Single-Cell Level Systems Biology

Agenda

A 1-hour networking reception will follow the symposium.

Abstracts

Leveraging Endogenous Phenotypic Variability to Quantitatively Analyze Signal Transduction at the Single Cell Level
Grégoire Altan-Bonnet, PhD, Memorial Sloan-Kettering Cancer Center

Single-cell analysis of signal transduction is applied to infer signal network connectivity or to analyze the endogenous phenotypic variability of isogenic populations of mammalian cells.
In a first part, we use single-cell phosphoprofiling to dissect the variability in cytokine response within T lymphocytes. We introduce a new methodology (named “ScatterSlice”) that leverages endogenous variability in expression of signaling components, to quantify their effects on downstream phospho-responses. We used this method to uncover an inverse relationship between responsiveness against two important cytokines (IL-2 and IL-7) in T cells. We have constructed a computational model of the interactions among the various subunits of the IL-2 and IL-7 receptors (e.g. common  chain), as well as with their ligands. To fit the model to the high-resolution data, we have employed a Bayesian framework that penalizes for deviation from both previously defined parameter values and the current data. Using this fitting algorithm, we identified the competition for common  chain as a mechanism for non-independence of IL-2 and IL-7 sensitivity. This single-cell study of differential responsiveness to cytokine among lymphocytes quantifies a key mechanism for the differentiation and survival of memory cells during an immune response.

In a second part, we describe how single-cell analysis of the noise in phospho-protein levels can be exploited to infer signaling network connectivity. Using an extension of the Chemical Langevin Equation theory to the analysis of flow cytometric phosphoproteomic data and accounting specifically for the empirically observed log-normal distributions of phosphoprotein concentration, we demonstrate that the topology of small network configurations, including link directionality (i.e., causality) can be inferred, even in the absence of temporally-resolved data samples. Applications to the MAPK cascade in lymphocytes yield new insights in this well characterized pathway [Work in collaboration with R.Prill, G. Cecchi & Gustavo Stolovitzky – IBM Yorktown Heights, NY]

Fluctuation Theory for Experimental Biology
Johan Paulsson, PhD, Harvard Medical School

Intracellular dynamics involves random fluctuations, delays, and nonlinearities—complications that typically make quantitative models analytically intractable. Most biological systems are also sparsely characterized, where one or two steps may be known in detail, others are known only qualitatively, and yet others have not even been identified. I will discuss some general mathematical frameworks that address these problems. I will also present some experimental results that exploit fluctuations to evaluate measured localization patterns in cells.

Optimal Information Processing in Biochemical Networks
Chris Wiggins, PhD, Columbia University

A variety of experimental results over the past decades provide examples of near-optimal information processing in biological networks, including in biochemical and transcriptional regulatory networks. Computing information–theoretic quantities requires first choosing or computing the joint probability distribution describing multiple nodes in such a network (for example, representing the probability distribution of finding an integer copy number of each of two interacting reactants or gene products while respecting the "intrinsic" small copy number noise constraining information transmission at the scale of the cell). I'll give an overview of some recent analytic and numerical work facilitating calculation of such joint distributions and the associated information, which in turn makes possible numerical optimization of information flow in models of noisy regulatory and biochemical networks. Illustrating cases include quantification of form–function relations, ideal design of regulatory cascades, and response to oscillatory driving.

A Tale of Two Switches
Narendra Maheshri, PhD, Massachusetts Institute of Technology

Positive feedback loops in gene and protein networks are often found in the decision-making circuitry of cells. They allow cells to mount and unambiguous response to external signals such as nutrient responses in microbes, cell-fate decisions in stem cells, and how to fight infections by the immune system. The same underlying interactions enable a population of genetically identical cells to respond heterogeneously to a fixed intermediate signal. The heterogeneous, often bimodal, response can lead to a population exhibiting multiple phenotypes that may often be advantageous.

Here I discuss two examples of transcriptional switches in budding yeast that are capable of generating a bimodal population-level response. These are both epigenetic switches as the particular response is heritable at the single cell level. Both switches rely on biological noise -- small number fluctuations in a particular molecular species. The first trans-encoded epigenetic switch is a synthetic transcriptional positive feedback loop that relies on small number fluctuations in a trans factor -- in this case a transcriptional activator. We use both modeling and experiments to demonstrate how random and intermittent bursts of transcription are necessary for the observed bimodal gene expression that was previously not predicted with deterministic models. The second cis-encoded epigenetic switch is the FLO11 gene, that slowly fluctuates between a silenced and competent promoter state -- a molecular transition that occurs in cis. Using the same modeling approach, we show the kinetic role of different activators is to largely stabilize the active state. In both cases, we highlight how a dynamical view of these switches can help explain both high-level functional behavior and lower level mechanistic details.

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