Multi-state Modeling of Biomolecules

Abstract

Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behavior of biological molecules or complexes that can adopt a large number of possible functional states. Biological signaling systems often rely on complexes of biological macromolecules that can undergo several functionally significant modifications that are mutually compatible. Thus, they can exist in a very large number of functionally different states. Modeling such multi-state systems poses two problems: the problem of how to describe and specify a multi-state system (the “specification problem”) and the problem of how to use a computer to simulate the progress of the system over time (the “computation problem”). To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states and towards rule-based formalisms that allow for implicit model specification, including the κ-calculus [1], BioNetGen [2]–[5], the Allosteric Network Compiler [6], and others [7], [8]. To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on ordinary differential equations, partial differential equations, or the Gillespie stochastic simulation algorithm [9], [10]. Given current computing technology, particle-based methods are sometimes the only possible option. Particle-based simulators fall into two further categories: nonspatial simulators, such as StochSim [11], DYNSTOC [12], RuleMonkey [9], [13], and the Network-Free Stochastic Simulator (NFSim) [14], and spatial simulators, including Meredys [15], SRSim [16], [17], and MCell [18]–[20]. Modelers can thus choose from a variety of tools, the best choice depending on the particular problem. Development of faster and more powerful methods is ongoing, promising the ability to simulate ever more complex signaling processes in the future.

From word models to executable models of signaling networks using automated assembly

“…We also tested whether differences in the way biological entities are named affects recognition and grounding; we found that Wip1, WIP-1, WIP1, PPM1D, and Protein phosphatase 1D as well as ATM, Atm, Box 3: Rule-based modeling and PySB Accurate simulation of biochemical systems requires that every species be explicitly tracked through time. The combinatorial nature of protein complex assembly, post-translational modification, and related processes causes the number of possible molecular states in many signaling networks to explode and exceed the capacity for efficient simulation (Stefan et al, 2014). For example, full enumeration of complexes involved in EGF signaling would require more than 10 19 molecular species differing in their states of oligomerization, phosphorylation, and adapter protein binding ).…”

Modeling for (physical) biologists: an introduction to the rule-based approach

“…Although cell signaling has been studied for decades, we have limited knowledge about how modifications and binding at different sites of a protein are coupled (e.g., [20, 24, 88]). We argue that rules expressing a high degree of modularity represent a more natural starting point for model development than making assumptions for the sake of limiting network size.…”

“…Here, we provide a brief introduction to rule-based modeling in systems biology, which is characterized by the use of local rules to represent biomolecular interactions [18–24]. In biology, rule-based modeling has most often been used to study cell signaling systems, but this modeling paradigm is more broadly applicable [23, 25–27].…”

BioNetFit: a fitting tool compatible with BioNetGen, NFsim and distributed computing environments

“…Biomolecular interactions can be represented by formalized rules (Chylek et al, 2014a;Stefan et al, 2014). Collections of rules form rule-based models, which provide concise representations of biomolecular interaction networks and can be analyzed to obtain insights into how system-level behavior emerges from biomolecular interactions (Chylek et al, 2014a;Stefan et al, 2014).…”

“…Collections of rules form rule-based models, which provide concise representations of biomolecular interaction networks and can be analyzed to obtain insights into how system-level behavior emerges from biomolecular interactions (Chylek et al, 2014a;Stefan et al, 2014). Rule-based models must be analyzed with specialized algorithms and software tools (Chylek et al, 2014a;Stefan et al, 2014), such as BioNetGen (Harris et al, 2015), which interprets models encoded in the BioNetGen language (BNGL) and provides deterministic, stochastic and hybrid forward simulation capabilities (Harris et al, 2015). To date, other critical methods of analysis, such as fitting (parameter estimation), have typically been applied ad hoc (e.g.…”