Phosphoproteomics-Based Modeling Defines the Regulatory Mechanism Underlying Aberrant EGFR Signaling

BACKGROUND: Mutation of the epidermal growth factor receptor (EGFR) results in a discordant cell signaling, leading to the development of various diseases. However, the mechanism underlying the alteration of downstream signaling due to such mutation has not yet been completely understood at the system level. Here, we report a phosphoproteomics-based methodology for characterizing the regulatory mechanism underlying aberrant EGFR signaling using computational network modeling. METHODOLOGY/PRINCIPAL FINDINGS: Our phosphoproteomic analysis of the mutation at tyrosine 992 (Y992), one of the multifunctional docking sites of EGFR, revealed network-wide effects of the mutation on EGF signaling in a time-resolved manner. Computational modeling based on the temporal activation profiles enabled us to not only rediscover already-known protein interactions with Y992 and internalization property of mutated EGFR but also further gain model-driven insights into the effect of cellular content and the regulation of EGFR degradation. Our kinetic model also suggested critical reactions facilitating the reconstruction of the diverse effects of the mutation on phosphoproteome dynamics. CONCLUSIONS/SIGNIFICANCE: Our integrative approach provided a mechanistic description of the disorders of mutated EGFR signaling networks, which could facilitate the development of a systematic strategy toward controlling disease-related cell signaling

Hybrid Modeling of Cancer Drug Resistance Mechanisms

Cancer is a multi-scale disease and its overwhelming complexity depends upon the multiple interwind events occurring at both molecular and cellular levels, making it very difficult for therapeutic advancements in cancer research. The resistance to cancer drugs is a significant challenge faced by scientists nowadays. The roots of the problem reside not only at the molecular level, due to multiple type of mutations in a single tumor, but also at the cellular level of drug interactions with the tumor. Tumor heterogeneity is the term used by oncologists for the involvement of multiple mutations in the development of a tumor at the sub-cellular level. The mechanisms for tumor heterogeneity are rigorously being explored as a reason for drug resistance in cancer patients. It is important to observe cell interactions not only at intra-tumoral level, but it is also essential to study the drug and tumor cell interactions at cellular level to have a complete picture of the mechanisms underlying drug resistance. The multi-scale nature of cancer drug resistance problem require modeling approaches that can capture all the multiple sub-cellular and cellular interaction factors with respect to dierent scales for time and space. Hybrid modeling offers a way to integrate both discrete and continuous dynamics to overcome this challenge. This research work is focused on the development of hybrid models to understand the drug resistance behaviors in colorectal and lung cancers. The common thing about the two types of cancer is that they both have dierent mutations at epidermal growth factor receptors (EGFRs) and they are normally treated with anti-EGFR drugs, to which they develop resistances with the passage of time. The acquiring of resistance is the sign of relapse in both kind of tumors. The most challenging task in colorectal cancer research nowadays is to understand the development of acquired resistance to anti-EGFR drugs. The key reason for this problem is the KRAS mutations appearance after the treatment with monoclonal antibodies (moAb). A hybrid model is proposed for the analysis of KRAS mutations behavior in colorectal cancer with respect to moAb treatments. The colorectal tumor hybrid model is represented as a single state automata, which shows tumor progression and evolution by means of mathematical equations for tumor sub-populations, immune system components and drugs for the treatment. The drug introduction is managed as a discrete step in this model. To evaluate the drug performance on a tumor, equations for two types of tumors cells are developed, i.e KRAS mutated and KRAS wild-type. Both tumor cell populations were treated with a combination of moAb and chemotherapy drugs. It is observed that even a minimal initial concentration of KRAS mutated cells before the treatment has the ability to make the tumor refractory to the treatment. Moreover, a small population of KRAS mutated cells has a strong influence on a large number of wild-type cells by making them resistant to chemotherapy. Patient's immune responses are specifically taken into considerations and it is found that, in case of KRAS mutations, the immune strength does not affect medication efficacy. Finally, cetuximab (moAb) and irinotecan (chemotherapy) drugs are analyzed as first-line treatment of colorectal cancer with few KRAS mutated cells. Results show that this combined treatment could be only effective for patients with high immune strengths and it should not be recommended as first-line therapy for patients with moderate immune strengths or weak immune systems because of a potential risk of relapse, with KRAS mutant cells acquired resistance involved with them. Lung cancer is more complicated then colorectal cancer because of acquiring of multiple resistances to anti-EGFR drugs. The appearance of EGFR T790M and KRAS mutations makes tumor resistant to a geftinib and AZD9291 drugs, respectively. The hybrid model for lung cancer consists of two non-resistant and resistant states of tumor. The non-resistant state is treated with geftinib drug until resistance to this drug makes tumor regrowth leading towards the resistant state. The resistant state is treated with AZD9291 drug for recovery. In this model the complete resistant state due to KRAS mutations is ignored because of the unavailability of parameter information and patient data. Each tumor state is evaluated by mathematical differential equations for tumor growth and progression. The tumor model consists of four tumor sub-population equations depending upon the type of mutations. The drug administration in this model is also managed as a discrete step for exact scheduling and dosages. The parameter values for the model are obtained by experiments performed in the laboratory. The experimental data is only available for the tumor progression along with the geftinib drug. The model is then fine tuned for obtaining the exact tumor growth patterns as observed in clinic, only for the geftinib drug. The growth rate for EGFR T790M tumor sub-population is changed to obtain the same tumor progression patterns as observed in real patients. The growth rate of mutations largely depends upon the immune system strength and by manipulating the growth rates for different tumor populations, it is possible to capture the factor of immune strength of the patient. The fine tuned model is then used to analyze the effect of AZD9291 drug on geftinib resistant state of the tumor. It is observed that AZD9291 could be the best candidate for the treatment of the EGFR T790M tumor sub-population. Hybrid modeling helps to understand the tumor drug resistance along with tumor progression due to multiple mutations, in a more realistic way and it also provides a way for personalized therapy by managing the drug administration in a strict pattern that avoid the growth of resistant sub-populations as well as target other populations at the same time. The only key to avoid relapse in cancer is the personalized therapy and the proposed hybrid models promises to do that

Discrete Semantics for Hybrid Automata

Many natural systems exhibit a hybrid behavior characterized by a set of continuous laws which are switched by discrete events. Such behaviors can be described in a very natural way by a class of automata called hybrid automata. Their evolution are represented by both dynamical systems on dense domains and discrete transitions. Once a real system is modeled in a such framework, one may want to analyze it by applying automatic techniques, such as Model Checking or Abstract Interpretation. Unfortunately, the discrete/continuous evolutions not only provide hybrid automata of great flexibility, but they are also at the root of many undecidability phenomena. This paper addresses issues regarding the decidability of the reachability problem for hybrid automata (i.e., "can the system reach a state a from a state b?") by proposing an "inaccurate" semantics. In particular, after observing that dense sets are often abstractions of real world domains, we suggest, especially in the context of biological simulation, to avoid the ability of distinguishing between values whose distance is less than a fixed \u3b5. On the ground of the above considerations, we propose a new semantics for first-order formul\ue6 which guarantees the decidability of reachability. We conclude providing a paradigmatic biological example showing that the new semantics mimics the real world behavior better than the precise one

Simulation-based model checking approach to cell fate specification during Caenorhabditis elegans vulval development by hybrid functional Petri net with extension

<p>Abstract</p> <p>Background</p> <p>Model checking approaches were applied to biological pathway validations around 2003. Recently, Fisher <it>et al</it>. have proved the importance of model checking approach by inferring new regulation of signaling crosstalk in <it>C. elegans </it>and confirming the regulation with biological experiments. They took a discrete and state-based approach to explore all possible states of the system underlying vulval precursor cell (VPC) fate specification for desired properties. However, since both discrete and continuous features appear to be an indispensable part of biological processes, it is more appropriate to use quantitative models to capture the dynamics of biological systems. Our key motivation of this paper is to establish a quantitative methodology to model and analyze <it>in silico </it>models incorporating the use of model checking approach.</p> <p>Results</p> <p>A novel method of modeling and simulating biological systems with the use of model checking approach is proposed based on hybrid functional Petri net with extension (HFPNe) as the framework dealing with both discrete and continuous events. Firstly, we construct a quantitative VPC fate model with 1761 components by using HFPNe. Secondly, we employ two major biological fate determination rules – Rule I and Rule II – to VPC fate model. We then conduct 10,000 simulations for each of 48 sets of different genotypes, investigate variations of cell fate patterns under each genotype, and validate the two rules by comparing three simulation targets consisting of fate patterns obtained from <it>in silico </it>and <it>in vivo </it>experiments. In particular, an evaluation was successfully done by using our VPC fate model to investigate one target derived from biological experiments involving hybrid lineage observations. However, the understandings of hybrid lineages are hard to make on a discrete model because the hybrid lineage occurs when the system comes close to certain thresholds as discussed by Sternberg and Horvitz in 1986. Our simulation results suggest that: Rule I that cannot be applied with qualitative based model checking, is more reasonable than Rule II owing to the high coverage of predicted fate patterns (except for the genotype of <it>lin-15ko; lin-12ko </it>double mutants). More insights are also suggested.</p> <p>Conclusion</p> <p>The quantitative simulation-based model checking approach is a useful means to provide us valuable biological insights and better understandings of biological systems and observation data that may be hard to capture with the qualitative one.</p

Méthodes systémiques d'analyse des données de simulation de modèles de voies de signalisation cellulaire

Les réseaux de pétri, un outil de modélisation polyvalent -- Une approche systémique en biologie moléculaire : le génie à la rencontre de la biologie -- Démarche de l'ensemble du travail de recherche et organisation générale du document -- Functional abstraction and spectral representation to visualize the system dynamics and the information flux in a biochemical model -- Petri net-based visualization of signal transduction pathway simulations -- Petri net-based method for the analysis of the dynamics of signal propagation in signaling pathways

Gene expression trees in lymphoid development

<p>Abstract</p> <p>Background</p> <p>The regulatory processes that govern cell proliferation and differentiation are central to developmental biology. Particularly well studied in this respect is the lymphoid system due to its importance for basic biology and for clinical applications. Gene expression measured in lymphoid cells in several distinguishable developmental stages helps in the elucidation of underlying molecular processes, which change gradually over time and lock cells in either the B cell, T cell or Natural Killer cell lineages. Large-scale analysis of these <it>gene expression trees </it>requires computational support for tasks ranging from visualization, querying, and finding clusters of similar genes, to answering detailed questions about the functional roles of individual genes.</p> <p>Results</p> <p>We present the first statistical framework designed to analyze gene expression data as it is collected in the course of lymphoid development through clusters of co-expressed genes and additional heterogeneous data. We introduce dependence trees for continuous variates, which model the inherent dependencies during the differentiation process naturally as gene expression trees. Several trees are combined in a mixture model to allow inference of potentially overlapping clusters of co-expressed genes. Additionally, we predict microRNA targets.</p> <p>Conclusion</p> <p>Computational results for several data sets from the lymphoid system demonstrate the relevance of our framework. We recover well-known biological facts and identify promising novel regulatory elements of genes and their functional assignments. The implementation of our method (licensed under the GPL) is available at <url>http://algorithmics.molgen.mpg.de/Supplements/ExpLym/</url>.</p

Biological knowledge management and gene network analysis: a heuristic road to System Biology

In order to understand the molecular basis of living cells and organisms, biologists over the past decades have been studying life's core molecular players: the genes. Most genes have a specific function, a role they play in the collective task of developing a cell and supporting all the aspects of keeping it alive. These genes do not perform their function randomly. Instead, after billions of years of evolution, nature's trial-and-error process, they have become parts of an utterly complex and intricate network, an interconnected mesh of genes that comprises signal detection cascades, enzymatic reactions, control mechanisms, etc. Over several past decades, experimental molecular biologists have sought mainly to study these genes via a one-by-one approach. However, with the advent of high-throughput experimental techniques, the number-crunching power of computers, and the realisation that many biological functions are the result of interactions between genes or their proteins, Biology's related field of Systems Biology has emerged. Here, one tries to combine the dispersed information produced by many researchers, in integrated assemblies called gene networks. Our research comprises the development of two new methods for improved information integration in the field of molecular Systems Biology. The first one aims to support an approach to acquire insights in the dynamics of gene networks (the behaviour of gene activities over time), called 'modelling and simulation' of genetic regulatory networks. Our second new method approaches the problem of how to collect and manage the information necessary to compose such genetic networks in the first place, based on scattered information in a dispersed and increasingly fast growing body of publications. These two methods form two separate parts in this thesis (chapters 2-4, and chapters 5-7). Chapter 1, section 1.3 provides an introductory, complete overview of this thesis. It is intended as a light introduction to my doctoral research, presented in an informal and entertaining way, and mainly addressed to my friends and family. It forms an introduction for the laymen to our work and the concepts that are important for this thesis. Chapters 2, 3 and 4 constitute Part 1 of this thesis. Chapter 2 gives a review of the various formalisms for modelling and simulation of gene networks, as a thorough background for our work presented in the following chapter. Chapter 3 describes SIM-plex, our new software tool that forms a bridge between a mathematical gene network modelling formalism, and the biologist, who usually is more an expert in the biology behind the gene network than a mathematician can ever be. It shields off the mathematics in a new way so as to enable biologists to experiment with modelling and simulation themselves. Chapter 4 describes the various applications that SIM-plex was used for. The research described in Part 2 of this thesis, chapters 5, 6 and 7, emerged from our own need for a better management of biological information. We experienced this necessity while we were building a larger genetic network for the Arabidopsis cell cycle, and it forms a general problem in biology. Chapter 5 gives a background of the currently existing methods for harvesting literature information, but comes to the conclusion that no existing automated or manual method displays sufficient potential to capture the largest part of information from literature in a structured way. In chapter 6, we describe our bold proposal of a new method to tackle this problem: MineMap, a community-based manual text-curation initiative. We describe the various aspects required to make such a project possible, based on our own experiences with our prototype application MineMap. This research is organised in a 'heuristic' way, in the sense that we built a first sketch and a working solution that also generated experiences for improvements in a next design. While chapter 6 describes our new ideas and concrete implementations in considerable detail, chapter 7 then illustrates the core concept behind MineMap

Analysis of drug resistance and the role of the stem cell niche in leukaemia

Glucocorticoids and etoposide are used to treat acute lymphoblastic leukaemia (ALL) as they induce death in lymphoblasts through the glucocorticoid receptor (GR) and p53. However, glucocorticoid resistance, cell death mechanisms and the contribution of the bone marrow microenvironment to drug response/resistance all require investigation. Using microenvironment-mimicking conditioned media (CM), dexamethasone (a synthetic glucocorticoid) and etoposide to treat glucocorticoid-sensitive (C7-14) and glucocorticoid-resistant (C1-15) cells, pathways by which the microenvironment exerts its chemoprotective effect have been investigated. CM reduced caspase-3/8 activation, downregulated RIPK1 (necroptotic marker), and limited chemotherapy-induced BECN1 downregulation, suggesting protective effects of CM. Glucocorticoids upregulated BIRC3 (which ubiquitinates RIPK1), whilst CM altered GR phosphorylation. GR occupancy was observed on the RIPK1, BECN1 and BIRC3 promoters and changed depending on its phosphorylation. High-molecular weight proteins reacting with the RIPK1 antibody increased with CM, and reduced following AT406 BIRC3 inhibitor treatment suggesting they represent ubiquitinated RIPK1. These results suggest mechanisms by which CM promotes survival, as well as indicating novel glucocorticoid-regulated pathways. Complementing laboratory investigation is the construction of a Boolean model of the GR interaction network (GEB052, GR “interactome”) containing 52 nodes (proteins, inputs/outputs) connected by 241 interactions. In silico mutations and analyses have generated predictions that were subsequently validated on a genome-wide scale via comparison to microarray data. GEB052 demonstrated high prediction accuracy, consistently achieving a better prediction rate than a randomised model. Quantitative algorithmic analysis via microarray superimposition has also been performed, and lastly the model has been preliminarily validated as a clinical tool via superimposition of patient microarray data and comparing model predictions to clinical data. In summary, this thesis provides novel insight into the effects of the microenvironment, and identifies new glucocorticoid-regulated pathways. The GEB052 model of GR signalling represents the novel application of this modelling approach to GR research, and generates accurate predictions

Development of constrained fuzzy logic for modeling biological regulatory networks and predicting contextual therapeutic effects

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 199-213).Upon exposure to environmental cues, protein modifications form a complex signaling network that dictates cellular response. In this thesis, we develop methods for using continuous logic-based models to aide our understanding of these signaling networks and facilitate data interpretation. We present a novel modeling framework called constrained fuzzy logic (cFL) that maintains a simple logic-based description of interactions with AND, OR, and NOT gates, but allows for intermediate species activities with mathematical functions relating input and output values (transfer functions). We first train a prior knowledge network (PKN) to data with cFL, which reveals what aspects of the dataset agree or disagree with prior knowledge. The cFL models are trained to a dataset describing signaling proteins in a hepatocellular carcinoma cell line after exposure to ligand cues in the presence or absence of small molecule inhibitors. We find that multiple models with differing topology and parameters explain the data equally well, and it is crucial to consider this non-identifiability during model training and subsequence analysis. Our trained models generate new biological understanding of network crosstalk as well as quantitative predictions of signaling protein activation. In our next applications of cFL, we explore the ability of models either constructed based solely on prior knowledge or trained to dedicated biochemical data to make predictions that answer the following questions: 1) What perturbations to species in the system are effective at accomplishing a clinical goal? and 2) In what environmental conditions are these perturbations effective? We find that we are able to make accurate predictions in both cases. Thus, we offer cFL as a flexible modeling methodology to assist data interpretation and hypothesis generation for choice of therapeutic targets.by Melody K. Morris.Ph.D