Team:Edinburgh/Phage Replication

Phage Replication

A basic activity in our biorefinery is the degradation of cellulose, due to the presence of enzymes. For our phage system, we are not only concerned with the activities and amount of enzymes, but also with the metabolism and activities of bacteriophage.

In particular, it would be good to verify that infected E. coli in the system don't get outcompeted and die out, since they are necessary for the system to work.

M13 Replication

M13 is a filamentous bacteriophage: a worm-like virus approximately 1 um long with a 10 nm diameter that infects only E. coli.

• The viral particle consists of a single-stranded, closed circular DNA core surrounded by a protein coat.
• Prior to virus assembly, the coat proteins are fixed in the bacterial membrane by transmembrane domains.
• During assembly, viral DNA is extruded through the membrane and enveloped by coat proteins.
• The ends of the assembled virus are capped by four minor coat proteins, and the length of the filament is covered by several thousand copies of the major coat protein pVIII.

From Slonczewski and Foster (2010).

• The M13 phage attacks E. coli (host), multiplies in the host cell cytoplasm, and is released without causing the bacteria's death (non-lytic).

Model construction

As a fundamental rule, the rate of change of population = production rate of population - loss rate of population

Model for non-lytic M13 phage:

Equations

• dx/dt=a*k1*x-b*v*x

Rate of change of quantity of uninfected E. coli equals to the uninfected E. coli replicate itself minus the E. coli infected by M13 phage.

• dy/dt=a*k2*y+b*v*x

Rate of change of quantity of infected E. coli equals to the quantity of infected E. coli replicate itself plus the E. coli infected by M13 phage.

• dv/dt=c*y-b*v*x-m*v

Rate of change of quantity of free phage equals to the phage released by infected E. coli minus the phage which is to infect an E. coli and the decayed phage.

X(t) — uninfected E. coli

Y(t) — infected E. coli

V(t) — free phage

a — replication coefficient of E. coli

b — transmission coefficient of phage

c — replication coefficient of phage

m — decay rate of phage

K1, K2 — account for the difference of the rate of replication between infected E. coli and uninfected E. coli

Simulations

The MATLAB code uses a Runge-Kutta method of order four to solve the system.

Figure1 simulation value: x0=2.00E3 y0=v0=2.00E5

The simulation runs under the condition that the amount of uninfected E. coli is significantly smaller than the other two.The quantity of uninfected E. coli keeps at a low level, which may have economic significance in practice, since our goal is to get free displayed phage. Besides, the figure also shows that the infected E. coli population dominates the population of free phage.

Figure2 simulation value: x0=v0=2.00E5 y0=2.00E3

In this case, the population of infected E. coli also dominates the population of free phage. However, the excess amount of uninfected E. coli results in large amount of free phage infecting E. coli. Therefore, over 15 hours, we will get least free phage among this three cases. And the increasing rate of free phage rise significantly in this case, this is probably because large amount of free phage infecting E. coli therefore leads to a significant rise in the amount of infected phage, which can release free phage later.

Figure3 simulation value: x0=y0=v0=2.00E5

A decrease of the slope of rise of uninfected E. coli is observed during the first 8 simulation hours. And even an increase of the slope of the fall of uninfected E. coli is observed later. This means probably that as the population of free phage increasing, more E. colis are infected by free phage. After 15 hours, we can get the most free phage among the three cases.

From these results it is evident that the population of the bacteriophage M13 primarily depends on the the population of infected E. coli, which is the host of bacteriophage. And the slowing down of bacterial metabolism seems has little effect on the reproduction of phage.

References

• Gregory AW， Sachdev SS (2000) [http://dx.doi.org/10.1006/jmbi.2000.3845 Design and Evolution of Artificial M13 coat Proteins], Journal of Molecular Biology, 300(1): 213-219, doi: 10.1006/jmbi.2000.3845
• Slonczewski JL, Foster JW (2010) [http://www.wwnorton.com/college/biology/microbiology2/ch/11/etopics.aspx Microbiology: An Evolving Science], 2nd edition. W. W. Norton & Company
• Payne RJH, jansen VAA (2001) [http://dx.doi.org/doi:10.1006/jtbi.2000.2198 Understanding Bacteriaphage therapy as a density-dependent kinetic process], Journal of Theoretical Biology, 208(1): 37-48, doi:10.1006/jtbi.2000.2198
• Cattoen C (2003) [http://msor.victoria.ac.nz/twiki/pub/Groups/GravityGroup/PreviousProjectsInAppliedMathematics/bacteria-phage_REPORT.pdf Bacteriaphage mathematical model applied to the cheese industry], Massey University, College of Sciences.