A novel computation tool for microbial community modeling predicts the evolution and diversification of E. coli in laboratory evolution experiments and gives insight into the underlying metabolic processes.
A mathematical model predicts the precise conditions for natural selection to favor the evolution of non-reproductive workers in insect colonies with haplodiploid genetics.
A mathematical modelling approach to understanding zebrafish stripe pattern formation exemplifies a biological rule-set sufficient to generate wild-type and a diverse range of mutant patterns.
The human immunodeficiency virus HIV-1 primarily spreads between cells using a method called cell-to-cell infection, suggesting that this process may be a target for anti-viral drugs.
Stochasticity introduced computationally into a gene expression oscillator creates heterogeneity in the time of differentiation of identical cells and offers robustness to the progenitor state and the outcome of cell division.
A stochastic model of phyllotaxis can explain the striking irregularities observed in the spiral patterns of plants and predicts that perturbation patterns provide key information about the underlying biochemical mechanisms.
Predictable patterns of fitness evolution observed in microbial evolution experiments can emerge generically as a consequence of widespread epistatic interactions between mutations.