A cellular automaton model of cellular signal transduction

Modeling and Simulation of Cell Signaling Networks for Subsequent Analytics Processes Using Big Data and Machine Learning

This work explores how much the traditional approach to modeling and simulation of biological systems, specifically cell signaling networks, can be increased and improved by integrating big data, data mining, and machine learning techniques. Specifically, we first model, simulate, validate, and calibrate the behavior of the PI3K/AKT/mTOR cancer-related signaling pathway. Subsequently, once the behavior of the simulated signaling network matches the expected behavior, the capacity of the computational simulation is increased to grow data (data farming). First, we use big data techniques to extract, collect, filter, and store large volumes of data describing all the interactions among the simulated cell signaling system components over time. Afterward, we apply data mining and machine learning techniques-specifically, exploratory data analysis, feature selection techniques, and supervised neural network models-to the resulting biological dataset to obtain new inferences and knowledge about this biological system. The results showed how the traditional approach to the simulation of biological systems could be enhanced and improved by incorporating big data, data mining, and machine learning techniques, which significantly contributed to increasing the predictive power of the simulation.

Dimensional Changes in Lipid Rafts from Human Brain Cortex Associated to Development of Alzheimer's Disease. Predictions from an Agent-Based Mathematical Model

Alzheimer’s disease (AD) is a neurodegenerative disease caused by abnormal functioning of critical physiological processes in nerve cells and aberrant accumulation of protein aggregates in the brain. The initial cause remains elusive—the only unquestionable risk factor for the most frequent variant of the disease is age. Lipid rafts are microdomains present in nerve cell membranes and they are known to play a significant role in the generation of hallmark proteinopathies associated to AD, namely senile plaques, formed by aggregates of amyloid β peptides. Recent studies have demonstrated that human brain cortex lipid rafts are altered during early neuropathological phases of AD as defined by Braak and Braak staging. The lipid composition and physical properties of these domains appear altered even before clinical symptoms are detected. Here, we use a coarse grain molecular dynamics mathematical model to predict the dimensional evolution of these domains using the experimental data reported by our group in human frontal cortex. The model predicts significant size and frequency changes which are detectable at the earliest neuropathological stage (ADI/II) of Alzheimer’s disease. Simulations reveal a lower number and a larger size in lipid rafts from ADV/VI, the most advanced stage of AD. Paralleling these changes, the predictions also indicate that non-rafts domains undergo simultaneous alterations in membrane peroxidability, which support a link between oxidative stress and AD progression. These synergistic changes in lipid rafts dimensions and non-rafts peroxidability are likely to become part of a positive feedback loop linked to an irreversible amyloid burden and neuronal death during the evolution of AD neuropathology.

'Molecular habituation' as a potential mechanism of gradual homeostatic loss with age

•

Constitutive signals indicate homeostatic dysregulation but their effect on signal transduction remains largely unexplored.

•

A theoretical approach is undertaken to examine how oxidative stress may affect redox signal transduction.

•

Constitutive signals can result in a ‘molecular habituation’ effect that interferes with information transmission.

•

The robustness of such a theoretical observation to the underlying methodology hints at the generality of this principle.

The ability of reactive oxygen species (ROS) to cause molecular damage has meant that chronic oxidative stress has been mostly studied from the point of view of being a source of toxicity to the cell. However, the known duality of ROS molecules as both damaging agents and cellular redox signals implies another perspective in the study of sustained oxidative stress. This is a perspective of studying oxidative stress as a constitutive signal within the cell. In this work, we adopt a theoretical perspective as an exploratory and explanatory approach to examine how chronic oxidative stress can interfere with signal processing by redox signalling pathways in the cell. We report that constitutive signals can give rise to a ‘molecular habituation’ effect that can prime for a gradual loss of biological function. This is because a constitutive signal in the environment has the potential to reduce the responsiveness of a signalling pathway through the prolonged activation of negative regulators. Additionally, we demonstrate how this phenomenon is likely to occur in different signalling pathways exposed to persistent signals and furthermore at different levels of biological organisation.

Lipid Raft Size and Lipid Mobility in Non-raft Domains Increase during Aging and Are Exacerbated in APP/PS1 Mice Model of Alzheimer's Disease. Predictions from an Agent-Based Mathematical Model

A connection between lipid rafts and Alzheimer's disease has been studied during the last decades. Mathematical modeling approaches have recently been used to correlate the effects of lipid composition changes in the physicochemical properties of raft-like membranes. Here we propose an agent based model to assess the effect of lipid changes in lipid rafts on the evolution and progression of Alzheimer's disease using lipid profile data obtained in an established model of familial Alzheimer's disease. We have observed that lipid raft size and lipid mobility in non-raft domains are two main factors that increase during age and are accelerated in the transgenic Alzheimer's disease mouse model. The consequences of these changes are discussed in the context of neurotoxic amyloid β production. Our agent based model predicts that increasing sterols (mainly cholesterol) and long-chain polyunsaturated fatty acids (LCPUFA) (mainly DHA, docosahexaenoic acid) proportions in the membrane composition might delay the onset and progression of the disease.

A simple computational cell: coupling boolean gene and protein networks

This article presents an abstract, tunable model containing two of the principal information-processing features of cells and explores its use with simulated evolution. The random Boolean model of genetic regulatory networks is extended to include a protein interaction network. The underlying behavior of the resulting two coupled dynamical networks is investigated before their evolvability is explored using a version of the NK model of fitness landscapes.

Modeling formalisms in Systems Biology

Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future.

A systems approach to bone pathophysiology

With evolving interest in multiscalar biological systems one could assume that reductionist approaches may not fully describe biological complexity. Instead, tools such as mathematical modeling, network analysis, and other multiplexed clinical- and research-oriented tests enable rapid analyses of high-throughput data parsed at the genomic, proteomic, metabolomic, and physiomic levels. A physiomic-level approach allows for recursive horizontal and vertical integration of subsystem coupling across and within spatiotemporal scales. Additionally, this methodology recognizes previously ignored subsystems and the strong, nonintuitively obvious and indirect connections among physiological events that potentially account for the uncertainties in medicine. In this review, we flip the reductionist research paradigm and review the concept of systems biology and its applications to bone pathophysiology. Specifically, a bone-centric physiome model is presented that incorporates systemic-level processes with their respective therapeutic implications.

Human disease classification in the postgenomic era: a complex systems approach to human pathobiology

Contemporary classification of human disease derives from observational correlation between pathological analysis and clinical syndromes. Characterizing disease in this way established a nosology that has served clinicians well to the current time, and depends on observational skills and simple laboratory tools to define the syndromic phenotype. Yet, this time-honored diagnostic strategy has significant shortcomings that reflect both a lack of sensitivity in identifying preclinical disease, and a lack of specificity in defining disease unequivocally. In this paper, we focus on the latter limitation, viewing it as a reflection both of the different clinical presentations of many diseases (variable phenotypic expression), and of the excessive reliance on Cartesian reductionism in establishing diagnoses. The purpose of this perspective is to provide a logical basis for a new approach to classifying human disease that uses conventional reductionism and incorporates the non-reductionist approach of systems biomedicine.

Modeling of the role of a Bax-activation switch in the mitochondrial apoptosis decision

We performed in silico modeling of the regulatory network of mitochondrial apoptosis through which we examined the role of a Bax-activation switch in governing the mitochondrial apoptosis decision. Two distinct modeling methods were used in this article. One is continuous and deterministic, comprised of a set of ordinary differential equations. The other, carried out in a discrete manner, is based on a cellular automaton, which takes stochastic fluctuations into consideration. We focused on dynamic properties of the mitochondrial apoptosis regulatory network. The roles of Bcl-2 family proteins in cellular responses to apoptotic stimuli were examined. In our simulations, a self-amplification process of Bax-activation is indicated. Further analysis suggests that the core module of Bax-activation is bistable in both deterministic and stochastic models, and this feature is robust to noise and wide ranges of parameter variation. When coupling with Bax-polymerization, it forms a one-way-switch, which governs irreversible behaviors of Bax-activation even with attenuation of apoptotic stimulus. Together with the growing biochemical evidence, we propose a novel molecular switch mechanism embedded in the mitochondrial apoptosis regulatory network and give a plausible explanation for the all-or-none, irreversible character of mitochondrial apoptosis.

Mathematical modeling of intracellular signaling pathways

Dynamic modeling and simulation of signal transduction pathways is an important topic in systems biology and is obtaining growing attention from researchers with experimental or theoretical background. Here we review attempts to analyze and model specific signaling systems. We review the structure of recurrent building blocks of signaling pathways and their integration into more comprehensive models, which enables the understanding of complex cellular processes. The variety of mechanisms found and modeling techniques used are illustrated with models of different signaling pathways. Focusing on the close interplay between experimental investigation of pathways and the mathematical representations of cellular dynamics, we discuss challenges and perspectives that emerge in studies of signaling systems.

Cellular Automata With Object-Oriented Features for Parallel Molecular Network Modeling

Cellular automata are an important modeling paradigm for studying the dynamics of large, parallel systems composed of multiple, interacting components. However, to model biological systems, cellular automata need to be extended beyond the large-scale parallelism and intensive communication in order to capture two fundamental properties characteristic of complex biological systems: hierarchy and heterogeneity. This paper proposes extensions to a cellular automata language, Cellang, to meet this purpose. The extended language, with object-oriented features, can be used to describe the structure and activity of parallel molecular networks within cells. Capabilities of this new programming language include object structure to define molecular programs within a cell, floating-point data type and mathematical functions to perform quantitative computation, message passing capability to describe molecular interactions, as well as new operators, statements, and built-in functions. We discuss relevant programming issues of these features, including the object-oriented description of molecular interactions with molecule encapsulation, message passing, and the description of heterogeneity and anisotropy at the cell and molecule levels. By enabling the integration of modeling at the molecular level with system behavior at cell, tissue, organ, or even organism levels, the program will help improve our understanding of how complex and dynamic biological activities are generated and controlled by parallel functioning of molecular networks. Index Terms-Cellular automata, modeling, molecular network, object-oriented.

Cellulat: an agent-based intracellular signalling model

The theory of behaviour-based systems (or autonomous agents) constitutes a useful approach for the modelling of intracellular signalling networks. In this sense, a cell can be seen as an adaptive autonomous agent or as a society of such agents, where each can exhibit a particular behaviour depending on its cognitive capabilities. We present an intracellular signalling model obtained by integrating several computational techniques into an agent-based paradigm. Cellulat, the model, takes into account two essential aspects of the intracellular signalling networks: (1) cognitive capacities, which are modelled as the agent abilities to interact with the surrounding medium and (2) a spatial organisation, this last obtained using a shared data structure through which the agents communicate between them. We propose a methodology for the modelling of intracellular signalling pathway using Cellulat and we discuss the goal of a virtual laboratory based on our model and presently under development.