Model-based assessment of mammalian cell metabolic functionalities using omics data.

Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org-CellFie).

An insight on Drosophila myogenesis and its assessment techniques.

Movement assisted by muscles forms the basis of various behavioural traits seen in Drosophila. Myogenesis involves developmental processes like cellular specification, differentiation, migration, fusion, adherence to tendons and neuronal innervation in a series of coordinated event well defined in body space and time. Gene regulatory networks are switched on-off, fine tuning at the right developmental stage to assist each cellular event. Drosophila is a holometabolous organism that undergoes myogenesis waves at two developmental stages, and is ideal for comparative analysis of the role of genes and genetic pathways conserved across phyla. In this review we have summarized myogenic events from the embryo to adult focussing on the somatic muscle development during the early embryonic stage and then on indirect flight muscles (IFM) formation required for adult life, emphasizing on recent trends of analysing muscle mutants and advances in Drosophila muscle biology.

Energetic costs of cellular and therapeutic control of stochastic mitochondrial DNA populations.

The dynamics of the cellular proportion of mutant mtDNA molecules is crucial for mitochondrial diseases. Cellular populations of mitochondria are under homeostatic control, but the details of the control mechanisms involved remain elusive. Here, we use stochastic modelling to derive general results for the impact of cellular control on mtDNA populations, the cost to the cell of different mtDNA states, and the optimisation of therapeutic control of mtDNA populations. This formalism yields a wealth of biological results, including that an increasing mtDNA variance can increase the energetic cost of maintaining a tissue, that intermediate levels of heteroplasmy can be more detrimental than homoplasmy even for a dysfunctional mutant, that heteroplasmy distribution (not mean alone) is crucial for the success of gene therapies, and that long-term rather than short intense gene therapies are more likely to beneficially impact mtDNA populations.

Propagation of Extrinsic Fluctuations in Biochemical Birth-Death Processes.

Biochemical reactions are often subject to a complex fluctuating environment, which means that the corresponding reaction rates may themselves be time-varying and stochastic. If the environmental noise is common to a population of downstream processes, then the resulting rate fluctuations will induce statistical correlations between them. In this paper we investigate how such correlations depend on the form of environmental noise by considering a simple birth-death process with dynamical disorder in the birth rate. In particular, we derive expressions for the second-order statistics of two birth-death processes evolving in the same noisy environment. We find that these statistics not only depend on the second-order statistics of the environment, but the full generator of the process describing it, thus providing useful information about the environment. We illustrate our theory by considering applications to stochastic gene transcription and cell sensing.

Modeling cell population dynamics.

 Quantitative modeling is quickly becoming an integral part of biology, due to the ability of mathematical models and computer simulations to generate insights and predict the behavior of living systems. Single-cell models can be incapable or misleading for inferring population dynamics, as they do not consider the interactions between cells via metabolites or physical contact, nor do they consider competition for limited resources such as nutrients or space. Here we examine methods that are commonly used to model and simulate cell populations. First, we cover simple models where analytic solutions are available, and then move on to more complex scenarios where computational methods are required. Overall, we present a summary of mathematical models used to describe cell population dynamics, which may aid future model development and highlights the importance of population modeling in biology.

Geometrical structures for radiation biology research as implemented in the TOPAS-nBio toolkit.

Computational simulations, such as Monte Carlo track structure simulations, offer a powerful tool for quantitatively investigating radiation interactions within cells. The modelling of the spatial distribution of energy deposition events as well as diffusion of chemical free radical species, within realistic biological geometries, can help provide a comprehensive understanding of the effects of radiation on cells. Track structure simulations, however, generally require advanced computing skills to implement. The TOPAS-nBio toolkit, an extension to TOPAS (TOol for PArticle Simulation), aims to provide users with a comprehensive framework for radiobiology simulations, without the need for advanced computing skills. This includes providing users with an extensive library of advanced, realistic, biological geometries ranging from the micrometer scale (e.g. cells and organelles) down to the nanometer scale (e.g. DNA molecules and proteins). Here we present the geometries available in TOPAS-nBio.

Statistical analysis of particle trajectories in living cells.

Recent advances in molecular biology and fluorescence microscopy imaging have made possible the inference of the dynamics of molecules in living cells. Such inference allows us to understand and determine the organization and function of the cell. The trajectories of particles (e.g., biomolecules) in living cells, computed with the help of object tracking methods, can be modeled with diffusion processes. Three types of diffusion are considered: (i) free diffusion, (ii) subdiffusion, and (iii) superdiffusion. The mean-square displacement (MSD) is generally used to discriminate the three types of particle dynamics. We propose here a nonparametric three-decision test as an alternative to the MSD method. The rejection of the null hypothesis, i.e., free diffusion, is accompanied by claims of the direction of the alternative (subdiffusion or superdiffusion). We study the asymptotic behavior of the test statistic under the null hypothesis and under parametric alternatives which are currently considered in the biophysics literature. In addition, we adapt the multiple-testing procedure of Benjamini and Hochberg to fit with the three-decision-test setting, in order to apply the test procedure to a collection of independent trajectories. The performance of our procedure is much better than the MSD method as confirmed by Monte Carlo experiments. The method is demonstrated on real data sets corresponding to protein dynamics observed in fluorescence microscopy.

Linear array of multi-substrate tracts for simultaneous assessment of cell adhesion, migration, and differentiation.

Cell migration, which is central to a wide variety of life processes, involves integration of the extracellular matrix (ECM) with the internal cytoskeleton and motor proteins via receptors spanning the plasma membrane. Cell migration can be induced by a variety of signals, including gradients of external soluble molecules, differences in ECM composition, or electrical gradients. Current in vitro methods to study cell migration only test one substrate at a time. Here, we present a method for assessing cell adhesion, migration, and differentiation in up to 20 different test conditions simultaneously, using only minute amounts of target substrate. Our system, which we call the linear array of multi-substrate cell migration assay (LAMA), has two configurations for direct comparison of one or two cell types in response to an array of ECM constituents under the same culture conditions. This culture model utilizes only nanogram amounts of test substrates and a minimal number of cells, which maximizes the use of limited and expensive test reagents. Moreover, LAMA can also be used for high-throughput screening of potential pharmaceuticals that target ECM-dependent cell behavior and differentiation.

DNA double strand break (DSB) induction and cell survival in iodine-enhanced computed tomography (CT).

A multi-scale Monte Carlo model is proposed to assess the dosimetric and biological impact of iodine-based contrast agents commonly used in computed tomography. As presented, the model integrates the general purpose MCNP6 code system for larger-scale radiation transport and dose assessment with the Monte Carlo damage simulation to determine the sub-cellular characteristics and spatial distribution of initial DNA damage. The repair-misrepair-fixation model is then used to relate DNA double strand break (DSB) induction to reproductive cell death. Comparisons of measured and modeled changes in reproductive cell survival for ultrasoft characteristic k-shell x-rays (0.25-4.55 keV) up to orthovoltage (200-500 kVp) x-rays indicate that the relative biological effectiveness (RBE) for DSB induction is within a few percent of the RBE for cell survival. Because of the very short range of secondary electrons produced by low energy x-ray interactions with contrast agents, the concentration and subcellular distribution of iodine within and near cellular targets have a significant impact on the estimated absorbed dose and number of DSB produced in the cell nucleus. For some plausible models of the cell-level distribution of contrast agent, the model predicts an increase in RBE-weighted dose (RWD) for the endpoint of DSB induction of 1.22-1.40 for a 5-10 mg ml-1 iodine concentration in blood compared to an RWD increase of 1.07 ± 0.19 from a recent clinical trial. The modeled RWD of 2.58 ± 0.03 is also in good agreement with the measured RWD of 2.3 ± 0.5 for an iodine concentration of 50 mg ml-1 relative to no iodine. The good agreement between modeled and measured DSB and cell survival estimates provides some confidence that the presented model can be used to accurately assess biological dose for other concentrations of the same or different contrast agents.

Effects of soft interactions and bound mobility on diffusion in crowded environments: a model of sticky and slippery obstacles.

Crowded environments modify the diffusion of macromolecules, generally slowing their movement and inducing transient anomalous subdiffusion. The presence of obstacles also modifies the kinetics and equilibrium behavior of tracers. While previous theoretical studies of particle diffusion have typically assumed either impenetrable obstacles or binding interactions that immobilize the particle, in many cellular contexts bound particles remain mobile. Examples include membrane proteins or lipids with some entry and diffusion within lipid domains and proteins that can enter into membraneless organelles or compartments such as the nucleolus. Using a lattice model, we studied the diffusive movement of tracer particles which bind to soft obstacles, allowing tracers and obstacles to occupy the same lattice site. For sticky obstacles, bound tracer particles are immobile, while for slippery obstacles, bound tracers can hop without penalty to adjacent obstacles. In both models, binding significantly alters tracer motion. The type and degree of motion while bound is a key determinant of the tracer mobility: slippery obstacles can allow nearly unhindered diffusion, even at high obstacle filling fraction. To mimic compartmentalization in a cell, we examined how obstacle size and a range of bound diffusion coefficients affect tracer dynamics. The behavior of the model is similar in two and three spatial dimensions. Our work has implications for protein movement and interactions within cells.

Using stem cell oxygen physiology to optimize blastocyst culture while minimizing hypoxic stress.

This review is a response to the Fellows Forum on testing 2% oxygen for best culture of human blastocysts (J Ass Reprod Gen 34:303-8, 1; J Ass Reprod Gen 34:309-14, 2) prior to embryo transfer. It is a general analysis in support of the position that an understanding of stem cell physiology and responses to oxygen are necessary for optimization of blastocyst culture in IVF and to enhance reproductive success in fertile women.

Architecture of the human interactome defines protein communities and disease networks.

The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein-protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidating how genome variation contributes to disease. Here we present BioPlex 2.0 (Biophysical Interactions of ORFeome-derived complexes), which uses robust affinity purification-mass spectrometry methodology to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein-coding genes from the human genome, and constitutes, to our knowledge, the largest such network so far. With more than 56,000 candidate interactions, BioPlex 2.0 contains more than 29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering of interacting proteins identified more than 1,300 protein communities representing diverse cellular activities. Genes essential for cell fitness are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2,000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization.

Monte Carlo N-Particle (MCNP) Modeling of the Cellular Dosimetry of 64Cu: Comparison with MIRDcell S Values and Implications for Studies of Its Cytotoxic Effects.

64Cu emits positrons as well as ß- particles and Auger and internal conversion electrons useful for radiotherapy. Our objective was to model the cellular dosimetry of 64Cu under different geometries commonly used to study the cytotoxic effects of 64Cu. METHODS: Monte Carlo N-Particle (MCNP) was used to simulate the transport of all particles emitted by 64Cu from the cell surface (CS), cytoplasm (Cy), or nucleus (N) of a single cell; monolayer in a well (radius = 0.32-1.74 cm); or a sphere (radius = 50-6,000 µm) of cells to calculate S values. The radius of the cell and N ranged from 5 to 12 µm and 2 to 11 µm, respectively. S values were obtained by MIRDcell for comparison. MCF7/HER2-18 cells were exposed in vitro to 64Cu-labeled trastuzumab. The subcellular distribution of 64Cu was measured by cell fractionation. The surviving fraction was determined in a clonogenic assay. RESULTS: The relative differences of MCNP versus MIRDcell self-dose S values (Sself) for 64Cu ranged from -0.2% to 3.6% for N to N (SN←N), 2.3% to 8.6% for Cy to N (SN←Cy), and -12.0% to 7.3% for CS to N (SN←CS). The relative differences of MCNP versus MIRDcell cross-dose S values were 25.8%-30.6% for a monolayer and 30%-34% for a sphere, respectively. The ratios of SN←N versus SN←Cy and SN←Cy versus SN←CS decreased with increasing ratio of the N of the cell versus radius of the cell and the size of the monolayer or sphere. The surviving fraction of MCF7 /: HER2-18 cells treated with 64Cu-labeled trastuzumab (0.016-0.368 MBq/µg, 67 nM) for 18 h versus the absorbed dose followed a linear survival curve with α = 0.51 ± 0.05 Gy-1 and R2 = 0.8838. This is significantly different from the linear quadratic survival curve of MCF7 /: HER2-18 cells exposed to γ-rays. CONCLUSION: MCNP- and MIRDcell-calculated S values agreed well. 64Cu in the N increases the dose to the N in isolated single cells but has less effect in a cell monolayer or small cluster of cells simulating a micrometastasis, and little effect in a sphere analogous to a tumor xenograft compared with 64Cu in the Cy or on the CS. The dose deposited by 64Cu is less effective for cell killing than γ-rays.

ML-Space: Hybrid Spatial Gillespie and Particle Simulation of Multi-Level Rule-Based Models in Cell Biology.

Spatio-temporal dynamics of cellular processes can be simulated at different levels of detail, from (deterministic) partial differential equations via the spatial Stochastic Simulation algorithm to tracking Brownian trajectories of individual particles. We present a spatial simulation approach for multi-level rule-based models, which includes dynamically hierarchically nested cellular compartments and entities. Our approach ML-Space combines discrete compartmental dynamics, stochastic spatial approaches in discrete space, and particles moving in continuous space. The rule-based specification language of ML-Space supports concise and compact descriptions of models and to adapt the spatial resolution of models easily.

Synthetic mechanobiology: engineering cellular force generation and signaling.

Mechanobiology seeks to understand and control mechanical and related biophysical communication between cells and their surroundings. While experimental efforts in this field have traditionally emphasized manipulation of the extracellular force environment, a new suite of approaches has recently emerged in which cell phenotype and signaling are controlled by directly engineering the cell itself. One route is to control cell behavior by modulating gene expression using conditional promoters. Alternatively, protein activity can be actuated directly using synthetic protein ligands, chemically induced protein dimerization, optogenetic strategies, or functionalized magnetic nanoparticles. Proof-of-principle studies are already demonstrating the translational potential of these approaches, and future technological development will permit increasingly precise control over cell mechanobiology and improve our understanding of the underlying signaling events.

Shift and adapt: the costs and benefits of karyotype variations.

Variation is the spice of life or, in the case of evolution, variation is the necessary material on which selection can act to enable adaptation. Karyotypic variation in ploidy (the number of homologous chromosome sets) and aneuploidy (imbalance in the number of chromosomes) are fundamentally different than other types of genomic variants. Karyotypic variation emerges through different molecular mechanisms than other mutational events, and unlike mutations that alter the genome at the base pair level, rapid reversion to the wild type chromosome number is often possible. Although karyotypic variation has long been noted and discussed by biologists, interest in the importance of karyotypic variants in evolutionary processes has spiked in recent years, and much remains to be discovered about how karyotypic variants are produced and subsequently selected.

Solving the chemical master equation by a fast adaptive finite state projection based on the stochastic simulation algorithm.

The mathematical framework of the chemical master equation (CME) uses a Markov chain to model the biochemical reactions that are taking place within a biological cell. Computing the transient probability distribution of this Markov chain allows us to track the composition of molecules inside the cell over time, with important practical applications in a number of areas such as molecular biology or medicine. However the CME is typically difficult to solve, since the state space involved can be very large or even countably infinite. We present a novel way of using the stochastic simulation algorithm (SSA) to reduce the size of the finite state projection (FSP) method. Numerical experiments that demonstrate the effectiveness of the reduction are included.

Modelling non-Markovian dynamics in biochemical reactions.

BACKGROUND: Biochemical reactions are often modelled as discrete-state continuous-time stochastic processes evolving as memoryless Markov processes. However, in some cases, biochemical systems exhibit non-Markovian dynamics. We propose here a methodology for building stochastic simulation algorithms which model more precisely non-Markovian processes in some specific situations. Our methodology is based on Constraint Programming and is implemented by using Gecode, a state-of-the-art framework for constraint solving. RESULTS: Our technique allows us to randomly sample waiting times from probability density functions that not necessarily are distributed according to a negative exponential function. In this context, we discuss an important case-study in which the probability density function is inferred from single-molecule experiments that describe the distribution of the time intervals between two consecutive enzymatically catalysed reactions. Noticeably, this feature allows some types of enzyme reactions to be modelled as non-Markovian processes. CONCLUSIONS: We show that our methodology makes it possible to obtain accurate models of enzymatic reactions that, in specific cases, fit experimental data better than the corresponding Markovian models.

Bayesian inference of reaction kinetics from single-cell recordings across a heterogeneous cell population.

Single-cell experimental techniques provide informative data to help uncover dynamical processes inside a cell. Making full use of such data requires dedicated computational methods to estimate biophysical process parameters and states in a model-based manner. In particular, the treatment of heterogeneity or cell-to-cell variability deserves special attention. The present article provides an introduction to one particular class of algorithms which employ marginalization in order to take heterogeneity into account. An overview of alternative approaches is provided for comparison. We treat two frequently encountered scenarios in single-cell experiments, namely, single-cell trajectory data and single-cell distribution data.