Sentence examples for mutants by setting from inspiring English sources

We therefore represent these mutants by setting <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0008337.e018.PNG" class= inline-graphic"/> for OX-ROP, and <img src="http://journals.plos.org/plosone/article/asset?id=info:doi/10.1371/journal.pone.0008337.e019.PNG" class="inline-graphic"/> for CA-rop.

Thus we could simulate both mutants by setting the appropriate FGF10 production rates.

Binding site mutations and null mutants were simulated by setting the regulator concentration to '0' in all nuclei.

The GPA2 Q 300 L (Gpa2 constitutively active) mutant was modeled by setting the concentration of GPa to 1 and the parameter V maxGPdeact to 0. The pde2Δ mutant model was simulated as described earlier.

UI mutant is investigated by setting UI = 0.

The crn-1 mutant is modeled by setting k6 = 0.

The STAT3-Y705F mutant was simulated by setting the STAT3 phosphorylation rate constant to zero.

The transfections were simulated by elevation of the STAT3 production rate from 0.211 to 5 and the MITF production rate from 1 to 5. The MITF mutant was simulated by setting the MITF-S409 phosphorylation rate constant to 5 and the MITF-S409 de-phosphorylation rate constant to zero.

Mutant patterns were generated by setting the levels of production of the relevant TF to zero or, for Gli /–, by setting the binding affinity of Gli to zero.

The expected number of mutant cells t′ time steps after the appearance of the first mutant cell with a surviving lineage is (3) Although there is stochasticity in the timing of the appearance of the first mutant cell with a surviving lineage, we can achieve a good approximation to the number of mutant cells at time t by setting t′ = t− τ in eqn (3).

Our model also allows us to explore the effect of different mtDNA types experiencing different selective pressures, by setting λ1 ≠ λ2 (mutant mtDNA experiences a proliferative advantage or disadvantage).