Model Of Signaling Network Research Articles

Reducing complexity: An iterative strategy for parameter determination in biological networks

The dynamics of biological networks are fundamental to a variety of processes in many areas of biology and medicine. Understanding of such networks on a systemic level is facilitated by mathematical models describing these networks. However, since mathematical models of signalling networks commonly aim to describe several highly connected biological quantities and many model parameters cannot be measured directly, quantitative dynamic models often present challenges with respect to model calibration. Here, we propose an iterative fitting routine to decompose the problem of fitting a system of coupled ordinary differential equations describing a signalling network into smaller subproblems. Parameters for each differential equation are estimated separately using a Differential Evolution algorithm while all other dynamic quantities in the model are treated as input to the system. The performance of this algorithm is evaluated on artificial networks with known structure and known model parameters and compared to a conventional optimisation procedure for the same problem. Our analysis indicates that the procedure results in a significantly higher quality of fit and more efficient reconstruction of the true parameters than the conventional algorithm.

Cooperation between Noncanonical Ras Network Mutations

Cancer develops after the acquisition of a collection of mutations that together create the cancer phenotype. How collections of mutations work together within a cell and whether there is selection for certain combinations of mutations are not well understood. We investigated this problem with a mathematical model of the Ras signaling network, including a computational random mutagenesis. Modeling and subsequent experiments revealed that mutations of the tumor suppressor gene NF1 can amplify the effects of other Ras pathway mutations, including weakly activating, noncanonical Ras mutants. Furthermore, analyzing recently available, large, cancer genomic data sets uncovered increased co-occurrence of NF1 mutations with mutations in other Ras network genes. Overall, these data suggest that combinations of Ras pathway mutations could serve the role of cancer "driver." More generally, this work suggests that mutations that result in network instability may promote cancer in a manner analogous to genomic instability.

A Virtual B Cell Lymphoma Model to Predict Effective Combination Therapy

Aberrant activation of the B cell receptor (BCR) signaling network drives survival and proliferation of many B cell malignancies, such as activated B cell like diffuse large B cell lymphoma (ABC-DLBCL). A number of small molecule inhibitors targeting various kinases in the BCR signaling network have been developed. However, clinical application of these targeted agents is facing several challenges such as low response rate and acquired drug resistance. The limited efficacy of single agent targeted therapy is at least partially due to pathway reactivation through crosstalks and compensatory circuits. By simultaneously repressing multiple nodes in the signaling network, combination therapy has the potential to completely extinguish signaling and induce more potent and durable response. The complexity of BCR signaling network makes it difficult to infer which combinations will be effective and synergistic. Given the large number of possible drug combinations, comprehensive experimental screening – including exploration of multiple dosages – is not practically feasible.Computational models of signaling networks that can accurately reconstruct signaling dynamics in silico may represent a useful alternative to experimental screening and trial-and-error experimental investigation. Here, we developed a computational model that integrates signal transduction, tumor growth, and drug kinetics to accurately simulate BCR signaling dynamics and the effect of drug-induced perturbation on signaling output and cell viability. We used this model to predict effective combinatorial therapy in silicoand validated some of these predictions using in vitro experiments.Based on experimentally verified protein-protein interactions, we constructed the first detailed kinetic model of the BCR signaling network covering three major signaling pathways downstream of BCR, namely NFkB, PI3K/AKT and MAPK. The model captured complex crosstalk between these three pathways and multiple feedback loops. Simulated kinase activation time courses under temporal antigen stimulus successfully recapitulated normal BCR signaling dynamics as reported in literature.Using published drug response data in the BCR signaling-dependent ABC-DLBCL cell line TMD8, we trained a tumor growth model which in combination with the kinetic model enabled reliable prediction of viability response of many drug combinations at various dosages. For example, predicted viability response of BTK inhibitor ibrutinib in combination with inhibitors targeting other kinases in the network, e.g. BKM-120 against PI3K, sotrastaurin against PKC-beta closely matched previously published experimental data in TMD8 (r>0.86,p<1e-11).We then sought to identify synergistic drug combinations by simulating viability response at 10x10 virtual dosages for each drug combination and estimating synergism using Bliss independence model. Computational screening predicted dual blockage of LYN and SYK as the most synergistic combination, which we confirmed experimentally by treating TMD8 cells with LYN inhibitor Dasatinib and SYK inhibitor R406 at multiple doses.Finally we sought to use our model to predict biomarkers of sensitivity and resistance to specific treatment strategies. By integrating expression levels of BCR signaling network components assessed by published primary DLBCL RNA-seq data, we simulated patient-specific drug responses and computed the correlation between expression level of specific components with viability response under specific treatments. We found that overexpression of PTP1B, which dephosphorylates BTK substrate PLCg2, predicts relative sensitivity to BTK inhibition. Supporting this prediction, we observed increased PTP1B expression in DLBCL cell lines sensitive to ibrutinib treatment, suggesting PTP1B as potential biomarker for ibrutinib sensitivity.In summary, this study provides a novel approach to computationally optimize combinatorial targeted therapy against aberrant BCR signaling and paves the way for the discovery of effective patient-specific drug combinations. DisclosuresMelnick:Bioreference: Scientific Advisory Board, Scientific Advisory Board Other; Calgene: Consultancy; Janssen: Research Funding; Genentech: Speakers Bureau.

Abstract PR04: Cooperation between Ras network mutations in cancer

Abstract Cancer genome sequencing often identifies mutations within the Ras signaling network other than the well-established KRAS, NRAS, and BRAF oncogene mutations and NF1 tumor suppressor gene mutations. The extent to which a novel mutation in a commonly mutated pathway can be assumed to have cancer promoting potential, and whether or not certain contextual conditions are required for the mutation to have cancer promoting effects, is not well understood. We investigated this problem with a computational model of the Ras signaling network, with focused cell-based experiments, and with statistical analysis of large cancer genomic data sets. We first applied our previously developed computational model of the Ras signaling network to identify potential synergistic combinations within the Ras network. For example, our modeling suggested that mutations to the tumor suppressor gene NF1 can amplify the effects of other Ras pathway mutations, including the effects of weakly-activating, noncanonical, Ras mutants. Experiments comparing the strength of signal produced by noncanonical Ras mutants expressed in neurofibromin deficient and wild-type cells confirmed this prediction. If such synergistic effects contribute to the development of cancer, we reasoned that there should be an increased rate of co-occurrence between the mutations that the model predicted to have cooperative potential. Analysis of large cancer genomic data sets from the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas identified increased rates of co-occurrence between mutations predicted to display synergy. Overall, our study suggests that there may be combinations of Ras pathway mutations that serve the role of cancer “driver”. This abstract is also presented as Poster B33. Citation Format: Edward C. Stites, Paul C. Trampont, Lisa B. Haney, Scott F. Walk, Kodi S. Ravichandran. Cooperation between Ras network mutations in cancer. [abstract]. In: Proceedings of the AACR Special Conference on RAS Oncogenes: From Biology to Therapy; Feb 24-27, 2014; Lake Buena Vista, FL. Philadelphia (PA): AACR; Mol Cancer Res 2014;12(12 Suppl):Abstract nr PR04. doi: 10.1158/1557-3125.RASONC14-PR04

Finding gene regulatory network candidates using the gene expression knowledge base.

BackgroundNetwork-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of ‘omics’ data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis.ResultsWe have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions.ConclusionsSemantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0386-y) contains supplementary material, which is available to authorized users.

Resolving Early Signaling Events in T-Cell Activation Leading to IL-2 and FOXP3 Transcription

Signal intensity and feedback regulation are known to be major factors in the signaling events stemming from the T-cell receptor (TCR) and its various coreceptors, but the exact nature of these relationships remains in question. We present a mathematical model of the complex signaling network involved in T-cell activation with cross-talk between the Erk, calcium, PKC and mTOR signaling pathways. The model parameters are adjusted to fit new and published data on TCR trafficking, Zap70, calcium, Erk and Isignaling. The regulation of the early signaling events by phosphatases, CD45 and SHP1, and the TCR dynamics are critical to determining the behavior of the model. Additional model corroboration is provided through quantitative and qualitative agreement with experimental data collected under different stimulating and knockout conditions. The resulting model is analyzed to investigate how signal intensity and feedback regulation affect TCR- and coreceptor-mediated signal transduction and their downstream transcriptional profiles to predict the outcome for a variety of stimulatory and knockdown experiments. Analysis of the model shows that: (1) SHP1 negative feedback is necessary for preventing hyperactivity in TCR signaling; (2) CD45 is required for TCR signaling, but also partially suppresses it at high expression levels; and (3) elevated FOXP3 and reduced IL-2 signaling, an expression profile often associated with T regulatory cells (Tregs), is observed when the system is subjected to weak TCR and CD28 costimulation or a severe reduction in CD45 activity.

Abstract 370: Modeling signaling networks in tumor immunology

Abstract Despite decades of successful discoveries, the overwhelming molecular complexity of cells has been a significant barrier to gain a fundamental understanding of how cells work as a whole, how they are perturbed in cancer, and how they interact with signals from the immune system. To achieve a more complete understanding, will require the assistance of network modeling. By developing computational network models we can introduce cancer mutations to simulate pharmacological perturbations, or loss-of-function and gain-of-function experiments and study the dynamics of biochemical networks change under different immune signaling states. We are developing and applying rule-base (kappa) network modeling of signaling networks to study how microenvironment immune factors influence a tumor's behavior. In particular, that of immune mediated cellular senescence in tumor cells. We have built a rule-base network model of the biochemical network regulating immune mediated cell senescence in melanomas, which carry a BRAFV600 mutation in the MAPK signaling network. Using computational methods we have characterized the crosstalk with cytokine signaling networks and the crosstalk with other growth signaling networks. The development of such a computational model may help researchers understand how BRAFV600 cells in melanomas communicate with their environment by secreting various cytokines and growth factors, as it has become clear that this immune ‘secretory phenotype’ can have pro- as well as anti-tumorigenic effects. By combining rule-based descriptions of the biochemical reactions associated to this phenotype with computer simulation we can open up new avenues for exploring such complex networks linked to melanoma progression. Citation Format: Trevor Clancy, Eivind Hovig. Modeling signaling networks in tumor immunology. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 370. doi:10.1158/1538-7445.AM2014-370

Network Modeling Approach to Predict Myofibroblast Differentiation

Fibrotic disease is a major cause of morbidity and mortality and is characterized by the transition of resident fibroblast cells into active myofibroblasts, identified by their expression of alpha smooth muscle actin. Myofibroblast differentiation is regulated by growth factor signaling and mechanical signals transduced through integrins, which converge at focal adhesion proteins (Src and FAK) and MAPK signaling, but lead to divergent outcomes. While details are known about individual pathways, little is known about their interactions. To this end, an ODE-based model of this cell signaling network was developed in parallel with in vitro experiments to analyze potential mechanisms of crosstalk and regulation of αSMA production. We found that cells lacking Src or FAK produce significantly less or more αSMA than wild type cells, respectively. Transforming growth factor beta 1 and fibroblast growth factor signal through ERK and MAPK p38 with different dynamic profiles to increase or decrease αSMA expression, respectively. Our model effectively recreated αSMA expression levels across a set of 22 experimental conditions and matched some features of transient phosphorylation of ERK and p38. These results support a potential mechanism for regulation of fibroblast differentiation: αSMA production is promoted by active p38 and Src and opposed by ERK.

Learning Boolean logic models of signaling networks with ASP

Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefore, automated inference of Boolean logical networks from experimental data is a fundamental question in this field. This paper addresses the problem consisting of learning from a prior knowledge network describing causal interactions and phosphorylation activities at a pseudo-steady state, Boolean logic models of immediate-early response in signaling transduction networks. The underlying optimization problem has been so far addressed through mathematical programming approaches and the use of dedicated genetic algorithms. In a recent work we have shown severe limitations of stochastic approaches in this domain and proposed to use Answer Set Programming (ASP), considering a simpler problem setting. Herein, we extend our previous work in order to consider more realistic biological conditions including numerical datasets, the presence of feedback-loops in the prior knowledge network and the necessity of multi-objective optimization. In order to cope with such extensions, we propose several discretization schemes and elaborate upon our previous ASP encoding. Towards real-world biological data, we evaluate the performance of our approach over in silico numerical datasets based on a real and large-scale prior knowledge network. The correctness of our encoding and discretization schemes are dealt with in Appendices A–B.

An Interaction Library for the FcεRI Signaling Network.

Antigen receptors play a central role in adaptive immune responses. Although the molecular networks associated with these receptors have been extensively studied, we currently lack a systems-level understanding of how combinations of non-covalent interactions and post-translational modifications are regulated during signaling to impact cellular decision-making. To fill this knowledge gap, it will be necessary to formalize and piece together information about individual molecular mechanisms to form large-scale computational models of signaling networks. To this end, we have developed an interaction library for signaling by the high-affinity IgE receptor, FcεRI. The library consists of executable rules for protein–protein and protein–lipid interactions. This library extends earlier models for FcεRI signaling and introduces new interactions that have not previously been considered in a model. Thus, this interaction library is a toolkit with which existing models can be expanded and from which new models can be built. As an example, we present models of branching pathways from the adaptor protein Lat, which influence production of the phospholipid PIP3 at the plasma membrane and the soluble second messenger IP3. We find that inclusion of a positive feedback loop gives rise to a bistable switch, which may ensure robust responses to stimulation above a threshold level. In addition, the library is visualized to facilitate understanding of network circuitry and identification of network motifs.

Comprehensive logic based analyses of Toll-like receptor 4 signal transduction pathway.

Among the 13 TLRs in the vertebrate systems, only TLR4 utilizes both Myeloid differentiation factor 88 (MyD88) and Toll/Interleukin-1 receptor (TIR)-domain-containing adapter interferon-β-inducing Factor (TRIF) adaptors to transduce signals triggering host-protective immune responses. Earlier studies on the pathway combined various experimental data in the form of one comprehensive map of TLR signaling. But in the absence of adequate kinetic parameters quantitative mathematical models that reveal emerging systems level properties and dynamic inter-regulation among the kinases/phosphatases of the TLR4 network are not yet available. So, here we used reaction stoichiometry-based and parameter independent logical modeling formalism to build the TLR4 signaling network model that captured the feedback regulations, interdependencies between signaling kinases and phosphatases and the outcome of simulated infections. The analyses of the TLR4 signaling network revealed 360 feedback loops, 157 negative and 203 positive; of which, 334 loops had the phosphatase PP1 as an essential component. The network elements' interdependency (positive or negative dependencies) in perturbation conditions such as the phosphatase knockout conditions revealed interdependencies between the dual-specific phosphatases MKP-1 and MKP-3 and the kinases in MAPK modules and the role of PP2A in the auto-regulation of Calmodulin kinase-II. Our simulations under the specific kinase or phosphatase gene-deficiency or inhibition conditions corroborated with several previously reported experimental data. The simulations to mimic Yersinia pestis and E. coli infections identified the key perturbation in the network and potential drug targets. Thus, our analyses of TLR4 signaling highlights the role of phosphatases as key regulatory factors in determining the global interdependencies among the network elements; uncovers novel signaling connections; identifies potential drug targets for infections.

Experimental and computational tools for analysis of signaling networks in primary cells.

Cellular information processing in signaling networks forms the basis of responses to environmental stimuli. At any given time, cells receive multiple simultaneous input cues, which are processed and integrated to determine cellular responses such as migration, proliferation, apoptosis, or differentiation. Protein phosphorylation events play a major role in this process and are often involved in fundamental biological and cellular processes such as protein-protein interactions, enzyme activity, and immune responses. Determining which kinases phosphorylate specific phospho sites poses a challenge; this information is critical when trying to elucidate key proteins involved in specific cellular responses. Here, methods to generate high-quality quantitative phosphorylation data from cell lysates originating from primary cells, and how to analyze the generated data to construct quantitative signaling network models, are presented. These models can subsequently be used to guide follow-up in vitro/in vivo validation studies.

Sequential and Opposing Activities of Wnt and BMP Coordinate Zebrafish Bone Regeneration

SUMMARYZebrafish fully regenerate lost bone, including after fin amputation, through a process mediated by dedifferentiated, lineage-restricted osteoblasts. Mechanisms controlling the osteoblast regenerative program from its initiation through reossification are poorly understood. We show that fin amputation induces a Wnt/β-catenin-dependent epithelial to mesenchymal transformation (EMT) of osteoblasts in order to generate proliferative Runx2+ preosteoblasts. Localized Wnt/β-catenin signaling maintains this progenitor population toward the distal tip of the regenerative blastema. As they become proximally displaced, preosteoblasts upregulate sp7 and subsequently mature into re-epithelialized Runx2−/sp7+ osteoblasts that extend preexisting bone. Auto-crine bone morphogenetic protein (BMP) signaling promotes osteoblast differentiation by activating sp7 expression and counters Wnt by inducing Dickkopf-related Wnt antagonists. As such, opposing activities of Wnt and BMP coordinate the simultaneous demand for growth and differentiation during bone regeneration. This hierarchical signaling network model provides a conceptual framework for understanding innate bone repair and regeneration mechanisms and rationally designing regenerative therapeutics.

From proteomes to complexomes in the era of systems biology

Protein complexes carry out almost the entire signaling and functional processes in the cell. The protein complex complement of a cell, and its network of complex-complex interactions, is referred to here as the complexome. Computational methods to predict protein complexes from proteomics data, resulting in network representations of complexomes, have recently being developed. In addition, key advances have been made toward understanding the network and structural organization of complexomes. We review these bioinformatics advances, and their discovery-potential, as well as the merits of integrating proteomics data with emerging methods in systems biology to study protein complex signaling. It is envisioned that improved integration of proteomics and systems biology, incorporating the dynamics of protein complexes in space and time, may lead to more predictive models of cell signaling networks for effective modulation.

Network based elucidation of drug response: from modulators to targets.

Network-based drug discovery aims at harnessing the power of networks to investigate the mechanism of action of existing drugs, or new molecules, in order to identify innovative therapeutic treatments. In this review, we describe some of the most recent advances in the field of network pharmacology, starting with approaches relying on computational models of transcriptional networks, then moving to protein and signaling network models and concluding with “drug networks”. These networks are derived from different sources of experimental data, or literature-based analysis, and provide a complementary view of drug mode of action. Molecular and drug networks are powerful integrated computational and experimental approaches that will likely speed up and improve the drug discovery process, once fully integrated into the academic and industrial drug discovery pipeline.

Activation of Intracellular Calcium by Multiple Wnt Ligands and Translocation of β-Catenin into the Nucleus: A CONVERGENT MODEL OF Wnt/Ca2+ AND Wnt/β-CATENIN PATHWAYS

Ca(2+) and β-catenin, a 92-kDa negatively charged transcription factor, transduce Wnt signaling via the non-canonical, Wnt/Ca(2+) and canonical, Wnt/β-catenin pathways independently. The nuclear envelope is a barrier to large protein entry, and this process is regulated by intracellular calcium [Ca(2+)]i and trans-nuclear potential. How β-catenin traverses the nuclear envelope is not well known. We hypothesized that Wnt/Ca(2+) and Wnt/β-catenin pathways act in a coordinated manner and that [Ca(2+)]i release facilitates β-catenin entry into the nucleus in mammalian cells. In a live assay using calcium dyes in PC3 prostate cancer cells, six Wnt peptides (3A, 4, 5A, 7A, 9B, and 10B) mobilized [Ca(2+)]i but Wnt11 did not. Based upon dwell time (range = 15-30 s) of the calcium waveform, these Wnts could be classified into three classes: short, 3A and 5A; long, 7A and 10B; and very long, 4 and 9B. Wnt-activated [Ca(2+)]i release was followed by an increase in intranuclear calcium and the depolarization of both the cell and nuclear membranes, determined by using FM4-64. In cells treated with Wnts 5A, 9B, and 10B, paradigm substrates for each Wnt class, increased [Ca(2+)]i was followed by β-catenin translocation into the nucleus in PC3, MCF7, and 253J, prostate, breast, and bladder cancer cell lines; both the increase in Wnt 5A, 9B, and 10B induced [Ca(2+)]i release and β-catenin translocation are suppressed by thapsigargin in PC3 cell line. We propose a convergent model of Wnt signaling network where Ca(2+) and β-catenin pathways may act in a coordinated, interdependent, rather than independent, manner.

Abstract A24: Network models of signaling and drug response in melanoma.

Abstract Novel combination therapies are potentially key to preventing emergence of resistance to initially successful single agent therapies such as RAF inhibitors in melanoma. In order to systematically nominate novel and effective drug combinations, we have developed an integrated systems biology technology (perturbation biology) for inferring quantitative and predictive network models of signaling. Our modeling strategy combines systematic perturbation experiments, measurement of response profiles to perturbations, inference of network models and simulation of models with in silico perturbations. As a key component of the overall strategy, we have adapted the belief propagation (BP) inference algorithm from statistical physics to construct signaling models with several hundreds of measured entities. In parallel, we have developed a pathway informatics tool to automatically extract prior information from multiple signaling databases. We applied this approach to derive signaling network models in RAF inhibitor resistant melanoma cells and nominate drug combinations to overcome resistance in melanoma. First, we systematically perturb melanoma cells with a set of targeted drugs as single and paired agents. Next, we quantitatively measured proteomic (total and phospho-protein levels) and phenotypic (e.g. cell cycle arrest, cellular viability) responses to perturbations. We incorporated the generic prior information and context specific perturbation response data to construct quantitative network models, which capture multiple oncogenic pathways in melanoma. As shown by cross validation calculations, use of prior information significantly improved predictive power of models. Using the inferred network models of signaling, we systematically predicted response to tens of thousands of in silico perturbations. Our modeling and simulation strategy expanded the volume of drug response profile from few thousand experimental data points to millions of in silico data points. In addition, our models provided quantitative descriptions of signaling events in melanoma. Our perturbation biology technology is suitable for applications in diverse areas of molecular biology beyond cancer research. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A24. Citation Format: Anil Korkut, Weiqing Wang, Evan Molinelli, Martin Miller, Poorvi Kaushik, Arman Aksoy, Xiaohong Jing, Nick Gauthier, Chris Sander. Network models of signaling and drug response in melanoma. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A24.

E-Learning with EMBS: Employing Medical Imaging in Drug Development [Continuing Education

The development of novel drugs is a lengthy process that requires years of preclinical and clinical research and many steps to ensure that the developed compound can be administered with minimal risk to patients. Drug effectiveness was traditionally assessed based on well-established clinical end points. However, secondary surrogate end points can also be assessed on the basis of biological markers quantified using imaging that serve as direct indicators of the drug's therapeutic efficiency. Compared to primary end points, biomarkers are expected to provide a shorter evaluation process without compromising the safety and efficiency of the developed drug. Modern imaging technologies enable multidimensional and multiparameter data visualization. As the spatial and temporal resolution has improved with newer imaging technologies, various modalities have been used to monitor substrate and protein dynamics and study the progression of therapy delivery in real time. For example, imaging data can provide the basis for the mathematical modeling of protein kinetics and biochemical signaling networks. While imaging modalities are now applied to provide secondary end points in some therapeutic areas, it is desirable to include imaging for end-point assessment in preclinical investigations by exploring models designed to report the activity of selected biomarkers that are detectable by certain imaging modalities. Moreover, with the advent of molecular probes, imaging will aid in not only visualizing gross anatomical structures but also visualizing cellular substructures and monitoring molecular dynamics. To complement the available knowledge on the scientific and operational use of medical imaging in drug development, the IEEE Engineering in Medicine and Biological Society (EMBS) has launched a recent education initiative to convey a series of tutorials on this topic to cater to specialists in the field as well as others interested in the concepts, methodologies, and applications. The educational material consists of slide presentations narrated by Prof. Mats Bergstroem. He has more than 30 years of experience in medical imaging and more than ten years of experience in the pharmaceutical industry.

Machines vs. Ensembles: Effective MAPK Signaling through Heterogeneous Sets of Protein Complexes

Despite the importance of intracellular signaling networks, there is currently no consensus regarding the fundamental nature of the protein complexes such networks employ. One prominent view involves stable signaling machines with well-defined quaternary structures. The combinatorial complexity of signaling networks has led to an opposing perspective, namely that signaling proceeds via heterogeneous pleiomorphic ensembles of transient complexes. Since many hypotheses regarding network function rely on how we conceptualize signaling complexes, resolving this issue is a central problem in systems biology. Unfortunately, direct experimental characterization of these complexes has proven technologically difficult, while combinatorial complexity has prevented traditional modeling methods from approaching this question. Here we employ rule-based modeling, a technique that overcomes these limitations, to construct a model of the yeast pheromone signaling network. We found that this model exhibits significant ensemble character while generating reliable responses that match experimental observations. To contrast the ensemble behavior, we constructed a model that employs hierarchical assembly pathways to produce scaffold-based signaling machines. We found that this machine model could not replicate the experimentally observed combinatorial inhibition that arises when the scaffold is overexpressed. This finding provides evidence against the hierarchical assembly of machines in the pheromone signaling network and suggests that machines and ensembles may serve distinct purposes in vivo. In some cases, e.g. core enzymatic activities like protein synthesis and degradation, machines assembled via hierarchical energy landscapes may provide functional stability for the cell. In other cases, such as signaling, ensembles may represent a form of weak linkage, facilitating variation and plasticity in network evolution. The capacity of ensembles to signal effectively will ultimately shape how we conceptualize the function, evolution and engineering of signaling networks.

Boolean Network Model Predicts Knockout Mutant Phenotypes of Fission Yeast

Boolean networks (or: networks of switches) are extremely simple mathematical models of biochemical signaling networks. Under certain circumstances, Boolean networks, despite their simplicity, are capable of predicting dynamical activation patterns of gene regulatory networks in living cells. For example, the temporal sequence of cell cycle activation patterns in yeasts S. pombe and S. cerevisiae are faithfully reproduced by Boolean network models. An interesting question is whether this simple model class could also predict a more complex cellular phenomenology as, for example, the cell cycle dynamics under various knockout mutants instead of the wild type dynamics, only. Here we show that a Boolean network model for the cell cycle control network of yeast S. pombe correctly predicts viability of a large number of known mutants. So far this had been left to the more detailed differential equation models of the biochemical kinetics of the yeast cell cycle network and was commonly thought to be out of reach for models as simplistic as Boolean networks. The new results support our vision that Boolean networks may complement other mathematical models in systems biology to a larger extent than expected so far, and may fill a gap where simplicity of the model and a preference for an overall dynamical blueprint of cellular regulation, instead of biochemical details, are in the focus.