chemotaxis? Can understanding these strategies help us find new ways to fight bacterial infections? I believe the way bacteria move and interact with their environment is a rich and fascinating topic, with important consequences for health and disease. My goal is to keep uncovering how these small organisms use clever movement strategies to survive and infect.\nOtte, S.Ɨ, Perez Ipiña, E. Ɨ, Pontier-Bres, R., Czerucka, D., \u0026amp; Peruani, F. (2021). Statistics of pathogenic bacteria in the search of host cells. Nature Communications, 12(1), 1-9 link. Ɨ co-first authors\nPerez Ipiña, E., Otte, S., Pontier-Bres, R., Czerucka, D., \u0026amp; Peruani, F. (2019). Bacteria display optimal transport near surfaces. Nature Physics, 15(6), 610-615 link.\n","externalUrl":null,"permalink":"/research/bacteria/","section":"Research","summary":"How do bacteria efficiently find host cells near surfaces? By alternating swimming with brief stops and adhesion, they enhance their ability to explore and interact with the environment during infection.","title":"In Search of a Host: How Bacterial Motility Drives Infections","type":"research"},{"content":" Eukaryotic cells are capable of detecting and responding to chemical gradients in their environment, a function essential for processes such as immune response, embryogenesis, and cancer metastasis. These behaviors are governed by physical mechanisms that operate both at the level of single cells and within multicellular groups. My research is focused on elucidating how cells, individually and collectively, acquire sensory information and translate this into directed movement, particularly in the presence of stochastic fluctuations.\nGradient Sensing by Single Cells: Quantitative Limits and Biological Variability # The initial step in gradient sensing involves ligand molecules binding to cell-surface receptors. The accuracy with which individual cells can estimate external concentrations is limited by several sources of noise:\nLigand-receptor binding fluctuations: Diffusive arrival and stochastic binding/unbinding of molecules introduce temporal noise into receptor occupancy. Cell-to-cell variability (CCV): Genetically identical cells can display substantial heterogeneity in molecular and functional properties, often exceeding ligand-receptor noise in magnitude. During my doctoral research, I developed mathematical frameworks to quantify the precision of concentration measurements by cells, accounting for relevant time scales of biological decision-making. Building on the foundational work of Berg and Purcell, these models provide analytic expressions for the autocorrelation of molecular input signals, valid across all time regimes.\nMain results include:\nShort-time accuracy: Corrections to standard concentration estimation can allow the error to decrease more rapidly for short integration times. This fast convergence is relevant for transient processes such as chemotaxis and binding dynamics in gene regulation. Applications: The framework explains observed phenomena, such as the precision of Bicoid gradient sensing in Drosophila embryos and changes in noise properties of enzymatic activity, by connecting measurement accuracy to transport and reaction kinetics. Collective Gradient Sensing: Role of Positional Information # Groups of cells frequently exhibit enhanced gradient detection compared to isolated cells. In our work, we consider how the accuracy of collective sensing depends on the ability of individual cells to localize themselves within the cluster—referred to as positional information.\nApplying a maximum likelihood estimation (MLE) approach, we found:\nImpact of uncertainty: Inaccuracies in positional information reduce the overall precision of gradient estimation, especially in steep gradients where spatial mislocalization results in significant concentration error. Edge contribution: Cells at the edge of a cluster contribute disproportionately to collective sensing; prioritizing positional information for edge cells is the most efficient allocation if information acquisition is limited. Scaling with group size: Larger clusters are less affected by positional uncertainty, as individual measurement errors are averaged out. Alternative Mechanisms: Tug-of-War Model # Cells may also coordinate movement without explicit positional knowledge. Based on observations in neural crest and lymphocyte clusters, we compared the MLE framework to a tug-of-war (ToW) model:\nCells polarize and exert force away from immediate neighbors via contact inhibition of locomotion, with polarization restricted to edge cells. In the presence of a gradient, edge cells at the front polarize more strongly, resulting in net directional migration. Comparison of the two models suggests:\nSmaller clusters or steep gradients: The ToW mechanism can yield more accurate orientation than strategies dependent on positional information, particularly when most cells are at the cluster edge. Larger clusters or shallow gradients: The MLE approach becomes optimal as the integration of multiple measurements compensates for positional uncertainty. We constructed phase diagrams delineating the conditions under which each strategy achieves superior accuracy, suggesting that cell clusters may employ distinct mechanisms in different contexts.\nPhase diagram showing the trade-off between MLE and tug‑of‑war models across cluster size and gradient steepness. Perez Ipiña, E., \u0026amp; Camley, B. A. (2022). Collective gradient sensing with limited positional information. Physical Review E, 105(4), 044410 link. Perez Ipiña, E., \u0026amp; Ponce Dawson, S. (2016). Fluctuations, correlations, and the estimation of concentrations inside cells. PLOS ONE, 11(3), e0151132 link. ","externalUrl":null,"permalink":"/research/gradient_sensing/","section":"Research","summary":"Eukaryotic cells sense noisy chemical signals in their environment to guide their directional movement.","title":"Cell Sensing and Navigation: From Individual Precision to Emergent Collective Behavior","type":"research"},{"content":"The physical and chemical characteristics of the cellular environment critically shape how cells move, organize, and interact. Environmental factors—including mechanical confinement, substrate composition, and chemical gradients—not only influence single-cell motility but also coordinate complex collective behaviors during development, tissue repair, and disease progression. Understanding how environmental conditions modulate cell migration provides key insights into diverse biological processes such as embryogenesis, immune surveillance, and cancer metastasis.\nEukaryotic Cell Migration in Confined Channels # Using microfluidic channels to model cancer cell invasion, our research shows that physical confinement strongly influences both collective migration and cell dissociation—or \u0026ldquo;rupture.\u0026rdquo; Within these constrained environments, cells detach from invasive strands, a process central to metastatic dissemination. Most rupture events involve single cells breaking away, though larger multicellular clusters (up to ~20 cells) can also detach in wider channels.\nDetachment propensity is not uniform among all cells. Highly motile \u0026ldquo;leader cells,\u0026rdquo; found at the migrating front, are especially prone to dissociation and largely account for the predominance of single-cell ruptures. These leader cells typically exhibit greater self-propulsion and reduced contact with surrounding neighbors, contributing to their likelihood of breaking off.\nAdhesive interactions and chemotactic signaling play key roles in regulating rupture dynamics. Cell–wall adhesion is essential for both invasion into narrow channels and the occurrence of ruptures, but excessive adhesion can inhibit detachment. Stronger cell–cell adhesion generally leads to fewer but larger rupture events. Notably, mathematical modeling indicates that effective cell–cell adhesion may decrease dynamically in narrow confinements, potentially explaining why rupture rates remain similar across different channel widths. Enhanced chemotactic gradients synchronize cell movement, resulting in larger and more rapid ruptures.\nAt the molecular level, confinement-induced dissociation is driven by activation of the RhoA/ROCK/Myosin IIA pathway and changes in microtubule dynamics. RhoA activation, mediated by GEF-H1, Ect2, and RacGAP1, promotes myosin II accumulation at junctions, generating contractile forces that disrupt cell–cell adhesions.\nImportantly, while unjamming (fluidization) of the tissue is a prerequisite for cell dissociation, it alone does not suffice to trigger rupture. Tissues may attain a fluid-like state without necessarily undergoing dissociation, indicating that factors beyond general tissue fluidity are necessary for rupture to occur.\nIn summary, cell migration and dissociation within confined environments reflect a complex interplay between physical constraints, cellular properties, and molecular signaling pathways—mechanisms with direct relevance to our understanding of cancer metastasis and related collective behaviors.\nWang, W., Law, R. A., Perez Ipiña, E., Konstantopoulos, K., \u0026amp; Camley, B. A. (2025). Confinement, jamming, and adhesion in cancer cells dissociating from a collectively invading strand. PRX Life 3, 013012 link.\nLaw, R. A., Kiepas, A.Ɨ, Desta, H. E. Ɨ, Perez Ipiña, E. Ɨ, Parlani, M., Lee, S. J., Yankaskas, C. L., Zhao, R., Mistriotis, P., Wang, N., Gu, Z., Kalab, P., Friedl, P., Camley, B. A., \u0026amp; Konstantopoulos, K. (2023). Cytokinesis machinery promotes cell dissociation from collectively migrating strands in confinement. Science Advances, 9(2), eabq6480 link. Ɨ co-second authors\n","externalUrl":null,"permalink":"/research/confinement_channels/","section":"Research","summary":"Physical barriers like narrow tissue channels can dramatically alter how cancer cells move and break away, revealing key mechanisms that underlie metastatic spread.","title":"How Physical Confinement Shapes Cancer Cell Migration and Invasion","type":"research"},{"content":"","externalUrl":null,"permalink":"/authors/","section":"Authors","summary":"","title":"Authors","type":"authors"},{"content":" During my doctoral years at the University of Buenos Aires, I took the 160 bus every day to get to the office. I spent countless hours at bus stops, witnessing a maddening yet intriguing pattern: I’d wait 20, sometimes 30 minutes without a single bus, and then—suddenly—three would arrive together.\nAt first, I blamed the drivers, the bus company, or the traffic. What were they doing—waiting to travel together and chat at red lights? Was I just unlucky? It didn’t add up. The more I thought about it, the more I realized it wasn’t random at all, but inevitable. Bus bunching isn’t a failure of planning; it’s the outcome of physics and feedback loops.\nA positive feedback loop # Imagine Bus A hits traffic or finds more passengers than usual at a stop. It falls slightly behind schedule. Now the next stop has had more time to accumulate passengers, so Bus A takes even longer to board them. It falls further behind. Meanwhile, Bus B behind it finds fewer passengers at each stop—it speeds up and catches Bus A.\nTraffic, weather, accidents—any event that delays a bus will push it toward this outcome. The gaps between buses don\u0026rsquo;t correct themselves; they grow.\nThis is a positive feedback loop. Evenly-spaced buses are an unstable equilibrium. Bus bunching is the stable one. Left alone, bunching is where all buses end up.\nSo why don\u0026rsquo;t we see it all the time?\nBus bunching emerges from a positive feedback loop: delayed buses pick up more passengers, falling further behind, while trailing buses catch up. Space-time diagram showing bus trajectories: initially evenly spaced buses progressively bunch together over time. Bus bunching makes service unpredictable: passengers face long waits followed by multiple buses at once, some packed and others nearly empty. It also wastes resources—several large buses traveling together clog traffic while leaving gaps in service elsewhere. The problem gets worse with longer routes, higher frequency, more passengers, unpredictable traffic, and slow boarding—all of which give the feedback loop more room to grow. Transit agencies fight back by holding early buses at stops, padding schedules with extra recovery time, using GPS to adjust speeds in real time, and reducing boarding variability with prepaid fares and multiple doors. Dedicated bus lanes help by removing traffic randomness. None of these eliminate bunching, but they slow it down.\nOne effective strategy to reduce bunching is to enhance the efficiency of passenger boarding. Bunching Beyond Buses # The same instability that bunches buses appears throughout nature. The ingredients are simple: units moving in sequence along a shared path, where the speed of each unit depends on local conditions. Whenever these conditions hold, bunching emerges.\nRibosomes translating mRNA bunch up behind slow codons. Molecular motors walking along microtubules cluster when one stalls. Ants following pheromone trails pile up on popular routes. Cars on a highway form the stop-and-go waves we call traffic jams. The mathematics underlying all these systems is remarkably similar—a positive feedback loop that amplifies small perturbations into large clusters.\nSometimes bunching is a problem to solve. Sometimes it\u0026rsquo;s a feature to exploit.\nCell Bunching in Cancer Metastasis # In cancer metastasis, tumor cells escape and invade surrounding tissue. Clinically, it is well established that cells travel from tumors as clusters, and these clusters are far more dangerous than single cells—they survive better in the bloodstream and are more likely to seed new tumors.\nBut why do cells cluster in the first place?\nAs a cell moves through the extracellular matrix toward blood vessels, it degrades the matrix and leaves behind a softened, remodeled track—a footprint. Follower cells encounter this footprint and move faster along it. They catch up to the leader. The same feedback loop that bunches buses bunches cells.\nThe footprint degrades over time, just as passengers accumulate at bus stops over time. Both create a memory effect: the longer since the last bus (or cell) passed, the more \u0026ldquo;resistance\u0026rdquo; the next one encounters. In buses, this means more passengers to board. In cells, it means a more intact matrix to degrade.\nThis memory effect controls whether bunching occurs. If buses run frequently, small delays compound before the system resets—bunching is inevitable. If buses run infrequently, fluctuations in boarding time aren\u0026rsquo;t enough for one bus to catch another. The same logic applies to cells: high escape rates from the tumor lead to clustering; low escape rates keep cells isolated.\nCells escaping a tumor leave behind a remodeled path that follower cells exploit, moving faster and catching up—the same feedback dynamics as bus bunching. This reframes metastatic clustering not as active coordination between cells, but as an emergent physical phenomenon—the inevitable consequence of cells moving along a shared path with memory. The same instability that frustrated me at Buenos Aires bus stops may help explain why cancer spreads the way it does.\nUnderstanding the physics of cell bunching opens new questions: Can we disrupt the footprint to prevent clustering? Can we tune matrix properties to keep cells isolated? The answers may lie in the same mathematics that transit engineers use to keep buses evenly spaced.\n","externalUrl":null,"permalink":"/projects/bondi/","section":"Projects","summary":"","title":"Bondi bunching","type":"projects"},{"content":"","externalUrl":null,"permalink":"/categories/","section":"Categories","summary":"","title":"Categories","type":"categories"},{"content":"I’m a scientist driven by curiosity and a fascination with how living systems work. Over the years, I’ve enjoyed moving between disciplines—physics, biology, and data science—chasing interesting problems and working with great people. My research has taken me from studying how cells move and communicate, to developing models that help us make sense of complex experimental data, and even to building tools for detecting odd behaviors in massive industrial datasets.\nI like finding patterns in noisy data, asking questions that cut across fields, and collaborating with researchers from different backgrounds. I’m comfortable diving into new topics, learning new techniques, and explaining tricky ideas in a way that makes sense to others. Along the way, I’ve mentored students, published in peer-reviewed journals, and presented at international conferences—experiences I value not just for the science, but for the teamwork and shared learning.\nI am interested in the diffusion and motility of microorganisms, such as bacteria and cells, the interaction with their habitats, the propagation of information inside biological systems, and the robustness of signal processing under fluctuating environments.\nIn my research, I aim to explain experimental observations with simple theoretical models that allow me to identify the fundamental elements underlying these biological processes. To do so, I combine tools from statistical physics, stochastic processes, and active matter physics together with detailed data analysis methods.\n","externalUrl":null,"permalink":"/about/","section":"Motility and Sensing in Microorganisms","summary":"","title":"Emiliano Perez Ipiña","type":"page"},{"content":"I’m a scientist driven by curiosity and a fascination with how living systems work. Over the years, I’ve enjoyed moving between disciplines—physics, biology, and data science—chasing interesting problems and working with great people. My research has taken me from studying how cells move and communicate, to developing models that help us make sense of complex experimental data, and even to building tools for detecting odd behaviors in massive industrial datasets.\nI like finding patterns in noisy data, asking questions that cut across fields, and collaborating with researchers from different backgrounds. I’m comfortable diving into new topics, learning new techniques, and explaining tricky ideas in a way that makes sense to others. Along the way, I’ve mentored students, published in peer-reviewed journals, and presented at international conferences—experiences I value not just for the science, but for the teamwork and shared learning.\nI am interested in the diffusion and motility of microorganisms, such as bacteria and cells, the interaction with their habitats, the propagation of information inside biological systems, and the robustness of signal processing under fluctuating environments.\nIn my research, I aim to explain experimental observations with simple theoretical models that allow me to identify the fundamental elements underlying these biological processes. To do so, I combine tools from statistical physics, stochastic processes, and active matter physics together with detailed data analysis methods.\n","externalUrl":null,"permalink":"/about_2/","section":"Emiliano Perez Ipiña","summary":"","title":"Emiliano Perez Ipiña","type":"about_2"},{"content":" I study how cells, from bacteria and algae to eukaryotic cells, move and how their environments shape and guide that motion. My goal is to uncover the physical principles that govern the interplay between motility, migration, and complex environments, with implications for development, immune response, bacterial infection, and cancer invasion. Previous Next My research combines theoretical modeling, data-driven analysis, and quantitative approaches to address questions such as:\nHow do microorganisms adapt their motility strategies to navigate through obstacles and confined spaces? In what ways do physical environments influence microbial search efficiency and infection potential? How do cells process noisy and fluctuating signals to achieve robust sensing and coordination? What roles do emergent behaviors—such as memory effects and collective exploration—play in cellular adaptation? By revealing how physical constraints and environmental cues shape the movement and sensory capabilities of living systems, this research provides insights relevant to infection dynamics, tissue organization, and biomaterial design. The goal is to advance understanding of the diverse and fascinating strategies microorganisms use to sense and respond to the world around them.\nExplore the site for current projects, publications, opportunities to collaborate, and personal side projects I enjoy outside the lab.\n","externalUrl":null,"permalink":"/","section":"Motility and Sensing in Microorganisms","summary":"","title":"Motility and Sensing in Microorganisms","type":"page"},{"content":"Here are several projects I have worked on in the past, spanning my academic research, industry experience, and personal hobbies.\nMultiple gradients Bondi model Outliers Fulbito ","externalUrl":null,"permalink":"/projects/","section":"Projects","summary":"","title":"Projects","type":"projects"},{"content":" (EDITORS\u0026rsquo; SUGGESTION) Perez Ipiña, E., \u0026amp; Camley, B. A. (2026). Competing chemical gradients change chemotactic dynamics and cell distribution. Physical Review E, 113(3), 034406. link.\nWang, W., Law, R. A., Perez Ipiña, E., Konstantopoulos, K., \u0026amp; Camley, B. A. (2025). Confinement, jamming, and adhesion in cancer cells dissociating from a collectively invading strand. PRX Life 3, 013012 link.\nPerez Ipiña, E., d\u0026rsquo;Alessandro, J., Ladoux, B., Camley, B. A. Deposited footprints let cells switch between confined, oscillatory, and exploratory migration. Proceedings of the National Academy of Sciences 121.22 (2024): e2318248121 link.\nLaw, R. A., Kiepas, A.Ɨ, Desta, H. E. Ɨ, Perez Ipiña, E. Ɨ, Parlani, M., Lee, S. J., Yankaskas, C. L., Zhao, R., Mistriotis, P., Wang, N., Gu, Z., Kalab, P., Friedl, P., Camley, B. A., \u0026amp; Konstantopoulos, K. (2023). Cytokinesis machinery promotes cell dissociation from collectively migrating strands in confinement. Science Advances, 9(2), eabq6480 link. Ɨ co-second authors\nPerez Ipiña, E., \u0026amp; Camley, B. A. (2022). Collective gradient sensing with limited positional information. Physical Review E, 105(4), 044410 link.\nOtte, S.Ɨ, Perez Ipiña, E. Ɨ, Pontier-Bres, R., Czerucka, D., \u0026amp; Peruani, F. (2021). Statistics of pathogenic bacteria in the search of host cells. Nature Communications, 12(1), 1-9 link. Ɨ co-first authors\nPerez Ipiña, E., Otte, S., Pontier-Bres, R., Czerucka, D., \u0026amp; Peruani, F. (2019). Bacteria display optimal transport near surfaces. Nature Physics, 15(6), 610-615 link.\nCarbó, N., Tarkowski, N., Perez Ipiña, E., Dawson, S. P., \u0026amp; Aguilar, P. S. (2017). Sexual pheromone modulates the frequency of cytosolic Ca2+ bursts in Saccharomyces cerevisiae. Molecular Biology of the Cell, 28(4), 501-510 link.\nPerez Ipiña, E., \u0026amp; Dawson, S. P. (2017). The effect of reactions on the formation and readout of the gradient of Bicoid. Physical Biology, 14(1), 016002 link.\nPerez Ipiña, E., \u0026amp; Ponce Dawson, S. (2016). Fluctuations, correlations, and the estimation of concentrations inside cells. PLOS ONE, 11(3), e0151132 link.\nPerez Ipiña, E., \u0026amp; Dawson, S. P. (2014). How long should a system be observed to obtain reliable concentration estimates from the measurement of fluctuations? Biophysical Journal, 107(11), 2674-2683 link.\nPiegari, E., Lopez, L., Perez Ipiña, E., \u0026amp; Ponce Dawson, S. (2014). Fluorescence fluctuations and equivalence classes of Ca2+ imaging experiments. PLOS ONE, 9(4), e95860 link.\nPerez Ipiña, E., \u0026amp; Dawson, S. P. (2013). From free to effective diffusion coefficients in fluorescence correlation spectroscopy experiments. Physical Review E, 87(2), 022706 link.\n","externalUrl":null,"permalink":"/publications/","section":"Publications","summary":"","title":"Publications","type":"publications"},{"content":" The Interplay between Motility, Migration, and the Environment in Microorganisms # The movement of microorganisms, including some eukaryotic cells and bacteria, is essential for numerous biological functions. The motility mechanisms of bacteria and eukaryotic cells are very different, but they share something in common: motility and its transport properties are the result of a complex interplay between the internal machinery of cells and the environment.\nCells need to sense and respond to environmental signals that tell them when and where to move. They also need to navigate complex landscapes, such as crawling across the extracellular matrix (ECM) or swimming through the bloodstream. In addition, cells can create their own signals by secreting and degrading chemoattractants to establish self-generated gradients or modify their environment by reorganizing the ECM and creating a footprint.\nTo fully understand the mechanisms underlying cell movement and migration, we need to study these phenomena in the context in which they occur. Thus, it is essential to consider the relationship between cells and their environment when studying the complex process of motility and migration.\n","externalUrl":null,"permalink":"/research/","section":"Research","summary":"","title":"Research","type":"research"},{"content":"","externalUrl":null,"permalink":"/series/","section":"Series","summary":"","title":"Series","type":"series"},{"content":"","externalUrl":null,"permalink":"/tags/","section":"Tags","summary":"","title":"Tags","type":"tags"},{"content":"Teaching has been an integral part of my path in science, and something I have truly enjoyed. Over the years, I have had the opportunity to teach physics at Universidad Católica Argentina, Universidad de Buenos Aires, and Johns Hopkins University, working with students from diverse academic backgrounds including physics, biology, geology, and engineering.\nTeaching Experience # Institution Course Role Hours Enrollment Johns Hopkins University Biological Physics Lecturer 4 15–25 (Advanced UG/Grad) Johns Hopkins University Statistical Mechanics Lecturer 2 15–25 (Advanced UG/Grad) Universidad de Buenos Aires Physics II Teaching Assistant 192 50–70 Universidad Católica Argentina Physics I Teaching Assistant 128 ~16 per section Universidad Católica Argentina Physics III Teaching Assistant 336 ~16 per section Universidad Católica Argentina Physics I Laboratory Lab Instructor 816 ~16 per section At Johns Hopkins, I delivered lectures on topics closely connected to my research, including diffusion, Brownian dynamics, and the Boltzmann distribution. At Universidad de Buenos Aires, I taught thermodynamics and optics to senior biology and geology students in large classes. At Universidad Católica Argentina, I taught mechanics, thermodynamics, and optics over seven semesters, led laboratory courses, and served as Laboratory Technical Advisor, where I helped modernize experimental infrastructure and introduce automated data acquisition systems.\nMentoring # I have also enjoyed mentoring students at various levels:\nMentored a Baltimore city high school student through the Johns Hopkins WISE program Co-mentored a senior undergraduate student for a research project Co-mentored a middle school student in the Ingenuity Project Baltimore Tutored students with learning disorders at the Cimarra Institute of Psychopedagogy in Argentina ","externalUrl":null,"permalink":"/teaching/","section":"Teaching","summary":"","title":"Teaching","type":"teaching"}]