Regulation of signal duration and the statistical dynamics of kinase activation by scaffold proteins

Scaffolding proteins that direct the assembly of multiple kinases into a spatially localized signaling complex are often essential for the maintenance of an appropriate biological response. Although scaffolds are widely believed to have dramatic effects on the dynamics of signal propagation, the mechanisms that underlie these consequences are not well understood. Here, Monte Carlo simulations of a model kinase cascade are used to investigate how the temporal characteristics of signaling cascades can be influenced by the presence of scaffold proteins. Specifically, we examine the effects of spatially localizing kinase components on a scaffold on signaling dynamics. The simulations 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. 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 at both early and late times. Scaffold concentrations that result in optimal signal amplitude also result in the broadest distributions of times over which kinases are activated. These calculations provide insights into one mechanism that describes how the duration of a signal can potentially be regulated in a scaffold mediated protein kinase cascade. Our results illustrate another complexity in the broad array of control properties that emerge from the physical effects of spatially localizing components of kinase cascades on scaffold proteins.Comment: 12 pages, 6 figure

Finding undetected protein associations in cell signaling by belief propagation

External information propagates in the cell mainly through signaling cascades and transcriptional activation, allowing it to react to a wide spectrum of environmental changes. High throughput experiments identify numerous molecular components of such cascades that may, however, interact through unknown partners. Some of them may be detected using data coming from the integration of a protein-protein interaction network and mRNA expression profiles. This inference problem can be mapped onto the problem of finding appropriate optimal connected subgraphs of a network defined by these datasets. The optimization procedure turns out to be computationally intractable in general. Here we present a new distributed algorithm for this task, inspired from statistical physics, and apply this scheme to alpha factor and drug perturbations data in yeast. We identify the role of the COS8 protein, a member of a gene family of previously unknown function, and validate the results by genetic experiments. The algorithm we present is specially suited for very large datasets, can run in parallel, and can be adapted to other problems in systems biology. On renowned benchmarks it outperforms other algorithms in the field.Comment: 6 pages, 3 figures, 1 table, Supporting Informatio

An Instruction Language for Self-Construction in the Context of Neural Networks

Biological systems are based on an entirely different concept of construction than human artifacts. They construct themselves by a process of self-organization that is a systematic spatio-temporal generation of, and interaction between, various specialized cell types. We propose a framework for designing gene-like codes for guiding the self-construction of neural networks. The description of neural development is formalized by defining a set of primitive actions taken locally by neural precursors during corticogenesis. These primitives can be combined into networks of instructions similar to biochemical pathways, capable of reproducing complex developmental sequences in a biologically plausible way. Moreover, the conditional activation and deactivation of these instruction networks can also be controlled by these primitives, allowing for the design of a “genetic code” containing both coding and regulating elements. We demonstrate in a simulation of physical cell development how this code can be incorporated into a single progenitor, which then by replication and differentiation, reproduces important aspects of corticogenesis

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