Bioengineering models of cell signaling

Strategies for rationally manipulating cell behavior in cell-based technologies and molecular therapeutics and understanding effects of environmental agents on physiological systems may be derived from a mechanistic understanding of underlying signaling mechanisms that regulate cell functions. Three crucial attributes of signal transduction necessitate modeling approaches for analyzing these systems: an ever-expanding plethora of signaling molecules and interactions, a highly interconnected biochemical scheme, and concurrent biophysical regulation. Because signal flow is tightly regulated with positive and negative feedbacks and is bidirectional with commands traveling both from outside-in and inside-out, dynamic models that couple biophysical and biochemical elements are required to consider information processing both during transient and steady-state conditions. Unique mathematical frameworks will be needed to obtain an integrated perspective on these complex systems, which operate over wide length and time scales. These may involve a two-level hierarchical approach wherein the overall signaling network is modeled in terms of effective "circuit" or "algorithm" modules, and then each module is correspondingly modeled with more detailed incorporation of its actual underlying biochemical/biophysical molecular interactions

PAI1 blocks NMDA receptor-mediated effects of tissue-type plasminogen activator on cell signaling and physiology

The fibrinolysis proteinase tissue-type plasminogen activator (tPA, also known as PLAT) triggers cell signaling and regulates cell physiology. In PC12 cells, Schwann cells and macrophages, the N-methyl-D-aspartate receptor (NMDA-R) mediates tPA signaling. Plasminogen activator inhibitor-1 (PAI1, also known as SERPINE1) is a rapidly acting inhibitor of tPA enzyme activity. Although tPA-initiated cell signaling is not dependent on its enzyme active site, we show that tPA signaling is neutralized by PAI1. In PC12 cells, PAI1 blocked the ERK1/2 activation mediated by tPA as well as neurite outgrowth. In Schwann cells, PAI1 blocked tPA-mediated ERK1/2 activation and cell migration. In macrophages, PAI1 blocked the ability of tPA to inhibit IκBα phosphorylation and cytokine expression. The cell signaling activity of tPA-PAI1 complex was rescued when the complex was formed with PAI1R76E, which binds to LRP1 with decreased affinity, by pre-treating cells with the LRP1 antagonist receptor-associated protein and upon LRP1 gene silencing. The inhibitory role of LRP1 in tPA-PAI1 complex-initiated cell signaling was unanticipated given the reported role of LRP1 as an NMDA-R co-receptor in signaling responses elicited by free tPA or α2-macroglobulin. We conclude that PAI1 functions as an in-hibitor not only of the enzyme activity of tPA but also of tPA receptor-mediated activities

On the emergence and evolution of artificial cell signaling networks

This PhD project is concerned with the evolution of Cell Signaling Networks (CSNs) in silico. CSNs are complex biochemical networks responsible for the coordination of cellular activities. We are investigating the possibility to build an evolutionary simulation platform that would allow the spontaneous emergence and evolution of Artificial Cell Signaling Networks (ACSNs). From a practical point of view, realizing and evolving ACSNs may provide novel computational paradigms for a variety of application areas. This work may also contribute to the biological understanding of the origins and evolution of real CSNs

Evolving artificial cell signaling networks using molecular classifier systems

Nature is a source of inspiration for computational techniques which have been successfully applied to a wide variety of complex application domains. In keeping with this we examine Cell Signaling Networks (CSN) which are chemical networks responsible for coordinating cell activities within their environment. Through evolution they have become highly efficient for governing critical control processes such as immunological responses, cell cycle control or homeostasis. Realising (and evolving) Artificial Cell Signaling Networks (ACSNs) may provide new computational paradigms for a variety of application areas. Our abstraction of Cell Signaling Networks focuses on four characteristic properties distinguished as follows: Computation, Evolution, Crosstalk and Robustness. These properties are also desirable for potential applications in the control systems, computation and signal processing field. These characteristics are used as a guide for the development of an ACSN evolutionary simulation platform. In this paper we present a novel evolutionary approach named Molecular Classifier System (MCS) to simulate such ACSNs. The MCS that we have designed is derived from Holland's Learning Classifier System. The research we are currently involved in is part of the multi disciplinary European funded project, ESIGNET, with the central question of the study of the computational properties of CSNs by evolving them using methods from evolutionary computation, and to re-apply this understanding in developing new ways to model and predict real CSNs

Cell signaling promoting protein carbonylation does not cause sulfhydryl oxidation: implications to the mechanism of redox signaling

Reactive oxygen species (ROS) have been recognized as second messengers, however, targeting mechanisms for ROS in cell signaling have not been defined. While ROS oxidizing protein cysteine thiols has been the most popular proposed mechanism, our laboratory proposed that ligand/receptor-mediated cell signaling involves protein carbonylation. Peroxiredoxin-6 (Prx6) is one protein that is carbonylated at 10 min after the platelet-derived growth factor (PDGF) stimulation of human pulmonary artery smooth muscle cells. In the present study, the SulfoBiotics Protein Redox State Monitoring Kit Plus (Dojindo Molecular Technologies) was used to test if cysteine residues of Prx6 are oxidized in response to the PDGF stimulation. Human Prx6 has a molecular weight of 25 kDa and contains two cysteine residues. The Dojindo system adds the 15 kDa Protein-SHifter if these cysteine residues are reduced in the cells. Results showed that, in untreated cells, the Prx6 molecule predominantly exhibited the 55 kDa band, indicating that both cysteine residues are reduced in the cells. Treatment of cells with 1 mM H 2O 2 caused the disappearance of the 55 kDa band and the appearance of a 40 kDa band, suggesting that the high concentration of H 2O 2 oxidized one of the two cysteine residues in the Prx6 molecule. By contrast, PDGF stimulation had no effects on the thiol status of the Prx6 molecule. We concluded that protein carbonylation is a more sensitive target of ROS during ligand/receptor-mediated cell signaling than sulfhydryl oxidation

An approach to evolving cell signaling networks in silico

Cell Signaling Networks(CSN) are complex bio-chemical networks which, through evolution, have become highly efficient for governing critical control processes such as immunological responses, cell cycle control or homeostasis. From a computational point of view, modeling Artificial Cell Signaling Networks (ACSNs) in silico may provide new ways to design computer systems which may have specialized application areas. To investigate these new opportunities, we review the key issues of modeling ACSNs identified as follows. We first present an analogy between analog and molecular computation. We discuss the application of evolutionary techniques to evolve biochemical networks for computational purposes. The potential roles of crosstalk in CSNs are then examined. Finally we present how artificial CSNs can be used to build robust real-time control systems. The research we are currently involved in is part of the multi disciplinary EU funded project, ESIGNET, with the central question of the study of the computational properties of CSNs by evolving them using methods from evolutionary computation, and to re-apply this understanding in developing new ways to model and predict real CSNs. This also complements the present requirements of Computational Systems Biology by providing new insights in micro-biology research

Editorial: Membrane lipids in T cell functions

Plasma membrane lipids play essential roles in regulating T cell signaling, differentiation, and effector functions. The major lipid species in the plasma membrane are glycerophospholipids, sphingolipids, and sterol lipids. TCR and costimulatory molecules lead to profound changes in the composition, distribution, and dynamic of plasma membrane lipids. For instance, cholesterol, sphingomyelin, and saturated phosphocholine are enriched at the contact zone between T cells and antigen-presenting cells during peptide/MHC complexes recognition, where they constitute a platform of lipid domains essential for optimal T cell signaling. Glycerophospholipid provide docking sites for binding pivotal signaling proteins as well as for their conformation, portioning, and mobility. Finally, plasma membrane lipids also act as second messengers with important immune-regulatory functions. This Research Topic contains seven articles that review the current understanding of the mechanisms and molecules involved in the metabolism and function of membrane lipids and how differences in their content may affect T cell functional properties

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

Inferring the Sign of Kinase-Substrate Interactions by Combining Quantitative Phosphoproteomics with a Literature-Based Mammalian Kinome Network

Protein phosphorylation is a reversible post-translational modification commonly used by cell signaling networks to transmit information about the extracellular environment into intracellular organelles for the regulation of the activity and sorting of proteins within the cell. For this study we reconstructed a literature-based mammalian kinase-substrate network from several online resources. The interactions within this directed graph network connect kinases to their substrates, through specific phosphosites including kinase-kinase regulatory interactions. However, the "signs" of links, activation or inhibition of the substrate upon phosphorylation, within this network are mostly unknown. Here we show how we can infer the "signs" indirectly using data from quantitative phosphoproteomics experiments applied to mammalian cells combined with the literature-based kinase-substrate network. Our inference method was able to predict the sign for 321 links and 153 phosphosites on 120 kinases, resulting in signed and directed subnetwork of mammalian kinase-kinase interactions. Such an approach can rapidly advance the reconstruction of cell signaling pathways and networks regulating mammalian cells.Comment: 5 page, 3 figures, IEEE-BIBE confrenc