Computational investigations into the orgins of 'short term' biochemical memory in T cell activation

Recent studies have reported that T cells can integrate signals between interrupted encounters with Antigen Presenting Cells (APCs) in such a way that the process of signal integration exhibits a form of memory. Here, we carry out a computational study using a simple mathematical model of T cell activation to investigate the ramifications of interrupted T cell-APC contacts on signal integration. We consider several mechanisms of how signal integration at these time scales may be achieved and conclude that feedback control of immediate early gene products (IEGs) appears to be a highly plausible mechanism that allows for effective signal integration and cytokine production from multiple exposures to APCs. Analysis of these computer simulations provides an experimental roadmap involving several testable predictions.Comment: 11 pages, published July 18th 200

Design principles of mammalian signaling networks : emergent properties at modular and global scales

Thesis (Ph. D.)--Massachusetts Institute of Technology, Biological Engineering Division, 2008.Includes bibliographical references (leaves 244-249).This thesis utilizes modeling approaches rooted in statistical physics and physical chemistry to investigate several aspects of cellular signal transduction at both the modular and global levels. Design principles of biological networks and cell signaling processes pertinent to disease progression emerge from these studies. It is my hope that knowledge of these principles may provide new mechanistic insights and conceptual frameworks for thinking about therapeutic intervention into diseases such as cancer and diabetes that arise from aberrant signaling. Areas of interest have emphasized the role of scaffold proteins in protein kinase cascades, modeling relevant biophysical processes related to T cell activation, design principles of signal transduction focusing on multisite phosphorylation, quantifying the notion of signal duration and the time scale dependence of signal detection, and entropy based models of network architecture inferred from proteomics data. These problems are detailed below. The assembly of multiple signaling proteins into a complex by a scaffold protein guides many cellular decisions. Despite recent advances, the overarching principles that govern scaffold function are not well understood. We carried out a computational study using kinetic Monte Carlo simulations to understand how spatial localization of kinases on a scaffold may regulate signaling under different physiological condition. Our studies identify regulatory properties of scaffold proteins that allow them to both amplify and attenuate incoming signals in different biological contexts. In a further, supplementary study, simulations also indicate that a major effect that scaffolds exert on the dynamics of cell signaling is to control how the activation of protein kinases is distributed over time[2].(cont.) Scaffolds can influence the timing of kinase activation by allowing for kinases to become activated over a broad range of times, thus allowing for signaling across a broad spectrum of time scales. T cells orchestrate the adaptive immune response and are central players in maintenance of functioning immune system. Recent studies have reported that T cells can integrate signals between interrupted encounters with Antigen Presenting Cells (APCs) in such a way that the process of signal integration exhibits a form of memory. We carried out a computational study using a simple mathematical model of T cell activation to investigate the ramifications of interrupted T cell-APC contacts on signal integration. We considered several mechanisms of how signal integration at these time scales may be achieved. In another study, we investigated the role of spatially localizing signaling components of the T cell signaling pathway into a structure known as the immunological synapse. We constructed a minimal mathematical model that offers a mechanism for how antigen quality can regulate signaling dynamics in the immunological synapse These studies involving the analysis of signaling dynamics led us to investigate how differences in signal duration might be detected. Signal duration (e.g. the time scales over which an active signaling intermediate persists) is a key regulator of biological decisions in myriad contexts such as cell growth, proliferation, and developmental lineage commitments. Accompanying differences in signal duration are numerous downstream biological processes that require multiple steps of biochemical regulation. We present an analysis that investigates how simple biochemical motifs that involve multiple stages of regulation can be constructed to differentially process signals that persist at different time scales[3].(cont.) Topological features of these networks that allow for different frequency dependent signal processing properties are identified. One role of multisite phosphorylation in cell signaling is also investigated. The utilization of multiple phosphorylation sites in regulating a biological response is ubiquitous in cell signaling. If each site contributes an additional, equivalent binding site, then one consequence of an increase in the number of phosphorylations may be to increase the probability that, upon disassociation, a ligand immediately rebinds to its receptor. How such effects may influence cell signaling systems is not well understood. A self-consistent integral equation formalism for ligand rebinding, in conjunction with Monte Carlo simulations, was employed to further investigate the effects of multiple, equivalent binding sites on shaping biological responses. Finally, this thesis also seeks to investigate cell signaling at a global scale. Advances in Mass Spectrometry based phosphoproteomics have allowed for the real-time quantitative monitoring of entire proteomes as signals propagate through complex networks in response to external signals. The trajectories of as many as 222 phosphorylated tyrosine sites can be simultaneously and reproducibly monitored at multiple time points. We develop and apply a method using the principle of maximum entropy to infer a model of network connectivity of these phosphorylation sites. The model predicts a core structure of signaling nodes, affinity dependent topological features of the network, and connectivity of signaling nodes that were hitherto unassociated with the canonical growth factor signaling network. Our combined results illustrate many complexities in the broad array of control properties that emerge from the physical effects that constrain signal propagation on complex biological networks.(cont.) It is the hope of this work that these studies bring coherence to seemingly paradoxical observations and suggest that cells have evolved design rules that enable biochemical motifs to regulate widely disparate cellular functions.by Jason W. Locasale.Ph.D

A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data

A major theme in constraint-based modeling is unifying experimental data, such as biochemical information about the reactions that can occur in a system or the composition and localization of enzyme complexes, with highthroughput data including expression data, metabolomics, or DNA sequencing. The desired result is to increase predictive capability resulting in improved understanding of metabolism. The approach typically employed when only gene (or protein) intensities are available is the creation of tissue-specific models, which reduces the available reactions in an organism model, and does not provide an objective function for the estimation of fluxes, which is an important limitation in many modeling applications. We develop a method, flux assignment with LAD (least absolute deviation) convex objectives and normalization (FALCON), that employs metabolic network reconstructions along with expression data to estimate fluxes. In order to use such a method, accurate measures of enzyme complex abundance are needed, so we first present a new algorithm that addresses quantification of complex abundance. Our extensions to prior techniques include the capability to work with large models and significantly improved run-time performance even for smaller models, an improved analysis of enzyme complex formation logic, the ability to handle very large enzyme complex rules that may incorporate multiple isoforms, and depending on the model constraints, either maintained or significantly improved correlation with experimentally measured fluxes. FALCON has been implemented in MATLAB and ATS, and can be downloaded from: https://github.com/bbarker/FALCON. ATS is not required to compile the software, as intermediate C source code is available, and binaries are provided for Linux x86-64 systems. FALCON requires use of the COBRA Toolbox, also implemented in MATLAB.Comment: 30 pages, 12 figures, 4 table

Allovalency revisited: an analysis of multisite phosphorylation and substrate rebinding

The utilization of multiple phosphorylation sites in regulating a biological response is ubiquitous in cell signaling. If each site contributes an additional, equivalent binding site, then one consequence of an increase in the number of phosphorylations may be to increase the probability that, upon disassociation, a ligand immediately rebinds to its receptor. How such effects may influence cell signaling systems has been less studied. Here, a self-consistent integral equation formalism for ligand rebinding, in conjunction with Monte Carlo simulations, is employed to further investigate the effects of multiple, equivalent binding sites on shaping biological responses. Multiple regimes that characterize qualitatively different physics due to the differential prevalence of rebinding effects are predicted. Calculations suggest that when ligand rebinding contributes significantly to the dose response, a purely allovalent model can influence the binding curves nonlinearly. The model also predicts that ligand rebinding in itself appears insufficient to generative a highly cooperative biological response.Comment: 44 pages, 5 figures; accepted Journal of Chemical Physic

Altered metabolism in cancer

Cancer cells have different metabolic requirements from their normal counterparts. Understanding the consequences of this differential metabolism requires a detailed understanding of glucose metabolism and its relation to energy production in cancer cells. A recent study in BMC Systems Biology by Vasquez et al. developed a mathematical model to assess some features of this altered metabolism. Here, we take a broader look at the regulation of energy metabolism in cancer cells, considering their anabolic as well as catabolic needs

Quantitative determinants of aerobic glycolysis identify flux through the enzyme GAPDH as a limiting step

Aerobic glycolysis or the Warburg Effect (WE) is characterized by the increased metabolism of glucose to lactate. It remains unknown what quantitative changes to the activity of metabolism are necessary and sufficient for this phenotype. We developed a computational model of glycolysis and an integrated analysis using metabolic control analysis (MCA), metabolomics data, and statistical simulations. We identified and confirmed a novel mode of regulation specific to aerobic glycolysis where flux through GAPDH, the enzyme separating lower and upper glycolysis, is the rate-limiting step in the pathway and the levels of fructose (1,6) bisphosphate (FBP), are predictive of the rate and control points in glycolysis. Strikingly, negative flux control was found and confirmed for several steps thought to be rate-limiting in glycolysis. Together, these findings enumerate the biochemical determinants of the WE and suggest strategies for identifying the contexts in which agents that target glycolysis might be most effective. DOI: http://dx.doi.org/10.7554/eLife.03342.00

Differential response to exercise in claudin-low breast cancer

Exposure to exercise following a breast cancer diagnosis is associated with reductions in the risk of recurrence. However, it is not known whether breast cancers within the same molecular-intrinsic subtype respond differently to exercise. Syngeneic mouse models of claudin-low breast cancer (i.e., EO771, 4TO7, and C3(1)SV40Tag-p16-luc) were allocated to a uniform endurance exercise treatment dose (forced treadmill exercise) or sham-exercise (stationary treadmill). Compared to sham-controls, endurance exercise treatment differentially affected tumor growth rate: 1- slowed (EO771), 2- accelerated (C3(1)SV40Tag-p16-luc), or 3- was not affected (4TO7). Differential sensitivity of the three tumor lines to exercise was paralleled by effects on intratumoral Ki-67, Hif1-α, and metabolic programming. Inhibition of Hif1-α synthesis by the cardiac glycoside, digoxin, completely abrogated exercise-accelerated tumor growth in C3(1)SV40Tag-p16-luc. These results suggest that intratumoral Hif1-α expression is an important determinant of claudin-low breast cancer adaptation to exercise treatment

Signal duration and the time scale dependence of signal integration in biochemical pathways

Signal duration (e.g. the time scales over which an active signaling intermediate persists) is a key regulator of biological decisions in myriad contexts such as cell growth, proliferation, and developmental lineage commitments. Accompanying differences in signal duration are numerous downstream biological processes that require multiple steps of biochemical regulation. Here, we present an analysis that investigates how simple biochemical motifs that involve multiple stages of regulation can be constructed to differentially process signals that persist at different time scales. We compute the dynamic gain within these networks and resulting power spectra to better understand how biochemical networks can integrate signals at different time scales. We identify topological features of these networks that allow for different frequency dependent signal processing properties. Our studies suggest design principles for why signal duration in connection with multiple steps of downstream regulation is a ubiquitous control motif in biochemical systems.Comment: 27 pages, 4 figure