L[m] | A[m] | ˉkcη[Kgs2m2] | ˉktη[Kgs2m2] | ˉkτ[Kgs2m2] | Btη[m] | Bcη[m] |
0.01 | 0.1 | 2π141014 | 10ˉkcη | 2π1013 | 10−8 | 10−7 |
Bτ0[m] | α1[1] | α2[1] | σtη[J/m3] | σcη[J/m3] | . | . |
5⋅10−8 | 10 | 14 | 8.385⋅106 | 8.912⋅107 | . | . |
Citation: S. Hossein Hosseini, Marc R. Roussel. Analytic delay distributions for a family of gene transcription models[J]. Mathematical Biosciences and Engineering, 2024, 21(6): 6225-6262. doi: 10.3934/mbe.2024273
[1] | Vito Crismale, Gianluca Orlando . A lower semicontinuity result for linearised elasto-plasticity coupled with damage in W1,γ, γ > 1. Mathematics in Engineering, 2020, 2(1): 101-118. doi: 10.3934/mine.2020006 |
[2] | Manuel Friedrich . Griffith energies as small strain limit of nonlinear models for nonsimple brittle materials. Mathematics in Engineering, 2020, 2(1): 75-100. doi: 10.3934/mine.2020005 |
[3] | Patrick Dondl, Martin Jesenko, Martin Kružík, Jan Valdman . Linearization and computation for large-strain visco-elasticity. Mathematics in Engineering, 2023, 5(2): 1-15. doi: 10.3934/mine.2023030 |
[4] | Gennaro Ciampa, Gianluca Crippa, Stefano Spirito . Propagation of logarithmic regularity and inviscid limit for the 2D Euler equations. Mathematics in Engineering, 2024, 6(4): 494-509. doi: 10.3934/mine.2024020 |
[5] | Edgard A. Pimentel, Miguel Walker . Potential estimates for fully nonlinear elliptic equations with bounded ingredients. Mathematics in Engineering, 2023, 5(3): 1-16. doi: 10.3934/mine.2023063 |
[6] | Francesca G. Alessio, Piero Montecchiari . Gradient Lagrangian systems and semilinear PDE. Mathematics in Engineering, 2021, 3(6): 1-28. doi: 10.3934/mine.2021044 |
[7] | Antonello Gerbi, Luca Dedè, Alfio Quarteroni . A monolithic algorithm for the simulation of cardiac electromechanics in the human left ventricle. Mathematics in Engineering, 2019, 1(1): 1-37. doi: 10.3934/Mine.2018.1.1 |
[8] | Mirco Piccinini . A limiting case in partial regularity for quasiconvex functionals. Mathematics in Engineering, 2024, 6(1): 1-27. doi: 10.3934/mine.2024001 |
[9] | Paola F. Antonietti, Ilario Mazzieri, Laura Melas, Roberto Paolucci, Alfio Quarteroni, Chiara Smerzini, Marco Stupazzini . Three-dimensional physics-based earthquake ground motion simulations for seismic risk assessment in densely populated urban areas. Mathematics in Engineering, 2021, 3(2): 1-31. doi: 10.3934/mine.2021012 |
[10] | Cristiana De Filippis . Optimal gradient estimates for multi-phase integrals. Mathematics in Engineering, 2022, 4(5): 1-36. doi: 10.3934/mine.2022043 |
A large amount of scientific literature deals with non-conservative physical systems, where it is necessary to use methods that are able to handle the related dissipation phenomena [3,16,28,38,54]. In engineering applications [39,40,62] a wide range of materials, like steel and concrete, experience dissipative phenomena such as damage and plasticity. That is why they are especially interesting for the engineering community. But building accurate description of these phenomena can be very difficult, especially for complex material systems like granular [39] or lattice-type [25,58] microstructure. Continuum damage [1,8] and plasticity [4,9,17,22,23,24,32,39,40,49,50] modeling have been vigorously pursued in the literature, considering the improvements based on phase field models [10,13,14,33,35,55] for shear bands and fracture. Multi-scale approaches [6,27,34,42], in addition to phenomenological ones [56,57], have been proposed. Their purpose is to link low-scale descriptions with continuum [15,29,31] to include complex emerging behaviors in the continuum. Besides, the strain gradient regularization of the elastic response [26,30] should be considered also in this non conservative context. In this paper we recap, in a new and better way, recently developed continuum model for granular materials undergoing damage and plastic deformations. Damage and plasticity are irreversible phenomena [2,32,49,50]. In this model, the irreversibility is taken into account by assuming that damage and plastic variables are non-decreasing quantities in time [59]. It has been extensively discussed in the literature, that deformed shapes of granular materials may be described in a continuum model by using the relative displacements of the barycenters of the grains, regardless their deformations. Thus, in a coarse-grained description, for a material with a granular micro-structure, the deformation energy as well as the energy dissipated due to damage and plasticity, are expressed in terms of these relative movements. The volumetric energy of deformation, i.e., the deformation energy per unit volume, is assumed to be the sum of deformation energies associated with each intergranular interaction. Each grain-grain interaction is identified by the orientation of the grain-grain direction that, in the continuum limit, are infinite in number. This approach has shown its efficiency in describing granular systems, both at the discrete and at the continuum levels [5,7,18,37,51,60,61]. We also use a variational approach [20,21]. First of all, we define an objective and reversible kinematic vector variable to measure the relative displacements between grains. Thus, we decompose the objective relative displacement between the grains [39,48] into two components. The first one is directed along the vector connecting the grain centroids, and it is called the normal component. The second is directed along the orthogonal direction and is called the tangent component. These components are decomposed into elastic and plastic parts. The functionals of elastic deformation and of dissipation energy are defined in terms of these reversible components [52,53] and of irreversible damage and plastic variables. Damage is defined by two variables, i.e., the normal and the tangential damage variables, that are both a function of grain-grain orientation. On the one part, plastic displacement does not have to be non-decreasing in time. On the other part, it is characterized as the difference between two non-diminishing plastic variables, that are the accumulated plastic relative displacement in tension and in compression, respectively. The evolution of the form of the body that is obtained without external load defines in this approach the evolution of the plastic strain. The elastic evolution of the damaged material is defined by the total elastic strain energy. This type of energy is explained in terms of the elastic energies related to each intergranular orientation. Therefore, dependencies are obtained for the standard elastic modules (4th order stiffness tensor), of the second gradient elastic modules (6th order stiffness tensor) and of the chiral interaction modules (5th order stiffness tensor). They are functions of parameters describing the damage-elasto-plastic state, describing micro-mechano-morphology, of each orientation. Besides, because of plastic effects, we obtain an expression also of the pre-stress (2nd order tensor) and of the pre-hyperstress (3rd order tensor). The hemi-variational approach [43,44,45,46] has been used to obtain Karush-Kuhn-Tucker (KKT) kind conditions, that drive the evolution of damage and plastic irreversible kinematic variables. According to the same derivation, also the Euler-Lagrange equations for the progress of the reversible placement function is obtained. It is worth to be noted that, since grain-pairs are oriented in different directions, for a given loading-sequence, they experience different loads and hence different damage and plastic evolution. Thus, the macroscopic response will be complex and with an intrinsically dependence upon path.
The content of this paper is organized as follows. Section 2 provides a rational recap of the model that was introduced in papers [12,41,44,47,59]. Section 3 provides the scheme of a possible numerical (or analytical) implementation of the model. Section 4 is devoted to the representation of the results for the homogeneous case, where no Finite Element implementation is necessary for the illustration of the results. Section 5 addresses a few concluding comments and future viewpoints.
In this section we will recap the model that has been investigated by the authors more widely in different papers [12,41,44,47,59].
Let B be the 2D reference configuration of a strain gradient elastic body. Its elastic energy U per unit area is assumed to take the following form
U=∫S1[12kη,D(uelη)2+12kτ,D(u2τ)],∀X∈B | (2.1) |
where the elastic part uelη of the normal displacement uη is postulated be equal to the difference of the total normal displacement uη and its plastic part uplη
uelη=uη−uplη, | (2.2) |
where uτ is the tangential displacement and both are defined as follows,
uη=LGijˆciˆcj+L24Gij,hˆciˆcjˆch, | (2.3) |
u2τ=4L2GijGab(δiaˆcjˆcb−ˆciˆcjˆcaˆcb)+2L3GijGab,c(δiaˆcjˆcbˆcc−ˆciˆcjˆcaˆcbˆcc) | (2.4) |
+L44Gij,hGam,n(δiaˆcjˆchˆcmˆcn−ˆciˆcjˆchˆcaˆcbˆcc), |
where the unit vector ˆc gives the direction of the considered grain-pair interaction and the domain S1 is the unit circle to which it belongs; the Green-Saint-Venant tensor G and its gradient are tensors of a 2nd and 3rd order,
G=12(FTF−I),∇G=FT∇F, | (2.5) |
respectively, where the deformation gradient F and its gradient are defined
F=∇χ,∇F=∇(∇χ) | (2.6) |
in terms of the placement function χ(X,t), that is a function of the position X and of time t. In Eq (2.3) and (2.4), L is the averaged grain-pair distance. Besides, the damaged tangent stiffness is kτ,D and the damaged normal stiffness is kη,D. The damaged normal stiffness is assumed to be asymmetric in tension and compression, i.e.,
kη,D=ktη,DΘ(uelη)+kcη,DΘ(−uelη), | (2.7) |
where ktη,D is the stiffness in tension that is assumed to be smaller than the stiffness in compression kcη,D≫ktη,D. Besides, the dividing line between tension and compression is given by the sign of the elastic normal displacement uelη. Thus, the Heaviside function Θ is here used. Damage is modeled, as we have already pointed out, with two variables, i.e., the normal damage Dη, and the tangent damage Dτ. The damage variables Dη and Dτ have the role to reduce the damaged normal stiffness kη,D (2.7) and the damaged tangent stiffness kτ,D, respectively,
kη,D=kη(1−Dη),kτ,D=kτ(1−Dτ), | (2.8) |
where the non-damaged normal stiffness kη and the non-damaged tangent stiffness kτ have been introduced. Definitions of non-damaged tension (ktη) and compression (kcη) normal stiffness through the following expressions
ktη,D=ktη(1−Dη),kcη,D=kcη(1−Dη), | (2.9) |
yield the analogous of (2.7) for the non-damaged normal stiffness, i.e.,
kη=ktηΘ(uelη)+kcηΘ(−uelη). | (2.10) |
We therefore obtain, by insertion of (2.10) into (2.8)1, the following expression for the damaged normal stiffness
kη,D=kη(1−Dη)=ktη(1−Dη)Θ(uelη)+kcη(1−Dη)Θ(−uelη). | (2.11) |
Insertion of (2.2), (2.3), (2.4) and (2.8) into (2.1) yield the elastic energy per unit area in a more compact form as
U=12CijabGijGab+MijabcGijGab,c+12DijhabcGij,hGab,c+PijGij+QijhGij,h, | (2.12) |
where, accounting for the symmetrization induced by the symmetry of the strain tensor G, the elastic stiffnesses C, M, D, P and Q are identified as follows
Cijab=L2∫S1kη(1−Dη)ˆciˆcjˆcaˆcb | (2.13) |
+L2∫S1kτ(1−Dτ)((δiaˆcjˆcb+δibˆcjˆca+δjaˆciˆcb+δjbˆciˆca)−4ˆciˆcjˆcaˆcb)Mijabc=14L3∫S1kη(1−Dη)ˆciˆcjˆcaˆcbˆcc | (2.14) |
+14L3∫S1kτ(1−Dτ)((δiaˆcjˆcb+δibˆcjˆca+δjaˆciˆcb+δjbˆciˆca)ˆcc−4ˆciˆcjˆcaˆcbˆcc)Dijhabc=116L4∫S1kη(1−Dη)ˆciˆcjˆchˆcaˆcbˆcc | (2.15) |
+116L4∫S1kτ(1−Dτ)((δiaˆcjˆcb+δibˆcjˆca+δjaˆciˆcb+δjbˆciˆca)ˆchˆcc−4ˆciˆcjˆchˆcaˆcbˆcc)Pij=−L∫S1kη(1−Dη)uplηˆciˆcj | (2.16) |
Qijh=−14L2∫S1kη(1−Dη)uplηˆciˆcjˆch | (2.17) |
According to the legacy of strain gradient continua [11,19], a consequence of the expression (2.12) for the elastic energy per unit area is the form of both the stress tensor S and the hyper stress tensor T, i.e.,
Sij=∂U∂Gij=Pij+CijabGab+MijabcGab,c,Tijh=∂U∂Gij,h=Qijh+DijhabcGab,c+MijhabGab, | (2.18) |
where P and Q take the roles of the pre-stress and the pre-hyper stress, respectively. We oversee that (i) the normal plastic displacement uplη has a direct influence, as expected, from (2.16)–(2.17) on the pre-stress and on the pre-hyper stress and (ii) damage variables Dη and Dτ has a direct influence from (2.13)–(2.17) on all the stiffness tensors.
Damage and plastic variables are dissipative in nature and their evolution are related to the form of the dissipation energy. The dissipation energy per unit area W is the energy dissipated because of irreversible phenomena. An additive decomposition of the dissipation energy is assumed in terms of WD=WηD+WτD, the energy dissipated because of damage phenomena (where WηD is the part that is due to the normal phenomena and WτD is the part that is due to tangential phenomena), and Wpl, the energy dissipated because of plasticity phenomena, i.e.,
W=WD+Wpl=WηD+WτD+Wpl, | (2.19) |
WηD=∫S112kcη(Bcη)2Θ(−uelη)[−Dη+2πtan(π2Dη)] | (2.20) |
+∫S112ktη(Btη)2Θ(uelη)[2+(Dη−1)(2−2log(1−Dη)+(log(1−Dη))2)],WτD=∫S112kτ[˜Bτ(uelη)]2[2+(Dτ−1)(2−2log(1−Dτ)+(log(1−Dτ))2)], | (2.21) |
Wpl=∫S1σtηλtη+σcηλcη, | (2.22) |
where Bcη and Btη are two characteristic displacements associated with normal damage dissipation in compression and in tension, respectively. The complicated forms of the assumed dissipated energy in (2.20)–(2.21) are devoted to obtain, in the subsection 2.5, an exponential and/or an arctangential damage evolution, as it will be proved in (2.42) and (2.43).
For cementitious materials it is intuitive that Btη≪Bcη. The reason is that, in tension, a smaller amount of elastic normal displacement is needed to activate damage mechanisms. Besides, ˜Bτ(uelη) is the characteristic displacement associated with tangent damage dissipation. It is assumed to depend on uelη, i.e., on the elastic part of the normal displacement as follows,
Bτ=˜Bτ(uelη)={Bτ0if uelη≥0Bτ0−α2uelηif 1−α1α2Bτ0≤uelη<0α1Bτ0if uelη<Bτ01−α1α2, | (2.23) |
where Bτ0, α1 and α2 are necessary constitutive parameters needed to express the function ˜Bτ(uelη). These parameters have the role to couple the two terms, namely the addends (2.20) and (2.21), of the damage dissipation energy WD per unit area. It is worth to be noted that usually, for cementitious materials and in elastic tension, the characteristic tangential displacement Bτ=Bτ0 is much lower than the one Bτ=α1Bτ0 it is necessary in elastic compression (α1≫1). Indeed, a smaller amount of elastic tangential displacement is needed in elastic extension to activate tangential damage mechanisms with respect to the tangential displacement that is needed in elastic compression. In other words, referring to Eq (2.23), this means both that Bτ0<Bτ0−α2uelη (which implies α2>0, as uelη<0 in compression) and Bτ0<<α1Bτ0 (which implies α1≫1).
The plastic dissipation energy function per unit area Wpl in (2.22) is assumed to linearly depend on the plastic multipliers λtη and λcη that are the plastic accumulated displacement in tension and in compression, respectively. The plastic normal displacement is defined as the following difference,
uplη=λtη−λcη. | (2.24) |
We will show at the end of Subsection 2.5 that the scalars σtη and σcη, defined in (2.22), dictate the yielding conditions (more specifically, they are the characteristic force that is necessary to apply to the grain-pair to activate plastic deformation for no-damage case) of the damage-elasto-plastic grain-pair interaction in tension and compression, respectively. It is worth to be noted that the identification of newly introduced constitutive parameters, i.e. of L, Btη, Bcη, Bτ0, α1 and α2, is necessary for the application of the present approach for modeling the mechanical behavior or real materials such as, e.g., concrete.
The energy functional is defined as the sum of the elastic and dissipation energy,
E(χ,Dη,Dτ,λtη,λcη)=∫BU+W, | (2.25) |
integrated over the 2D reference configuration B. It is a functional of the fundamental kinematical descriptors of the model, i.e. the placement
χ(X,t), | (2.26) |
that is a function of position X and time t, and the 4 irreversible descriptors
Dη(X,ˆc,t),Dτ(X,ˆc,t),λtη(X,ˆc,t),λcη(X,ˆc,t), | (2.27) |
that are functions not only of position X and time t but also on the orientation ˆc.
Damage (Dη and Dτ) and plastic (λtη and λcη) variables are defined both by two variables that are non-decreasing in time. These inequality assumptions,
∂Dη∂t≥0,∂Dτ∂t≥0,∂λtη∂t≥0,∂λcη∂t≥0,∀X∈B,∀ˆc∈S1, | (2.28) |
imply a generalization of standard variational principle into a so-called hemivariational principle.
Let us introduce a monotonously increasing time sequence Ti∈{Ti}i=0,…,M with Ti∈R and M∈N and give initial datum on each of the fundamental kinematic quantities for i=0, i.e., for time T0. A family of placements χ defines the motion for each time t=T0,T1,…,TM. The set AMt of kinematically admissible placements is defined for a given time t and the set AVt is defined as the corresponding space of kinematically admissible variations, i.e., υ=δχ∈AVt. Admissible variations β of the irreversible kinematic quantities (Dη,Dτ,λtη,λcη) must be positive, namely
β=δDη,δDτ,δλtη,δλcη∈R+×R+×R+×R+. | (2.29) |
By definition, the first variation δE of the energy functional (2.25) is calculated as
δE=E(χ+δχ,Dη+δDη,Dτ+δDτ,λtη+δλtη,λcη+δλcη)−E(χ,Dη,Dτ,λtη,λcη). | (2.30) |
Besides, the increment of (2.26-2.27), i.e. of the fundamental kinematic quantities, at t=Ti is given by the difference between these quantities as evaluated at times t=Ti and t=Ti−1, namely
(Δχ,ΔDη,ΔDτ,Δλtη,Δλcη)Ti=(χ,Dη,Dτ,λtη,λcη)Ti−(χ,Dη,Dτ,λtη,λcη)Ti−1. | (2.31) |
The same definition is utilized for the increment ΔE of the energy functional
ΔE=E(χ+Δχ,Dη+ΔDη,Dτ+ΔDτ,λtη+Δλtη,λcη+Δλcη)−E(χ,Dη,Dτ,λtη,λcη). | (2.32) |
Finally, as a matter of facts, the hemi-variational principle is formulated as follows
ΔE≤δE∀υ=δχ∈AVt,∀β=(δDη,δDτ,δλtη,δλcη)∈R+×R+×R+×R+. | (2.33) |
It is worth to be noted here that introducing the three vectors
A=(∂E∂χ,∂E∂Dη,∂E∂Dτ,∂E∂λtη,∂E∂λcη),B=(Δχ,ΔDη,ΔDτ,Δλtη,Δλcη),C=(υ,β), | (2.34) |
where A is intended as the Frechet derivative of the energy functional, the first variation δE of the energy functional in (2.30) and its increment ΔE in (2.32) are represented as linear functional of the variation C and the increment B as follows,
δE=A⋅C,ΔE=A⋅B, | (2.35) |
so that the hemi-variational principle (2.33) can be formulated
A⋅B≤A⋅(υ,β)∀υ∈AVt,∀β∈R+×R+×R+×R+. | (2.36) |
As remarked in [36], the inequality (2.33) states that the actual energy release rate is not smaller than any possible one. Thus, it constitutes a kind of principle of maximum energy release rate.
First of all, the reversibility of the admissible placement variation υ=δχ∈AVt implies
E(χ+δχ,Dη,Dτ,λtη,λcη)−E(χ,Dη,Dτ,λtη,λcη)=∂E∂χδχ=0,∀υ=δχ∈AVt, | (2.37) |
that correspond to standard strain gradient elasticity equations for fixed values of irreversible kinematic quantities (Dη,Dτ,λtη,λcη). Equation (2.37) is derived simply evaluating the inequality (2.36) both for δχ=Δχ+ˆδχ and β=(ΔDη,ΔDτ,Δλtη,Δλcη) and for δχ=Δχ−ˆδχ and β=(ΔDη,ΔDτ,Δλtη,Δλcη), where ˆδχ is another arbitrary variation that in (2.37) takes the same symbol δχ just for the sake of simplicity. Secondly, following [47], the variational inequality (2.36) implies the following KKT conditions on the 4 irreversible kinematic descriptors (Dη,Dτ,λtη,λcη)
{Dη−˜Dη(uη,λtη,λcη)}ΔDη=0 | (2.38) |
{Dτ−˜Dτ(uτ)}ΔDτ=0 | (2.39) |
{λtη−˜λtη(uη,λcη,Dη,Dτ)}Δλtη=0 | (2.40) |
{λcη−˜λcη(uη,λtη,Dη,Dτ)}Δλcη=0, | (2.41) |
where the derivation of (2.38) is done simply evaluating the inequality (2.36) both for δχ=Δχ and β=(2ΔDη,ΔDτ,Δλtη,Δλcη) and for δχ=Δχ and β=(0,ΔDτ,Δλtη,Δλcη), the derivation of (2.39) is done simply evaluating the inequality (2.36) both for δχ=Δχ and β=(ΔDη,2ΔDτ,Δλtη,Δλcη) and for δχ=Δχ and β=(ΔDη,0,Δλtη,Δλcη), the derivation of (2.40) is done simply evaluating the inequality (2.36) both for δχ=Δχ and β=(ΔDη,ΔDτ,2Δλtη,Δλcη) and for δχ=Δχ and β=(ΔDη,ΔDτ,0,Δλcη) and the derivation of (2.41) is done simply evaluating the inequality (2.36) both for δχ=Δχ and β=(ΔDη,ΔDτ,Δλtη,2Δλcη) and for δχ=Δχ and β=(ΔDη,ΔDτ,Δλtη,0). In (2.38)–(2.41) the auxiliary threshold functions ˜Dη(uη,λtη,λcη), ˜Dτ(uτ), ˜λtη(uη,λcη,Dη,Dτ) and ˜λcη(uη,λtη,Dη,Dτ) have been defined as follows,
˜Dη(uη,λtη,λcη)={1−exp(−uη−λtη+λcηBtη),uelη=uη−λtη+λcη>0,2πarctan(−uη−λtη+λcηBcη),uelη=uη−λtη+λcη<0, | (2.42) |
˜Dτ(uτ)=1−exp(−|uτ|Bτ), | (2.43) |
˜λtη(uη,λcη,Dη,Dτ)=λcη−σtηkη(1−Dη)+uη+kτBτkη(1−Dη)∂˜Bτ∂uelη[Dτ∫0[log(1−x)]2dx], | (2.44) |
˜λcη(uη,λtη,Dη,Dτ)=λtη−σcηkη(1−Dη)−uη−kτBτkη(1−Dη)∂˜Bτ∂uelη[Dτ∫0[log(1−x)]2dx]. | (2.45) |
From Eqs (2.42) and (2.43) the meaning of Btη, Bcη and Bτ as characteristic displacements for the activation of the damage phenomena is evident at least for the loading case. Besides, we observe from (2.44) and (2.45) that with no damage, the meaning of the scalars σtη and σcη as those characteristic forces that dictate the yielding conditions in tension and compression, is also explained. However, the presence of the normal damage Dη makes higher such effective characteristic displacement that, in the failure case (with Dη→1), becomes infinite.
In this Section, the implementation of the model previously presented is divided in the following 5 steps.
1) Null initial, i.e., at time t=0, conditions is assumed on the displacement field for all the points of the body
u(X,t=0)=χ(X,t=0)−X=0,∀X∈B | (3.1) |
and and the same for damage and plastic irreversible descriptors both for all the points of the body and for all the directions,
{Dη=˘Dη(ˆc,X,t=0)=0,Dτ=˘Dτ(ˆc,X,t=0)=0λtη=˘λtη(ˆc,X,t=0)=0,λcη=˘λcη(ˆc,X,t=0)=0,∀ˆc∈S1∀X∈B | (3.2) |
2) Initial isotropy is assumed, that means that non-damaged stiffnesses kcη, ktη and kτ are assumed to be initially constant ∀ˆc∈S1 and ∀X∈B
kcη=˜kcη(ˆc,X,t=0)=ˉkcη2π,ktη=˜ktη(ˆc,X,t=0)=ˉktη2π,kτ=˜kτ(ˆc,X,t=0)=ˉkτ2π, | (3.3) |
where ˉkcη, ˉktη and ˉkτ are the averaged non-damaged initial stiffnesses.
3) Numerical values of the parameters of the model are assumed and here are reported in Tables 1 and 2.
L[m] | A[m] | ˉkcη[Kgs2m2] | ˉktη[Kgs2m2] | ˉkτ[Kgs2m2] | Btη[m] | Bcη[m] |
0.01 | 0.1 | 2π141014 | 10ˉkcη | 2π1013 | 10−8 | 10−7 |
Bτ0[m] | α1[1] | α2[1] | σtη[J/m3] | σcη[J/m3] | . | . |
5⋅10−8 | 10 | 14 | 8.385⋅106 | 8.912⋅107 | . | . |
αc | αt |
2 10−6m/s | 10−7m/s |
4) The elastic stiffness tensors (C,M,D), as well as the pre-stress and pre hyperstress tensors (P,Q) are calculated according to Eqs (2.13)–(2.17) at time t=0 with the initial input (3.1)–(3.3). These ingredients with proper boundary conditions are the input for a standard variational principle in (2.37), where the dissipation energy becomes simply an additive constant that does not influence the minimization process. Such a minimization can be performed analytically (as in the homogeneous cases of Section 4) or, more generally, with the use of a Finite Element Method (FEM). Thus, we obtain the displacement field at i=1,
u(X,t=Ti),∀X∈B. | (3.4) |
5) With (3.4) we compute the new irreversible descriptors (Dη,Dτ,λtη,λcη) via the KKT conditions (2.38)–(2.41) at i=1,
{Dη=˘Dη(ˆc,X,t=Ti),Dτ=˘Dτ(ˆc,X,t=Ti)λtη=˘λtη(ˆc,X,t=Ti),λcη=˘λcη(ˆc,X,t=Ti),∀ˆc∈S1∀X∈B | (3.5) |
Thus, we iterate the points 4 and 5 for all those time sequence Ti∈{Ti}i=0,…,M with Ti∈R and M∈N of the researched time history defined at the beginning of Subsection 2.4.
Constitutive parameters are depicted in Table 1. Thus, from [12,59] we have an equivalent initial Young modulus in compression Ec or in tension Et and Poisson ratio in compression νc or in tension νt, that yields in compression
Ec=L2kcηkcη+4kτ3kcη+4kτ=299GPa,νc=kcη−4kτ3kcη+4kτ=0.32 | (4.1) |
or in tension
Et=L2ktηktη+4kτ3ktη+4kτ=34.4GPa,νc=ktη−4kτ3ktη+4kτ=0.22. | (4.2) |
Let us solve the problem in Figure 1. The imposed displacement is
δ(t)=αt | (4.3) |
and, consequently, the displacement is trivially deduced in all the body,
u1=αAtX1,u2=0,∀X∈B. | (4.4) |
Thus, the strain and strain gradient are
G11=αAt+12(αAt)2,G12=G22=0,∇G=0, | (4.5) |
and the relative displacements from (2.3) and (2.4) are
uη=LG11cosθ=L[αAt+12(αAt)2]cosθ, | (4.6) |
u2τ=4L2G11G11(cos4θ−cos2θ)=(L[αAt+12(αAt)2]sin2θ)2, | (4.7) |
where a standard parameterization of the unit vector ˆc has been used in terms of an angle θ, i.e.,
ˆc1=cosθ,ˆc2=sinθ. | (4.8) |
The stress response is given in terms of the components of the stress tensors in (2.18),
S11=P11+C1111G11, | (4.9) |
S22=P22+C2211G11, | (4.10) |
S12=P12+C1211G11, | (4.11) |
T=0 | (4.12) |
that implies
S11=−L∫2π0[kη(1−Dη)(λtη−λcη)cos2θ]dθ | (4.13) |
+G11L2∫2π0[kη(1−Dη)cos4θ+kτ(1−Dτ)(4cos2θ−4cos4θ)]dθ,S22=−L∫2π0[kη(1−Dη)(λtη−λcη)sin2θ]dθ | (4.14) |
+G11L2∫2π0[kη(1−Dη)sin2θcos2θ−4kτ(1−Dτ)cos4θ]dθ,S12=0,T=0. | (4.15) |
In tension
α=αt>0 | (4.16) |
the normal displacement uη≥0 is positive in any direction (i.e., ∀θ∈[0,2π]) and therefore from (2.45) we have
λcη=0. | (4.17) |
From (2.42) and (2.44) we have
˜Dη(uη,λtη,λcη)=1−exp(−uη−λtηBtη),˜λtη(uη,λcη,Dη,Dτ)=uη−σtηkη(1−Dη). | (4.18) |
At the beginning of the time history (i.e., with 0<uη≪L from (4.6)) we have from (4.18)2 that ˜λtη(uη,λcη,Dη,Dτ)<0 that means an analytical solution for the accumulation in tension and for the normal damage,
λtη=0,Dη=1−exp(−uηBtη). | (4.19) |
The solution (4.19) is valid only before the threshold condition (2.40) with (2.44)
˜λtη(uη,λcη,Dη,Dτ)=uη−σtηkη(1−Dη)=0 | (4.20) |
is satisfied. Thus, by insertion of (4.19)2 into (4.20) we have,
(1−Dη)=σtηkηuη=exp(−uηBtη). | (4.21) |
The nonlinear algebraic equation (4.21) defines a Lambert function, does not have an analytical solution and can be solved only numerically or graphically, e.g., in Figure 2, from which it is clear, with the constitutive parameters that we have chosen in Table 1, that the condition (4.21) is satisfied for no values of the normal displacement uη, that implies that (4.19)1 is always valid in this investigated case and no plastic behavior takes place. Besides, a different choice of the normal damage characteristic displacement Btη would give a different result. From Figure 2 it is in fact also shown that by assigning a value of the normal damage characteristic displacement Btη equal to 20 times that assigned in Table 1, there exist a value for the normal displacement uη=ˇuη that satisfies (4.21),
σtηkηuη=exp(−uηBtη),⇒uη=ˇuη | (4.22) |
that implies that (4.19)1 is not valid and plasticity takes a role in this new investigated case. The new analytical solution comes from (4.18), from which we derive the nonlinear algebraic equation for normal damage
1−Dη=exp(−σtηBtηkη(1−Dη)),⇒Dη=ˇDη,uη>ˇuη | (4.23) |
and for accumulation of tension
λtη=uη−σtηkη(1−ˇDη),uη>ˇuη | (4.24) |
We observe from (4.23) that the normal damage Dη=ˇDη at which the condition uη=ˇuη>0 holds is constant with respect to the normal displacement uη and therefore with respect to time. This implies that the accumulation in tension in (4.24) evolves linearly with the normal displacement.
The response is calculated by (4.13)–(4.15) and it is graphically represented in Figure 3. We observe (S11 in the left-hand side of Figure 3) that, after a first part where we have an increasing function of time, in a second part of the response the softening induced by damage is evident and a peak reaction is observed as well as a descending curve. A positive reaction is observed also in the orthogonal direction (S22 in the right-hand side of Figure 3). The behavior in the orthogonal direction is almost equivalent. However, a second hardening stage is observed because of a non trivial evolution of the equivalent (the response is not anymore isotropic) Poisson ratio.
The softening behavior is due the non homogeneous evolution of the damage variables with respect to the grain-pair orientation ˆc∈S1 or, equivalently, to the angle θ∈[0,2π], as it is shown in Figure 4.
The anisotropic behavior is also explicated in the evolution of the ratio C2222/C1111 between the vertical and the horizontal stiffness in Figure 5.
In the left-hand side picture of Figure 4 normal damage evolution is shown and it is evident that the horizontal grain-pair orientation, i.e., around θ=kπ, with k∈Z, are the most affected by the damage effect. In the right-hand side picture of Figure 4 tangential damage evolution is shown and it is evident that the oblique grain-pair orientation, i.e., around θ=π/4+kπ/2, with k∈Z, are the most affected by the damage effect. It is also observed that the velocity of the damage evolution is constitutively driven by the damage characteristic displacements Btη, Bcη and Bτ. It is also worth to be noted from Figure 5 that the anisotropy between vertical and horizontal stiffness on the one hand in an elastic isotropic simulation should be maintained at 1 for the entire history of deformation. Here, on the other hand, it goes from 1 at t=0 (i.e., initial isotropic behavior) to 60 at t=20s (i.e., we have anisotropic behavior induced by deformation).
As it is shown in Figure 2 on the one hand no plastic behavior occurs in this case. On the other hand, increasing 20 times the normal damage characteristic displacement (Btη→20Btη), the response changes dramatically. In Figure 6 the stress response is shown in this second case and the softening behavior is very much attenuated.
In Figure 7 normal and tangent damage evolution are also different. In fact, e.g., maximum normal damage is not any more equal to the admissible value Dη≃1 but to the constant value Dη=ˇDη≃0.4 that was analytically calculated in (4.23).
The non trivial plastic displacement evolution uplη=λtη−λcη is therefore shown in Figure 8. It is evident that the plastic displacement occurs only around the horizontal direction.
In compression
α=−αc<0. | (4.25) |
the relative displacement uη≤0 is negative in any direction (i.e., ∀θ∈[0,2π]) and therefore from (2.44) we have
λtη=0. | (4.26) |
From (2.42) and (2.44) we have
˜Dη(uη,λtη,λcη)=2πarctan(−uη+λcηBcη),˜λcη(uη,λcη,Dη,Dτ)=−uη−σcηkη(1−Dη). | (4.27) |
At the beginning of the time history (i.e., with 0≥uη≫−L from (4.6)) we have from (4.27)2 that ˜λcη(uη,λcη,Dη,Dτ)<0 that means an analytical solution for the accumulation in compression and for the normal damage as follows,
λcη=0,Dη=2πarctan(−uηBcη). | (4.28) |
The solution (4.28) is valid only before the threshold condition (2.41) with (2.45)
˜λcη(uη,λcη,Dη,Dτ)=−uη−σcηkη(1−Dη)=0 | (4.29) |
is satisfied. Thus, by insertion of (4.28)2 into (4.29) we have,
(1−Dη)=−σcηkηuη=1−2πarctan(−uηBcη). | (4.30) |
With the constitutive parameters that we have chosen in Table 1 this condition happens at uη=ˉuη<0 and therefore we have (4.28) for uη<ˉuη
λcη=−uη−σcηkη(1−Dη),Dη=2πarctan(−uη+λcηBcη). | (4.31) |
By insertion of (4.31) into (4.30) we also obtain a nonlinear algebraic equation
Dη=2πarctan(σcηkηBcη(1−Dη)),⇒Dη=ˉDη | (4.32) |
the solution of which gives the normal damage Dη=ˉDη at which the condition uη=ˉuη<0 holds and that is constant with respect to the normal displacement uη and therefore with respect to time. This implies that the accumulation in compression evolves linearly with the normal displacement by insertion of (4.32) into (4.31)2,
λcη=−uη−σcηkη(1−ˉDη),uη<ˉuη<0 | (4.33) |
This response is calculated by (4.13)–(4.15) and it is graphically represented in Figure 9. We observe (S11<0 in the left-hand side of Figure 9) that, after a first part where we have an decreasing function of time, in a second part of the response the softening induced by damage is evident and a peak reaction is observed as well as a slightly increasing curve. A negative reaction is observed also in the orthogonal direction (S22<0 in the right-hand side of Figure 9). The behavior in the orthogonal direction is almost equivalent except for the slightly increasing part again because of a non trivial evolution of the equivalent (the response is not anymore isotropic) Poisson ratio.
The softening behavior is due the non homogeneous evolution of the damage variables with respect to the grain-pair orientation ˆc∈S1 or, equivalently, to the angle θ∈[0,2π], as it is shown in Figure 10.
The anisotropic behavior is also explicated in the evolution of the ratio C2222/C1111 between the vertical and the horizontal stiffness in Figure 11.
In the left-hand side picture of Figure 10 normal damage evolution is shown and it is evident that the horizontal grain-pair orientation, i.e., around θ=kπ, with k∈Z, are the most affected by the damage effect. In the right-hand side picture of Figure 10 tangential damage evolution is shown and it is evident that the oblique grain-pair orientation, i.e., around θ=π/4+kπ/2, with k∈Z, are the most affected by the damage effect. This behavior is nevertheless obscured by the non constant dependence of the damage tangential characteristic displacement Bτ with respect to the normal displacement in compression that was made explicit in (2.23). We have that the higher is the compression the lower is the damage velocity evolution. Thus, the orientation that are more in compression, i.e., θ=kπ, with k∈Z, have lower damage velocity evolution with respect to the orientation that are less in compression, i.e., θ=π/2+kπ, with k∈Z, where damage have new peaks.
It is also worth to be noted from Figure 11 that the anisotropy between vertical and horizontal stiffness on the one hand in an elastic isotropic simulation should be maintained at 1 for the entire history of deformation. Here, on the other hand, it goes from 1 at t=0 (i.e., initial isotropic behavior) to 13 at t=20s (i.e., anisotropic behavior induced by deformation).
The non trivial plastic displacement evolution uplη=λtη−λcη is therefore shown in Figure 12. It is evident that the plastic displacement occurs only around the horizontal direction.
This paper recaps and updates a recently developed continuum model for granular materials in order to handle with those important dissipative phenomena as damage and plasticity. The novelty of such a recap is original. The reason is that here for the first time the dissipation energy and the variational inequality are defined directly integrated over the orientation space. This makes the Euler Lagrange equations related to the displacement field (i.e., the elastic Partial Differential Equations PDEs and Boundary Conditions BCs) already integrated over that space. The advantage is that we derive directly the equations that we use for the numerical integration and we do not need to make an artificial integration after their derivation. Its application to an analytical homogeneous case is considered. Plasticity is given by two separate kinematic descriptors (i.e., the accumulation in tension and the accumulation in compression), that are the two plastic multipliers, for every position, time, and grain pair orientation. A hemi-variational principle was adopted to derive the governing equations, from which we obtain Karush-Kuhn-Tucker (KKT)-kind conditions that specify the progression of damage and plasticity relating to each pair of grains interaction. For the case of homogeneous deformation, an analytical solution for the displacement field is assumed and damage and plastic evolution have been derived. It is worth to remark that for non homogeneous deformation, the computation of non homogeneous strain can be reached, e.g., with a Finite Element method according to the scheme developed in [47,59], where the presence of strain gradient terms in the PDEs related to the elastic evolution guarantees the overlook of the problem of the mesh-dependence results. Different loading patterns are experienced by different grain-pairs that are oriented in different directions, resulting in complex anisotropic behavior due to damage and plastic evolution. Competition between damage and plasticity dissipative phenomena is demonstrated in these simulations. We show that, for specific parameters, the evolution of plasticity may stop the growth of damage and vice versa. Besides, the presence of newly conceived constitutive parameters that are present in the dissipation energy functional, imposes a fundamental outlook related to their identification. In the presented model we have the inclusion of simple local plastic interactions that contribute to a complex plastic response of the material as a whole. Finally, no additional assumptions, out of the form of the dissipation energy and such as flow rules, are required to describe the plastic behavior and it is worth to point out that the plastic strain is compatible with the existence of a placement function.
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 899546.
All authors declare no conflicts of interest in this paper
[1] |
D. R. Larson, D. Zenklusen, B. Wu, J. A. Chao, R. H. Singer, Real-time observation of transcription initiation and elongation on an endogeneous yeast gene, Science, 332 (2011), 475–478. https://doi.org/10.1126/science.1202142 doi: 10.1126/science.1202142
![]() |
[2] |
S. Buratowski, S. Hahn, L. Guarente, P. A. Sharp, Five intermediate complexes in transcription initiation by RNA polymerase Ⅱ, Cell, 56 (1989), 549–561. https://doi.org/10.1016/0092-8674(89)90578-3 doi: 10.1016/0092-8674(89)90578-3
![]() |
[3] |
A. Dvir, J. W. Conaway, R. C. Conaway, Mechanism of transcription initiation and promoter escape by RNA polymerase Ⅱ, Curr. Opin. Genet. Dev., 11 (2001), 209–214. https://doi.org/10.1016/S0959-437X(00)00181-7 doi: 10.1016/S0959-437X(00)00181-7
![]() |
[4] |
X. Darzacq, Y. Shav-Tal, V. de Turris, Y. Brody, S. M. Shenoy, R. D. Phair, et al., In vivo dynamics of RNA polymerase Ⅱ transcription, Nat. Struct. Mol. Biol., 14 (2007), 796–806. https://doi.org/10.1038/nsmb1280 doi: 10.1038/nsmb1280
![]() |
[5] | J. C. Venter, M. D. Adams, E. W. Myers, P. W. Li, R. J. Mural, G. G. Sutton, et al., The sequence of the human genome, Science, 291 (2001), 1304–1351. |
[6] |
C. N. Tennyson, H. J. Klamut, R. G. Worton, The human dystrophin gene requires 16 hours to be transcribed and is cotranscriptionally spliced, Nat. Genet., 9 (1995), 184–190. https://doi.org/10.1038/ng0295-184 doi: 10.1038/ng0295-184
![]() |
[7] |
J.-F. Lemay, F. Bachand, Fail-safe transcription termination: Because one is never enough, RNA Biol., 12 (2015), 927–932. https://doi.org/10.1080/15476286.2015.1073433 doi: 10.1080/15476286.2015.1073433
![]() |
[8] |
R. Ben-Yishay, Y. Shav-Tal, The dynamic lifecycle of mRNA in the nucleus, Curr. Opin. Cell Biol., 58 (2019), 69–75. https://doi.org/10.1016/j.ceb.2019.02.007 doi: 10.1016/j.ceb.2019.02.007
![]() |
[9] |
B. Daneholt, Assembly and transport of a premessenger RNP particle, Proc. Natl. Acad. Sci. U.S.A., 98 (2001), 7012–7017. https://doi.org/10.1073/pnas.111145498 doi: 10.1073/pnas.111145498
![]() |
[10] |
J. Sheinberger, Y. Shav-Tal, The dynamic pathway of nuclear RNA in eukaryotes, Nucleus, 4 (2013), 195–205. https://doi.org/10.4161/nucl.24434 doi: 10.4161/nucl.24434
![]() |
[11] | A. Chaudhuri, S. Das, B. Das, Localization elements and zip codes in the intracellular transport and localization of messenger RNAs in Saccharomyces cerevisiae, WIREs RNA, 11 (2020), e1591. |
[12] |
U. Schmidt, E. Basyuk, M.-C. Robert, M. Yoshida, J.-P. Villemin, D. Auboeuf, et al., Real-time imaging of cotranscriptional splicing reveals a kinetic model that reduces noise: Implications for alternative splicing regulation, J. Cell Biol., 193 (2011), 819–829. https://doi.org/10.1083/jcb.201009012 doi: 10.1083/jcb.201009012
![]() |
[13] |
P. Cramer, A. Srebrow, S. Kadener, S. Werbajh, M. de la Mata, G. Melen, et al., Coordination between transcription and pre-mRNA processing, FEBS Lett., 498 (2001), 179–182. https://doi.org/10.1016/S0014-5793(01)02485-1 doi: 10.1016/S0014-5793(01)02485-1
![]() |
[14] |
A. Babour, C. Dargemont, F. Stutz, Ubiquitin and assembly of export competent mRNP, Biochim. Biophys. Acta, 1819 (2012), 521–530. https://doi.org/10.1016/j.bbagrm.2011.12.006 doi: 10.1016/j.bbagrm.2011.12.006
![]() |
[15] |
R. A. Coleman, B. F. Pugh, Slow dimer dissociation of the TATA binding protein dictates the kinetics of DNA binding, Proc. Natl. Acad. Sci. U.S.A., 94 (1997), 7221–7226. https://doi.org/10.1073/pnas.94.14.7221 doi: 10.1073/pnas.94.14.7221
![]() |
[16] |
J. F. Kugel, J. A. Goodrich, A kinetic model for the early steps of RNA synthesis by human RNA polymerase Ⅱ, J. Biol. Chem., 275 (2000), 40483–40491. https://doi.org/10.1074/jbc.M006401200 doi: 10.1074/jbc.M006401200
![]() |
[17] |
A. Kalo, I. Kanter, A. Shraga, J. Sheinberger, H. Tzemach, N. Kinor, et al., Cellular levels of signaling factors are sensed by β-actin alleles to modulate transcriptional pulse intensity, Cell Rep., 11 (2015), 419–432. https://doi.org/10.1016/j.celrep.2015.03.039 doi: 10.1016/j.celrep.2015.03.039
![]() |
[18] |
R. D. Bliss, P. R. Painter, A. G. Marr, Role of feedback inhibition in stabilizing the classical operon, J. Theor. Biol., 97 (1982), 177–193. https://doi.org/10.1016/0022-5193(82)90098-4 doi: 10.1016/0022-5193(82)90098-4
![]() |
[19] |
F. Buchholtz, F. W. Schneider, Computer simulation of T3/T7 phage infection using lag times, Biophys. Chem., 26 (1987), 171–179. https://doi.org/10.1016/0301-4622(87)80020-0 doi: 10.1016/0301-4622(87)80020-0
![]() |
[20] |
S. N. Busenberg, J. M. Mahaffy, The effects of dimension and size for a compartmental model of repression, SIAM J. Appl. Math., 48 (1988), 882–903. https://doi.org/10.1137/0148049 doi: 10.1137/0148049
![]() |
[21] |
J. Lewis, Autoinhibition with transcriptional delay: A simple mechanism for the zebrafish somitogenesis oscillator, Curr. Biol., 13 (2003), 1398–1408. https://doi.org/10.1016/S0960-9822(03)00534-7 doi: 10.1016/S0960-9822(03)00534-7
![]() |
[22] |
N. A. M. Monk, Oscillatory expression of Hes1, p53, and NF-κB driven by transcriptional time delays, Curr. Biol., 13 (2003), 1409–1413. https://doi.org/10.1016/S0960-9822(03)00494-9 doi: 10.1016/S0960-9822(03)00494-9
![]() |
[23] |
L.-J. Chiu, M.-Y. Ling, E.-H. Wu, C.-X. You, S.-T. Lin, C.-C. Shu, The distributed delay rearranges the bimodal distribution at protein level, J. Taiwan Inst. Chem. Eng., 137 (2022), 104436. https://doi.org/10.1016/j.jtice.2022.104436 doi: 10.1016/j.jtice.2022.104436
![]() |
[24] |
M. Jansen, P. Pfaffelhuber, Stochastic gene expression with delay, J. Theor. Biol., 364 (2015), 355–363. https://doi.org/10.1016/j.jtbi.2014.09.031 doi: 10.1016/j.jtbi.2014.09.031
![]() |
[25] |
K. Rateitschak, O. Wolkenhauer, Intracellular delay limits cyclic changes in gene expression, Math. Biosci., 205 (2007), 163–179. https://doi.org/10.1016/j.mbs.2006.08.010 doi: 10.1016/j.mbs.2006.08.010
![]() |
[26] |
M. R. Roussel, On the distribution of transcription times, BIOMATH, 2 (2013), 1307247. https://doi.org/10.11145/j.biomath.2013.07.247 doi: 10.11145/j.biomath.2013.07.247
![]() |
[27] |
M. R. Roussel, R. Zhu, Stochastic kinetics description of a simple transcription model, Bull. Math. Biol., 68 (2006), 1681–1713. https://doi.org/10.1007/s11538-005-9048-6 doi: 10.1007/s11538-005-9048-6
![]() |
[28] | S. Vashishtha, Stochastic modeling of eukaryotic transcription at the single nucleotide level, M.Sc. thesis, University of Lethbridge, 2011, URL https://www.uleth.ca/dspace/handle/10133/3190. |
[29] |
V. Pelechano, S. Chávez, J. E. Pérez-Ortín, A complete set of nascent transcription rates for yeast genes, PLoS One, 5 (2010), e15442. https://doi.org/10.1371/journal.pone.0015442 doi: 10.1371/journal.pone.0015442
![]() |
[30] |
T. Muramoto, D. Cannon, M. Gierliński, A. Corrigan, G. J. Barton, J. R. Chubb, Live imaging of nascent RNA dynamics reveals distinct types of transcriptional pulse regulation, Proc. Natl. Acad. Sci. U.S.A., 109 (2012), 7350–7355. https://doi.org/10.1073/pnas.1117603109 doi: 10.1073/pnas.1117603109
![]() |
[31] |
A. Raj, C. S. Peskin, D. Tranchina, D. Y. Vargas, S. Tyagi, Stochastic mRNA synthesis in mammalian cells, PLoS Biol., 4 (2006), e309. https://doi.org/10.1371/journal.pbio.0040309 doi: 10.1371/journal.pbio.0040309
![]() |
[32] |
D. M. Suter, N. Molina, D. Gatfield, K. Schneider, U. Schibler, F. Naef, Mammalian genes are transcribed with widely different bursting kinetics, Science, 332 (2011), 472–474. https://doi.org/10.1126/science.1198817 doi: 10.1126/science.1198817
![]() |
[33] |
I. Jonkers, H. Kwak, J. T. Lis, Genome-wide dynamics of Pol Ⅱ elongation and its interplay with promoter proximal pausing, chromatin, and exons, eLife, 3 (2014), e02407. https://doi.org/10.7554/eLife.02407 doi: 10.7554/eLife.02407
![]() |
[34] |
P. K. Parua, G. T. Booth, M. Sansó, B. Benjamin, J. C. Tanny, J. T. Lis, et al., A Cdk9-PP1 switch regulates the elongation-termination transition of RNA polymerase Ⅱ, Nature, 558 (2018), 460–464. https://doi.org/10.1038/s41586-018-0214-z doi: 10.1038/s41586-018-0214-z
![]() |
[35] |
L. Bai, R. M. Fulbright, M. D. Wang, Mechanochemical kinetics of transcription elongation, Phys. Rev. Lett., 98 (2007), 068103. https://doi.org/10.1103/PhysRevLett.98.068103 doi: 10.1103/PhysRevLett.98.068103
![]() |
[36] |
L. Bai, A. Shundrovsky, M. D. Wang, Sequence-dependent kinetic model for transcription elongation by RNA polymerase, J. Mol. Biol., 344 (2004), 335–349. https://doi.org/10.1016/j.jmb.2004.08.107 doi: 10.1016/j.jmb.2004.08.107
![]() |
[37] |
F. Jülicher, R. Bruinsma, Motion of RNA polymerase along DNA: a stochastic model, Biophys. J., 74 (1998), 1169–1185. https://doi.org/10.1016/S0006-3495(98)77833-6 doi: 10.1016/S0006-3495(98)77833-6
![]() |
[38] |
H.-Y. Wang, T. Elston, A. Mogilner, G. Oster, Force generation in RNA polymerase, Biophys. J., 74 (1998), 1186–1202. https://doi.org/10.1016/S0006-3495(98)77834-8 doi: 10.1016/S0006-3495(98)77834-8
![]() |
[39] |
T. D. Yager, P. H. Von Hippel, A thermodynamic analysis of RNA transcript elongation and termination in Escherichia coli, Biochemistry, 30 (1991), 1097–1118. https://doi.org/10.1021/bi00218a032 doi: 10.1021/bi00218a032
![]() |
[40] |
S. J. Greive, J. P. Goodarzi, S. E. Weitzel, P. H. von Hippel, Development of a "modular" scheme to describe the kinetics of transcript elongation by RNA polymerase, Biophys. J., 101 (2011), 1155–1165. https://doi.org/10.1016/j.bpj.2011.07.042 doi: 10.1016/j.bpj.2011.07.042
![]() |
[41] |
T. Filatova, N. Popovic, R. Grima, Statistics of nascent and mature rna fluctuations in a stochastic model of transcriptional initiation, elongation, pausing, and termination, Bull. Math. Biol., 83 (2021), 3. https://doi.org/10.1007/s11538-020-00827-7 doi: 10.1007/s11538-020-00827-7
![]() |
[42] |
A. N. Boettiger, P. L. Ralph, S. N. Evans, Transcriptional regulation: Effects of promoter proximal pausing on speed, synchrony and reliability, PLoS Comput. Biol., 7 (2011), e1001136. https://doi.org/10.1371/journal.pcbi.1001136 doi: 10.1371/journal.pcbi.1001136
![]() |
[43] | X. Xu, N. Kumar, A. Krishnan, R. V. Kulkarni, Stochastic modeling of dwell-time distributions during transcriptional pausing and initiation, in 52nd IEEE Conference on Decision and Control, 2013, 4068–4073. |
[44] |
M. Hamano, Stochastic transcription elongation via rule based modelling, Electron. Notes Theor. Comput. Sci., 326 (2016), 73–88. https://doi.org/10.1016/j.entcs.2016.09.019 doi: 10.1016/j.entcs.2016.09.019
![]() |
[45] | S. Klumpp, T. Hwa, Stochasticity and traffic jams in the transcription of ribosomal RNA: Intriguing role of termination and antitermination, Proceedings of the National Academy of Sciences. |
[46] |
A. S. Ribeiro, O.-P. Smolander, T. Rajala, A. Häkkinen, O. Yli-Harja, Delayed stochastic model of transcription at the single nucleotide level, J. Computat. Biol., 16 (2009), 539–553. https://doi.org/10.1089/cmb.2008.0153 doi: 10.1089/cmb.2008.0153
![]() |
[47] |
M. J. Schilstra, C. L. Nehaniv, Stochastic model of template-directed elongation processes in biology, BioSystems, 102 (2010), 55–60. https://doi.org/10.1016/j.biosystems.2010.07.006 doi: 10.1016/j.biosystems.2010.07.006
![]() |
[48] |
A. Garai, D. Chowdhury, D. Chowdhury, T. V. Ramakrishnan, Stochastic kinetics of ribosomes: single motor properties and collective behavior, Phys. Rev. E, 80 (2009), 011908. https://doi.org/10.1103/PhysRevE.79.011916 doi: 10.1103/PhysRevE.79.011916
![]() |
[49] |
A. Garai, D. Chowdhury, T. V. Ramakrishnan, Fluctuations in protein synthesis from a single RNA template: Stochastic kinetics of ribosomes, Phys. Rev. E, 79 (2009), 011916. https://doi.org/10.1103/PhysRevE.79.011916 doi: 10.1103/PhysRevE.79.011916
![]() |
[50] |
L. Mier-y-Terán-Romero, M. Silber, V. Hatzimanikatis, The origins of time-delay in template biopolymerization processes, PLoS Comput. Biol., 6 (2010), e1000726. https://doi.org/10.1371/journal.pcbi.1000726 doi: 10.1371/journal.pcbi.1000726
![]() |
[51] | L. S. Churchman, J. S. Weissman, Nascent transcript sequencing visualizes transcription at nucleotide resolution, Nature, 469 (2011), 368–373. |
[52] |
K. C. Neuman, E. A. Abbondanzieri, R. Landick, J. Gelles, S. M. Block, Ubiquitous transcriptional pausing is independent of RNA polymerase backtracking, Cell, 115 (2003), 437 – 447. https://doi.org/10.1016/S0092-8674(03)00845-6 doi: 10.1016/S0092-8674(03)00845-6
![]() |
[53] |
R. Landick, The regulatory roles and mechanism of transcriptional pausing, Biochem. Soc. Trans., 34 (2006), 1062–1066. https://doi.org/10.1042/BST0341062 doi: 10.1042/BST0341062
![]() |
[54] |
V. Epshtein, F. Toulmé, A. R. Rahmouni, S. Borukhov, E. Nudler, Transcription through the roadblocks: the role of RNA polymerase cooperation, EMBO J., 22 (2003), 4719–4727. https://doi.org/10.1093/emboj/cdg452 doi: 10.1093/emboj/cdg452
![]() |
[55] |
S. Klumpp, Pausing and backtracking in transcription under dense traffic conditions, J. Stat. Phys., 142 (2011), 1252–1267. https://doi.org/10.1007/s10955-011-0120-3 doi: 10.1007/s10955-011-0120-3
![]() |
[56] | M. Voliotis, N. Cohen, C. Molina-París, T. B. Liverpool, Fluctuations, pauses, and backtracking in DNA transcription, Biophys. J., 94 (2008), 334–348. |
[57] | J. Li, D. S. Gilmour, Promoter proximal pausing and the control of gene expression, Curr. Opin. Genet. Dev., 21 (2011), 231–235. |
[58] | S. Nechaev, K. Adelman, Pol Ⅱ waiting in the starting gates: Regulating the transition from transcription initiation into productive elongation, Biochim. Biophys. Acta, 1809 (2011), 34 – 45. |
[59] |
P. B. Rahl, C. Y. Lin, A. C. Seila, R. A. Flynn, S. McCuine, C. B. Burge, et al., c-Myc regulates transcriptional pause release, Cell, 141 (2010), 432 – 445. https://doi.org/10.1016/j.cell.2010.03.030 doi: 10.1016/j.cell.2010.03.030
![]() |
[60] |
P. Feng, A. Xiao, M. Fang, F. Wan, S. Li, P. Lang, et al., A machine learning-based framework for modeling transcription elongation, Proc. Natl. Acad. Sci. U.S.A., 118 (2021), e2007450118. https://doi.org/10.1073/pnas.2007450118 doi: 10.1073/pnas.2007450118
![]() |
[61] |
B. Zamft, L. Bintu, T. Ishibashi, C. Bustamante, Nascent RNA structure modulates the transcriptional dynamics of RNA polymerases, Proc. Natl. Acad. Sci. U.S.A., 109 (2012), 8948–8953. https://doi.org/10.1073/pnas.1205063109 doi: 10.1073/pnas.1205063109
![]() |
[62] |
R. D. Alexander, S. A. Innocente, J. D. Barrass, J. D. Beggs, Splicing-dependent RNA polymerase pausing in yeast, Mol. Cell, 40 (2010), 582–593. https://doi.org/10.1016/j.molcel.2010.11.005 doi: 10.1016/j.molcel.2010.11.005
![]() |
[63] |
N. Gromak, S. West, N. J. Proudfoot, Pause sites promote transcriptional termination of mammalian RNA polymerase Ⅱ, Mol. Cell. Biol., 26 (2006), 3986–3996. https://doi.org/10.1128/MCB.26.10.3986-3996.2006 doi: 10.1128/MCB.26.10.3986-3996.2006
![]() |
[64] |
N. MacDonald, Time delay in prey-predator models, Math. Biosci., 28 (1976), 321–330. https://doi.org/10.1016/0025-5564(76)90130-9 doi: 10.1016/0025-5564(76)90130-9
![]() |
[65] |
N. MacDonald, Time lag in a model of a biochemical reaction sequence with end product inhibition, J. Theor. Biol., 67 (1977), 549–556. https://doi.org/10.1016/0022-5193(77)90056-X doi: 10.1016/0022-5193(77)90056-X
![]() |
[66] | N. MacDonald, Biological Delay Systems: Linear Stability Theory, Cambridge, Cambridge, 1989. |
[67] |
M. Barrio, A. Leier, T. T. Marquez-Lago, Reduction of chemical reaction networks through delay distributions, J. Chem. Phys., 138 (2013), 104114. https://doi.org/10.1063/1.4793982 doi: 10.1063/1.4793982
![]() |
[68] |
A. Leier, M. Barrio, T. T. Marquez-Lago, Exact model reduction with delays: Closed-form distributions and extensions to fully bi-directional monomolecular reactions, J. R. Soc. Interface, 11 (2014), 20140108. https://doi.org/10.1098/rsif.2014.0108 doi: 10.1098/rsif.2014.0108
![]() |
[69] |
I. R. Epstein, Differential delay equations in chemical kinetics: Some simple linear model systems, J. Chem. Phys., 92 (1990), 1702–1712. https://doi.org/10.1063/1.458052 doi: 10.1063/1.458052
![]() |
[70] |
D. Bratsun, D. Volfson, L. S. Tsimring, J. Hasty, Delay-induced stochastic oscillations in gene regulation, Proc. Natl. Acad. Sci. U.S.A., 102 (2005), 14593–14598. https://doi.org/10.1073/pnas.0503858102 doi: 10.1073/pnas.0503858102
![]() |
[71] |
M. R. Roussel, R. Zhu, Validation of an algorithm for delay stochastic simulation of transcription and translation in prokaryotic gene expression, Phys. Biol., 3 (2006), 274. https://doi.org/10.1088/1478-3975/3/4/005 doi: 10.1088/1478-3975/3/4/005
![]() |
[72] |
B. H. Jennings, Pausing for thought: Disrupting the early transcription elongation checkpoint leads to developmental defects and tumourigenesis, BioEssays, 35 (2013), 553–560. https://doi.org/10.1002/bies.201200179 doi: 10.1002/bies.201200179
![]() |
[73] |
H. Kwak, N. J. Fuda, L. J. Core, J. T. Lis, Precise maps of RNA polymerase reveal how promoters direct initiation and pausing, Science, 339 (2013), 950–953. https://doi.org/10.1126/science.1229386 doi: 10.1126/science.1229386
![]() |
[74] |
A. R. Hieb, S. Baran, J. A. Goodrich, J. F. Kugel, An 8nt RNA triggers a rate-limiting shift of RNA polymerase Ⅱ complexes into elongation, EMBO J., 25 (2006), 3100–3109. https://doi.org/10.1038/sj.emboj.7601197 doi: 10.1038/sj.emboj.7601197
![]() |
[75] |
T. J. Stasevich, Y. Hayashi-Takanaka, Y. Sato, K. Maehara, Y. Ohkawa, K. Sakata-Sogawa, et al., Regulation of RNA polymerase Ⅱ activation by histone acetylation in single living cells, Nature, 516 (2014), 272–275. https://doi.org/10.1038/nature13714 doi: 10.1038/nature13714
![]() |
[76] |
B. Steurer, R. C. Janssens, B. Geverts, M. E. Geijer, F. Wienholz, A. F. Theil, et al., Live-cell analysis of endogeneous GFP-RPB1 uncovers rapid turnover of initiating and promoter-paused RNA polymerase Ⅱ, Proc. Natl. Acad. Sci. U.S.A., 115 (2018), E4368–E4376. https://doi.org/10.1073/pnas.1717920115 doi: 10.1073/pnas.1717920115
![]() |
[77] |
J. Liu, D. Hansen, E. Eck, Y. J. Kim, M. Turner, S. Alamos, et al., Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage, PLoS Comput. Biol., 17 (2021), e1008999. https://doi.org/10.1371/journal.pcbi.1008999 doi: 10.1371/journal.pcbi.1008999
![]() |
[78] |
A. Kremling, Comment on mathematical models which describe transcription and calculate the relationship between mrna and protein expression ratio, Biotech. Bioeng., 96 (2007), 815–819. https://doi.org/10.1002/bit.21065 doi: 10.1002/bit.21065
![]() |
[79] |
N. Mitarai, S. Pedersen, Control of ribosome traffic by position-dependent choice of synonymous codons, Phys. Biol., 10 (2013), 056011. https://doi.org/10.1088/1478-3975/10/5/056011 doi: 10.1088/1478-3975/10/5/056011
![]() |
[80] |
M. R. Roussel, The use of delay differential equations in chemical kinetics, J. Phys. Chem., 100 (1996), 8323–8330. https://doi.org/10.1021/jp9600672 doi: 10.1021/jp9600672
![]() |
[81] |
R. A. Coleman, B. F. Pugh, Evidence for functional binding and stable sliding of the TATA binding protein on nonspecific DNA, J. Biol. Chem., 270 (1995), 13850–13859. https://doi.org/10.1074/jbc.270.23.13850 doi: 10.1074/jbc.270.23.13850
![]() |
[82] |
A. Dasgupta, S. A. Juedes, R. O. Sprouse, D. T. Auble, Mot1-mediated control of transcription complex assembly and activity, EMBO J., 24 (2005), 1717–1729. https://doi.org/10.1038/sj.emboj.7600646 doi: 10.1038/sj.emboj.7600646
![]() |
[83] |
R. O. Sprouse, T. S. Karpova, F. Mueller, A. Dasgupta, J. G. McNally, D. T. Auble, Regulation of TATA-binding protein dynamics in living yeast cells, Proc. Natl. Acad. Sci. U.S.A., 105 (2008), 13304–13308. https://doi.org/10.1073/pnas.0801901105 doi: 10.1073/pnas.0801901105
![]() |
[84] | S. H. Hosseini, Analytic Solutions for Stochastic Models of Transcription, Master's thesis, University of Lethbridge, 2016, URL https://www.uleth.ca/dspace/handle/10133/4791. |
[85] |
H.-J. Woo, Analytical theory of the nonequilibrium spatial distribution of RNA polymerase translocations, Phys. Rev. E, 74 (2006), 011907. https://doi.org/10.1103/PhysRevE.74.011907 doi: 10.1103/PhysRevE.74.011907
![]() |
[86] |
M. H. Larson, J. Zhou, C. D. Kaplan, M. Palangat, R. D. Kornberg, R. Landick, et al., Trigger loop dynamics mediate the balance between the transcriptional fidelity and speed of RNA polymerase Ⅱ, Proc. Natl. Acad. Sci. U.S.A., 109 (2012), 6555–6560. https://doi.org/10.1073/pnas.1200939109 doi: 10.1073/pnas.1200939109
![]() |
[87] |
A. C. M. Cheung, P. Cramer, Structural basis of RNA polymerase Ⅱ backtracking, arrest and reactivation, Nature, 471 (2011), 249–253. https://doi.org/10.1038/nature09785 doi: 10.1038/nature09785
![]() |
[88] |
G. Brzyżek, S. Świeżewski, Mutual interdependence of splicing and transcription elongation, Transcription, 6 (2015), 37–39. https://doi.org/10.1080/21541264.2015.1040146 doi: 10.1080/21541264.2015.1040146
![]() |
[89] |
M. Imashimizu, M. L. Kireeva, L. Lubkowska, D. Gotte, A. R. Parks, J. N. Strathem, et al., Intrinsic translocation barrier as an initial step in pausing by RNA polymerase Ⅱ, J. Mol. Biol., 425 (2013), 697–712. https://doi.org/10.1016/j.jmb.2012.12.002 doi: 10.1016/j.jmb.2012.12.002
![]() |
[90] |
J. W. Roberts, Molecular basis of transcriptional pausing, Science, 344 (2014), 1226–1227. https://doi.org/10.1126/science.1255712 doi: 10.1126/science.1255712
![]() |
[91] |
J. Singh, R. A. Padgett, Rates of in situ transcription and splicing in large human genes, Nat. Struct. Mol. Biol., 16 (2009), 1128–1133. https://doi.org/10.1038/nsmb.1666 doi: 10.1038/nsmb.1666
![]() |
[92] |
E. Rosonina, S. Kaneko, J. L. Manley, Terminating the transcript: breaking up is hard to do, Genes Dev., 20 (2006), 1050–1056. https://doi.org/10.1101/gad.1431606 doi: 10.1101/gad.1431606
![]() |
[93] |
E. A. Abbondanzieri, W. J. Greenleaf, J. W. Shaevitz, R. Landick, S. M. Block, Direct observation of base-pair stepping by RNA polymerase, Nature, 438 (2005), 460–465. https://doi.org/10.1038/nature04268 doi: 10.1038/nature04268
![]() |
[94] |
L. M. Hsu, Promoter clearance and escape in prokaryotes, Biochim. Biophys. Acta, 1577 (2002), 191–207. https://doi.org/10.1016/S0167-4781(02)00452-9 doi: 10.1016/S0167-4781(02)00452-9
![]() |
[95] |
H. Kimura, K. Sugaya, P. R. Cook, The transcription cycle of RNA polymerase Ⅱ in living cells, J. Cell Biol., 159 (2002), 777–782. https://doi.org/10.1083/jcb.200206019 doi: 10.1083/jcb.200206019
![]() |
[96] |
H. A. Ferguson, J. F. Kugel, J. A. Goodrich, Kinetic and mechanistic analysis of the RNA polymerase Ⅱ transcription reaction at the human interleukin-2 promoter, J. Mol. Biol., 314 (2001), 993–1006. https://doi.org/10.1006/jmbi.2000.5215 doi: 10.1006/jmbi.2000.5215
![]() |
[97] |
D. A. Jackson, F. J. Iborra, E. M. M. Manders, P. R. Cook, Numbers and organization of RNA polymerases, nascent transcripts, and transcription units in HeLa nuclei, Mol. Biol. Cell, 9 (1998), 1523–1536. https://doi.org/10.1091/mbc.9.6.1523 doi: 10.1091/mbc.9.6.1523
![]() |
[98] |
P. J. Hurtado, A. S. Kirosingh, Generalizations of the 'linear chain trick': Incorporating more flexible dwell time distributions into mean field ODE models, J. Math. Biol., 79 (2019), 1831–1883. https://doi.org/10.1007/s00285-019-01412-w doi: 10.1007/s00285-019-01412-w
![]() |
[99] | H. Golstein, Classical Mechanics, chapter 12, Addison-Wesley, Reading, Massachusetts, 1980. |
[100] |
H. G. Othmer, A continuum model for coupled cells, J. Math. Biol., 17 (1983), 351–369. https://doi.org/10.1007/BF00276521 doi: 10.1007/BF00276521
![]() |
[101] |
C. J. Roussel, M. R. Roussel, Reaction-diffusion models of development with state-dependent chemical diffusion coefficients, Prog. Biophys. Mol. Biol., 86 (2004), 113–160. https://doi.org/10.1016/j.pbiomolbio.2004.03.001 doi: 10.1016/j.pbiomolbio.2004.03.001
![]() |
[102] |
D. Sulsky, R. R. Vance, W. I. Newman, Time delays in age-structured populations, J. Theor. Biol., 141 (1989), 403–422. https://doi.org/10.1016/S0022-5193(89)80122-5 doi: 10.1016/S0022-5193(89)80122-5
![]() |
[103] |
G. Bel, B. Munsky, I. Nemenman, The simplicity of completion time distributions for common complex biochemical processes, Phys. Biol., 7 (2010), 016003. https://doi.org/10.1088/1478-3975/7/1/016003 doi: 10.1088/1478-3975/7/1/016003
![]() |
[104] | P. Billingsley, Probability and Measure, Wiley, New York, 1995. |
[105] |
G. Bar-Nahum, V. Epshtein, A. E. Ruckenstein, R. Rafikov, A. Mustaev, E. Nudler, A ratchet mechanism of transcription elongation and its control, Cell, 120 (2005), 183–193. https://doi.org/10.1016/j.cell.2004.11.045 doi: 10.1016/j.cell.2004.11.045
![]() |
[106] |
J. W. Shaevitz, E. A. Abbondanzieri, R. Landick, S. M. Block, Backtracking by single RNA polymerase molecules observed at near-base-pair resolution, Nature, 426 (2003), 684–687. https://doi.org/10.1038/nature02191 doi: 10.1038/nature02191
![]() |
[107] |
M. A. Gibson, J. Bruck, Efficient exact stochastic simulation of chemical systems with many species and many channels, J. Phys. Chem. A, 104 (2000), 1876–1889. https://doi.org/10.1021/jp993732q doi: 10.1021/jp993732q
![]() |
[108] |
H. T. Banks, J. Catenacci, S. Hu, A comparison of stochastic systems with different types of delays, Stoch. Anal. Appl., 31 (2013), 913–955. https://doi.org/10.1080/07362994.2013.806217 doi: 10.1080/07362994.2013.806217
![]() |
[109] | Y.-L. Feng, J.-M. Dong, X.-L. Tang, Non-Markovian effect on gene transcriptional systems, Chin. Phys. Lett., 33. https://doi.org/10.1088/0256-307X/33/10/108701 |
[110] |
J. Lloyd-Price, A. Gupta, A. S. Ribeiro, Sgns2: A compartmental stochastic chemical kinetics simulator for dynamic cell populations, Bioinformatics, 28 (2012), 3004–3005. https://doi.org/10.1093/bioinformatics/bts556 doi: 10.1093/bioinformatics/bts556
![]() |
[111] | T. Maarleveld, StochPy User Guide, Release 2.3.0, 2015, URL https://sourceforge.net/projects/stochpy/files/stochpy_userguide_2.3.pdf/download. |
[112] |
A. S. Ribeiro, J. Lloyd-Price, SGN Sim, a stochastic genetic networks simulator, Bioinformatics, 23 (2007), 777–779. https://doi.org/10.1093/bioinformatics/btm004 doi: 10.1093/bioinformatics/btm004
![]() |
[113] |
D. T. Gillespie, A general method for numerically simulating the stochastic time evolution of coupled chemical reactions, J. Comput. Phys., 22 (1976), 403–434. https://doi.org/10.1016/0021-9991(76)90041-3 doi: 10.1016/0021-9991(76)90041-3
![]() |
[114] |
R. J. Sims Ⅲ, R. Belotserkovskaya, D. Reinberg, Elongation by RNA polymerase Ⅱ: the short and long of it, Genes Dev., 18 (2004), 2437–2468. https://doi.org/10.1101/gad.1235904 doi: 10.1101/gad.1235904
![]() |
[115] |
E. A. M. Trofimenkoff, M. R. Roussel, Small binding-site clearance delays are not negligible in gene expression modeling, Math. Biosci., 325 (2020), 108376. https://doi.org/10.1016/j.mbs.2020.108376 doi: 10.1016/j.mbs.2020.108376
![]() |
[116] |
V. Epshtein, E. Nudler, Cooperation between RNA polymerase molecules in transcription elongation, Science, 300 (2003), 801–805. https://doi.org/10.1126/science.1083219 doi: 10.1126/science.1083219
![]() |
[117] | C. Jia, L. Y. Wang, G. G. Yin, M. Q. Zhang, Single-cell stochastic gene expression kinetics with coupled positive-plus-negative feedback, Phys. Rev. E, 100. https://doi.org/10.1103/PhysRevE.100.052406 |
[118] |
J. Szavits-Nossan, R. Grima, Uncovering the effect of RNA polymerase steric interactions on gene expression noise: Analytical distributions of nascent and mature RNA numbers, Phys. Rev. E, 108 (2023), 034405. https://doi.org/10.1103/PhysRevE.108.034405 doi: 10.1103/PhysRevE.108.034405
![]() |
[119] |
P. Bokes, J. R. King, A. T. A. Wood, M. Loose, Transcriptional bursting diversifies the behaviour of a toggle switch: Hybrid simulation of stochastic gene expression, Bull. Math. Biol., 75 (2013), 351–371. https://doi.org/10.1007/s11538-013-9811-z doi: 10.1007/s11538-013-9811-z
![]() |
[120] |
M. Dobrzyński, F. J. Bruggeman, Elongation dynamics shape bursty transcription and translation, Proc. Natl. Acad. Sci. U.S.A., 106 (2009), 2583–2588. https://doi.org/10.1073/pnas.0803507106 doi: 10.1073/pnas.0803507106
![]() |
[121] |
L. Cai, N. Friedman, X. S. Xie, Stochastic protein expression in individual cells at the single molecule level, Nature, 440 (2006), 358–362. https://doi.org/10.1038/nature04599 doi: 10.1038/nature04599
![]() |
[122] |
A. J. M. Larsson, P. Johnsson, M. Hagemann-Jensen, L. Hartmanis, O. R. Faridani, B. Reinius, et al., Genomic encoding of transcriptional burst kinetics, Nature, 565 (2019), 251–254. https://doi.org/10.1038/s41586-018-0836-1 doi: 10.1038/s41586-018-0836-1
![]() |
[123] |
Y. Wan, D. G. Anastasakis, J. Rodriguez, M. Palangat, P. Gudla, G. Zaki, et al., Dynamic imaging of nascent RNA reveals general principles of transcription dynamics and stochastic splice site selection, Cell, 184 (2021), 2878–2895. https://doi.org/10.1016/j.cell.2021.04.012 doi: 10.1016/j.cell.2021.04.012
![]() |
[124] |
C. Jia, Y. Li, Analytical time-dependent distributions for gene expression models with complex promoter switching mechanisms, SIAM J. Appl. Math., 83 (2023), 1572–1602. https://doi.org/10.1137/22M147219X doi: 10.1137/22M147219X
![]() |
[125] | B. W. Lindgren, G. W. McElrath, D. A. Berry, Introduction to Probability and Statistics, 154–156, 4th edition, Macmillan, New York, 1978. |
[126] |
M. Janisch, Kolmogorov's strong law of large numbers holds for pairwise uncorrelated random variables, Theory Probab. Appl., 66 (2021), 263–275. https://doi.org/10.4213/tvp5459 doi: 10.4213/tvp5459
![]() |
[127] |
B. Li, J. A. Weber, Y. Chen, A. L. Greenleaf, D. S. Gilmour, Analyses of promoter-proximal pausing by RNA polymerase Ⅱ on the hsp70 heat shock gene promoter in a Drosophila nuclear extract, Mol. Cell. Biol., 16 (1996), 5433–5443. https://doi.org/10.1128/MCB.16.10.5433 doi: 10.1128/MCB.16.10.5433
![]() |
[128] | R.-J. Murphy, Stochastic Modeling of the Torpedo Mechanism of Eukaryotic Transcription Termination, Master's thesis, University of Lethbridge, 2017, URL https://www.uleth.ca/dspace/handle/10133/4906. |
[129] |
B. Choi, Y.-Y. Cheng, S. Cinar, W. Ott, M. R. Bennett, K. Josić, et al., Bayesian inference of distributed time delay in transcriptional and translational regulation, Bioinformatics, 36 (2020), 586–593. https://doi.org/10.1093/bioinformatics/btz574 doi: 10.1093/bioinformatics/btz574
![]() |
[130] |
H. Hong, M. J. Cortez, Y.-Y. Cheng, H. J. Kim, B. Choi, K. Josić, et al., Inferring delays in partially observed gene regulation processes, Bioinformatics, 39 (2023), btad670. https://doi.org/10.1093/bioinformatics/btad670 doi: 10.1093/bioinformatics/btad670
![]() |
[131] | D. Holcman, Z. Schuss, The narrow escape problem, SIAM Rev., 56 (2014), 213–257. https://doi.org/10.1137/120898395 |
[132] |
M. R. Roussel, T. Tang, Simulation of mRNA diffusion in the nuclear environment, IET Syst. Biol., 6 (2012), 125–133. https://doi.org/10.1049/iet-syb.2011.0032 doi: 10.1049/iet-syb.2011.0032
![]() |
[133] | S. Tang, Mathematical Modeling of Eukaryotic Gene Expression, PhD thesis, University of Lethbridge, 2010, URL https://www.uleth.ca/dspace/handle/10133/2567. |
![]() |
![]() |
1. | José Manuel Torres Espino, Jaime Heman Espinoza Sandoval, Chuong Anthony Tran, Roberto Fedele, Emilio Turco, Francesco dell’Isola, Luca Placidi, Anil Misra, Francisco James León Trujillo, Emilio Barchiesi, 2023, Chapter 13, 978-3-031-26185-5, 191, 10.1007/978-3-031-26186-2_13 | |
2. | Krzysztof Jankowski, Marek Pawlikowski, Janusz Domański, Multi-scale constitutive model of human trabecular bone, 2022, 0935-1175, 10.1007/s00161-022-01161-0 | |
3. | Francesco dell'Isola, Maximilian Stilz, The «materialization» of forces: Why confounding mathematical concept and physical entity makes the design of metamaterials arduous, 2023, 103, 0044-2267, 10.1002/zamm.202200433 | |
4. | C. A. Tran, E. Barchiesi, A new block-based approach for the analysis of damage in masonries undergoing large deformations, 2022, 0935-1175, 10.1007/s00161-022-01178-5 | |
5. | Rossana Dimitri, Martina Rinaldi, Marco Trullo, Francesco Tornabene, Theoretical and computational investigation of the fracturing behavior of anisotropic geomaterials, 2022, 0935-1175, 10.1007/s00161-022-01141-4 | |
6. | B. Cagri Sarar, M. Erden Yildizdag, B. Emek Abali, A multi-scale homogenization framework for design and strain-gradient modeling of additively manufactured parts fabricated by particulate composites, 2024, 36, 0935-1175, 1629, 10.1007/s00161-024-01320-5 | |
7. | M. Erden Yildizdag, Bekir Cagri Sarar, Antonello Salvatori, Gino D’Ovidio, Emilio Turco, Analysis of transmission and reflection characteristics of linear plane waves in pantographic lattices, 2023, 74, 0044-2275, 10.1007/s00033-023-02074-x | |
8. | Angelo Scrofani, Emilio Barchiesi, Bernardino Chiaia, Anil Misra, Luca Placidi, Fluid diffusion related aging effect in a concrete dam modeled as a Timoshenko beam, 2023, 11, 2325-3444, 313, 10.2140/memocs.2023.11.313 | |
9. | Olga Chekeres, Vladimir Salnikov, Francesco D’Annibale, From approximation of dissipative systems to representative space-time volume elements for metamaterials, 2024, 36, 0935-1175, 1597, 10.1007/s00161-024-01318-z | |
10. | Boumediene Nedjar, Zeinab Awada, Standard gradient models and application to continuum damage in shell structures, 2023, 74, 0044-2275, 10.1007/s00033-023-02055-0 | |
11. | Abdo Kandalaft, Anil Misra, Luca Placidi, Valerii Maksimov, Dmitry Timofeev, 2024, 9780323906470, 55, 10.1016/B978-0-323-90646-3.00035-6 | |
12. | Nurettin Yilmaz, Bekir Cagri Sarar, Chuong Anthony Tran, Mustafa Erden Yildizdag, Emilio Barchiesi, 2024, 9780323906470, 98, 10.1016/B978-0-323-90646-3.00045-9 | |
13. | Nurettin Yilmaz, M. Erden Yildizdag, Francesco Fabbrocino, Luca Placidi, Anil Misra, Emergence of critical state in granular materials using a variationally‐based damage‐elasto‐plastic micromechanical continuum model, 2024, 48, 0363-9061, 3369, 10.1002/nag.3795 | |
14. | Emilio Turco, Emilio Barchiesi, Andrea Causin, Francesco dell’Isola, Margherita Solci, Kresling tube metamaterial exhibits extreme large-displacement buckling behavior, 2023, 134, 00936413, 104202, 10.1016/j.mechrescom.2023.104202 | |
15. | Emilio Barchiesi, Stefanos Mavrikos, Ivan Giorgio, Costas Grigoropoulos, Maria Farsari, Francesco dell’Isola, Gordon Zyla, Complex mechanical properties of 3D micro-metric pantographic metamaterials fabricated by two-photon polymerization, 2024, 36, 0935-1175, 1755, 10.1007/s00161-024-01327-y | |
16. | Nasrin Rezaei, M. Erden Yildizdag, Emilio Turco, Anil Misra, Luca Placidi, Strain-gradient finite elasticity solutions to rigid bar pull-out test, 2024, 36, 0935-1175, 607, 10.1007/s00161-024-01285-5 | |
17. | Hoang Nguyen, Weican Li, Zdeněk P. Bažant, Yuri Bazilevs, Isogeometric smooth crack-band model (isCBM) using spress–sprain relations adapted to microplane theory, 2023, 181, 00225096, 105470, 10.1016/j.jmps.2023.105470 | |
18. | Luca Placidi, Francesco dell’Isola, Abdou Kandalaft, Raimondo Luciano, Carmelo Majorana, Anil Misra, A granular micromechanic-based model for Ultra High Performance Fiber-Reinforced Concrete (UHP FRC), 2024, 297, 00207683, 112844, 10.1016/j.ijsolstr.2024.112844 | |
19. | Nasrin Rezaei, Johannes Riesselmann, Anil Misra, Daniel Balzani, Luca Placidi, A procedure for the experimental identification of the strain gradient characteristic length, 2024, 75, 0044-2275, 10.1007/s00033-023-02181-9 | |
20. | Rachele Allena, Daria Scerrato, Alberto M. Bersani, Ivan Giorgio, Functional adaptation of bone mechanical properties using a diffusive stimulus originated by dynamic loads in bone remodelling, 2024, 75, 0044-2275, 10.1007/s00033-024-02230-x | |
21. | Fabio De Angelis, An internal variable treatment of evolutive problems in hardening plasticity and viscoplasticity with singularities, 2023, 35, 0935-1175, 1807, 10.1007/s00161-023-01227-7 | |
22. | Yury Solyaev, Self-consistent homogenization approach for polycrystals within second gradient elasticity, 2023, 132, 00936413, 104162, 10.1016/j.mechrescom.2023.104162 | |
23. | Chuong Anthony Tran, Francisco James Leòn Trujillo, Antonello Salvatori, Margherita Solci, Andrea Causin, Luca Placidi, Emilio Barchiesi, A hemivariational damageable elastoplastic vertex-spring model for masonry analysis, 2024, 1081-2865, 10.1177/10812865241233008 | |
24. | Marcin Białas, Giuliano Aretusi, An analytical model for debonding of composite cantilever beams under point loads, 2025, 37, 0935-1175, 10.1007/s00161-024-01332-1 | |
25. | Michele Tepedino, Francesco D’Annibale, Ivan Giorgio, Ewa Bednarczyk, Daniel George, Predictive models for bone remodeling during orthodontic tooth movement: a scoping review on the “biological metamaterial” periodontal ligament interface, 2025, 37, 0935-1175, 10.1007/s00161-024-01336-x | |
26. | E. Yousefimiab, A. Kendibilir, Y. Yalcin, C. Cardillo, E. Aydogan, A. Kefal, Thermomechanical process modelling and simulation for additive manufacturing of nanoparticle dispersed Inconel 718 alloys, 2025, 37, 0935-1175, 10.1007/s00161-024-01346-9 | |
27. | Pouria Mazinani, Christian Cardillo, Peiman Mosaddegh, Evaluating corneal biomechanics using shear wave elastography and finite element modeling: sensitivity analysis and parametric optimization, 2025, 37, 0935-1175, 10.1007/s00161-024-01340-1 | |
28. | Fabio De Angelis, On the formulation of evolutive laws and complementarity conditions for non-smooth elastoplastic materials, 2025, 37, 0935-1175, 10.1007/s00161-024-01341-0 | |
29. | Anil Misra, Luca Placidi, Emergence of bimodulus (tension–compression asymmetric) behavior modeled in granular micromechanics framework, 2025, 1081-2865, 10.1177/10812865241299340 | |
30. | Nurettin Yilmaz, Luca Placidi, Anil Misra, Francesco Fabbrocino, A parametric study on a granular micromechanics continuum-based hemivariational approach: unraveling the emergence of critical states in granular materials, 2025, 13, 2325-3444, 25, 10.2140/memocs.2025.13.25 | |
31. | Heliang You, Meizhen Xiang, Yuhang Jing, Licheng Guo, Zhiqiang Yang, A strain gradient phase field model for heterogeneous materials based on two-scale asymptotic homogenization, 2025, 00225096, 106104, 10.1016/j.jmps.2025.106104 | |
32. | Rachele Allena, Daria Scerrato, Alberto Bersani, Ivan Giorgio, Simulating bone healing with bio-resorbable scaffolds in a three-dimensional system: insights into graft resorption and integration, 2025, 353, 1873-7234, 479, 10.5802/crmeca.291 |
L[m] | A[m] | ˉkcη[Kgs2m2] | ˉktη[Kgs2m2] | ˉkτ[Kgs2m2] | Btη[m] | Bcη[m] |
0.01 | 0.1 | 2π141014 | 10ˉkcη | 2π1013 | 10−8 | 10−7 |
Bτ0[m] | α1[1] | α2[1] | σtη[J/m3] | σcη[J/m3] | . | . |
5⋅10−8 | 10 | 14 | 8.385⋅106 | 8.912⋅107 | . | . |
αc | αt |
2 10−6m/s | 10−7m/s |