STAT3 Knockdown Induces Tumor Formation by MDA-MB-231 Cells.

STAT3 plays a central role in oncogenesis by mediating cell survival, growth, and differentiation. It is constitutively activated in breast cancer. We investigated the role of STAT3 in tumor development by knocking down STAT3 levels in MDA-MB-231 triple negative breast cancer cells using short hairpin RNA. The tumor forming potential of these STAT3-depleted cells was assessed by xenografts in immunocompromised NOD SCID mice. Contrary to its accepted tumor promoting role, we found STAT3 to be a negative regulator of growth in MDA-MB-231- derived tumors. Although similar observations have been made in thyroid carcinoma and lung adenocarcinoma xenograft studies, our novel results showed for the first time that the role of STAT3 in promoting tumorigenesis may be context-specific, and that STAT3 may actually be a negative regulator of certain breast-cancer types. Studies to identify the mechanisms of STAT3's negative regulatory role may be useful in developing STAT3-based therapeutics.

Quantitative investigation of MDA-MB-231 breast cancer cell motility: dependence on epidermal growth factor concentration and its gradient.

Enhanced cell motility is one of the primary features of cancer. Accumulated evidence demonstrates that Epidermal Growth Factor Receptor (EGFR) mediated pathways play an important role in breast cancer cell proliferation and migration. We have quantified the MDA-MB-231 breast cancer cell migration in response to the stimulation of EGFR pathways with their ligand EGF to determine how the cell motility of MDA-MB-231 cells depends on the ligand concentration and gradient. Analysis at the single cell level combined with mathematical modeling and the ability to vary the ligand concentration and gradients locally using microfluidic devices allowed us to separate the unique contributions of ligand concentration and ligand gradient to cell motility. We tracked the motility of 6600 cells individually using time lapse imaging under varying EGF stimulation conditions. Trajectory analysis of the tracked cells using non-linear multivariate regression models showed that: (i) cell migration of MDA-MB-231 breast cancer cells depends on the ligand gradient but not on the ligand concentration. This observation was valid for both the total (direction independent) and directed (along gradient direction) cell velocities. Although the dependence of the directed motility on ligand gradient is to be expected, the dependence of the total velocity solely on ligand gradient was an unexpected novel observation. (ii) Enhancement of the motilities of individual cells in a population upon exposure to the ligand was highly heterogeneous, and only a very small percentage of cells responded strongly to the external stimuli. Separating out the non-responding cells using quantitative analysis of individual cell motilities enabled us to establish that enhanced motility of the responding cells indeed increases monotonically with increasing EGF gradient. (iii) A large proportion of cells in a population were unresponsive to ligand stimulation, and their presence introduced considerable random intrinsic variability to the observations. This indicated that studying cell motilities at the individual cell level is necessary to better capture the biological reality and that population averaging methods should be avoided. Studying motilities at the individual cell level is particularly important to understand the biological processes that are possibly driven by the action of a small portion of cells in a population, such as metastasis. We discuss the implications of our results on the total and chemotactic movement of cancer cells in the tumor microenvironment.

EGFR signaling pathways are wired differently in normal 184A1L5 human mammary epithelial and MDA-MB-231 breast cancer cells.

Because of differences in the downstream signaling patterns of its pathways, the role of the human epidermal growth factor family of receptors (HER) in promoting cell growth and survival is cell line and context dependent. Using two model cell lines, we have studied how the regulatory interaction network among the key proteins of HER signaling pathways may be rewired upon normal to cancerous transformation. We in particular investigated how the transcription factor STAT3 and several key kinases' involvement in cancer-related signaling processes differ between normal 184A1L5 human mammary epithelial (HME) and MDA-MB-231 breast cancer epithelial cells. Comparison of the responses in these cells showed that normal-to-cancerous cellular transformation causes a major re-wiring of the growth factor initiated signaling. In particular, we found that: i) regulatory interactions between Erk, p38, JNK and STAT3 are triangulated and tightly coupled in 184A1L5 HME cells, and ii) STAT3 is only weakly associated with the Erk-p38-JNK pathway in MDA-MB-231 cells. Utilizing the concept of pathway substitution, we predicted how the observed differences in the regulatory interactions may affect the proliferation/survival and motility responses of the 184A1L5 and MDA-MB-231 cells when exposed to various inhibitors. We then validated our predictions experimentally to complete the experiment-computation-experiment iteration loop. Validated differences in the regulatory interactions of the 184A1L5 and MDA-MB-231 cells indicated that instead of inhibiting STAT3, which has severe toxic side effects, simultaneous inhibition of JNK together with Erk or p38 could be a more effective strategy to impose cell death selectively to MDA-MB-231 cancer cells while considerably lowering the side effects to normal epithelial cells. Presented analysis establishes a framework with examples that would enable cell signaling researchers to identify the signaling network structures which can be used to predict the phenotypic responses in particular cell lines of interest.

Constitutive activation of STAT3 in breast cancer cells: A review.

Signal transducer and activator of transcription 3 (STAT3) is constitutively activated in numerous cancer types, including more than 40% of breast cancers. In contrast to tight regulation of STAT3 as a latent transcription factor in normal cells, its signaling in breast cancer oncogenesis is multifaceted. Signaling through the IL-6/JAK/STAT3 pathway initiated by the binding of IL-6 family of cytokines (i.e., IL-6 and IL-11) to their receptors have been implicated in breast cancer development. Receptors with intrinsic kinase activity such as EGFR and VEGFR directly or indirectly induce STAT3 activation in various breast cancer types. Aberrant STAT3 signaling promotes breast tumor progression through deregulation of the expression of downstream target genes which control proliferation (Bcl-2, Bcl-xL, Survivin, Cyclin D1, c-Myc and Mcl-1), angiogenesis (Hif1α and VEGF) and epithelial-mesenchymal transition (Vimentin, TWIST, MMP-9 and MMP-7). These multiple modes of STAT3 regulation therefore make it a central linking point for a multitude of signaling processes. Extensive efforts to target STAT3 activation in breast cancer had no remarkable success in the past because the highly interconnected nature of STAT3 signaling introduces lack of selectivity in pathway identification for STAT3 targeted molecular therapies or because its role in tumorigenesis may not be as critical as it was thought. This review provides a full spectrum of STAT3's involvement in breast cancer by consolidating the knowledge about its role in breast cancer development at multiple levels: its differential regulation by different receptor signaling pathways, its downstream target genes, and modification of its transcriptional activity by its coregulatory transcription factors.

Integrated analysis reveals that STAT3 is central to the crosstalk between HER/ErbB receptor signaling pathways in human mammary epithelial cells.

Human epidermal growth factor receptors (HER, also known as ErbB) drive cellular proliferation, pro-survival and stress responses by activating several downstream kinases, in particular ERK, p38 MAPK, JNK (SAPK), the PI3K/AKT, as well as various transcriptional regulators such as STAT3. When co-expressed, the first three members of HER family (HER1-3) can form homo- and hetero-dimers, and there is considerable evidence suggesting that the receptor dimers differentially activate intracellular signaling pathways. To better understand the interactions in this system, we pursued multi-factorial experiments where HER dimerization patterns and signaling pathways were rationally perturbed. We measured the activation of HER1-3 receptors and of the sentinel signaling proteins ERK, AKT, p38 MAPK, JNK, STAT3 as a function of time in a panel of human mammary epithelial (HME) cells expressing different levels of HER1-3 stimulated with various ligand combinations. We hypothesized that the HER dimerization pattern is a better predictor of downstream signaling than the total receptor activation levels. We validated this hypothesis using a combination of model-based analysis to quantify the HER dimerization patterns, and by clustering the activation data in multiple ways to confirm that the HER receptor dimer is a better predictor of the signaling through p38 MAPK, ERK and AKT pathways than the total HER receptor expression and activation levels. We then pursued combinatorial inhibition studies to identify the causal regulatory interactions between sentinel signaling proteins. Quantitative analysis of the collected data using the modular response analysis (MRA) and its Bayesian Variable Selection Algorithm (BVSA) version allowed us to obtain a consensus regulatory interaction model, which revealed that STAT3 occupies a central role in the crosstalk between the studied pathways in HME cells. Results of the BVSA/MRA and cluster analysis were in agreement with each other.

Reconstruction of biofilm images: combining local and global structural parameters.

Digitized images can be used for quantitative comparison of biofilms grown under different conditions. Using biofilm image reconstruction, it was previously found that biofilms with a completely different look can have nearly identical structural parameters and that the most commonly utilized global structural parameters were not sufficient to uniquely define these biofilms. Here, additional local and global parameters are introduced to show that these parameters considerably increase the reliability of the image reconstruction process. Assessment using human evaluators indicated that the correct identification rate of the reconstructed images increased from 50% to 72% with the introduction of the new parameters into the reconstruction procedure. An expanded set of parameters especially improved the identification of biofilm structures with internal orientational features and of structures in which colony sizes and spatial locations varied. Hence, the newly introduced structural parameter sets helped to better classify the biofilms by incorporating finer local structural details into the reconstruction process.

Model-based analysis of HER activation in cells co-expressing EGFR, HER2 and HER3.

The HER/ErbB family of receptor tyrosine kinases drives critical responses in normal physiology and cancer, and the expression levels of the various HER receptors are critical determinants of clinical outcomes. HER activation is driven by the formation of various dimer complexes between members of this receptor family. The HER dimer types can have differential effects on downstream signaling and phenotypic outcomes. We constructed an integrated mathematical model of HER activation, and trafficking to quantitatively link receptor expression levels to dimerization and activation. We parameterized the model with a comprehensive set of HER phosphorylation and abundance data collected in a panel of human mammary epithelial cells expressing varying levels of EGFR/HER1, HER2 and HER3. Although parameter estimation yielded multiple solutions, predictions for dimer phosphorylation were in agreement with each other. We validated the model using experiments where pertuzumab was used to block HER2 dimerization. We used the model to predict HER dimerization and activation patterns in a panel of human mammary epithelial cells lines with known HER expression levels in response to stimulations with ligands EGF and HRG. Simulations over the range of expression levels seen in various cell lines indicate that: i) EGFR phosphorylation is driven by HER1-HER1 and HER1-HER2 dimers, and not HER1-HER3 dimers, ii) HER1-HER2 and HER2-HER3 dimers both contribute significantly to HER2 activation with the EGFR expression level determining the relative importance of these species, and iii) the HER2-HER3 dimer is largely responsible for HER3 activation. The model can be used to predict phosphorylated dimer levels for any given HER expression profile. This information in turn can be used to quantify the potencies of the various HER dimers, and can potentially inform personalized therapeutic approaches.

Integrated experimental and model-based analysis reveals the spatial aspects of EGFR activation dynamics.

The epidermal growth factor receptor (EGFR) belongs to the ErbB family of receptor tyrosine kinases, and controls a diverse set of cellular responses relevant to development and tumorigenesis. ErbB activation is a complex process involving receptor-ligand binding, receptor dimerization, phosphorylation, and trafficking (internalization, recycling and degradation), which together dictate the spatio-temporal distribution of active receptors within the cell. The ability to predict this distribution, and elucidation of the factors regulating it, would help to establish a mechanistic link between ErbB expression levels and the cellular response. Towards this end, we constructed mathematical models to determine the contributions of receptor dimerization and phosphorylation to EGFR activation, and to examine the dependence of these processes on sub-cellular location. We collected experimental datasets for EGFR activation dynamics in human mammary epithelial cells, with the specific goal of model parameterization, and used the data to estimate parameters for several alternate models. Model-based analysis indicated that: (1) signal termination via receptor dephosphorylation in late endosomes, prior to degradation, is an important component of the response, (2) less than 40% of the receptors in the cell are phosphorylated at any given time, even at saturating ligand doses, and (3) receptor phosphorylation kinetics at the cell surface and early endosomes are comparable. We validated the last finding by measuring the EGFR dephosphorylation rates at various times following ligand addition both in whole cells and in endosomes using ELISAs and fluorescent imaging. Overall, our results provide important information on how EGFR phosphorylation levels are regulated within cells. This study demonstrates that an iterative cycle of experiments and modeling can be used to gain mechanistic insight regarding complex cell signaling networks.

Modeling microbial dynamics in heterogeneous environments: growth on soil carbon sources.

We have developed a new kinetic model to study how microbial dynamics are affected by the heterogeneity in the physical structure of the environment and by different strategies for hydrolysis of polymeric carbon. The hybrid model represented the dynamics of substrates and enzymes using a continuum representation and the dynamics of the cells were modeled individually. Individual-based biological model allowed us to explicitly simulate microbial diversity, and to model cell physiology as regulated via optimal allocation of cellular resources to enzyme synthesis, control of growth rate by protein synthesis capacity, and shifts to dormancy. This model was developed to study how microbial community functioning is influenced by local environmental conditions in heterogeneous media such as soil and by the functional attributes of individual microbes. Microbial community dynamics were simulated at two spatial scales: micro-pores that resemble 6-20-µm size portions of the soil physical structure and in 111-µm size soil aggregates with a random pore structure. Different strategies for acquisition of carbon from polymeric cellulose were investigated. Bacteria that express membrane-associated hydrolase had different growth and survival dynamics in soil pores than bacteria that release extracellular hydrolases. The kinetic differences suggested different functional niches for these two microbe types in cellulose utilization. Our model predicted an emergent behavior in which co-existence of membrane-associated hydrolase and extracellular hydrolases releasing organisms led to higher cellulose utilization efficiency and reduced stochasticity. Our analysis indicated that their co-existence mutually benefits these organisms, where basal cellulose degradation activity by membrane-associated hydrolase-expressing cells shortened the soluble hydrolase buildup time and, when enzyme buildup allowed for cellulose degradation to be fast enough to sustain exponential growth, all the organisms in the community shared the soluble carbon product and grew together. Although pore geometry affected the kinetics of cellulose degradation, the patterns observed for the bacterial community dynamics in the 6-20 µm-sized micro-pores were relevant to the dynamics in the more complex 111-µm-sized porous soil aggregates, implying that micro-scale studies can be useful approximations to aggregate scale studies when local effects on microbial dynamics are studied. As shown with examples in this study, various functional niches of the bacterial communities can be investigated using complex predictive mathematical models where the role of key environmental aspects such as the heterogeneous three-dimensional structure, functional niches of the community members, and environmental biochemical processes are directly connected to microbial metabolism and maintenance in an integrated model.

An adaptive coarse graining method for signal transduction in three dimensions.

The spatio-temporal landscape of the plasma membrane regulates activation and signal transduction of membrane bound receptors by restricting their two-dimensional mobility and by inducing receptor clustering. This regulation also extends to complex formation between receptors and adaptor proteins, which are the intermediate signaling molecules involved in cellular signaling that relay the received cues from cell surface to cytoplasm and eventually to the nucleus. Although their investigation poses challenging technical difficulties, there is a crucial need to understand the impact of the receptor diffusivity, clustering, and spatial heterogeneity, and of receptor-adaptor protein complex formation on the cellular signal transduction patterns. Building upon our earlier studies, we have developed an adaptive coarse-grained Monte Carlo method that can be used to investigate the role of diffusion, clustering and membrane corralling on receptor association and receptor-adaptor protein complex formation dynamics in three dimensions. The new Monte Carlo lattice based approach allowed us to introduce spatial resolution on the 2-D plasma membrane and to model the cytoplasm in three-dimensions. Being a multi-resolution approach, our new method makes it possible to represent various parts of the cellular system at different levels of detail and enabled us to utilize the locally homogeneous assumption when justified (e.g., cytoplasmic region away from the cell membrane) and avoid its use when high spatial resolution is needed (e.g., cell membrane and cytoplasmic region near the membrane) while keeping the required computational complexity manageable. Our results have shown that diffusion has a significant impact on receptor-receptor dimerization and receptor-adaptor protein complex formation kinetics. We have observed an "adaptor protein hopping" mechanism where the receptor binding proteins may hop between receptors to form short-lived transient complexes. This increased residence time of the adaptor proteins near cell membrane and their ability to frequently change signaling partners may explain the increase in signaling efficiency when receptors are clustered. We also hypothesize that the adaptor protein hopping mechanism can cause concurrent or sequential activation of multiple signaling pathways, thus leading to crosstalk between diverse biological functions.

Spatial aspects in biological system simulations.

Mathematical models of the dynamical properties of biological systems aim to improve our understanding of the studied system with the ultimate goal of being able to predict system responses in the absence of experimentation. Despite the enormous advances that have been made in biological modeling and simulation, the inherently multiscale character of biological systems and the stochasticity of biological processes continue to present significant computational and conceptual challenges. Biological systems often consist of well-organized structural hierarchies, which inevitably lead to multiscale problems. This chapter introduces and discusses the advantages and shortcomings of several simulation methods that are being used by the scientific community to investigate the spatiotemporal properties of model biological systems. We first describe the foundations of the methods and then describe their relevance and possible application areas with illustrative examples from our own research. Possible ways to address the encountered computational difficulties are also discussed.

Rapid and sustained nuclear-cytoplasmic ERK oscillations induced by epidermal growth factor.

Although the ERK pathway has a central role in the response of cells to growth factors, its regulatory structure and dynamics are incompletely understood. To investigate ERK activation in real time, we expressed an ERK-GFP fusion protein in human mammary epithelial cells. On EGF stimulation, we observed sustained oscillations of the ERK-GFP fusion protein between the nucleus and cytoplasm with a periodicity of approximately 15 min. The oscillations were persistent (>45 cycles), independent of cell cycle phase, and were highly dependent on cell density, essentially disappearing at confluency. Oscillations occurred even at ligand doses that elicited very low levels of ERK phosphorylation, and could be detected biochemically in both transfected and nontransfected cells. Mathematical modeling revealed that negative feedback from phosphorylated ERK to the cascade input was necessary to match the robustness of the oscillation characteristics observed over a broad range of ligand concentrations. Our characterization of single-cell ERK dynamics provides a quantitative foundation for understanding the regulatory structure of this signaling cascade.

HER/ErbB receptor interactions and signaling patterns in human mammary epithelial cells.

BACKGROUND: Knowledge about signaling pathways is typically compiled based on data gathered using different cell lines. This approach implicitly assumes that the cell line dependence is not important. However, different cell lines do not always respond to a particular stimulus in the same way, and lack of coherent data collected from closely related cellular systems can be detrimental to the efforts to understand the regulation of biological processes. To address this issue, we created a clone library of human mammary epithelial (HME) cells that expresses different levels of HER2 and HER3 receptors in combination with endogenous EGFR/HER1. Using our clone library, we have quantified the receptor activation patterns and systematically tested the validity of the existing hypotheses about the interaction patterns between HER1-3 receptors. RESULTS: Our study identified HER2 as the dominant dimerization partner for both EGFR and HER3. Contrary to earlier suggestions, we find that lateral interactions with HER2 do not lead to strong transactivation between EGFR and HER3, i.e., EGFR activation and HER3 activation are only weakly linked in HME cells. We also find that observed weak transactivation is uni-directional where stimulation of EGFR leads to HER3 activation whereas HER3 stimulation does not activate the EGFR. Repeating our experiments at lower cell confluency established that cell confluency is not a major factor in the observed interaction patterns. We have also quantified the dependence of the kinetics of Erk and Akt activation on different HER receptors. We found that HER3 signaling makes the strongest contribution to Akt activation and that, stimulation of either EGFR or HER3 leads to significant Erk activation. CONCLUSION: Our study shows that clone cell libraries can be a powerful resource in systems biology research by making it possible to differentiate between various hypotheses in a consistent cellular background. Using our constructed clone library we profiled the cell signaling patterns to establish the role of HER2 in the crosstalk between EGFR and HER3 receptors in HME cells. Our results for HME cells show that the weak linkage between EGFR and HER3 pathways can lead to distinct downstream cellular signaling patterns in response to the ligands of these two receptors.

Kinetic modeling of biological systems.

The dynamics of how the constituent components of a natural system interact defines the spatio-temporal response of the system to stimuli. Modeling the kinetics of the processes that represent a biophysical system has long been pursued with the aim of improving our understanding of the studied system. Due to the unique properties of biological systems, in addition to the usual difficulties faced in modeling the dynamics of physical or chemical systems, biological simulations encounter difficulties that result from intrinsic multi-scale and stochastic nature of the biological processes.This chapter discusses the implications for simulation of models involving interacting species with very low copy numbers, which often occur in biological systems and give rise to significant relative fluctuations. The conditions necessitating the use of stochastic kinetic simulation methods and the mathematical foundations of the stochastic simulation algorithms are presented. How the well-organized structural hierarchies often seen in biological systems can lead to multi-scale problems and the possible ways to address the encountered computational difficulties are discussed. We present the details of the existing kinetic simulation methods and discuss their strengths and shortcomings. A list of the publicly available kinetic simulation tools and our reflections for future prospects are also provided.

Quantifying the effects of co-expressing EGFR and HER2 on HER activation and trafficking.

The human epidermal growth factor receptor (HER) system is an intricately regulated system that plays critical roles in development and tumorigenesis. Here, we apply integrated experimentation and modeling to analyze HER receptor activation in a panel of cell lines expressing endogenous levels of EGFR/HER1 and different levels of HER2. A mathematical model that includes the fundamental processes involved in receptor activation and trafficking was used to fit the experimental data, and values of the independent parameters for active receptor dimer formation affinities, trafficking rates and relative phosphorylation levels were estimated. Obtained parameter values quantitatively support the existing ideas on the effect of HER2 on EGFR dynamics, and enable us to predict the abundances of various phosphorylated receptor dimers in the cell lines.

Receptor downregulation and desensitization enhance the information processing ability of signalling receptors.

BACKGROUND: In addition to initiating signaling events, the activation of cell surface receptors also triggers regulatory processes that restrict the duration of signaling. Acute attenuation of signaling can be accomplished either via ligand-induced internalization of receptors (endocytic downregulation) or via ligand-induced receptor desensitization. These phenomena have traditionally been viewed in the context of adaptation wherein the receptor system enters a refractory state in the presence of sustained ligand stimuli and thereby prevents the cell from over-responding to the ligand. Here we use the epidermal growth factor receptor (EGFR) and G-protein coupled receptors (GPCR) as model systems to respectively examine the effects of downregulation and desensitization on the ability of signaling receptors to decode time-varying ligand stimuli. RESULTS: Using a mathematical model, we show that downregulation and desensitization mechanisms can lead to tight and efficient input-output coupling thereby ensuring synchronous processing of ligand inputs. Frequency response analysis indicates that upstream elements of the EGFR and GPCR networks behave like low-pass filters with the system being able to faithfully transduce inputs below a critical frequency. Receptor downregulation and desensitization increase the filter bandwidth thereby enabling the receptor systems to decode inputs in a wider frequency range. Further, system-theoretic analysis reveals that the receptor systems are analogous to classical mechanical over-damped systems. This analogy enables us to metaphorically describe downregulation and desensitization as phenomena that make the systems more resilient in responding to ligand perturbations thereby improving the stability of the system resting state. CONCLUSION: Our findings suggest that in addition to serving as mechanisms for adaptation, receptor downregulation and desensitization can play a critical role in temporal information processing. Furthermore, engineering metaphors such as the ones described here could prove to be invaluable in understanding the design principles of biological systems.

Cell surface receptors for signal transduction and ligand transport: a design principles study.

Receptors constitute the interface of cells to their external environment. These molecules bind specific ligands involved in multiple processes, such as signal transduction and nutrient transport. Although a variety of cell surface receptors undergo endocytosis, the systems-level design principles that govern the evolution of receptor trafficking dynamics are far from fully understood. We have constructed a generalized mathematical model of receptor-ligand binding and internalization to understand how receptor internalization dynamics encodes receptor function and regulation. A given signaling or transport receptor system represents a particular implementation of this module with a specific set of kinetic parameters. Parametric analysis of the response of receptor systems to ligand inputs reveals that receptor systems can be characterized as being: i) avidity-controlled where the response control depends primarily on the extracellular ligand capture efficiency, ii) consumption-controlled where the ability to internalize surface-bound ligand is the primary control parameter, and iii) dual-sensitivity where both the avidity and consumption parameters are important. We show that the transferrin and low-density lipoprotein receptors are avidity-controlled, the vitellogenin receptor is consumption-controlled, and the epidermal growth factor receptor is a dual-sensitivity receptor. Significantly, we show that ligand-induced endocytosis is a mechanism to enhance the accuracy of signaling receptors rather than merely serving to attenuate signaling. Our analysis reveals that the location of a receptor system in the avidity-consumption parameter space can be used to understand both its function and its regulation.

A domain-based approach to predict protein-protein interactions.

BACKGROUND: Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI) networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins. RESULTS: DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms. CONCLUSION: We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed using the DomainGA scores are reasonably low, and the erroneous predictions can be filtered further using supplementary approaches such as those based on literature search or other prediction methods.

Multinomial tau-leaping method for stochastic kinetic simulations.

We introduce the multinomial tau-leaping (MtauL) method for general reaction networks with multichannel reactant dependencies. The MtauL method is an extension of the binomial tau-leaping method where efficiency is improved in several ways. First, tau-leaping steps are determined simply and efficiently using a priori information and Poisson distribution-based estimates of expectation values for reaction numbers over a tentative tau-leaping step. Second, networks are partitioned into closed groups of reactions and corresponding reactants in which no group reactant set is found in any other group. Third, product formation is factored into upper-bound estimation of the number of times a particular reaction occurs. Together, these features allow larger time steps where the numbers of reactions occurring simultaneously in a multichannel manner are estimated accurately using a multinomial distribution. Furthermore, we develop a simple procedure that places a specific upper bound on the total reaction number to ensure non-negativity of species populations over a single multiple-reaction step. Using two disparate test case problems involving cellular processes--epidermal growth factor receptor signaling and a lactose operon model--we show that the tau-leaping based methods such as the MtauL algorithm can significantly reduce the number of simulation steps thus increasing the numerical efficiency over the exact stochastic simulation algorithm by orders of magnitude.

Modeling the effects of HER/ErbB1-3 coexpression on receptor dimerization and biological response.

The human epidermal growth factor receptor (HER/ErbB) system comprises the epidermal growth factor receptor (EGFR/HER1) and three other homologs, namely HERs 2-4. This receptor system plays a critical role in cell proliferation and differentiation and receptor overexpression has been associated with poor prognosis in cancers of the epithelium. Here, we examine the effect of coexpressing varying levels of HERs 1-3 on the receptor dimerization patterns using a detailed kinetic model for HER/ErbB dimerization and trafficking. Our results indicate that coexpression of EGFR with HER2 or HER3 biases signaling to the cell surface and retards signal downregulation. In addition, simultaneous coexpression of HERs 1-3 leads to an abundance of HER2-HER3 heterodimers, which are known to be potent inducers of cell growth and transformation. Our new approach to use parameter dependence analysis in experimental design reveals that measurements of HER3 phosphorylation and HER2 internalization ratio may prove to be especially useful for the estimation of critical model parameters. Further, we examine the effect of receptor dimerization patterns on biological response using a simple phenomenological model. Results indicate that coexpression of EGFR with HER2 and HER3 at low to moderate levels may enable cells to match the response of a high HER2 expresser.