Agent-based modeling for the 2D molecular self-organization of realistic molecules

Agent-Based Modeling in Molecular Systems Biology

Molecular systems orchestrating the biology of the cell typically involve a complex web of interactions among various components and span a vast range of spatial and temporal scales. Computational methods have advanced our understanding of the behavior of molecular systems by enabling us to test assumptions and hypotheses, explore the effect of different parameters on the outcome, and eventually guide experiments. While several different mathematical and computational methods are developed to study molecular systems at different spatiotemporal scales, there is still a need for methods that bridge the gap between spatially-detailed and computationally-efficient approaches. In this review, we summarize the capabilities of agent-based modeling (ABM) as an emerging molecular systems biology technique that provides researchers with a new tool in exploring the dynamics of molecular systems/pathways in health and disease.

In Silico Generation of Peptides by Replica Exchange Monte Carlo: Docking-Based Optimization of Maltose-Binding-Protein Ligands

Short peptides can be designed in silico and synthesized through automated techniques, making them advantageous and versatile protein binders. A number of docking-based algorithms allow for a computational screening of peptides as binders. Here we developed ex-novo peptides targeting the maltose site of the Maltose Binding Protein, the prototypical system for the study of protein ligand recognition. We used a Monte Carlo based protocol, to computationally evolve a set of octapeptides starting from a polialanine sequence. We screened in silico the candidate peptides and characterized their binding abilities by surface plasmon resonance, fluorescence and electrospray ionization mass spectrometry assays. These experiments showed the designed binders to recognize their target with micromolar affinity. We finally discuss the obtained results in the light of further improvement in the ex-novo optimization of peptide based binders.

Capturing the embryonic stages of self-assembly - design rules for molecular computation

The drive towards organic computing is gaining momentum. Interestingly, the building blocks for such architectures is based on molecular ensembles extending from nucleic acids to synthetic molecules. Advancement in this direction requires devising precise nanoscopic experiments and model calculations to decipher the mechanisms governing the integration of a large number of molecules over time at room-temperature. Here, we report on ultrahigh-resolution scanning tunnelling microscopic measurements to register the motion of molecules in the absence of external stimulus in liquid medium. We observe the collective behavior of individual molecules within a swarm which constantly iterate their position to attain an energetically favourable site. Our approach provides a consistent pathway to register molecular self-assembly in sequential steps from visualising thermodynamically driven repair of defects up until the formation of a stable two-dimensional configuration. These elemental findings on molecular surface dynamics, self-repair and intermolecular kinetic pathways rationalised by atom-scale simulations can be explored for developing new models in algorithmic self-assembly to realisation of evolvable hardware.

Interactively illustrating polymerization using three-level model fusion

BACKGROUND

Research in cell biology is steadily contributing new knowledge about many aspects of physiological processes, both with respect to the involved molecular structures as well as their related function. Illustrations of the spatio-temporal development of such processes are not only used in biomedical education, but also can serve scientists as an additional platform for in-silico experiments.

RESULTS

In this paper, we contribute a new, three-level modeling approach to illustrate physiological processes from the class of polymerization at different time scales. We integrate physical and empirical modeling, according to which approach best suits the different involved levels of detail, and we additionally enable a form of interactive steering, while the process is illustrated. We demonstrate the suitability of our approach in the context of several polymerization processes and report from a first evaluation with domain experts.

CONCLUSION

We conclude that our approach provides a new, hybrid modeling approach for illustrating the process of emergence in physiology, embedded in a densely filled environment. Our approach of a complementary fusion of three systems combines the strong points from the different modeling approaches and is capable to bridge different spatial and temporal scales.

Born-Haber cycle for monolayer self-assembly at the liquid-solid interface: assessing the enthalpic driving force

The driving force for self-assembly is the associated gain in free energy with decisive contributions from both enthalpy and entropy differences between final and initial state. For monolayer self-assembly at the liquid-solid interface, solute molecules are initially dissolved in the liquid phase and then become incorporated into an adsorbed monolayer. In this work, we present an adapted Born-Haber cycle for obtaining precise enthalpy values for self-assembly at the liquid-solid interface, a key ingredient for a profound thermodynamic understanding of this process. By choosing terephthalic acid as a model system, it is demonstrated that all required enthalpy differences between well-defined reference states can be independently and consistently assessed by both experimental and theoretical methods, giving in the end a reliable value of the overall enthalpy gain for self-assembly of interfacial monolayers. A quantitative comparison of enthalpy gain and entropy cost reveals essential contributions from solvation and dewetting, which lower the entropic cost and render monolayer self-assembly a thermodynamically favored process.