Simulating Microbial Systems: Addressing Model Uncertainty/Incompleteness via Multiscale and Entropy Methods

Abstract

Most systems of interest in the natural and engineering sciences are multiscale in character. Typically available models are incomplete or uncertain. Thus, a probabilistic approach is required. We present a deductive multiscale approach to address such problems, focusing on virus and cell systems to demonstrate the ideas. There is usually an underlying physical model, all factors in which (e.g., particle masses, charges, and force constants) are known. For example, the underlying model can be cast in terms of a collection of N-atoms evolving via Newton's equations. When the number of atoms is 10(6) or more, these physical models cannot be simulated directly. However, one may only be interested in a coarse-grained description, e.g., in terms of molecular populations or overall system size, shape, position, and orientation. The premise of this chapter is that the coarse-grained equations should be derived from the underlying model so that a deductive calibration-free methodology is achieved. We consider a reduction in resolution from a description for the state of N-atoms to one in terms of coarse-grained variables. This implies a degree of uncertainty in the underlying microstates. We present a methodology for modeling microbial systems that integrates equations for coarse-grained variables with a probabilistic description of the underlying fine-scale ones. The implementation of our strategy as a general computational platform (SimEntropics™) for microbial modeling and prospects for developments and applications are discussed.

Multiscale simulation of microbe structure and dynamics

“…In effect, OPs filter out the high frequency atomistic fluctuations from the low frequency coherent modes. This property of OPs enables them to serve as the basis of a multiscale approach for simulating microbial dynamics (Cheluvaraja and Ortoleva, 2010; Ortoleva, 2005; Pankavich et al, 2008; Singharoy et al, 2010a). A number of types of OPs have been identified through our work and an extensive set of studies on phase transitions and related phenomena.…”

“…This conceptual scheme has been used to derive a framework for multiscale modeling of biological systems (Cheluvaraja and Ortoleva, 2010; Singharoy et al, 2011a). Similar schemes are designed and implemented for investigating electrostatic (Singharoy et al, 2010b) and quantum (Iyengar and Ortoleva, 2008; Shreif and Ortoleva, 2010a, b) properties of nanosystems.…”

Discovering Free Energy Basins for Macromolecular Systems via Guided Multiscale Simulation

“…5,26,27 Let $\underset{true¯}{r}\equiv \{{\stackrel{true⃑}{r}}_1,{\stackrel{true⃑}{r}}_2,\dots {\stackrel{true⃑}{r}}_{N}true\}$ denote the all-atom configuration of the structure of interest. In the approach adopted here, OPs $\underset{true¯}{\Phi }\equiv true\{{\stackrel{true⃑}{\Phi }}_1,{\stackrel{true⃑}{\Phi }}_2,\mathrm{\dots }{\stackrel{⃑}{\Phi }}_{N_{OP}}true\}$ describe overall structure of the system.…”

“…APPROACH A natural framework for casting an FE basin discovery algorithm is deductive multiscale analysis. 5,26,27 Let r̲…”

“…A natural framework for casting an FE basin discovery algorithm is deductive multiscale analysis. ,, Let r̲ ≡ { r ⇀ 1 , r ⇀ 2 , ... , r ⇀ N } denote the all-atom configuration of the structure of interest. In the approach adopted here, OPs Φ̲ ≡ { Φ ⇀ 1 , Φ ⇀ 2 , ... , Φ ⇀ N OP } describe the overall structure of the system.…”