An Introduction to Rule-based Modeling of Immune Receptor Signaling

Cells process external and internal signals through chemical interactions. Cells that constitute the immune system (e.g., antigen presenting cell, T-cell, B-cell, mast cell) can have different functions (e.g., adaptive memory, inflammatory response) depending on the type and number of receptor molecules on the cell surface and the specific intracellular signaling pathways activated by those receptors. Explicitly modeling and simulating kinetic interactions between molecules allows us to pose questions about the dynamics of a signaling network under various conditions. However, the application of chemical kinetics to biochemical signaling systems has been limited by the complexity of the systems under consideration. Rule-based modeling (BioNetGen, Kappa, Simmune, PySB) is an approach to address this complexity. In this chapter, by application to the Fc$\varepsilon$RI receptor system, we will explore the origins of complexity in macromolecular interactions, show how rule-based modeling can be used to address complexity, and demonstrate how to build a model in the BioNetGen framework. Open source BioNetGen software and documentation are available at http://bionetgen.org.Comment: 5 figure

Plasmonic atoms and plasmonic molecules

The proposed paradigm of plasmonic atoms and plasmonic molecules allows one to describe and predict the strongly localized plasmonic oscillations in the clusters of nanoparticles and some other nanostructures in uniform way. Strongly localized plasmonic molecules near the contacting surfaces might become the fundamental elements (by analogy with Lego bricks) for a construction of fully integrated opto-electronic nanodevices of any complexity and scale of integration.Comment: 30 pages, 16 figure

Stochastic noise reduction upon complexification: positively correlated birth-death type systems

Cell systems consist of a huge number of various molecules that display specific patterns of interactions, which have a determining influence on the cell's functioning. In general, such complexity is seen to increase with the complexity of the organism, with a concomitant increase of the accuracy and specificity of the cellular processes. The question thus arises how the complexification of systems - modeled here by simple interacting birth-death type processes - can lead to a reduction of the noise - described by the variance of the number of molecules. To gain understanding of this issue, we investigated the difference between a single system containing molecules that are produced and degraded, and the same system - with the same average number of molecules - connected to a buffer. We modeled these systems using Ito stochastic differential equations in discrete time, as they allow straightforward analytical developments. In general, when the molecules in the system and the buffer are positively correlated, the variance on the number of molecules in the system is found to decrease compared to the equivalent system without a buffer. Only buffers that are too noisy by themselves tend to increase the noise in the main system. We tested this result on two model cases, in which the system and the buffer contain proteins in their active and inactive state, or protein monomers and homodimers. We found that in the second test case, where the interconversion terms are non-linear in the number of molecules, the noise reduction is much more pronounced; it reaches up to 20% reduction of the Fano factor with the parameter values tested in numerical simulations on an unperturbed birth-death model. We extended our analysis to two arbitrary interconnected systems.Comment: 38 pages, 5 figures, to appear in J. Theor. Bio

Exploring molecular complexity in the Galactic Center with ALMA

The search for complex organic molecules (COMs) in the ISM has revealed chemical species of ever greater complexity. This search relies heavily on the progress made in the laboratory to characterize the rotational spectra of these molecules. Observationally, the advent of ALMA with its high angular resolution and sensitivity has allowed to reduce the spectral confusion and detect low-abundance molecules that could not be probed before. We present results of the EMoCA survey conducted with ALMA toward the star-forming region Sgr B2(N). This spectral line survey aims at deciphering the molecular content of Sgr B2(N) in order to test the predictions of astrochemical models and gain insight into the chemical processes at work in the ISM. We report on the tentative detection of N-methylformamide, on deuterated COMs, and on the detection of a branched alkyl molecule. Prospects for probing molecular complexity in the ISM even further are discussed at the end.Comment: Invited contribution to appear in "Astrochemistry VII -- Through the Cosmos from Galaxies to Planets", proceedings of the IAU Symposium No. 332, 2017, Puerto Varas, Chile. M. Cunningham, T. Millar and Y. Aikawa, eds. (12 pages, 8 figures

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schr\"odinger equation is mapped onto a non-linear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross-validation over more than seven thousand small organic molecules yields a mean absolute error of ~10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves

Non-Duality

Self – organizing is closely connected to the phenomenon of life. The creation of complexity, which is necessary for the creation of life, is connected to the process of storing information in molecules of which the living cell is constituted. All living systems, either unicellular or multicellular organisms, are extremely complex systems compared to all the other species of the non living matter that exist in the Universe. Complexity is the result of effect processes, leading to systems with great organization, containing large information stocks. This organization of the molecules of a living organism, a result of accumulation of information, is what makes them able to produce useful work. The useful work involves both the fulfillment of basic biological processes, such as metabolism and reproduction, and the further increase in the information content that builds up in living systems. This last process is subject to the great chain of evolution of biological systems, this development is governed by the law of natural selection. The capability of reproduction, mutation and metabolism are necessary conditions for the latter

A Novel A Priori Simulation Algorithm for Absorbing Receivers in Diffusion-Based Molecular Communication Systems

A novel a priori Monte Carlo (APMC) algorithm is proposed to accurately simulate the molecules absorbed at spherical receiver(s) with low computational complexity in diffusion-based molecular communication (MC) systems. It is demonstrated that the APMC algorithm achieves high simulation efficiency since by using this algorithm, the fraction of molecules absorbed for a relatively large time step length precisely matches the analytical result. Therefore, the APMC algorithm overcomes the shortcoming of the existing refined Monte Carlo (RMC) algorithm which enables accurate simulation for a relatively small time step length only. Moreover, for the RMC algorithm, an expression is proposed to quickly predict the simulation accuracy as a function of the time step length and system parameters, which facilitates the choice of simulation time step for a given system. Furthermore, a rejection threshold is proposed for both the RMC and APMC algorithms to significantly save computational complexity while causing an extremely small loss in accuracy.Comment: 11 pages, 14 figures, submitted to IEEE Transactions on NanoBioscience. arXiv admin note: text overlap with arXiv:1803.0463