Investigating Differential Dynamics of the MAPK Signaling Cascade Using a Multi-Parametric Global Sensitivity Analysis

Cell growth critically depends on signalling pathways whose regulation is the focus of intense research. Without utilizing a priori knowledge of the relative importance of pathway components, we have applied in silico computational methods to the EGF-induced MAPK cascade. Specifically, we systematically perturbed the entire parameter space, including initial conditions, using a Monte Carlo approach, and investigate which protein components or kinetic reaction steps contribute to the differentiation of ERK responses. The model, based on previous work by Brightman and Fell (2000), is composed of 28 reactions, 27 protein molecules, and 48 parameters from both mass action and Michaelis-Menten kinetics. Our multi-parametric systems analysis confirms that Raf inactivation is one of the key steps regulating ERK responses to be either transient or sustained. Furthermore, the results of amplitude-differential ERK phosphorylations within the transient case are mainly attributed to the balance between activation and inactivation of Ras while duration-differential ERK responses for the sustained case are, in addition to Ras, markedly affected by dephospho-/phosphorylation of both MEK and ERK. Our sub-module perturbations showed that MEK and ERK''s contribution to this differential ERK activation originates from fluctuations in intermediate pathway module components such as Ras and Raf, implicating a cooperative regulatory mode among the key components. The initial protein concentrations of corresponding reactions such as Ras, GAP, and Raf also influence the distinct signalling outputs of ERK activation. We then compare these results with those obtained from a single-parametric perturbation approach using an overall state sensitivity (OSS) analysis. The OSS findings indicate a more pronounced role of ERK''s inhibitory feedback effect on catalysing the dissociation of the SOS complex. Both approaches reveal the presence of multiple specific reactions involved in the distinct dynamics of ERK responses and the cell fate decisions they trigger. This work adds a mechanistic insight of the contribution of key pathway components, thus may support the identification of biomarkers for pharmaceutical drug discovery processes.     

Characterizing the normal proteome of human ciliary body

Background

The ciliary body is the circumferential muscular tissue located just behind the iris in the anterior chamber of the eye. It plays a pivotal role in the production of aqueous humor, maintenance of the lens zonules and accommodation by changing the shape of the crystalline lens. The ciliary body is the major target of drugs against glaucoma as its inhibition leads to a drop in intraocular pressure. A molecular study of the ciliary body could provide a better understanding about the pathophysiological processes that occur in glaucoma. Thus far, no large-scale proteomic investigation has been reported for the human ciliary body.

Results

In this study, we have carried out an in-depth LC-MS/MS-based proteomic analysis of normal human ciliary body and have identified 2,815 proteins. We identified a number of proteins that were previously not described in the ciliary body including importin 5 (IPO5), atlastin-2 (ATL2), B-cell receptor associated protein 29 (BCAP29), basigin (BSG), calpain-1 (CAPN1), copine 6 (CPNE6), fibulin 1 (FBLN1) and galectin 1 (LGALS1). We compared the plasma proteome with the ciliary body proteome and found that the large majority of proteins in the ciliary body were also detectable in the plasma while 896 proteins were unique to the ciliary body. We also classified proteins using pathway enrichment analysis and found most of proteins associated with ubiquitin pathway, EIF2 signaling, glycolysis and gluconeogenesis.

Conclusions

More than 95% of the identified proteins have not been previously described in the ciliary body proteome. This is the largest catalogue of proteins reported thus far in the ciliary body that should provide new insights into our understanding of the factors involved in maintaining the secretion of aqueous humor. The identification of these proteins will aid in understanding various eye diseases of the anterior segment such as glaucoma and presbyopia.     

Multiscale agent-based cancer modeling

Agent-based modeling (ABM) is an in silico technique that is being used in a variety of research areas such as in social sciences, economics and increasingly in biomedicine as an interdisciplinary tool to study the dynamics of complex systems. Here, we describe its applicability to integrative tumor biology research by introducing a multi-scale tumor modeling platform that understands brain cancer as a complex dynamic biosystem. We summarize significant findings of this work, and discuss both challenges and future directions for ABM in the field of cancer research.     

Analysis and 3D reconstruction of heterogeneity in malignant brain tumors: an interdisciplinary case study using a novel computational visualization approach

OBJECTIVE: To explore how a multidisciplinary approach, combining modern visualization and image processing techniques with innovative experimental studies, can augment the understanding of tumor development. STUDY DESIGN: We analyzed histologic sections of a microscopic brain tumor and reconstructed these slices into a 3D representation. We processed these slices to: (1) identify tumor boundaries, (2) isolate proliferating tumor cells, and (3) segment the tumor into regions based on the density of proliferating cells. We then reconstructed the 3D shape of the tumor using a constrained deformable surface approach. RESULTS: This novel method allows the analyst to (1) see specific properties of histologic slices in the 3D environment with animation, (2) switch 2D "views" dynamically, and (3) see relationships between the 3D structure and structure on a plane. CONCLUSION: Using this method to analyze a specific "case," we were also able to shed light on the limitations of a widely held assumption about the shape of expanding microscopic solid tumors as well as find more indications that such tumors behave as adaptive biosystems. Implications of these case study results, as well as future applications of the method for tumor biology research, are discussed.     

Does tumor growth follow a "universal law"?

A general model for the ontogenetic growth of living organisms has been recently proposed. Here we investigate the extension of this model to the growth of solid malignant tumors. A variety of in vitro and in vivo data are analysed and compared with the prediction of a "universal" law, relating properly rescaled tumor masses and tumor growth times. The results support the notion that tumor growth follows such a universal law. Several important implications of this finding are discussed, including its relevance for tumor metastasis and recurrence, cell turnover rates, angiogenesis and invasion.     

Simulated brain tumor growth dynamics using a three-dimensional cellular automaton

We have developed a novel and versatile three-dimensional cellular automaton model of brain tumor growth. We show that macroscopic tumor behavior can be realistically modeled using microscopic parameters. Using only four parameters, this model simulates Gompertzian growth for a tumor growing over nearly three orders of magnitude in radius. It also predicts the composition and dynamics of the tumor at selected time points in agreement with medical literature. We also demonstrate the flexibility of the model by showing the emergence, and eventual dominance, of a second tumor clone with a different genotype. The model incorporates several important and novel features, both in the rules governing the model and in the underlying structure of the model. Among these are a new definition of how to model proliferative and non-proliferative cells, an isotropic lattice, and an adaptive grid lattice.     

Simulating the impact of a molecular 'decision-process' on cellular phenotype and multicellular patterns in brain tumors

Experimental evidence indicates that human brain cancer cells proliferate or migrate, yet do not display both phenotypes at the same time. Here, we present a novel computational model simulating this cellular decision-process leading up to either phenotype based on a molecular interaction network of genes and proteins. The model's regulatory network consists of the epidermal growth factor receptor (EGFR), its ligand transforming growth factor-alpha (TGF alpha), the downstream enzyme phospholipaseC-gamma (PLC gamma) and a mitosis-associated response pathway. This network is activated by autocrine TGF alpha secretion, and the EGFR-dependent downstream signaling this step triggers, as well as modulated by an extrinsic nutritive glucose gradient. Employing a framework of mass action kinetics within a multiscale agent-based environment, we analyse both the emergent multicellular behavior of tumor growth and the single-cell molecular profiles that change over time and space. Our results show that one can indeed simulate the dichotomy between cell migration and proliferation based solely on an EGFR decision network. It turns out that these behavioral decisions on the single cell level impact the spatial dynamics of the entire cancerous system. Furthermore, the simulation results yield intriguing experimentally testable hypotheses also on the sub-cellular level such as spatial cytosolic polarization of PLC gamma towards an extrinsic chemotactic gradient. Implications of these results for future works, both on the modeling and experimental side are discussed.