## CTW: Modeling and Inference from Single Molecule to Cells

### Organizers

Jayajit Das
Departments of Pediatrics, Physics, Integrated Biomedical Sciences, The Ohio State University
John Fricks
Statistics, Pennsylvania State University
Dept. of Chemical and Biological Engineering, Colorado State University
Steve Presse
Department of Physics and Chemistry & Chemical Biology, Indiana University--Purdue University

Recent advances in in vitro and in vivo experimental techniques now generate data on living systems from single molecule length scales to whole cell populations and time scales not previously achievable. Concurrently, there have been significant advances in the mathematical modeling of living systems. Despite these advances, models rarely capture the complex and nuanced mechanisms that detailed biological data are poised to provide.

Here we are focused on three critical challenges:

1) How to use data collected at small length scales to draw quantitative inferences about emergent biological phenomena at larger scales. In other words, integrating models valid at smaller scales to explain behavior at larger scales such as an entire cell or even a tissue.

2) How to build first-principles models motivated from the data and, subsequently, infer model features and parameters that will provide a principled mechanistic understanding of the system.

3) How to use in vitro data (collected under presumably vastly different conditions than its poorly controlled in vivo counter-part) to help motivate and build in vivo models.

### Accepted Speakers

Nitin Baliga
NA, Institute for Systems Biology
Mark Bathe
Biological Engineering, Massachusetts Institute of Technology
Nicolas Buchon
Entomology, Cornell University
Chao Du
Statistics, University of Virginia
Kingshuk Ghosh
Physics and Astronomy, University of Denver
Todd Gingrich
Physics, Massachusetts Institute of Technology
Irina Gopich
Laboratory of Chemical Physics, NIDDK, National Institutes of Health
Kevin Janes
Biomedical Engineering, University of Virginia
Gavin King
Physics and Astronomy, University of Missouri
Lisa Lapidus
Physics and Astronomy, Michigan State University
Andre Levchenko
Yale Systems Biology Institute, Yale University
Dinah Loerke
Physics and Astronomy, University of Denver
Vasileios Maroulas
Mathematics, University of Tennessee
Anastasios Matzavinos
Applied Mathematics, Brown University
Scott McKinley
Mathematics, Tulane University
Andrew Mugler
Physics and Astronomy, Purdue University
Brian Munsky
Chemical and Biological Engineering, Colorado State University
Phil Nelson
Physics and Astronomy, University of Pennsylvania
Raghuveer Parthasarathy
Physics, University of Oregon
Cosma Shalizi
Center for the Neural Basis of Cognition, Carnegie-Mellon University
Doug Shepherd
Albert Siryaporn
Physics, University of California, Irvine
Erdal Toprak
Green Center for Systems Biology, University of Texas Southwestern Medical Center
Nils Walter
Chemistry, University of Michigan
Monday, February 8, 2016
Time Session
07:45 AM

Shuttle to MBI

08:00 AM
08:30 AM

Breakfast

08:30 AM
08:45 AM

Welcome, overview, introductions: Marty Golubitsky

08:45 AM
09:00 AM

Introduction by Workshop Organizers

09:00 AM
09:45 AM
Kingshuk Ghosh - Biophysical proteome: principles, evolution and universality.
Protein sequence encodes complex network of interactions and it is difficult to decipher simple rules in protein science. In spite of this challenge, approximate and semi-empirical rules can be found to describe biophysical properties of different proteins. Using statistical mechanical models tested against multitude of data, our goal is to unravel such universal features of proteins. Our next goal is to extend these transferrable laws in a high throughput manner to model the entire collection of proteins inside an organism, called the proteome. The application at the proteome level allows us to bridge the gap between molecular biophysics and cellular physics and provides us evolutionary insights. With this approach we will try to address some questions of broad interest: i) Why are cells so sensitive to temperature? ii) How do thermophilic proteins (derived from organisms that thrive at high temperature) withstand high temperatures compared to their mesophilic (organisms that live at room temperature) counterparts? iii) What is the evolutionary implication of distribution of different rate processes in a cell and how are they optimized? iv) How do salts slow down cell growth?
09:45 AM
10:30 AM
Gavin King - Probing peptide-lipid interactions from a mechanical single-molecule perspective
Peptide interactions with the complex environment of the phospholipid bilayer determine the three-dimensional structure of membrane proteins. Understanding these interactions is important, because they dictate partitioning, folding, stability, and ultimately the function of this large class of proteins. These interactions not only determine the final folded structures of membrane proteins, they underlie the biological assembly processes guided by the SecYEG translocon complex. Lipid-protein interactions have been studied extensively using biochemical assays, but elucidating mechanistic details from such assays has proven to be challenging. In this talk I will discuss and demonstrate a single molecule approach to measure the interaction of a peptide that is decorated at the tip of atomic force microscope with a supported lipid bilayer. Inspired by work from Stephen WhiteÃ¢â‚¬â„¢s group*, we used synthetic peptides of the form W-L-X-L-L, where the Ã¢â‚¬Å“guestÃ¢â‚¬? X is an amino acid of interest. In another line of inquiry, we used constructs based on SecA, the ATPase of the general secretory system. The mechanical nature of the method allows folding/unfolding data to be collected in physiological buffer solution, without resorting to chemical denaturation. Experiments with short peptides allow identification of the nature of the peptide-lipid interaction at the single amino-acid level in a near-native environment. Looking towards the future, such measurements, together with modeling, may help provide a basis for improved understanding of the folding/unfolding of membrane proteins and their partitioning into the membrane.
10:30 AM
11:00 AM

Break

11:00 AM
11:45 AM
Lisa Lapidus - Monomer dynamics control the first steps of aggregation and folding
An important aspect of protein folding is understanding how folding competes with aggregation, which leads to diseases such as ParkinsonÃ¢â‚¬â„¢s and AlzheimerÃ¢â‚¬â„¢s. The complexity and dynamics of unfolded protein ensembles may be the ultimate speed limit of folding and play a crucial role in aggregation. In my lab over the past several years we have investigated the reconfiguration dynamics unfolded proteins by measuring the rate of intramolecular diffusion, the rate one part of the chain diffuses relative to another. We have measured diffusion coefficients ranging over three orders of magnitude and observed that aggregation-prone sequences tend to fall in the middle of this range. In this talk, I shall present our experiments on alpha-synuclein, the AlzheimerÃ¢â‚¬â„¢s peptide and various prion sequences. We correlated intramolecular diffusion of the disordered protein with solution conditions that promote aggregation. Finally, we have begun measurements on small molecule aggregation inhibitors and found that some can prevent aggregation by shifting intramolecular diffusion out of the dangerous middle range.
11:45 AM
12:30 PM
Steve Presse - Enzymes stepping on landmines
Abstract not submitted
12:30 PM
02:00 PM

Lunch Break

02:00 PM
02:45 PM
Irina Gopich - Maximum Likelihood Analysis in Single-Molecule FRET
Single-molecule fluorescence spectroscopy is widely used to study macromolecular dynamics. The output of such measurements is a sequence of photons of different colors separated by random time intervals. In the experiments with pulsed lasers, fluorescence lifetimes can also be monitored. To improve the range of the measured dynamics at a given photon count rate, we consider each and every photon and use a maximum likelihood method to get the information about fast conformational dynamics. For a photon trajectory with recorded photon colors, interphoton times, and delay times (relative to laser pulses), the parameters of a model describing molecular dynamics are obtained by maximizing the appropriate likelihood function [1-3]. We discuss various aspects of the maximum likelihood analysis, such as likelihood functions and their applicability, the accuracy of the extracted parameters [4], their sensitivity to the model assumptions [3], and the influence of fast blinking [5]. The method has been applied to study fast folding proteins [3, 5].

[1] I.V. Gopich, A. Szabo. Decoding the pattern of photon colors in single-molecule FRET. J. Phys. Chem. B.; v. 113, 10965â€“10973 (2009).
[2] I.V. Gopich, A. Szabo. Theory of the energy transfer efficiency and fluorescence lifetime distribution in single-molecule FRET. Proc. Natl. Acad. Sci. U.S.A.; v. 109, 7747 (2012).
[3] H. S. Chung and I. V. Gopich. Fast single-molecule FRET spectroscopy: theory and experiment. Phys. Chem. Chem. Phys.; v. 16, 18644-18657 (2014).
[4] I. V. Gopich. Accuracy of Maximum Likelihood Estimates of a Two-State Model in Single-Molecule FRET. J. Chem. Phys.; v. 142, 034110 (2015).
[5] H. S. Chung, J. M. Louis, and I. V. Gopich. Analysis of Fluorescence Lifetime and Energy Transfer Efficiency in Single-Molecule Photon Trajectories of Fast-folding Proteins. J. Phys. Chem. B; (2016), in press.
02:45 PM
03:00 PM

Break

03:00 PM
04:00 PM
Phil Nelson - Old news and new news about single-photon absorption sensitivity in human vision
It is sometimes said that "Our eyes can see single photons." This article begins by finding a more precise version of that claim and reviewing evidence gathered for it up to around 1985 in two distinct realms, those of human psychophysics and single-cell physiology. Finding a single framework that accommodates both kinds of result is then a nontrivial challenge, and one that sets severe quantitative constraints on any model of dim-light visual processing. I'll present a new model that accomplishes this task, and compare it to recent experiments.
04:00 PM
06:00 PM

Reception and Poster session in MBI lounge

06:00 PM

Shuttle pick-up from MBI

Tuesday, February 9, 2016
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:45 AM
John Fricks - Time Series Analysis of Diffusion with Transient Binding
In cellular systems, Brownian forces play a dominant role in the movement of small (and not so small) particles such as vesicles, organelles, etc. However, proteins and other macromolecules bind to one another, altering the underlying Brownian dynamics. In this talk, classical approaches in the biophysical literature to time series which switch between bound and unbound states will be presented, and an alternative approach using stochastic expectation-maximization algorithm (EM) combined with particle filters will be proposed. As an example system, molecular motors, such as kinesin, switch between weakly and strongly bound states, as well as directed transport. I will discuss the analysis of such a system along with the ramifications for multi-motor-cargo complexes found in living cells.
09:45 AM
10:30 AM
Erdal Toprak - Fighting antibiotic resistance by exploiting antibiotic hypersensitivity
Antibiotic resistance is a worldwide public health problem. The lack of effective antibiotic therapies against resistant pathogens has led to prolonged treatments, increased morbidity, and burgeoning health care costs. Pathogenic bacteria are increasingly resistant to antibiotics leaving clinicians with limited options for treatment. For combating both the short- and long-term ineffectiveness of antibiotics, we have developed a novel approach to modulate the intrinsic antibiotic resistance level of bacteria. We can now decrease bacterial antibiotic resistance and design antibiotic therapies using lower doses of drugs. Reducing the amount of antibiotic to clear infections will impact patient well-being and reduce the antibiotic load in the clinic which exacerbates spread of antibiotic resistance.
10:30 AM
11:00 AM

Break

11:00 AM
11:45 AM
Nils Walter - RNA Pathways Dissected at the Single Molecule Level: The Power of Integrating Experimental and Computational Approaches
Nature employs nanoscale machines that self-assemble into dynamic structures of complex architecture and functionality. Single molecule fluorescence microscopy offers a non-invasive tool to probe and ultimately dissect and control these nanoassemblies in real-time, often with the aid of computational tools to interpret and complement the experimental data. In particular, single molecule fluorescence resonance energy transfer (smFRET) allows us to measure biologically relevant distances and changes thereof at the 2-8 nm scale, whereas complementary super-resolution localization techniques based on Gaussian fitting of imaged point spread functions measure distances in the 10 nm and longer range. In this talk, I will describe how we have developed Single Molecule Cluster Analysis (SiMCAn) based on a vast smFRET dataset to dissect the complex conformational dynamics of a pre-mRNA as it is spliced by the spliceosomal processing machinery. In addition, I will demonstrate how a single molecule systems biology can be implemented that feeds super-resolved single particle tracking data into lattice-based Monte-Carlo simulations to generate novel hypotheses on the gene regulation of mRNAs by microRNAs during RNA silencing.
11:45 AM
12:30 PM
Doug Shepherd - Information from Fluctuations: Utilizing Single-Cell Analyses to Probe Disease Mechanisms
Abstract not submitted
12:30 PM
02:00 PM

Lunch Break

02:00 PM
02:45 PM
Anastasios Matzavinos - Mesoscopic modeling of DNA transport in an array of entropic barriers
In this talk, we discuss dissipative particle dynamics (DPD) simulations of the dispersion of DNA molecules conveyed by a pressure-driven fluid flow across a periodic array of entropic barriers. We compare our simulations with nanofluidic experiments, which show the DNA to transition between various types of behaviors as the pressure is increased, and discuss physical insights afforded by the ability of the DPD method to explicitly model flows in the system. Finally, we present anomalous diffusion phenomena that emerge in both experiment and simulation, and we illustrate similarities between this system and Brownian motion in a tilted periodic potential. This is a joint work with Clark Bowman, Daniel Kim, and Derek Stein.
02:45 PM
03:00 PM

Break

03:00 PM
03:45 PM
Vasileios Maroulas - Tracking intracellular movements
We focus on the biological problem of tracking organelles as they move through cells. In the past, most intracellular movements were recorded manually, however, the results are too incomplete to capture the full complexity of organelle motions. An automated tracking algorithm promises to provide a complete analysis of noisy microscopy data. In this talk, we will get exposed to two such algorithms. The first algorithmic implementation adopts statistical techniques from a Bayesian random set point of view. Instead of considering each individual organelle, we examine a random set whose members are the organelle states and we establish a Bayesian filtering algorithm involving such set-states. The second method combines stochastic filtering estimates of the displacement field with the topological properties on the space of trajectories. Both methods were used to analyze real data.
03:45 PM
04:30 PM

Informal Discussion -- the future of protein biophysics -- Moderators: Lisa Lapidus and Doug Shephard

04:30 PM

Shuttle pick-up from MBI

Wednesday, February 10, 2016
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:45 AM
Nitin Baliga - A systems biology approach to characterize and rationally manipulate complex behaviors
I will speak about a data-driven approach to decipher and model the dynamics of gene regulatory networks. I will discuss specific applications of this approach to (in no particular order):

Decipher disease-perturbed networks in brain cancer for characterizing etiology of the disease, accurate diagnosis, and rational combination therapy.
Characterize drug tolerance networks in Mycobacterium tuberculosis to potentiate pathogen killing and prevent resistance with combination drugs.
(Time permitting): Understand the mechanistic underpinnings of microbial population collapse in fluctuating environments.
09:45 AM
10:30 AM
Jayajit Das - Connecting the dots across time: Gleaning signaling mechanisms from single cell snapshot data
Individual isogenic immune cells respond to identical stimuli with unique signaling kinetics. Shapes of kinetic trajectories describing time evolution of abundances of multiple signaling molecules in single cells contain key information regarding signaling mechanisms. However, it can be challenging to measure many signaling reporters simultaneously in single cell in experiments. Flow and mass cytometry experiments can assay a large number of proteins (4 to100) but individual cells are not tracked in these experiments, therefore, such measurements only provide a statistical description of the signaling kinetics, e.g., mean abundances, or, covariance between protein abundances. Is there a way to reconstruct signaling trajectories, even approximately, in individual cells using cytometry data? We address this question affirmatively by using a novel method based on identification of a dynamical invariant pertaining to chemical reaction networks. We validate our method in data obtained from in silico networks and published single cell experiments. We apply our trajectory reconstruction method to analyze mass cytometry data for NaturalKiller (NK) cells to decipher mechanisms underlying NK cell cytotoxic responses.
10:30 AM
11:00 AM

Break

11:00 AM
11:45 AM
John Sekar - Unraveling regulation from reaction mechanisms
No abstract submitted.
11:45 AM
12:30 PM
Andrew Mugler - Physical limits to collective sensing by communicating cells
Single cells sense their environment with remarkable precision. At the same time, cells have evolved diverse mechanisms for communicating. How are sensing and communication related? I will describe recent theoretical and experimental results in which this question is explored in several contexts, including gradient detection by connected epithelial cells, and collective invasion of breast cancer cells. I will show how communication allows cells to perform qualitatively new behaviors that single cells cannot perform alone. Moreover, I will demonstrate that minimal mathematical modeling yields fundamental limits to the precision of sensing, and that these limits are critically altered by cell-to-cell communication. This work extends the study of cellular sensing and information processing to collective ensembles.
12:30 PM
02:00 PM

Lunch Break

02:00 PM
02:45 PM
Chao Du - Intrinsic Noise in Nonlinear Gene Regulation Inference
Cellular intrinsic noise plays an essential role in the regulatory interactions between genes. Although a variety of quantitative methods are used to study gene regulation system, the role of intrinsic noises has largely been overlooked. Using the Kolmogorov backward equation (master equation), we formulate a causal and mechanistic Markov model. This framework recognizes the discrete, nonlinear and stochastic natures of gene regulation and presents a more realistic description of the physical systems than many existing methods. Within this framework, we develop an associated moment-based statistical method, aiming for inferring the unknown regulatory relations. By analyzing the observed distributions of gene expression measurements from both unperturbed and perturbed steady-states of gene regulation systems, this method is able to learn valuable information concerning regulatory mechanisms. This design allows us to estimate the model parameters with a simple convex optimization algorithm. We apply this approach to a synthetic system that resembles a genetic toggle switch and demonstrate that this algorithm can recover the regulatory parameters efficiently and accurately.
02:45 PM
03:00 PM

Break

03:00 PM
03:45 PM
Kevin Janes - Regulation of Flagellar Motors in Salmonella
Regulated changes in gene expression underlie many biological processes, but globally profiling cell-to-cell variations in transcriptional regulation is problematic when measuring single cells. We have developed an approach, called stochastic profiling, that applies probability theory to transcriptome-wide measurements of small pools of cells to identify single-cell regulatory heterogeneities (Nat Methods 7:311-7 [2010]). I will talk about work in progress that applies stochastic profiling as a tool for uncovering the mechanistic basis of phenotypes that are incompletely penetrant. Regulatory-state frequencies are matched to downstream phenotype frequencies to converge upon a tractable set of candidate states worth of follow-up experimentation. Using the ErbB2 oncoprotein as a model trigger for an incompletely penetrant phenotype, we identify a handful of surprising candidates that significantly affect penetrance when perturbed. Stochastic profiling remains the only method compatible with cells microdissected in situ and thereby opens exciting opportunities in the areas of tissue morphogenesis and cancer.
03:45 PM
04:30 PM

Informal Discussion -- bridging theory and experiments at the single-cell level -- Moderators: Moderators: Dinah Loerke, Andrew Mugler, Andre Levchenko

04:30 PM

Shuttle pick-up from MBI

Thursday, February 11, 2016
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:45 AM
Scott McKinley - Intracellular transport: The paradox of codependence among antagonistic motors
Transport in neurons is intrinsically bidirectional, with each movement modality carried out by molecular motors in either the kinesin (anterograde) or the dynein (retrograde) families. Because all motors are present at a given time there must be competition and/or cooperation among motors that simultaneously bind a single vesicle to nearby microtubules. The prevailing tug-of-war model captures this dynamic, but fails to account for a recently recognized phenomenon: that in many situations, disabling one family of motors somehow inhibits the performance of motors that are working in the opposite direction. In this talk we will survey a few proposed mechanisms that may account for this behavior and will look at recent work that focuses on a potential role played by the helper protein dynactin.
09:45 AM
10:30 AM
Nicolas Buchon - Maintaining homeostasis in the Drosophila midgut: a quantitative study
The midgut of *Drosophila* is a highly compartmentalized organ and a major site of interaction between the fly and microbes, both benign and pathogenic. Upon infection, both the virulence of the pathogen ingested and the immune response itself inflict damage to the gut epithelium. This damage is repaired by an acceleration of epithelium renewal that combines increased delamination of enterocytes with reprogramming of intestinal stem cells to proliferate and regenerate the gut epithelium. The proper regulation of epithelium renewal, as well as its coordination with immune effector mechanisms, is required to maintain intestinal homeostasis and organismal health. However, it remains unclear how epithelium renewal is quantitatively regulated upon infection. In this talk, we will present data on the mechanisms that control cell dynamics upon infection, and how epithelium renewal is quantitatively regulated along the gut.
10:30 AM
11:15 AM
Ashok Prasad - Interpreting changes in cell shape: cancer invasion and metastases
Cells from different tissues typically look quite different from each other even when cultured on plastic or glass slides under identical conditions. This leads us to formulate the hypothesis that cell shape is a function of the cytoskeletal properties of those cells, and begs the question as to what information changes in cell shape carry. This question becomes all the more interesting for cancer, since invasive cancer cells are reported to have altered mechanical properties compared to non-invasive cancer cells. Inspired by this reasoning we study shape characteristics of paired osteosarcoma cell lines, each consisting of a less metastatic parental line and a more metastatic line, derived from the former by in vivo selection. Statistical analysis shows that shape characteristics of the metastatic cell lines are partly overlapping but on average distinguishable from the parental line. Significantly the shape changes fall into two categories, with three paired cell lines displaying a more mesenchymal-like morphology, while the fourth displaying a change towards a more rounded morphology. A neural network algorithm could distinguish between samples of the less metastatic cells from the more metastatic cells with near perfect accuracy. Thus subtle changes in shape carry information about the genetic changes that lead to invasiveness and metastasis of osteosarcoma cancer cells. The next challenge is to link these changes in shape with changes in mechanical cytoskeletal parameters. I will briefly discuss ongoing experiments to infer these cellular mechanical properties by studying internal fluctuations of organelles.
11:15 AM
11:45 AM

Break

11:45 AM
12:30 PM
Mark Bathe - Inferring neuronal synapse structure and dynamics in situ using high-resolution fluorescence imaging and analysis
The neuronal synapse is the core functional unit of the brain, governing neuronal signal transmission in normal and diseased states. Individual neurons contain up to thousands of synapses that interconnect cells to form highly intricate, spatially extended circuits that govern learning, memory, fear, and anxiety, amongst other core brain functions. Neuronal diseases including autism spectrum disorder, schizophrenia, and Alzheimerâ€™s disease are associated with genetic variations in synaptic proteins, which may impact synapse formation, stability, and signal transmission. Understanding the impact of genetic variation on synapse function requires in situ characterization of synaptic protein localization, copy number, and protein-protein interactions in intact cells and tissues. Toward this end, I will present ongoing efforts in our lab to develop super-resolution fluorescence imaging and analysis approaches that enable in situ mapping of (1) the translational dynamics of individual messenger RNAs that regulate synaptic protein expression; (2) the localization and copy number of individual synaptic proteins; and (3) the dynamics of amyloid aggregation within individual synapses that impact their degeneration in amyloidopathies. These approaches integrate dynamic high-resolution fluorescence imaging with model-based computational analysis to infer super-resolved structure and dynamics of proteins and RNAs pertinent to normal brain development, as well as a range of neuronal diseases.
12:30 PM
01:15 PM
Raghuveer Parthasarathy - Visualizing microbial population dynamics in the larval zebrafish gut
In each of our digestive tracts, trillions of microbes representing hundreds of species colonize local environments, reproduce, and compete with one another. The resulting ecosystems influence many aspects their hostâ€™s development and health. Little is known about how gut microbial communities vary in space and time: how they grow, fluctuate, and respond to perturbations. To address this, we apply light sheet fluorescence microscopy to a model system that combines a realistic /in vivo/ environment with a high degree of experimental control: larval zebrafish with defined subsets of commensal bacterial species. Light sheet microscopy enables three-dimensional imaging with high resolution over the entire intestine, providing visualizations that would be difficult or impossible to achieve otherwise. Quantitative analysis of image data enables measurement of bacterial abundances and distributions and the construction of realistic models of population dynamics. I will describe this approach and focus especially on recent experiments in which a colonizing bacterial species is challenged by the invasion of a second species. Imaging reveals dramatic population collapses driven by peristaltic activity, which differentially affects the two species due to their distinct community architectures. Our findings demonstrate that stochastic perturbations and the physical properties of the host environment can play major roles in determining population dynamics in the vertebrate gut.
01:15 PM
02:45 PM

Lunch Break

02:45 PM
03:30 PM
Brian Munsky - Using Noise to Quantify, Predict, and Control Single-Cell Gene Regulation
Stochastic fluctuations can cause genetically identical cells to exhibit wildly different behaviors. Often labeled "noise," these fluctuations are frequently considered a nuisance that compromises cellular responses, complicates modeling, makes predictive understanding and control all but impossible. However, if we examine cellular fluctuations more closely and match them to discrete stochastic analyses, we discover virtually untapped, yet powerful sources of information and opportunities. In this talk, I will present our collaborative endeavors to integrate single-cell experiments with precise stochastic analyses to gain new insight and quantitatively predictive understanding for Mitogen Activated Protein Kinase (MAPK) signal-activated gene regulation. I will explain how we experimentally quantify transcription dynamics at high temporal (1-minute) and spatial (1-molecule) resolutions; how we use precise computational analyses to model this data and efficiently infer biological mechanisms and parameters; how we predict and evaluate the extent to which model constraints (i.e., data) and uncertainty (i.e., model complexity) contribute to our understanding. I will finish with the discussion of new opportunities in which noise analysis not only helps us to better understand gene regulation phenomena, but where it actually introduces new opportunities to more precisely control these phenomena.
03:30 PM
03:45 PM

Break

03:45 PM
04:30 PM

Informal Discussion -- A statistician's wish list for experiments -- Moderator: Ed Ionides, John Fricks

04:45 PM

Shuttle pick-up from MBI

06:30 PM
07:00 PM

Cash Bar

07:00 PM
09:00 PM

Banquet in the Fusion Room @ Crowne Plaza Hotel

Friday, February 12, 2016
Time Session
08:00 AM

Shuttle to MBI

08:15 AM
09:00 AM

Breakfast

09:00 AM
09:45 AM
Albert Siryaporn - Bacterial colonization in fluid flow networks
Bacteria encounter a variety of mechanical forces during the course of growth and infection. Our lab explores how bacteria detect and respond to forces generated by fluid flow, which is common in many bacterial habitats and host organisms. We find that surprisingly, the bacterium Pseudomonas aeruginosa moves upstream, in the opposite direction, of flow. Cells attach to surfaces at the liquid-surface interface and are oriented upstream by the force of the flow. We detail this mechanism of upstream migration through single-cell measurements and modeling and explore the consequences of this behavior at the multi-cellular level. In particular, we explore how bacteria colonize complex flow networks found in the vasculature of host organisms. Our results show that the interplay between flow and bacterial physiology plays a critical role in determining colonization, competition between different bacterial species, and the dispersal of bacteria. Importantly, our model establishes a foundation for understanding how bacteria grow and spread during pathogenesis.
09:45 AM
10:30 AM
Todd Gingrich - Dynamic fluctuations in cyclic processes
Stochastic processes that consume energy to drive dynamics around a cycle are ubiquitous in biology. Because these processes are stochastic, each realization differs. Consequently, extracted dynamical properties, like the average cycling rate, fluctuate depending on the particular trajectory that was observed. I address how the probability of these fluctuations can be strongly affected by subtle perturbations when there is a dynamic phase transition. By focusing on a simple toy model, I highlight one mechanism for such a dynamic phase transition to arise.
10:30 AM
11:00 AM

Break

11:00 AM
12:00 PM

Informal discussion/workshop wrap up

12:00 PM

Shuttle pick up (one to hotel, one to airport)

Name Email Affiliation
Baliga, Nitin nbaliga@systemsbiology.org NA, Institute for Systems Biology
Bathe, Mark mark.bathe@mit.edu Biological Engineering, Massachusetts Institute of Technology
Buchon, Nicolas nicolas.buchon@cornell.edu Entomology, Cornell University
Bundschuh, Ralf bundschuh@mps.ohio-state.edu Departments of Physics, Chemistry&Biochemistry, Division of Hematology, The Ohio State University
Das, Jayajit jayajit.das@nationwidechildrens.org Departments of Pediatrics, Physics, Integrated Biomedical Sciences, The Ohio State University
Didier, Gustavo gdidier@tulane.edu Mathematics, Tulane University
Du, Chao cd2wb@eservices.virginia.edu Statistics, University of Virginia
Fricks, John fricks@stat.psu.edu Statistics, Pennsylvania State University
Ghosh, Kingshuk Kingshuk.Ghosh@du.edu Physics and Astronomy, University of Denver
Gingrich, Todd tgingrich@gmail.com Physics, Massachusetts Institute of Technology
Gopich, Irina irina.gopich@nih.gov Laboratory of Chemical Physics, NIDDK, National Institutes of Health
Janes, Kevin kjanes@virginia.edu Biomedical Engineering, University of Virginia
Jashnsaz, Hossein hjashnsa@iupui.edu Physics, Indiana University--Purdue University
Kim, Jae Kyoung kim.5052@mbi.osu.edu Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology (KAIST)
King, Gavin kinggm@missouri.edu Physics and Astronomy, University of Missouri
Lakhani, Vinal Vinal.Lakhani@NationwideChildrens.org Department of Pediatrics, The Ohio State University
Lapidus, Lisa lapidus@msu.edu Physics and Astronomy, Michigan State University
Levchenko, Andre andre.levchenko@yale.edu Yale Systems Biology Institute, Yale University
Loerke, Dinah Dinah.Loerke@du.edu Physics and Astronomy, University of Denver
Maroulas, Vasileios maroulas@math.utk.edu Mathematics, University of Tennessee
Matzavinos, Anastasios Anastasios_Matzavinos@Brown.edu Applied Mathematics, Brown University
McKinley, Scott scott.mckinley@ufl.edu Mathematics, Tulane University
Mugler, Andrew amugler@purdue.edu Physics and Astronomy, Purdue University
Munsky, Brian brian.munsky@colostate.edu Chemical and Biological Engineering, Colorado State University
Nelson, Phil nelson@physics.upenn.edu Physics and Astronomy, University of Pennsylvania
Parthasarathy, Raghuveer raghu@uoregon.edu Physics, University of Oregon
Poirier, Michael poirier.18@osu.edu Department of Physics, The Ohio State University
Prasad, Ashok ashokp@engr.colostate.edu Dept. of Chemical and Biological Engineering, Colorado State University
Presse, Steve spresse@iupui.edu Department of Physics and Chemistry & Chemical Biology, Indiana University--Purdue University
Ramchandran, Sundaram sundarramchandran@hotmail.com
Sekar, John johnarul.sekar@gmail.com Computational and Systems Biology, University of Pittsburgh
Shalizi, Cosma cshalizi@cmu.edu Center for the Neural Basis of Cognition, Carnegie-Mellon University
Shepherd, Douglas douglas.shepherd@ucdenver.edu Physics, University of Colorado
Siryaporn, Albert asirya@princeton.edu Physics, University of California, Irvine
Smith, J. Darby j.darby.smith@ufl.edu Mathematics, University of Florida
Toprak, Erdal Erdal.Toprak@utsouthwestern.edu Green Center for Systems Biology, University of Texas Southwestern Medical Center
Tsekouras, Konstantinos ktsekour@iupui.edu Physics, IUPUI
Walter, Nils nwalter@umich.edu Chemistry, University of Michigan
Xu, Wenlong ericxu0605@gmail.com Chemical and Biological Engineering, Colorado State University
A systems biology approach to characterize and rationally manipulate complex behaviors
I will speak about a data-driven approach to decipher and model the dynamics of gene regulatory networks. I will discuss specific applications of this approach to (in no particular order):

Decipher disease-perturbed networks in brain cancer for characterizing etiology of the disease, accurate diagnosis, and rational combination therapy.
Characterize drug tolerance networks in Mycobacterium tuberculosis to potentiate pathogen killing and prevent resistance with combination drugs.
(Time permitting): Understand the mechanistic underpinnings of microbial population collapse in fluctuating environments.
Inferring neuronal synapse structure and dynamics in situ using high-resolution fluorescence imaging and analysis
The neuronal synapse is the core functional unit of the brain, governing neuronal signal transmission in normal and diseased states. Individual neurons contain up to thousands of synapses that interconnect cells to form highly intricate, spatially extended circuits that govern learning, memory, fear, and anxiety, amongst other core brain functions. Neuronal diseases including autism spectrum disorder, schizophrenia, and AlzheimerÃ¢â‚¬â„¢s disease are associated with genetic variations in synaptic proteins, which may impact synapse formation, stability, and signal transmission. Understanding the impact of genetic variation on synapse function requires in situ characterization of synaptic protein localization, copy number, and protein-protein interactions in intact cells and tissues. Toward this end, I will present ongoing efforts in our lab to develop super-resolution fluorescence imaging and analysis approaches that enable in situ mapping of (1) the translational dynamics of individual messenger RNAs that regulate synaptic protein expression; (2) the localization and copy number of individual synaptic proteins; and (3) the dynamics of amyloid aggregation within individual synapses that impact their degeneration in amyloidopathies. These approaches integrate dynamic high-resolution fluorescence imaging with model-based computational analysis to infer super-resolved structure and dynamics of proteins and RNAs pertinent to normal brain development, as well as a range of neuronal diseases.
Maintaining homeostasis in the Drosophila midgut: a quantitative study
The midgut of *Drosophila* is a highly compartmentalized organ and a major site of interaction between the fly and microbes, both benign and pathogenic. Upon infection, both the virulence of the pathogen ingested and the immune response itself inflict damage to the gut epithelium. This damage is repaired by an acceleration of epithelium renewal that combines increased delamination of enterocytes with reprogramming of intestinal stem cells to proliferate and regenerate the gut epithelium. The proper regulation of epithelium renewal, as well as its coordination with immune effector mechanisms, is required to maintain intestinal homeostasis and organismal health. However, it remains unclear how epithelium renewal is quantitatively regulated upon infection. In this talk, we will present data on the mechanisms that control cell dynamics upon infection, and how epithelium renewal is quantitatively regulated along the gut.
Connecting the dots across time: Gleaning signaling mechanisms from single cell snapshot data
Individual isogenic immune cells respond to identical stimuli with unique signaling kinetics. Shapes of kinetic trajectories describing time evolution of abundances of multiple signaling molecules in single cells contain key information regarding signaling mechanisms. However, it can be challenging to measure many signaling reporters simultaneously in single cell in experiments. Flow and mass cytometry experiments can assay a large number of proteins (4 to100) but individual cells are not tracked in these experiments, therefore, such measurements only provide a statistical description of the signaling kinetics, e.g., mean abundances, or, covariance between protein abundances. Is there a way to reconstruct signaling trajectories, even approximately, in individual cells using cytometry data? We address this question affirmatively by using a novel method based on identification of a dynamical invariant pertaining to chemical reaction networks. We validate our method in data obtained from in silico networks and published single cell experiments. We apply our trajectory reconstruction method to analyze mass cytometry data for NaturalKiller (NK) cells to decipher mechanisms underlying NK cell cytotoxic responses.
Intrinsic Noise in Nonlinear Gene Regulation Inference
Cellular intrinsic noise plays an essential role in the regulatory interactions between genes. Although a variety of quantitative methods are used to study gene regulation system, the role of intrinsic noises has largely been overlooked. Using the Kolmogorov backward equation (master equation), we formulate a causal and mechanistic Markov model. This framework recognizes the discrete, nonlinear and stochastic natures of gene regulation and presents a more realistic description of the physical systems than many existing methods. Within this framework, we develop an associated moment-based statistical method, aiming for inferring the unknown regulatory relations. By analyzing the observed distributions of gene expression measurements from both unperturbed and perturbed steady-states of gene regulation systems, this method is able to learn valuable information concerning regulatory mechanisms. This design allows us to estimate the model parameters with a simple convex optimization algorithm. We apply this approach to a synthetic system that resembles a genetic toggle switch and demonstrate that this algorithm can recover the regulatory parameters efficiently and accurately.
Time Series Analysis of Diffusion with Transient Binding
In cellular systems, Brownian forces play a dominant role in the movement of small (and not so small) particles such as vesicles, organelles, etc. However, proteins and other macromolecules bind to one another, altering the underlying Brownian dynamics. In this talk, classical approaches in the biophysical literature to time series which switch between bound and unbound states will be presented, and an alternative approach using stochastic expectation-maximization algorithm (EM) combined with particle filters will be proposed. As an example system, molecular motors, such as kinesin, switch between weakly and strongly bound states, as well as directed transport. I will discuss the analysis of such a system along with the ramifications for multi-motor-cargo complexes found in living cells.
Biophysical proteome: principles, evolution and universality.
Protein sequence encodes complex network of interactions and it is difficult to decipher simple rules in protein science. In spite of this challenge, approximate and semi-empirical rules can be found to describe biophysical properties of different proteins. Using statistical mechanical models tested against multitude of data, our goal is to unravel such universal features of proteins. Our next goal is to extend these transferrable laws in a high throughput manner to model the entire collection of proteins inside an organism, called the proteome. The application at the proteome level allows us to bridge the gap between molecular biophysics and cellular physics and provides us evolutionary insights. With this approach we will try to address some questions of broad interest: i) Why are cells so sensitive to temperature? ii) How do thermophilic proteins (derived from organisms that thrive at high temperature) withstand high temperatures compared to their mesophilic (organisms that live at room temperature) counterparts? iii) What is the evolutionary implication of distribution of different rate processes in a cell and how are they optimized? iv) How do salts slow down cell growth?
Dynamic fluctuations in cyclic processes
Stochastic processes that consume energy to drive dynamics around a cycle are ubiquitous in biology. Because these processes are stochastic, each realization differs. Consequently, extracted dynamical properties, like the average cycling rate, fluctuate depending on the particular trajectory that was observed. I address how the probability of these fluctuations can be strongly affected by subtle perturbations when there is a dynamic phase transition. By focusing on a simple toy model, I highlight one mechanism for such a dynamic phase transition to arise.
Maximum Likelihood Analysis in Single-Molecule FRET
Single-molecule fluorescence spectroscopy is widely used to study macromolecular dynamics. The output of such measurements is a sequence of photons of different colors separated by random time intervals. In the experiments with pulsed lasers, fluorescence lifetimes can also be monitored. To improve the range of the measured dynamics at a given photon count rate, we consider each and every photon and use a maximum likelihood method to get the information about fast conformational dynamics. For a photon trajectory with recorded photon colors, interphoton times, and delay times (relative to laser pulses), the parameters of a model describing molecular dynamics are obtained by maximizing the appropriate likelihood function [1-3]. We discuss various aspects of the maximum likelihood analysis, such as likelihood functions and their applicability, the accuracy of the extracted parameters [4], their sensitivity to the model assumptions [3], and the influence of fast blinking [5]. The method has been applied to study fast folding proteins [3, 5].

[1] I.V. Gopich, A. Szabo. Decoding the pattern of photon colors in single-molecule FRET. J. Phys. Chem. B.; v. 113, 10965Ã¢â‚¬â€œ10973 (2009).
[2] I.V. Gopich, A. Szabo. Theory of the energy transfer efficiency and fluorescence lifetime distribution in single-molecule FRET. Proc. Natl. Acad. Sci. U.S.A.; v. 109, 7747 (2012).
[3] H. S. Chung and I. V. Gopich. Fast single-molecule FRET spectroscopy: theory and experiment. Phys. Chem. Chem. Phys.; v. 16, 18644-18657 (2014).
[4] I. V. Gopich. Accuracy of Maximum Likelihood Estimates of a Two-State Model in Single-Molecule FRET. J. Chem. Phys.; v. 142, 034110 (2015).
[5] H. S. Chung, J. M. Louis, and I. V. Gopich. Analysis of Fluorescence Lifetime and Energy Transfer Efficiency in Single-Molecule Photon Trajectories of Fast-folding Proteins. J. Phys. Chem. B; (2016), in press.
Regulation of Flagellar Motors in Salmonella
Regulated changes in gene expression underlie many biological processes, but globally profiling cell-to-cell variations in transcriptional regulation is problematic when measuring single cells. We have developed an approach, called stochastic profiling, that applies probability theory to transcriptome-wide measurements of small pools of cells to identify single-cell regulatory heterogeneities (Nat Methods 7:311-7 [2010]). I will talk about work in progress that applies stochastic profiling as a tool for uncovering the mechanistic basis of phenotypes that are incompletely penetrant. Regulatory-state frequencies are matched to downstream phenotype frequencies to converge upon a tractable set of candidate states worth of follow-up experimentation. Using the ErbB2 oncoprotein as a model trigger for an incompletely penetrant phenotype, we identify a handful of surprising candidates that significantly affect penetrance when perturbed. Stochastic profiling remains the only method compatible with cells microdissected in situ and thereby opens exciting opportunities in the areas of tissue morphogenesis and cancer.
Probing peptide-lipid interactions from a mechanical single-molecule perspective
Peptide interactions with the complex environment of the phospholipid bilayer determine the three-dimensional structure of membrane proteins. Understanding these interactions is important, because they dictate partitioning, folding, stability, and ultimately the function of this large class of proteins. These interactions not only determine the final folded structures of membrane proteins, they underlie the biological assembly processes guided by the SecYEG translocon complex. Lipid-protein interactions have been studied extensively using biochemical assays, but elucidating mechanistic details from such assays has proven to be challenging. In this talk I will discuss and demonstrate a single molecule approach to measure the interaction of a peptide that is decorated at the tip of atomic force microscope with a supported lipid bilayer. Inspired by work from Stephen WhiteÃ¢â‚¬â„¢s group*, we used synthetic peptides of the form W-L-X-L-L, where the Ã¢â‚¬Å“guestÃ¢â‚¬? X is an amino acid of interest. In another line of inquiry, we used constructs based on SecA, the ATPase of the general secretory system. The mechanical nature of the method allows folding/unfolding data to be collected in physiological buffer solution, without resorting to chemical denaturation. Experiments with short peptides allow identification of the nature of the peptide-lipid interaction at the single amino-acid level in a near-native environment. Looking towards the future, such measurements, together with modeling, may help provide a basis for improved understanding of the folding/unfolding of membrane proteins and their partitioning into the membrane.
Monomer dynamics control the first steps of aggregation and folding
An important aspect of protein folding is understanding how folding competes with aggregation, which leads to diseases such as ParkinsonÃ¢â‚¬â„¢s and AlzheimerÃ¢â‚¬â„¢s. The complexity and dynamics of unfolded protein ensembles may be the ultimate speed limit of folding and play a crucial role in aggregation. In my lab over the past several years we have investigated the reconfiguration dynamics unfolded proteins by measuring the rate of intramolecular diffusion, the rate one part of the chain diffuses relative to another. We have measured diffusion coefficients ranging over three orders of magnitude and observed that aggregation-prone sequences tend to fall in the middle of this range. In this talk, I shall present our experiments on alpha-synuclein, the AlzheimerÃ¢â‚¬â„¢s peptide and various prion sequences. We correlated intramolecular diffusion of the disordered protein with solution conditions that promote aggregation. Finally, we have begun measurements on small molecule aggregation inhibitors and found that some can prevent aggregation by shifting intramolecular diffusion out of the dangerous middle range.
Coupling models and experiments to understand guidance of cell migration

TBD

Biomechanics of intercalation: Linking live-cell analysis and modeling

During embryonic development, epithelial tissues often lengthen along one dimension to establish a longer body or organ axis. During the extension of the germ band (GBE for short) in Drosophila, the embryonic epithelium narrows in one direction and elongates in the other through cell intercalation, i.e. through the directional insertion of cells into neighboring rows. It is known that the symmetry breaking of the process is provided by a system of planar-polarized localization of actin, myosin and adhesion molecules to specific cell-cell interfaces.

Physical models of the process have interpreted it as resulting from the generation of anisotropic interfacial line tension to promote contraction of interfaces between anterior-posterior (AP) neighbors, followed by volume conservation providing a restoring force to passively' elongate new interfaces between dorsal-ventral (DV) neighbors. In order to expand our fundamental understanding of tissue lengthening mechanisms, our goal has been to subject our mechanistic models of intercalation to quantitative tests that combine the large-scale analysis of experimental data and the predictions of simulations.

Using automated computational analysis of live-cell data, we determine cell dynamics in both the planar and apical-basal axis. Our results run counter to physical predictions based on the prevailing line tension models, and instead suggest that interface remodeling occurs through independent sliding displacements of these vertices. In addition, we note key discrepancies between the passive' model and experimental data during the extension phase of GBE, which lead us to propose an active mechanism by introducing an additional anisotropic interfacial interaction during the process of extension.

Tracking intracellular movements
We focus on the biological problem of tracking organelles as they move through cells. In the past, most intracellular movements were recorded manually, however, the results are too incomplete to capture the full complexity of organelle motions. An automated tracking algorithm promises to provide a complete analysis of noisy microscopy data. In this talk, we will get exposed to two such algorithms. The first algorithmic implementation adopts statistical techniques from a Bayesian random set point of view. Instead of considering each individual organelle, we examine a random set whose members are the organelle states and we establish a Bayesian filtering algorithm involving such set-states. The second method combines stochastic filtering estimates of the displacement field with the topological properties on the space of trajectories. Both methods were used to analyze real data.
Mesoscopic modeling of DNA transport in an array of entropic barriers
In this talk, we discuss dissipative particle dynamics (DPD) simulations of the dispersion of DNA molecules conveyed by a pressure-driven fluid flow across a periodic array of entropic barriers. We compare our simulations with nanofluidic experiments, which show the DNA to transition between various types of behaviors as the pressure is increased, and discuss physical insights afforded by the ability of the DPD method to explicitly model flows in the system. Finally, we present anomalous diffusion phenomena that emerge in both experiment and simulation, and we illustrate similarities between this system and Brownian motion in a tilted periodic potential. This is a joint work with Clark Bowman, Daniel Kim, and Derek Stein.
Intracellular transport: The paradox of codependence among antagonistic motors
Transport in neurons is intrinsically bidirectional, with each movement modality carried out by molecular motors in either the kinesin (anterograde) or the dynein (retrograde) families. Because all motors are present at a given time there must be competition and/or cooperation among motors that simultaneously bind a single vesicle to nearby microtubules. The prevailing tug-of-war model captures this dynamic, but fails to account for a recently recognized phenomenon: that in many situations, disabling one family of motors somehow inhibits the performance of motors that are working in the opposite direction. In this talk we will survey a few proposed mechanisms that may account for this behavior and will look at recent work that focuses on a potential role played by the helper protein dynactin.
Physical limits to collective sensing by communicating cells
Single cells sense their environment with remarkable precision. At the same time, cells have evolved diverse mechanisms for communicating. How are sensing and communication related? I will describe recent theoretical and experimental results in which this question is explored in several contexts, including gradient detection by connected epithelial cells, and collective invasion of breast cancer cells. I will show how communication allows cells to perform qualitatively new behaviors that single cells cannot perform alone. Moreover, I will demonstrate that minimal mathematical modeling yields fundamental limits to the precision of sensing, and that these limits are critically altered by cell-to-cell communication. This work extends the study of cellular sensing and information processing to collective ensembles.
Using Noise to Quantify, Predict, and Control Single-Cell Gene Regulation
Stochastic fluctuations can cause genetically identical cells to exhibit wildly different behaviors. Often labeled "noise," these fluctuations are frequently considered a nuisance that compromises cellular responses, complicates modeling, makes predictive understanding and control all but impossible. However, if we examine cellular fluctuations more closely and match them to discrete stochastic analyses, we discover virtually untapped, yet powerful sources of information and opportunities. In this talk, I will present our collaborative endeavors to integrate single-cell experiments with precise stochastic analyses to gain new insight and quantitatively predictive understanding for Mitogen Activated Protein Kinase (MAPK) signal-activated gene regulation. I will explain how we experimentally quantify transcription dynamics at high temporal (1-minute) and spatial (1-molecule) resolutions; how we use precise computational analyses to model this data and efficiently infer biological mechanisms and parameters; how we predict and evaluate the extent to which model constraints (i.e., data) and uncertainty (i.e., model complexity) contribute to our understanding. I will finish with the discussion of new opportunities in which noise analysis not only helps us to better understand gene regulation phenomena, but where it actually introduces new opportunities to more precisely control these phenomena.
Old news and new news about single-photon absorption sensitivity in human vision
It is sometimes said that "Our eyes can see single photons." This article begins by finding a more precise version of that claim and reviewing evidence gathered for it up to around 1985 in two distinct realms, those of human psychophysics and single-cell physiology. Finding a single framework that accommodates both kinds of result is then a nontrivial challenge, and one that sets severe quantitative constraints on any model of dim-light visual processing. I'll present a new model that accomplishes this task, and compare it to recent experiments.
Visualizing microbial population dynamics in the larval zebrafish gut
In each of our digestive tracts, trillions of microbes representing hundreds of species colonize local environments, reproduce, and compete with one another. The resulting ecosystems influence many aspects their hostÃ¢â‚¬â„¢s development and health. Little is known about how gut microbial communities vary in space and time: how they grow, fluctuate, and respond to perturbations. To address this, we apply light sheet fluorescence microscopy to a model system that combines a realistic /in vivo/ environment with a high degree of experimental control: larval zebrafish with defined subsets of commensal bacterial species. Light sheet microscopy enables three-dimensional imaging with high resolution over the entire intestine, providing visualizations that would be difficult or impossible to achieve otherwise. Quantitative analysis of image data enables measurement of bacterial abundances and distributions and the construction of realistic models of population dynamics. I will describe this approach and focus especially on recent experiments in which a colonizing bacterial species is challenged by the invasion of a second species. Imaging reveals dramatic population collapses driven by peristaltic activity, which differentially affects the two species due to their distinct community architectures. Our findings demonstrate that stochastic perturbations and the physical properties of the host environment can play major roles in determining population dynamics in the vertebrate gut.
Interpreting changes in cell shape: cancer invasion and metastases
Cells from different tissues typically look quite different from each other even when cultured on plastic or glass slides under identical conditions. This leads us to formulate the hypothesis that cell shape is a function of the cytoskeletal properties of those cells, and begs the question as to what information changes in cell shape carry. This question becomes all the more interesting for cancer, since invasive cancer cells are reported to have altered mechanical properties compared to non-invasive cancer cells. Inspired by this reasoning we study shape characteristics of paired osteosarcoma cell lines, each consisting of a less metastatic parental line and a more metastatic line, derived from the former by in vivo selection. Statistical analysis shows that shape characteristics of the metastatic cell lines are partly overlapping but on average distinguishable from the parental line. Significantly the shape changes fall into two categories, with three paired cell lines displaying a more mesenchymal-like morphology, while the fourth displaying a change towards a more rounded morphology. A neural network algorithm could distinguish between samples of the less metastatic cells from the more metastatic cells with near perfect accuracy. Thus subtle changes in shape carry information about the genetic changes that lead to invasiveness and metastasis of osteosarcoma cancer cells. The next challenge is to link these changes in shape with changes in mechanical cytoskeletal parameters. I will briefly discuss ongoing experiments to infer these cellular mechanical properties by studying internal fluctuations of organelles.
Enzymes stepping on landmines
Abstract not submitted
Unraveling regulation from reaction mechanisms
No abstract submitted.
Information from Fluctuations: Utilizing Single-Cell Analyses to Probe Disease Mechanisms
Abstract not submitted
Bacterial colonization in fluid flow networks
Bacteria encounter a variety of mechanical forces during the course of growth and infection. Our lab explores how bacteria detect and respond to forces generated by fluid flow, which is common in many bacterial habitats and host organisms. We find that surprisingly, the bacterium Pseudomonas aeruginosa moves upstream, in the opposite direction, of flow. Cells attach to surfaces at the liquid-surface interface and are oriented upstream by the force of the flow. We detail this mechanism of upstream migration through single-cell measurements and modeling and explore the consequences of this behavior at the multi-cellular level. In particular, we explore how bacteria colonize complex flow networks found in the vasculature of host organisms. Our results show that the interplay between flow and bacterial physiology plays a critical role in determining colonization, competition between different bacterial species, and the dispersal of bacteria. Importantly, our model establishes a foundation for understanding how bacteria grow and spread during pathogenesis.
Fighting antibiotic resistance by exploiting antibiotic hypersensitivity
Antibiotic resistance is a worldwide public health problem. The lack of effective antibiotic therapies against resistant pathogens has led to prolonged treatments, increased morbidity, and burgeoning health care costs. Pathogenic bacteria are increasingly resistant to antibiotics leaving clinicians with limited options for treatment. For combating both the short- and long-term ineffectiveness of antibiotics, we have developed a novel approach to modulate the intrinsic antibiotic resistance level of bacteria. We can now decrease bacterial antibiotic resistance and design antibiotic therapies using lower doses of drugs. Reducing the amount of antibiotic to clear infections will impact patient well-being and reduce the antibiotic load in the clinic which exacerbates spread of antibiotic resistance.
RNA Pathways Dissected at the Single Molecule Level: The Power of Integrating Experimental and Computational Approaches
Nature employs nanoscale machines that self-assemble into dynamic structures of complex architecture and functionality. Single molecule fluorescence microscopy offers a non-invasive tool to probe and ultimately dissect and control these nanoassemblies in real-time, often with the aid of computational tools to interpret and complement the experimental data. In particular, single molecule fluorescence resonance energy transfer (smFRET) allows us to measure biologically relevant distances and changes thereof at the 2-8 nm scale, whereas complementary super-resolution localization techniques based on Gaussian fitting of imaged point spread functions measure distances in the 10 nm and longer range. In this talk, I will describe how we have developed Single Molecule Cluster Analysis (SiMCAn) based on a vast smFRET dataset to dissect the complex conformational dynamics of a pre-mRNA as it is spliced by the spliceosomal processing machinery. In addition, I will demonstrate how a single molecule systems biology can be implemented that feeds super-resolved single particle tracking data into lattice-based Monte-Carlo simulations to generate novel hypotheses on the gene regulation of mRNAs by microRNAs during RNA silencing.

Regulation of Flagellar Motors in Salmonella
Kevin Janes Regulated changes in gene expression underlie many biological processes, but globally profiling cell-to-cell variations in transcriptional regulation is problematic when measuring single cells. We have developed an approach, called stochastic profili

Interpreting changes in cell shape: cancer invasion and metastases
Ashok Prasad Cells from different tissues typically look quite different from each other even when cultured on plastic or glass slides under identical conditions. This leads us to formulate the hypothesis that cell shape is a function of the cytoskeletal properti

Visualizing microbial population dynamics in the larval zebrafish gut
Raghuveer Parthasarathy In each of our digestive tracts, trillions of microbes representing hundreds of species colonize local environments, reproduce, and compete with one another. The resulting ecosystems influence many aspects their hostâ€™s development

Bacterial colonization in fluid flow networks
Albert Siryaporn Bacteria encounter a variety of mechanical forces during the course of growth and infection. Our lab explores how bacteria detect and respond to forces generated by fluid flow, which is common in many bacterial habitats and host organisms. We find th

Using Noise to Quantify, Predict, and Control Single-Cell Gene Regulation
Brian Munsky Stochastic fluctuations can cause genetically identical cells to exhibit wildly different behaviors. Often labeled "noise," these fluctuations are frequently considered a nuisance that compromises cellular responses, complicates modeling, m

Biophysical proteome: principles, evolution and universality.
Kingshuk Ghosh Protein sequence encodes complex network of interactions and it is difficult to decipher simple rules in protein science. In spite of this challenge, approximate and semi-empirical rules can be found to describe biophysical properties of different pr

Monomer dynamics control the first steps of aggregation and folding
Lisa Lapidus An important aspect of protein folding is understanding how folding competes with aggregation, which leads to diseases such as ParkinsonÃ¢â‚¬â„¢s and AlzheimerÃ¢â‚¬â„¢s. The complexity and dynamics of unfolded protein ensembles may be the ultimate sp

Enzymes stepping on landmines
Steve Presse Abstract not submitted

Old news and new news about single-photon absorption sensitivity in human vision
Phil Nelson It is sometimes said that "Our eyes can see single photons." This article begins by finding a more precise version of that claim and reviewing evidence gathered for it up to around 1985 in two distinct realms, those of human psychophysics a

Time Series Analysis of Diffusion with Transient Binding
John Fricks In cellular systems, Brownian forces play a dominant role in the movement of small (and not so small) particles such as vesicles, organelles, etc. However, proteins and other macromolecules bind to one another, altering the underlying Brownian dynami

RNA Pathways Dissected at the Single Molecule Level: The Power of Integrating Experimental and Computational Approaches
Nils Walter Nature employs nanoscale machines that self-assemble into dynamic structures of complex architecture and functionality. Single molecule fluorescence microscopy offers a non-invasive tool to probe and ultimately dissect and control these nanoassemblie

Mesoscopic modeling of DNA transport in an array of entropic barriers
Anastasios Matzavinos In this talk, we discuss dissipative particle dynamics (DPD) simulations of the dispersion of DNA molecules conveyed by a pressure-driven fluid flow across a periodic array of entropic barriers. We compare our simulations with nanofluidic experiments

Tracking intracellular movements
Vasileios Maroulas We focus on the biological problem of tracking organelles as they move through cells. In the past, most intracellular movements were recorded manually, however, the results are too incomplete to capture the full complexity of organelle motions. An au

Connecting the dots across time: Gleaning signaling mechanisms from single cell snapshot data
Jayajit Das Individual isogenic immune cells respond to identical stimuli with unique signaling kinetics. Shapes of kinetic trajectories describing time evolution of abundances of multiple signaling molecules in single cells contain key information regarding sig

Physical limits to collective sensing by communicating cells
Andrew Mugler Single cells sense their environment with remarkable precision. At the same time, cells have evolved diverse mechanisms for communicating. How are sensing and communication related? I will describe recent theoretical and experimental results in which

Videos

### Print

Full Schedule Participant List