Identification of signaling pathways related to drug efficacy in hepatocellular carcinoma via integration of phosphoproteomic, genomic and clinical data.

Hepatocellular Carcinoma (HCC) is one of the leading causes of death worldwide, with only a handful of treatments effective in unresectable HCC. Most of the clinical trials for HCC using new generation interventions (drug-targeted therapies) have poor efficacy whereas just a few of them show some promising clinical outcomes [1]. This is amongst the first studies where the mode of action of some of the compounds extensively used in clinical trials is interrogated on the phosphoproteomic level, in an attempt to build predictive models for clinical efficacy.

Modulating cell-to-cell variability and sensitivity to death ligands by co-drugging.

TRAIL (tumor necrosis factor-related apoptosis-inducing ligand) holds promise as an anti-cancer therapeutic but efficiently induces apoptosis in only a subset of tumor cell lines. Moreover, even in clonal populations of responsive lines, only a fraction of cells dies in response to TRAIL and individual cells exhibit cell-to-cell variability in the timing of cell death. Fractional killing in these cell populations appears to arise not from genetic differences among cells but rather from differences in gene expression states, fluctuations in protein levels and the extent to which TRAIL-induced death or survival pathways become activated.

Cells surviving fractional killing by TRAIL exhibit transient but sustainable resistance and inflammatory phenotypes.

When clonal populations of human cells are exposed to apoptosis-inducing agents, some cells die and others survive. This fractional killing arises not from mutation but from preexisting, stochastic differences in the levels and activities of proteins regulating apoptosis. Here we examine the properties of cells that survive treatment with agonists of two distinct death receptors, tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and anti-FasR antibodies.

Properties of cell death models calibrated and compared using Bayesian approaches.

Using models to simulate and analyze biological networks requires principled approaches to parameter estimation and model discrimination. We use Bayesian and Monte Carlo methods to recover the full probability distributions of free parameters (initial protein concentrations and rate constants) for mass-action models of receptor-mediated cell death. The width of the individual parameter distributions is largely determined by non-identifiability but covariation among parameters, even those that are poorly determined, encodes essential information.

An expanding role for cell biologists in drug discovery and pharmacology.

The profound challenges facing clinicians, who must prescribe drugs in the face of dramatic variability in response, and the pharmaceutical industry, which must develop new drugs despite ever-rising costs, represent opportunities for cell biologists interested in rethinking the conceptual basis of pharmacology and drug discovery. Much better understanding is required of the quantitative behaviors of networks targeted by drugs in cells, tissues, and organisms. Cell biologists interested in these topics should learn more about the basic structure of drug development campaigns and hone their quantitative and programming skills.

Construction of cell type-specific logic models of signaling networks using CellNOpt.

Mathematical models are useful tools for understanding protein signaling networks because they provide an integrated view of pharmacological and toxicological processes at the molecular level. Here we describe an approach previously introduced based on logic modeling to generate cell-specific, mechanistic and predictive models of signal transduction. Models are derived from a network encoding prior knowledge that is trained to signaling data, and can be either binary (based on Boolean logic) or quantitative (using a recently developed formalism, constrained fuzzy logic).

Kinome-wide selectivity profiling of ATP-competitive mammalian target of rapamycin (mTOR) inhibitors and characterization of their binding kinetics.

An intensive recent effort to develop ATP-competitive mTOR inhibitors has resulted in several potent and selective molecules such as Torin1, PP242, KU63794, and WYE354. These inhibitors are being widely used as pharmacological probes of mTOR-dependent biology. To determine the potency and specificity of these agents, we have undertaken a systematic kinome-wide effort to profile their selectivity and potency using chemical proteomics and assays for enzymatic activity, protein binding, and disruption of cellular signaling.

Lyapunov exponents and phase diagrams reveal multi-factorial control over TRAIL-induced apoptosis.

Receptor-mediated apoptosis proceeds via two pathways: one requiring only a cascade of initiator and effector caspases (type I behavior) and the second requiring an initiator-effector caspase cascade and mitochondrial outer membrane permeabilization (type II behavior). Here, we investigate factors controlling type I versus II phenotypes by performing Lyapunov exponent analysis of an ODE-based model of cell death. The resulting phase diagrams predict that the ratio of XIAP to pro-caspase-3 concentrations plays a key regulatory role: type I behavior predominates when the ratio is low and type II behavior when the ratio is high.

p31(comet) acts to ensure timely spindle checkpoint silencing subsequent to kinetochore attachment.

The spindle assembly checkpoint links the onset of anaphase to completion of chromosome-microtubule attachment and is mediated by the binding of Mad and Bub proteins to kinetochores of unattached or maloriented chromosomes. Mad2 and BubR1 traffic between kinetochores and the cytosol, thereby transmitting a "wait anaphase" signal to the anaphase-promoting complex. It is generally assumed that this signal dissipates automatically upon kinetochore-microtubule binding, but it has been shown that under conditions of nocodazole-induced arrest p31(comet), a Mad2-binding protein, is required for mitotic progression.

Rapid phospho-turnover by receptor tyrosine kinases impacts downstream signaling and drug binding.

Epidermal growth factor receptors (ErbB1-4) are oncogenic receptor tyrosine kinases (RTKs) that regulate diverse cellular processes. In this study, we combine measurement and mathematical modeling to quantify phospho-turnover at ErbB receptors in human cells and to determine the consequences for signaling and drug binding. We find that phosphotyrosine residues on ErbB1 have half-lives of a few seconds and therefore turn over 100-1000 times in the course of a typical immediate-early response to ligand.

Comparing signaling networks between normal and transformed hepatocytes using discrete logical models.

Substantial effort in recent years has been devoted to constructing and analyzing large-scale gene and protein networks on the basis of "omic" data and literature mining. These interaction graphs provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or drugs. Conversely, traditional approaches to analyzing cell signaling are narrow in scope and cannot easily make use of network-level data.

Adaptive informatics for multifactorial and high-content biological data.

Whereas genomic data are universally machine-readable, data from imaging, multiplex biochemistry, flow cytometry and other cell- and tissue-based assays usually reside in loosely organized files of poorly documented provenance. This arises because the relational databases used in genomic research are difficult to adapt to rapidly evolving experimental designs, data formats and analytic algorithms. Here we describe an adaptive approach to managing experimental data based on semantically typed data hypercubes (SDCubes) that combine hierarchical data format 5 (HDF5) and extensible markup language (XML) file types.

Measuring and modeling apoptosis in single cells.

Cell death plays an essential role in the development of tissues and organisms, the etiology of disease, and the responses of cells to therapeutic drugs. Here we review progress made over the last decade in using mathematical models and quantitative, often single-cell, data to study apoptosis. We discuss the delay that follows exposure of cells to prodeath stimuli, control of mitochondrial outer membrane permeabilization, switch-like activation of effector caspases, and variability in the timing and probability of death from one cell to the next.

Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli.

Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements.

Classic and contemporary approaches to modeling biochemical reactions.

Recent interest in modeling biochemical networks raises questions about the relationship between often complex mathematical models and familiar arithmetic concepts from classical enzymology, and also about connections between modeling and experimental data. This review addresses both topics by familiarizing readers with key concepts (and terminology) in the construction, validation, and application of deterministic biochemical models, with particular emphasis on a simple enzyme-catalyzed reaction. Networks of coupled ordinary differential equations (ODEs) are the natural language for describing enzyme kinetics in a mass action approximation.

Networks inferred from biochemical data reveal profound differences in toll-like receptor and inflammatory signaling between normal and transformed hepatocytes.

Systematic study of cell signaling networks increasingly involves high throughput proteomics, transcriptional profiling, and automated literature mining with the aim of assembling large scale interaction networks. In contrast, functional analysis of cell signaling usually focuses on a much smaller sets of proteins and eschews computation but focuses directly on cellular responses to environment and perturbation. We sought to combine these two traditions by collecting cell response measures on a reasonably large scale and then attempting to infer differences in network topology between two cell types.

Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction.

Large-scale protein signalling networks are useful for exploring complex biochemical pathways but do not reveal how pathways respond to specific stimuli. Such specificity is critical for understanding disease and designing drugs. Here we describe a computational approach--implemented in the free CNO software--for turning signalling networks into logical models and calibrating the models against experimental data.

Non-genetic cell-to-cell variability and the consequences for pharmacology.

Recent advances in single-cell assays have focused attention on the fact that even members of a genetically identical group of cells or organisms in identical environments can exhibit variability in drug sensitivity, cellular response, and phenotype. Underlying much of this variability is stochasticity in gene expression, which can produce unique proteomes even in genetically identical cells. Here we discuss the consequences of non-genetic cell-to-cell variability in the cellular response to drugs and its potential impact for the treatment of human disease.

Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis.

In microorganisms, noise in gene expression gives rise to cell-to-cell variability in protein concentrations. In mammalian cells, protein levels also vary and individual cells differ widely in their responsiveness to uniform physiological stimuli. In the case of apoptosis mediated by TRAIL (tumour necrosis factor (TNF)-related apoptosis-inducing ligand) it is common for some cells in a clonal population to die while others survive-a striking divergence in cell fate.

Fuzzy logic analysis of kinase pathway crosstalk in TNF/EGF/insulin-induced signaling.

When modeling cell signaling networks, a balance must be struck between mechanistic detail and ease of interpretation. In this paper we apply a fuzzy logic framework to the analysis of a large, systematic dataset describing the dynamics of cell signaling downstream of TNF, EGF, and insulin receptors in human colon carcinoma cells. Simulations based on fuzzy logic recapitulate most features of the data and generate several predictions involving pathway crosstalk and regulation.

Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data.

The ErbB signaling pathways, which regulate diverse physiological responses such as cell survival, proliferation and motility, have been subjected to extensive molecular analysis. Nonetheless, it remains poorly understood how different ligands induce different responses and how this is affected by oncogenic mutations. To quantify signal flow through ErbB-activated pathways we have constructed, trained and analyzed a mass action model of immediate-early signaling involving ErbB1-4 receptors (EGFR, HER2/Neu2, ErbB3 and ErbB4), and the MAPK and PI3K/Akt cascades.

Cytokine-induced signaling networks prioritize dynamic range over signal strength.

Signaling networks respond to diverse stimuli, but how the state of the signaling network is relayed to downstream cellular responses is unclear. We modeled how incremental activation of signaling molecules is transmitted to control apoptosis as a function of signal strength and dynamic range. A linear relationship between signal input and response output, with the dynamic range of signaling molecules uniformly distributed across activation states, most accurately predicted cellular responses.

Flexible informatics for linking experimental data to mathematical models via DataRail.

Motivation: Linking experimental data to mathematical models in biology is impeded by the lack of suitable software to manage and transform data. Model calibration would be facilitated and models would increase in value were it possible to preserve links to training data along with a record of all normalization, scaling, and fusion routines used to assemble the training data from primary results.

Results: We describe the implementation of DataRail, an open source MATLAB-based toolbox that stores experimental data in flexible multi-dimensional arrays, transforms arrays so as to maximize information content, and then constructs models using internal or external tools.

Common effector processing mediates cell-specific responses to stimuli.

The fundamental components of many signalling pathways are common to all cells. However, stimulating or perturbing the intracellular network often causes distinct phenotypes that are specific to a given cell type. This 'cell specificity' presents a challenge in understanding how intracellular networks regulate cell behaviour and an obstacle to developing drugs that treat signalling dysfunctions.

Direct Lyapunov exponent analysis enables parametric study of transient signalling governing cell behaviour.

Computational models aid in the quantitative understanding of cell signalling networks. One important goal is to ascertain how multiple network components work together to govern cellular responses, that is, to determine cell 'signal-response' relationships. Several methods exist to study steady-state signals in the context of differential equation-based models.