Biophysics
biophysics
MedUni PhD Recruitment
Medical University of Vienna invites applications for all currently open Ph.D. positions within their 18 Ph.D. programs. We encourage ambitious and creative young scientists to develop their original research project in the field of Behavioural Biology, Biochemistry, Biophysics, Bioinformatics & Machine Learning, Cancer, Cardiovascular Systems, Drug Targets & Drug Development, Endocrinology & Metabolism, Biomedical Engineering, Mathematics & Statistics, Immunology, Medical Physics, Mental Health, Molecular and Cellular Biology, Neuroscience and Public Health with the assistance of our renowned and international scientists . Benefit from a well-established and connected network within the science community and built important relations with your peers at our university. On top of it, become an expert in your field! All project information can be found online under https://www.meduniwien.ac.at/web/en/studies-further-education/phd-doctoral-programmes/phd-programme-un094/phd-opportunities/ Apply online till 20.11.2022
Arvind Kumar
Postdoctoral researcher positions are available in computational neuroscience. The projects will entail modelling of biological neural networks, either reduced rate-models or data-driven biophysical models or analysis of neural data. Each selected candidate will work in close collaboration with other PIs in the dBrain consortium. dBRAIN is an interdisciplinary initiative to better understand neurodegenerative diseases such as Parkinson’s disease and Alzheimer’s disease. We combine computational modeling, machine learning and topological data analysis to identify causal links among disease biomarkers and disease symptoms. This understanding should improve diagnosis, prediction of the disease progression and suggest better therapies. There are 3 positions available and the selected candidates with work with Arvind Kumar [https://www.kth.se/profile/arvindku?l=en] Jeanette Hellgren Kotaleski [https://www.kth.se/profile/jeanette?l=en] Erik Fransen [https://www.kth.se/profile/erikf?l=en] Apply: https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:390546/where:4/
Miguel Aguilera
The postdoc position is focused on self-organized network modelling. The project aims to develop a theory of learning in liquid brains, focusing on how liquid brains learn and their adaptive potential when embodied as an agent interacting with a changing external environment. The goal is to extend the concept of liquid brains from a theoretical concept to a useful tool for the machine learning community. This could lead to more open-ended, self-improving systems, exploiting fluid reconfiguration of nodes as an adaptive dimension which is generally unexplored. This could also allow modes of learning that avoid catastrophic forgetting, as reconfigurations in the network are based on reversible movement patterns. This could have important implications for new paradigms like edge computing.
N/A
The PostDoctoral researcher will conduct research activities in modelling and simulation of reward-modulated prosocial behavior and decision-making. The position is part of a larger effort to uncover the computational and mechanistic bases of prosociality and empathy at the behavioral and circuit levels. The role involves working at the interface between experimental data (animal behavior and electrophysiology) and theoretical modelling, with an emphasis on Multi-Agent Reinforcement Learning and neural population dynamics.
Michael J Frank, PhD
The Carney Institute for Brain Science at Brown University is seeking Postdoctoral Fellows to join the NIMH funded T32 Training Program in Computational Psychiatry. The program’s goal is to train research fellows with expertise in computational cognitive and systems neuroscience, capable of collaborating with clinical researchers to advance knowledge of psychiatric disorders and treatments. Eligible research topics include brain and cognitive modeling over multiple scales and levels of analysis (ranging from biophysics to artificial intelligence), and the use of these models to understand mechanisms of psychiatric disorders with the ultimate goal of improving treatments. The program applies an apprenticeship model in which fellows work with a primary research trainer in a computational field and a secondary research mentor in clinical psychiatry. In this apprenticeship model, the trainer works closely with the fellow and a secondary clinical psychiatry mentor, who is conducting research in areas such as neuroimaging, neurostimulation, digital phenotyping, and/or animal models. The list of eligible faculty trainers can be found on the Training Program in Computational Psychiatry’s website.
Prof. Erik De Schutter
The Computational Neuroscience unit at the Okinawa Institute of Science and Technology, Japan has an opening for a postdoctoral researcher or technician to contribute to the software development of the nanoscale simulator of neuronal electrophysiology and molecular properties STEPS. The software developer will join the STEPS team and contribute to maintenance and further expansion of the software capacities. Recent versions of STEPS improved its parallel performance and added modeling of vesicles. The ideal candidate will have a scientific background but also possess good programming skills, but experienced software engineers can also apply.
Dr. Fleur Zeldenrust
For the Vidi project ‘Top-down neuromodulation and bottom-up network computation,’ we seek a postdoc to study neuromodulators in efficient spike-coding networks. Using our lab’s data on dopamine, acetylcholine, and serotonin from the mouse barrel cortex, you’ll derive models connecting single cells, networks, and behavior. The aim of this project is to explain the effects of neuromodulation on task performance in biologically realistic spiking recurrent neural networks (SRNNs). You will use the efficient spike coding framework, in which a network is not trained by a learning paradigm but deduced using mathematically rigorous rules that enforce efficient coding (i.e. maximally informative spikes). You will study how the network’s structural properties such as neural heterogeneity influence decoding performance and efficiency. You will incorporate realistic network properties of the (barrel) cortex based on our lab’s measurements and incorporate the cellular effects of dopamine, acetylcholine and serotonin we have measured over the past years into the network, to investigate their effects on representations, network activity measures such as dimensionality, and decoding performance. You will build on the single cell data, network models and analysis methods available in our group, and your results will be incorporated into our group’s further research to develop and validate efficient coding models of (somatosensory) perception. Therefore, we are looking for a team player who is willing to learn from the other group members and to share their knowledge with them.
Dr. Fleur Zeldenrust
For the NWO project ‘DBI2’ we are looking for a PhD candidate to study predictive error responses in the auditory cortex. The main goal is to design an experimental approach to distinguish between two alternative theories of predictive coding and processing. Predictive coding and predictive processing are compelling theories to explain brain function. The idea that the brain continually maintains and updates an internal model of the outside world, and compares the incoming input with the expectations generated by this model, can explain many phenomena including adaptive behaviour and sensory effects such as oddball responses. However, until now there is no consensus on how such predictive coding could be implemented in real neural tissue. Importantly, there are two alternative theories on how error signals in predictive processing could be coded in neural signals: either as (1) top down signals from ‘higher order’ brain areas (hierarchical predictive coding, [2]) or (2) local signals, resulting in membrane potentials reflecting error signals ([3], for a review, see [1]). The goal of the research presented here, under the primary supervision of Dr Zeldenrust, is to design an experimental approach to distinguish between these two theoretical approaches. Measuring error signals in neural tissue is experimentally challenging. Therefore, a direct exchange between theory and experiment is needed, so that hypotheses and specific predictions about which neurons to record from and stimulate and the results expected can be quickly updated for the design of optimal experiments. The student will work in close collaboration with the Englitz lab, so that there is a direct link between modelling, data analysis and experiment. As a PhD candidate you will use data on oddball paradigms [4,5], which provide the ability to directly observe predictions and distinguish them from prediction errors. The data are a combined approach of widefield imaging of the entire auditory cortex with local and layer-specific imaging using 2-photon recordings in the same animals. To directly test the top-down hypothesis, neurons in subareas of the prefrontal cortex will be transfected with an inhibitory opsin (eNpHR3.0) to modulate their top-down influence. You will develop a model of the hierarchical interaction between the auditory cortex and the prefrontal cortex, in which error signals are either coded as top-down (theory 1) or local (theory 2). You will use this model to formulate testable predictions, distinguishing theory 1 from theory 2. These predictions will be tested by both analysing existing data from the Englitz lab and formulating new experimental paradigms that are suitable to distinguish between the local and top-down hypothesis. [1] N’dri, A. W., Gebhardt, W., Teulière, C., Zeldenrust, F., Rao, R. P. N., Triesch, J., & Ororbia, A. (2024). Predictive Coding with Spiking Neural Networks: A Survey (arXiv:2409.05386). arXiv. https://doi.org/10.48550/arXiv.2409.05386 [2] Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), 79–87. [3] Zeldenrust, F., Gutkin, B., & Denéve, S. (2021). Efficient and robust coding in heterogeneous recurrent networks. PLOS Computational Biology, 17(4), e1008673. https://doi.org/10.1371/journal.pcbi.1008673 [4] Nieto-Diego, J. & Malmierca, M. S. Topographic Distribution of Stimulus-Specific Adaptation across Auditory Cortical Fields in the Anesthetized Rat. (2016) PLOS Biol. 14, e1002397 [5] Lao-Rodríguez, A. B. ... Englitz B, (2023) Neuronal responses to omitted tones in the auditory brain: A neuronal correlate for predictive coding. Sci. Adv. 9, eabq8657 * We will give you a temporary employment contract (1.0 FTE) of 1.5 years, after which your performance will be evaluated. If the evaluation is positive, your contract will be extended by 2.5 years (4-year contract). * You will receive a starting salary of €2,901 gross per month based on a 38-hour working week, which will increase to €3,707 in the fourth year (salary scale P). * You will receive an 8% holiday allowance and an 8,3% end-of-year bonus. * We offer Dual Career Coaching. The Dual Career Coaching assists your partner via support, tools, and resources to improve their chances of independently finding employment in the Netherlands. * You will receive extra days off. With full-time employment, you can choose between 30 or 41 days of annual leave instead of the statutory 20.
dr. Fleur Zeldenrust
My lab is looking for a PhD candidate in Modelling Predictive Error Responses: https://www.ru.nl/en/working-at/job-opportunities/phd-position-in-computational-neuroscience-modelling-predictive-error-responses For the NWO project ‘DBI2’ we are looking for a PhD candidate to study predictive error responses in the auditory cortex. The main goal is to design an experimental approach to distinguish between two alternative theories of predictive coding and processing. Predictive coding and predictive processing are compelling theories to explain brain function. The idea that the brain continually maintains and updates an internal model of the outside world, and compares the incoming input with the expectations generated by this model, can explain many phenomena including adaptive behaviour and sensory effects such as oddball responses. However, until now there is no consensus on how such predictive coding could be implemented in real neural tissue. Importantly, there are two alternative theories on how error signals in predictive processing could be coded in neural signals: either as (1) top down signals from ‘higher order’ brain areas (hierarchical predictive coding, [2]) or (2) local signals, resulting in membrane potentials reflecting error signals ([3], for a review, see [1]). The goal of the research presented here, under the primary supervision of Dr Zeldenrust, is to design an experimental approach to distinguish between these two theoretical approaches. Measuring error signals in neural tissue is experimentally challenging. Therefore, a direct exchange between theory and experiment is needed, so that hypotheses and specific predictions about which neurons to record from and stimulate and the results expected can be quickly updated for the design of optimal experiments. The student will work in close collaboration with the Englitz lab, so that there is a direct link between modelling, data analysis and experiment. As a PhD candidate you will use data on oddball paradigms [4,5], which provide the ability to directly observe predictions and distinguish them from prediction errors. The data are a combined approach of widefield imaging of the entire auditory cortex with local and layer-specific imaging using 2-photon recordings in the same animals. To directly test the top-down hypothesis, neurons in subareas of the prefrontal cortex will be transfected with an inhibitory opsin (eNpHR3.0) to modulate their top-down influence. You will develop a model of the hierarchical interaction between the auditory cortex and the prefrontal cortex, in which error signals are either coded as top-down (theory 1) or local (theory 2). You will use this model to formulate testable predictions, distinguishing theory 1 from theory 2. These predictions will be tested by both analysing existing data from the Englitz lab and formulating new experimental paradigms that are suitable to distinguish between the local and top-down hypothesis. [1] N’dri, A. W., Gebhardt, W., Teulière, C., Zeldenrust, F., Rao, R. P. N., Triesch, J., & Ororbia, A. (2024). Predictive Coding with Spiking Neural Networks: A Survey (arXiv:2409.05386). arXiv. https://doi.org/10.48550/arXiv.2409.05386 [2] Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), 79–87. [3] Zeldenrust, F., Gutkin, B., & Denéve, S. (2021). Efficient and robust coding in heterogeneous recurrent networks. PLOS Computational Biology, 17(4), e1008673. https://doi.org/10.1371/journal.pcbi.1008673 [4] Nieto-Diego, J. & Malmierca, M. S. Topographic Distribution of Stimulus-Specific Adaptation across Auditory Cortical Fields in the Anesthetized Rat. (2016) PLOS Biol. 14, e1002397 [5] Lao-Rodríguez, A. B. ... Englitz B, (2023) Neuronal responses to omitted tones in the auditory brain: A neuronal correlate for predictive coding. Sci. Adv. 9, eabq8657 We offer * We will give you a temporary employment contract (1.0 FTE) of 1.5 years, after which your performance will be evaluated. If the evaluation is positive, your contract will be extended by 2.5 years (4-year contract). * You will receive a starting salary of €2,901 gross per month based on a 38-hour working week, which will increase to €3,707 in the fourth year (salary scale P). * You will receive an 8% holiday allowance and an 8,3% end-of-year bonus. * We offer Dual Career Coaching. The Dual Career Coaching assists your partner via support, tools, and resources to improve their chances of independently finding employment in the Netherlands. * You will receive extra days off. With full-time employment, you can choose between 30 or 41 days of annual leave instead of the statutory 20.
“Brain theory, what is it or what should it be?”
n the neurosciences the need for some 'overarching' theory is sometimes expressed, but it is not always obvious what is meant by this. One can perhaps agree that in modern science observation and experimentation is normally complemented by 'theory', i.e. the development of theoretical concepts that help guiding and evaluating experiments and measurements. A deeper discussion of 'brain theory' will require the clarification of some further distictions, in particular: theory vs. model and brain research (and its theory) vs. neuroscience. Other questions are: Does a theory require mathematics? Or even differential equations? Today it is often taken for granted that the whole universe including everything in it, for example humans, animals, and plants, can be adequately treated by physics and therefore theoretical physics is the overarching theory. Even if this is the case, it has turned out that in some particular parts of physics (the historical example is thermodynamics) it may be useful to simplify the theory by introducing additional theoretical concepts that can in principle be 'reduced' to more complex descriptions on the 'microscopic' level of basic physical particals and forces. In this sense, brain theory may be regarded as part of theoretical neuroscience, which is inside biophysics and therefore inside physics, or theoretical physics. Still, in neuroscience and brain research, additional concepts are typically used to describe results and help guiding experimentation that are 'outside' physics, beginning with neurons and synapses, names of brain parts and areas, up to concepts like 'learning', 'motivation', 'attention'. Certainly, we do not yet have one theory that includes all these concepts. So 'brain theory' is still in a 'pre-newtonian' state. However, it may still be useful to understand in general the relations between a larger theory and its 'parts', or between microscopic and macroscopic theories, or between theories at different 'levels' of description. This is what I plan to do.
How fly neurons compute the direction of visual motion
Detecting the direction of image motion is important for visual navigation, predator avoidance and prey capture, and thus essential for the survival of all animals that have eyes. However, the direction of motion is not explicitly represented at the level of the photoreceptors: it rather needs to be computed by subsequent neural circuits, involving a comparison of the signals from neighboring photoreceptors over time. The exact nature of this process represents a classic example of neural computation and has been a longstanding question in the field. Much progress has been made in recent years in the fruit fly Drosophila melanogaster by genetically targeting individual neuron types to block, activate or record from them. Our results obtained this way demonstrate that the local direction of motion is computed in two parallel ON and OFF pathways. Within each pathway, a retinotopic array of four direction-selective T4 (ON) and T5 (OFF) cells represents the four Cartesian components of local motion vectors (leftward, rightward, upward, downward). Since none of the presynaptic neurons is directionally selective, direction selectivity first emerges within T4 and T5 cells. Our present research focuses on the cellular and biophysical mechanisms by which the direction of image motion is computed in these neurons.
Development of an open-source femtosecond fiber laser system for multiphoton microscopy
This talk will present a low-cost protocol for fabricating an easily constructed femtosecond (fs) fiber laser system suitable for routine multiphoton microscopy (1060–1080 nm, 1 W average power, 70 fs pulse duration, 30–70 MHz repetition rate). Concepts well-known in the laser physics community essential to proper laser operation, but generally obscure to biophysicists and biomedical engineers, will be clarified. The parts list (~$13K US dollars), the equipment list (~$40K+), and the intellectual investment needed to build the laser will be described. A goal of the presentation will be to engage with the audience to discuss trade-offs associated with a custom-built fs fiber laser versus purchasing a commercial system. I will also touch on my research group’s plans to further develop this custom laser system for multiplexed cancer imaging as well as recent developments in the field that promise even higher performance fs fiber lasers for approximately the same cost and ease of construction.
Self-perception: mechanosensation and beyond
Brain-organ communications play a crucial role in maintaining the body's physiological and psychological homeostasis, and are controlled by complex neural and hormonal systems, including the internal mechanosensory organs. However, the progress has been slow due to technical hurdles: the sensory neurons are deeply buried inside the body and are not readily accessible for direct observation, the projection patterns from different organs or body parts are complex rather than converging into dedicate brain regions, the coding principle cannot be directly adapted from that learned from conventional sensory pathways. Our lab apply the pipeline of "biophysics of receptors-cell biology of neurons-functionality of neural circuits-animal behaviors" to explore the molecular and neural mechanisms of self-perception. In the lab, we mainly focus on the following three questions: 1, The molecular and cellular basis for proprioception and interoception. 2, The circuit mechanisms of sensory coding and integration of internal and external information. 3, The function of interoception in regulating behavior homeostasis.
Setting network states via the dynamics of action potential generation
To understand neural computation and the dynamics in the brain, we usually focus on the connectivity among neurons. In contrast, the properties of single neurons are often thought to be negligible, at least as far as the activity of networks is concerned. In this talk, I will contradict this notion and demonstrate how the biophysics of action-potential generation can have a decisive impact on network behaviour. Our recent theoretical work shows that, among regularly firing neurons, the somewhat unattended homoclinic type (characterized by a spike onset via a saddle homoclinic orbit bifurcation) particularly stands out: First, spikes of this type foster specific network states - synchronization in inhibitory and splayed-out/frustrated states in excitatory networks. Second, homoclinic spikes can easily be induced by changes in a variety of physiological parameters (like temperature, extracellular potassium, or dendritic morphology). As a consequence, such parameter changes can even induce switches in network states, solely based on a modification of cellular voltage dynamics. I will provide first experimental evidence and discuss functional consequences of homoclinic spikes for the design of efficient pattern-generating motor circuits in insects as well as for mammalian pathologies like febrile seizures. Our analysis predicts an interesting role for homoclinic action potentials as an integral part of brain dynamics in both health and disease.
The Standard Model of the Retina
The science of the retina has reached an interesting stage of completion. There exists now a consensus standard model of this neural system - at least in the minds of many researchers - that serves as a baseline against which to evaluate new claims. The standard model links phenomena from molecular biophysics, cell biology, neuroanatomy, synaptic physiology, circuit function, and visual psychophysics. It is further supported by a normative theory explaining what the purpose is of processing visual information this way. Most new reports of retinal phenomena fit squarely within the standard model, and major revisions seem increasingly unlikely. Given that our understanding of other brain circuits with comparable complexity is much more rudimentary, it is worth considering an example of what success looks like. In this talk I will summarize what I think are the ingredients that led to this mature understanding of the retina. Equally important, a number of practices and concepts that are currently en vogue in neuroscience were not needed or indeed counterproductive. I look forward to debating how these lessons might extend to other areas of brain research.
Homeostatic Plasticity in Health and Disease
Dr. Davis will present a summary regarding the identification and characterization of mechanisms of homeostatic plasticity as they relate to the control of synaptic transmission. He will then provide evidence of translation to the mammalian neuromuscular junction and central synapses, and provide tangible links to the etiology of neurological disease.
Retinoblastoma: Canadian global leadership
Active dynamics and tunable mechanics of actin-microtubule composites
Controlling flows and defects in biomolecular active liquid crystals
Analyzing Retinal Disease Using Electron Microscopic Connectomics
John DowlingJohn E. Dowling received his AB and PhD from Harvard University. He taught in the Biology Department at Harvard from 1961 to 1964, first as an Instructor, then as assistant professor. In 1964 he moved to Johns Hopkins University, where he held an appointment as associate professor of Ophthalmology and Biophysics. He returned to Harvard as professor of Biology in 1971, was the Maria Moors Cabot Professor of Natural Sciences from 1971-2001, Harvard College professor from 1999-2004 and is presently the Gordon and Llura Gund Professor of Neurosciences. Dowling was chairman of the Biology Department at Harvard from 1975 to 1978 and served as associate dean of the faculty of Arts and Sciences from 1980 to 1984. He was Master of Leverett House at Harvard from 1981-1998 and currently serves as president of the Corporation of The Marine Biological Laboratory in Woods Hole. He is a Fellow of the American Academy of Arts and Sciences, a member of the National Academy of Sciences and a member of the American Philosophical Society. Awards that Dowling received include the Friedenwald Medal from the Association of Research in Ophthalmology and Vision in 1970, the Annual Award of the New England Ophthalmological Society in 1979, the Retinal Research Foundation Award for Retinal Research in 1981, an Alcon Vision Research Recognition Award in 1986, a National Eye Institute's MERIT award in 1987, the Von Sallman Prize in 1992, The Helen Keller Prize for Vision Research in 2000 and the Llura Ligget Gund Award for Lifetime Achievement and Recognition of Contribution to the Foundation Fighting Blindness in 2001. He was granted an honorary MD degree by the University of Lund (Sweden) in 1982 and an honorary Doctor of Laws degree from Dalhousie University (Canada) in 2012. Dowling's research interests have focused on the vertebrate retina as a model piece of the brain. He and his collaborators have long been interested in the functional organization of the retina, studying its synaptic organization, the electrical responses of the retinal neurons, and the mechanisms underlying neurotransmission and neuromodulation in the retina. Dowling became interested in zebrafish as a system in which one could explore the development and genetics of the vertebrate retina about 20 years ago. Part of his research team has focused on retinal development in zebrafish and the role of retinoic acid in early eye and photoreceptor development. A second group has developed behavioral tests to isolate mutations, both recessive and dominant, specific to the visual system.
Locomotion of Helicobacter pylori: Cell geometry and active confinement
How Chromosomes align on the spindle
Interpreting the Mechanisms and Meaning of Sensorimotor Beta Rhythms with the Human Neocortical Neurosolver (HNN) Neural Modeling Software
Electro- and magneto-encephalography (EEG/MEG) are the leading methods to non-invasively record human neural dynamics with millisecond temporal resolution. However, it can be extremely difficult to infer the underlying cellular and circuit level origins of these macro-scale signals without simultaneous invasive recordings. This limits the translation of E/MEG into novel principles of information processing, or into new treatment modalities for neural pathologies. To address this need, we developed the Human Neocortical Neurosolver (HNN: https://hnn.brown/edu ), a new user-friendly neural modeling tool designed to help researchers and clinicians interpret human imaging data. A unique feature of HNN’s model is that it accounts for the biophysics generating the primary electric currents underlying such data, so simulation results are directly comparable to source localized data. HNN is being constructed with workflows of use to study some of the most commonly measured E/MEG signals including event related potentials, and low frequency brain rhythms. In this talk, I will give an overview of this new tool and describe an application to study the origin and meaning of 15-29Hz beta frequency oscillations, known to be important for sensory and motor function. Our data showed that in primary somatosensory cortex these oscillations emerge as transient high power ‘events’. Functionally relevant differences in averaged power reflected a difference in the number of high-power beta events per trial (“rate”), as opposed to changes in event amplitude or duration. These findings were consistent across detection and attention tasks in human MEG, and in local field potentials from mice performing a detection task. HNN modeling led to a new theory on the circuit origin of such beta events and suggested beta causally impacts perception through layer specific recruitment of cortical inhibition, with support from invasive recordings in animal models and high-resolution MEG in humans. In total, HNN provides an unpresented biophysically principled tool to link mechanism to meaning of human E/MEG signals.
Research seminar: How actin pulls the nucleus through constrictions
Tutorial: Cell mimics to study active movements and deformations by actin assembly
Physics of flow sensing by cancer cells
Bacteria, soil, carbon, and biosurfactants:From climate related themes to bacterial spreading in unsaturated porous media
Active recognition at immune cell interfaces
Power at the nanoscale: Speed, strength, and efficiency in biological motors
Research talk: Spontaneous ciliary waves
Tutorial talk: Ciliated tissues from form to function
Picocalorimeter sensors for liquid samples with applications to chemical reactions and biochemistry
Spatio-temporal control over near critical-point operation ensures fidelity of bacterial genome partition
Elastically limited liquid-liquid phase separation within cells
Enzyme driven active matter
Metachronal waves in swarms of nematode Turbatrix aceti
Surprising twists in nucleosomal DNA with implications for higher-order chromatin folding
Modeling the composition and dynamics of contractile ring constriction
The structural origins of cartilage shear mechanics and fracture properties
Novel Object Detection and Multiplexed Motion Representation in Retinal Bipolar Cells
Detection of motion is essential for survival, but how the visual system processes moving stimuli is not fully understood. Here, based on a detailed analysis of glutamate release from bipolar cells, we outline the rules that govern the representation of object motion in the early processing stages. Our main findings are as follows: (1) Motion processing begins already at the first retinal synapse. (2) The shape and the amplitude of motion responses cannot be reliably predicted from bipolar cell responses to stationary objects. (3) Enhanced representation of novel objects - particularly in bipolar cells with transient dynamics. (4) Response amplitude in bipolar cells matches visual salience reported in humans: suddenly appearing objects > novel motion > existing motion. These findings can be explained by antagonistic interactions in the center-surround receptive field, demonstrate that despite their simple operational concepts, classical center-surround receptive fields enable sophisticated visual computations.
Research talk: Giant vesicles in electric fields
Tutorial talk: Electromechanics of biomembranes
Tutorial: inference in biological physics
States of (active) matter: the single cell perspective
Making connections: how epithelial tissues guarantee folding
Molecular to cellular mechanics probed by high-speed force microscopy
The role of the fluid bilayer in kinesin-driven vesicle transport
Feeling for functional changes in cells
The pathophysiology of prodromal Parkinson’s disease
Studying the pathophysiology of late stage Parkinson’s disease (PD) – after the patients have experienced severe neuronal loss – has helped develop various symptomatic treatments for PD (e.g., deep brain stimulation). However, it has been of limited use in developing neuroprotective disease-modifying therapies (DMTs), because DMTs require interventions at much earlier stages of PD when vulnerable neurons are still intact. Because PD patients exhibit various non-motor prodromal symptoms (ie, symptoms that predate diagnosis), understanding the pathophysiology underlying these symptom could lead to earlier diagnosis and intervention. In my talk, I will present a recently elucidated example of how PD pathologies alter the channel biophysics of intact vagal motoneurons (known to be selectively vulnerable in PD) to drive dysautonomia that is reminiscent of prodromal PD. I will discuss how elucidating the pathophysiology of prodromal symptoms can lead to earlier diagnosis through the development of physiological biomarkers for PD.
Research talk: insight into protein allostery from designed mechanical networks
Tutorial talk: an application of persistent homology to allostery
Traffic jams and U-turns: motility of swimming cells in viscosity gradients
Regulation of cytoplasmic density
Curved protein IRSp53 driven protrusion initiation
From topological defects to fruiting bodies in bacterial colonies
Neural network-like collective dynamics of molecules
Adventures in DNA replication using single molecule biophysics
Research talk: Columns of ants--mechanics, force chains, and waves
Tutorial talk: Janssen's effect--an old problem with a new spin
Motor guidance by long range communication through the microtubule highway
From individual to collective intermittent motion: from bacteria to sheep
Bacterial active nematics: how modeling can be really quantitative
Transcription factor dynamics and nuclear organization during early embryonic development
Opposite response of cancer cells to substrate viscoelasticity
Cytoskeletal interaction forces: from filament architecture to network mechanics
Functional consequences of microscopic skin features on snake locomotion
Beating of artificial cilia
Research talk: Is Escherichia coli information limited when navigating chemical gradients?
Tutorial talk: Bacterial Chemotaxis
Bridging biophysics and computation with differentiable simulation
Bernstein Conference 2024