Satya N. V. Arjunan

Space in systems biology of signaling pathways--towards intracellular molecular crowding in silico.

How cells utilize intracellular spatial features to optimize their signaling characteristics is still not clearly understood. The physical distance between the cell‐surface receptor and the gene expression machinery, fast reactions, and slow protein diffusion coefficients are some of the properties that contribute to their intricacy. This article reviews computational frameworks that can help biologists to elucidate the implications of space in signaling pathways. We argue that intracellular macromolecular crowding is an important modeling issue, and describe how recent simulation methods can reproduce this phenomenon in either implicit, semi‐explicit or fully explicit representation.

A new multicompartmental reaction-diffusion modeling method links transient membrane attachment of E. coli MinE to E-ring formation

Many important cellular processes are regulated by reaction-diffusion (RD) of molecules that takes place both in the cytoplasm and on the membrane. To model and analyze such multicompartmental processes, we developed a lattice-based Monte Carlo method, Spatiocyte that supports RD in volume and surface compartments at single molecule resolution. Stochasticity in RD and the excluded volume effect brought by intracellular molecular crowding, both of which can significantly affect RD and thus, cellular processes, are also supported. We verified the method by comparing simulation results of diffusion, irreversible and reversible reactions with the predicted analytical and best available numerical solutions. Moreover, to directly compare the localization patterns of molecules in fluorescence microscopy images with simulation, we devised a visualization method that mimics the microphotography process by showing the trajectory of simulated molecules averaged according to the camera exposure time. In the rod-shaped bacterium Escherichia coli, the division site is suppressed at the cell poles by periodic pole-to-pole oscillations of the Min proteins (MinC, MinD and MinE) arising from carefully orchestrated RD in both cytoplasm and membrane compartments. Using Spatiocyte we could model and reproduce the in vivo MinDE localization dynamics by accounting for the previously reported properties of MinE. Our results suggest that the MinE ring, which is essential in preventing polar septation, is largely composed of MinE that is transiently attached to the membrane independently after recruited by MinD. Overall, Spatiocyte allows simulation and visualization of complex spatial and reaction-diffusion mediated cellular processes in volumes and surfaces. As we showed, it can potentially provide mechanistic insights otherwise difficult to obtain experimentally.

Sequential logic model deciphers dynamic transcriptional control of gene expressions.

Background Cellular signaling involves a sequence of events from ligand binding to membrane receptors through transcription factors activation and the induction of mRNA expression. The transcriptional-regulatory system plays a pivotal role in the control of gene expression. A novel computational approach to the study of gene regulation circuits is presented here. Methodology Based on the concept of finite state machine, which provides a discrete view of gene regulation, a novel sequential logic model (SLM) is developed to decipher control mechanisms of dynamic transcriptional regulation of gene expressions. The SLM technique is also used to systematically analyze the dynamic function of transcriptional inputs, the dependency and cooperativity, such as synergy effect, among the binding sites with respect to when, how much and how fast the gene of interest is expressed. Principal Findings SLM is verified by a set of well studied expression data on endo16 of Strongylocentrotus purpuratus (sea urchin) during the embryonic midgut development. A dynamic regulatory mechanism for endo16 expression controlled by three binding sites, UI, R and Otx is identified and demonstrated to be consistent with experimental findings. Furthermore, we show that during transition from specification to differentiation in wild type endo16 expression profile, SLM reveals three binary activities are not sufficient to explain the transcriptional regulation of endo16 expression and additional activities of binding sites are required. Further analyses suggest detailed mechanism of R switch activity where indirect dependency occurs in between UI activity and R switch during specification to differentiation stage. Conclusions/Significance The sequential logic formalism allows for a simplification of regulation network dynamics going from a continuous to a discrete representation of gene activation in time. In effect our SLM is non-parametric and model-independent, yet providing rich biological insight. The demonstration of the efficacy of this approach in endo16 is a promising step for further application of the proposed method.

A Computational Framework for Bioimaging Simulation.

Using bioimaging technology, biologists have attempted to identify and document analytical interpretations that underlie biological phenomena in biological cells. Theoretical biology aims at distilling those interpretations into knowledge in the mathematical form of biochemical reaction networks and understanding how higher level functions emerge from the combined action of biomolecules. However, there still remain formidable challenges in bridging the gap between bioimaging and mathematical modeling. Generally, measurements using fluorescence microscopy systems are influenced by systematic effects that arise from stochastic nature of biological cells, the imaging apparatus, and optical physics. Such systematic effects are always present in all bioimaging systems and hinder quantitative comparison between the cell model and bioimages. Computational tools for such a comparison are still unavailable. Thus, in this work, we present a computational framework for handling the parameters of the cell models and the optical physics governing bioimaging systems. Simulation using this framework can generate digital images of cell simulation results after accounting for the systematic effects. We then demonstrate that such a framework enables comparison at the level of photon-counting units.

Particle Simulation of Oxidation Induced Band 3 Clustering in Human Erythrocytes.

Oxidative stress mediated clustering of membrane protein band 3 plays an essential role in the clearance of damaged and aged red blood cells (RBCs) from the circulation. While a number of previous experimental studies have observed changes in band 3 distribution after oxidative treatment, the details of how these clusters are formed and how their properties change under different conditions have remained poorly understood. To address these issues, a framework that enables the simultaneous monitoring of the temporal and spatial changes following oxidation is needed. In this study, we established a novel simulation strategy that incorporates deterministic and stochastic reactions with particle reaction-diffusion processes, to model band 3 cluster formation at single molecule resolution. By integrating a kinetic model of RBC antioxidant metabolism with a model of band 3 diffusion, we developed a model that reproduces the time-dependent changes of glutathione and clustered band 3 levels, as well as band 3 distribution during oxidative treatment, observed in prior studies. We predicted that cluster formation is largely dependent on fast reverse reaction rates, strong affinity between clustering molecules, and irreversible hemichrome binding. We further predicted that under repeated oxidative perturbations, clusters tended to progressively grow and shift towards an irreversible state. Application of our model to simulate oxidation in RBCs with cytoskeletal deficiency also suggested that oxidation leads to more enhanced clustering compared to healthy RBCs. Taken together, our model enables the prediction of band 3 spatio-temporal profiles under various situations, thus providing valuable insights to potentially aid understanding mechanisms for removing senescent and premature RBCs.

E-Cell System

Analytical techniques in computational cell biology such as kinetic parameter estimation, Metabolic Control Analysis (MCA) and bifurcation analysis require large numbers of repetitive simulation runs with different input parameters. The requirements for significant computational resources imposed by those analytical methods have led to an increasing interest in the use of parallel and distributed computing technologies. We developed a Python-scripting environment that can execute the above mathematical analyses. Also, where possible, it automatically and transparently parallelizes them on either (1) stand-alone PCs, (2) shared-memory multiprocessor (SMP) servers, (3) cluster systems, or (4) a computational grid infrastructure. We named this environment E-Cell Session Manager (ESM). It involves user-friendly flat application program interfaces (APIs) for scripting and a pure object-oriented programming environment for sophisticated implementation of a user’s analysis. In this chapter, fundamental concepts related to the design and the ESM architecture are introduced. We also describe an estimation of the parameters with some script examples executed on ESM. Introduction Computer simulations are often used to understand complex biological mechanisms, reproducing dynamic behavior in cells, organs and individuals. Simulation models are important for simultaneously understanding the complex processing of biological phenomena and for revealing their mechanisms in vivo. To establish an in silico model to capture biological behavior, qualitative structural information concerning cellular elements including gene networks, metabolic pathways and cascades of signal transductions, along with parameters of reaction rates characterizing the dynamics of the model must be provided precisely and in sufficient detail. Quantitative parameters available from literature or public databases deteriorate the credibility of such constructed models because they often show noise and are measured under different conditions. Recently, a number of high-throughput measurement devices to perform time-course quantitative studies have been developed; these have been aimed at accumulating sufficient and accurate data that can be used for cell simulations.1 Thus, development of sophisticated parameter estimation methods to determine parameters unavailable from observable data and to build quantitative models are required. Estimation of parameters for large-scale models requires high-performance computing facilities because a number of simulation runs must be repeated using different parameters to produce models that represent specific time-courses. Generic parameter estimation approaches based on 34 E-Cell System: Basic Concepts and Applications global optimizations such as genetic algorithm iterate independent simulations, which can be executed on coarse-grained parallel environments, e.g., cluster machines and grid infrastructures. A number of cell simulators implementing parameter estimation functions with parallel computing have been developed. Systems Biology Workbench (SBW) is an extensible and general framework that includes a biological simulation engine and parameter optimization modules.2 Grid Cellware is an integrated simulation environment implementing the adaptive Swarm algorithm for parameter estimation.3 OBIYagns is a parameter estimation system based on an epigone genetic algorithm called distance independent diversity control (DIDC) and has a Web-based graphical interface.4 These systems exploit clusters or grid infrastructures to distribute simulation runs to reduce the total calculation time. After constructing the structure and parameters needed in a simulation model, they need to be evaluated by comparing them with known biological data. At this stage, the validity of the model is investigated; this includes the ability to reproduce inter/intra cellular behaviors or its quantitative properties including sensitivity or stability of parameters and analyses using Metabolic Control Analysis (MCA), bifurcation analysis. These analyses can be parallelized at a coarse-grained level because they also repeat independent simulations with different parameters. Typical in silico experiments can also be parallelized in the same way such as over/under-activate a/some intercellular substrate(s) to virtually simulate gene knockout or overexpression and the cultivation of cells with different intracellular conditions such as pH or temperature to maximize/minimize concentrations of cellular products. Since many simulation applications in computational cell biology require repetitive runs of simulation sessions with different models and boundary parameters, distributed computation schemes are highly suitable for such applications. Here, we discuss a scheme for job-level parallelism, or distributed computing. There is already some middleware software available for the assignment of jobs to distributed environments, e.g., Portable Batch System (PBS, http://www.pbs.org/), Load Sharing Facility (LSF, http://www. platform.com/), Sun Grid Engine (SGE, http://wwws.sun.com/software/gridware/) at the cluster level and Globus toolkit5 at the grid level. While these low-level infrastructures are extremely powerful, they are not compatible with each other, nor are they readily accessible to an average computational biologist. On the other hand, higher-level parallelization systems with a Web-based user interface such as OBIYagns may help computer neophytes. Though these systems provide editable workflow functions such as myGrid6 and ProGenGrid7, they lack programming flexibility to implement a user’s analysis algorithm for various research purposes. In this chapter, we describe the architecture and the design of a distributed computing module of E-Cell3, called E-Cell Session Manager (ESM).8 ESM was developed to produce higher-level APIs to provide users with a scripting environment and to transparently distribute multiple E-Cell sessions on stand-alone PC, SMP, cluster and grid environments. We also introduce parameter estimation scripts built on ESM as an example.