Biology subjects

Farheen, N.

Publications and source records attributed to Farheen, N..

3 recordsLinked to original sources

bioRxiv2026

How many mutations does it take to evolve a new function?

Biochemical and genetic networks in cells evolve to produce new functions. Evolution is guided by the genotype to phenotype map which describes how mutations at the molecular scale lead to changes in network function at the cellular scale. The number of genotypes is astronomically large which makes these maps very high dimensional. In this context, the simple question: How many mutations does it take to evolve a new function?, is usually hard to answer. Here we consider this question in the context of a simple Boolean-network model of a genotype-phenotype map. The nodes and edges of the Boolean network represent the genotype while the Boolean function the network computes is the phenotype. The network evolves by rewiring of its edges, while keeping the number of nodes fixed. We compute the number of mutations needed to achieve a new Boolean function as a function of the number of nodes in the network. We show that the number of mutations scales with the number of nodes of the network as a power-law with an exponent determined by local geometrical features of the genotype-phenotype map. Our theoretical study emphasizes the role of the genotype-phenotype map in determining the time to evolve a new function.

evolutionary biology↗

bioRxiv2024

Mechanisms of synergistic Mediator recruitment in RNA polymerase II transcription activation revealed by single-molecule fluorescence

Transcription activators trigger transcript production by RNA Polymerase II (RNApII) via the Mediator coactivator complex. Here the dynamics of activator, Mediator, and RNApII binding at promoter DNA were analyzed using multi-wavelength single-molecule microscopy of fluorescently labeled proteins in budding yeast nuclear extract. Binding of Mediator and RNApII to the template required activator and an upstream activator sequence (UAS), but not a core promoter. While Mediator and RNApII sometimes bind as a pre-formed complex, more commonly Mediator binds first and subsequently recruits RNApII to form a preinitiation complex precursor (pre-PIC) tethered to activators on the UAS. Interestingly, Mediator occupancy has a highly non-linear response to activator concentration, and fluorescence intensity measurements show Mediator preferentially associates with templates having at least two activators bound. Statistical mechanical modeling suggests this "synergy" is not due to cooperative binding between activators, but instead occurs when multiple DNA-bound activator molecules simultaneously interact with a single Mediator.

biochemistry↗

bioRxiv2019

Frustration and Fidelity in Influenza Genome Assembly

The genome of the influenza virus consists of eight distinct single-stranded RNA segments, each encoding proteins essential for the viral life cycle. When the virus infects a host cell these segments must be replicated and packaged into new budding virions. The viral genome is assembled with remarkably high fidelity: experiments reveal that most virions contain precisely one copy of each of the eight RNA segments. Cell-biological studies suggest that genome assembly is mediated by specific reversible and irreversible interactions between the RNA segments and their associated proteins. However, the precise inter-segment interaction network remains unresolved. Here we computationally predict that tree-like irreversible interaction networks guarantee high-fidelity genome assembly, while cyclic interaction networks lead to futile or frustrated off-pathway products. We test our prediction against multiple experimental datasets. We find that tree-like networks capture the nearest-neighbor statistics of RNA segments in packaged virions, as observed by EM tomography. Just eight tree-like networks (of a possible 262,144) optimally capture both the nearest-neighbor data as well as independently measured RNA-RNA contact propensities. These eight do not include the previously-proposed hub-and-spoke and linear networks. Rather, each predicted network combines hub-like and linear features, consistent with evolutionary models of interaction gain and loss.

biophysics↗