09:00 to 09:45 Rosalind Allen Inherent variability in the kinetics of amyloid fibril formation Co-authors: Juraj Szavits-Nossan, Kym Eden, Ryan Morris, Martin Evans and Cait MacPheeIn small volumes, the kinetics of filamentous protein self-assembly is expected to show significant variability, arising from intrinsic molecular noise. We introduce a simple stochastic model including nucleation and autocatalytic growth via elongation and fragmentation, which allows us to predict the effects of molecular noise on the kinetics of autocatalytic self-assembly. We derive an analytic expression for the lag-time distribution, which agrees well with experimental results for the fibrillation of bovine insulin. Our analysis shows that significant lag-time variability can arise from both primary nucleation and from autocatalytic growth and should provide a way to extract mechanistic information on early-stage aggregation from small-volume experiments. INI 1 09:45 to 10:30 Muruhan Rathinam Analysis of Monte Carlo estimators for parametric sensitivities in stochastic chemical kinetics Co-author: Ting Wang (University of Delaware) We provide an overview of some of the major Monte Carlo approaches for parametric sensitivities in stochastic chemical systems. The efficiency of a Monte Carlo approach depends in part on the variance of the estimator. It has been numerically observed that in several examples, that the finite difference (FD) and the (regularized) pathwise differentiation (RPD) methods tend to have lower variance than the Girsanov Tranformation (GT) estimator while the latter has the advantage of being unbiased. We present a theoretical explanation in terms of system volume asymptotics for the larger variance of the GT approach when compared to the FD methods. We also present an analysis of efficiency of the FD and GT methods in terms of desired error and system volume. INI 1 10:30 to 11:00 Morning Coffee 11:00 to 11:45 David Doty "No We Can't": Impossibility of efficient leader election by chemical reactions Co-author: David Soloveichik (University of Texas, Austin) Suppose a chemical system requires a single molecule of a certain species $L$. Preparing a solution with just a single copy of $L$ is a difficult task to achieve with imprecise pipettors. Could we engineer artificial reactions (a chemical election algorithm, so to speak) that whittle down an initially large count of $L$ to 1? Yes, with the reaction $L+L \to L+F$: whenever two candidate leaders encounter each other, one drops out of the race. In volume $v$ convergence to a single $L$ requires expected time proportional to $v$; the final reaction --- two lone $L$'s seeking each other in the vast expanse of volume $v$ --- dominates the whole expected time.One might hope that more cleverly designed reactions could elect a leader more quickly. We dash this hope: $L+L \to L+F$, despite its sloth, is the fastest chemical algorithm for leader election there is (subject to some reasonable constraints on the reactions). The techniques generalize to establish lower bounds on the time required to do other computational tasks, such as computing which of two species $X$ or $Y$ holds an initial majority.Democracy works... but it's painstakingly slow.Related Links http://web.cs.ucdavis.edu/~doty/papers/slepprlt.pdf - Paper INI 1 11:45 to 12:30 Jay Newby First-passage time to clear the way for receptor-ligand binding in a crowded environment I will present theoretical support for a hypothesis about cell-cell contact, which plays a critical role in immune function. A fundamental question for all cell-cell interfaces is how receptors and ligands come into contact, despite being separated by large molecules, the extracellular fluid, and other structures in the glycocalyx. The cell membrane is a crowded domain filled with large glycoproteins that impair interactions between smaller pairs of molecules, such as the T cell receptor and its ligand, which is a key step in immunological information processing and decision-making. A first passage time problem allows us to gauge whether a reaction zone can be cleared of large molecules through passive diffusion on biologically relevant timescales. I combine numerical and asymptotic approaches to obtain a complete picture of the first passage time, which shows that passive diffusion alone would take far too long to account for experimentally observed cell-cell contact format ion times. The result suggests that cell-cell contact formation may involve previously unknown active mechanical processes. INI 1 12:30 to 13:30 Lunch at Wolfson Court 14:00 to 14:45 John Albeck Linking dynamic signaling events within the same cell In intracellular signaling pathways, biochemical activation events are transmitted from one node within the signaling network to another.  Recent work examining the information capacity of signaling pathways has concluded that most signaling pathways have limited abilities to resolve different strengths of inputs.  However, these studies are based on data in which only a single signal is measured in each cell, in response to a given cell, with the limitation that transmission of a signal from one signaling node to another cannot be directly observed.  Other published data suggest that single cells may have a much higher capacity to transmit quantitative information, which is obscured by population heterogeneity.  To better understand the properties of information transmission through biochemical cascades in individual cells, we have developed a panel of live-cell reporters to monitor multiple signaling events in the cell proliferation and growth network (CPGN).  These reporters include activity biosensors for the kinases ERK, Akt, mTOR, and AMPK, and CRISPR-based reporters for ERK target gene expression.  Experimental analysis with these tools reveals the temporal and quantitative linkage properties between nodes of the CPGN.  I will discuss two studies currently underway in our lab.  The first examines the how the CPGN manages the interplay between ATP-producing and ATP-consuming processes during cell proliferation; we find that loss of Akt signaling results in unstable levels of ATP and NADH in proliferating cells.  The second project focuses on how variations in amplitude and duration of ERK activity control the expression of the target gene Fra-1, which is involved in metastasis; here, we show that cancer therapeutics directed at inhibiting this pathway create strikingly different kinetics of ERK activity at the single-cell level, with distinct effects on Fra-1 expression. INI 1 14:45 to 15:30 Aleksandra Walczak tba INI 1 15:30 to 16:00 Afternoon Tea 16:00 to 16:45 Vahid Shahrezaei Inference of size dependence of transcription parameters from single cell data using multi-scale models of gene expression Co-authors: Anthony Bowman (Imperial College London), Xi-Ming Sun (MRC CSC), Samuel Marguerat (MRC CSC)Gene expression is affected by both random timing of reactions (intrinsic noise) and interaction with global stochastic systems in the cells (extrinsic noise). A challenge in inferring parameters of gene expression using models of stochastic gene expression is that these models usually only inlcude intrinsic noise. However, experimental distributions of transcripts are strongly influenced by extrinsic effects including cell cycle and cell division. Here, we present a multi-scale approach in stochastic gene expression to deal with this problem. We apply our methodology to data obtained using single molecule Fish technique in fission yeast. The data suggests cell size influences transcription parameters. We use Approximate Bayesian Computation (ABC) along with sequential Monte Carlo to infer the dependence of gene expression parameters on cell size. Our analysis reveals a linear increase of transcription burst size during the cell cycle. INI 1