Self-selection of evolutionary strategies: adaptive versus non-adaptive forces.

The evolution of complex genetic networks is shaped over the course of many generations through multiple mechanisms. These mechanisms can be broken into two predominant categories: adaptive forces, such as natural selection, and non-adaptive forces, such as recombination, genetic drift, and random mutation. Adaptive forces are influenced by the environment, where individuals better suited for their ecological niche are more likely to reproduce. This adaptive force results in a selective pressure which creates a bias in the reproduction of individuals with beneficial traits. Non-adaptive forces, in contrast, are not influenced by the environment: Random mutations occur in offspring regardless of whether they improve the fitness of the offspring. Both adaptive and non-adaptive forces play critical roles in the development of a species over time, and both forces are intrinsically linked to one another. We hypothesize that even under a simple sexual reproduction model, selective pressure will result in changes in the mutation rate and genome size. We tested this hypothesis by evolving Boolean networks using a modified genetic algorithm. Our results demonstrate that changes in environmental signals can result in selective pressure which affects mutation rate.

Stimulus-responsive self-assembly of protein-based fractals by computational design.

Fractal topologies, which are statistically self-similar over multiple length scales, are pervasive in nature. The recurrence of patterns in fractal-shaped branched objects, such as trees, lungs and sponges, results in a high surface area to volume ratio, which provides key functional advantages including molecular trapping and exchange. Mimicking these topologies in designed protein-based assemblies could provide access to functional biomaterials. Here we describe a computational design approach for the reversible self-assembly of proteins into tunable supramolecular fractal-like topologies in response to phosphorylation. Guided by atomic-resolution models, we develop fusions of Src homology 2 (SH2) domain or a phosphorylatable SH2-binding peptide, respectively, to two symmetric, homo-oligomeric proteins. Mixing the two designed components resulted in a variety of dendritic, hyperbranched and sponge-like topologies that are phosphorylation-dependent and self-similar over three decades (~10 nm-10 µm) of length scale, in agreement with models from multiscale computational simulations. Designed assemblies perform efficient phosphorylation-dependent capture and release of cargo proteins.

Boolean Modeling in Quantitative Systems Pharmacology: Challenges and Opportunities.

Drug research and development has a high attrition rate, with many promising drugs failing for efficacy or safety in the clinic. Increased use of detailed modeling approaches like quantitative systems pharmacology (QSP) may help in reducing overall failure rate, by helping the industry in decisions to fail early and cheaply, or to focus on patients and drug combinations that are more likely to respond or synergize, respectively. QSP offers computational methods to simulate how well different therapies may work in a patient, and therefore to better predict drug performance and reduce the cost in the development of new drug therapies. However, the development of detailed models requires a significant amount of biological data, and models often require knowledge of specific mechanisms. Coarse-grained, network-based models, such as Boolean and logic models, provide a tool for simulating complex systems without knowledge of specific mechanisms. These tools can be used to make early predictions about a biological system and can facilitate the development of more complex models. We offer a literature review of how Boolean modeling techniques are used in the identification of novel drug targets, as well as how they fall into the pipeline of developing in-depth ordinary differential equation models.