Research article Special Issues

What we talk about when we talk about protein hydrolyzate-based biostimulants

  • Protein hydrolyzates (PHs) are a well-known group of plant biostimulants that are obtained by thermal, chemical, enzymatic or mixed hydrolysis from animal or plant sources. PHs are mainly applied to plants with the aim to improve their nutrition level, stimulate metabolism, and increase resistance to abiotic stress. Chemically, the PHs are a mixture of free amino acids, oligo- and polypeptides. Considering the differences in the source materials and hydrolytic processes used for their production it is not surprising that PHs are a very complex and inhomogeneous category of biostimulants. This is a critical point that should be considered by both manufacturers and potential users of such products. With the aim to identify a putative correlation among PHs physic-chemical properties, the different source materials and the hydrolytic process used for their production, 22 PHs available on the market were analyzed for: (i) total amino acids concentration, (ii) free amino acids concentration, (iii) degree of racemization, (iv) degree of hydrolysis and (v) apparent average molecular size. The obtained results confirmed the inhomogeneity PHs. However, the chosen parameters were able to provide information about the above-mentioned correlation. Total amino acids in particular were useful for the identification of the original source material. The degree of racemization was useful for the identification of the type of hydrolysis used during production (chemical or enzymatic). The degree of hydrolysis and free amino acid concentrations were useful for the identification of the intensity of the hydrolytic process. The average molecular size was useful for the identification of polydispersity of the peptide size. In conclusion, the PHs can be adequately characterized only through a multi-analytical approach.

    Citation: Luciano Cavani, Alja Margon, Luigi Sciubba, Claudio Ciavatta, Claudio Marzadori. What we talk about when we talk about protein hydrolyzate-based biostimulants[J]. AIMS Agriculture and Food, 2017, 2(3): 221-232. doi: 10.3934/agrfood.2017.3.221

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  • Protein hydrolyzates (PHs) are a well-known group of plant biostimulants that are obtained by thermal, chemical, enzymatic or mixed hydrolysis from animal or plant sources. PHs are mainly applied to plants with the aim to improve their nutrition level, stimulate metabolism, and increase resistance to abiotic stress. Chemically, the PHs are a mixture of free amino acids, oligo- and polypeptides. Considering the differences in the source materials and hydrolytic processes used for their production it is not surprising that PHs are a very complex and inhomogeneous category of biostimulants. This is a critical point that should be considered by both manufacturers and potential users of such products. With the aim to identify a putative correlation among PHs physic-chemical properties, the different source materials and the hydrolytic process used for their production, 22 PHs available on the market were analyzed for: (i) total amino acids concentration, (ii) free amino acids concentration, (iii) degree of racemization, (iv) degree of hydrolysis and (v) apparent average molecular size. The obtained results confirmed the inhomogeneity PHs. However, the chosen parameters were able to provide information about the above-mentioned correlation. Total amino acids in particular were useful for the identification of the original source material. The degree of racemization was useful for the identification of the type of hydrolysis used during production (chemical or enzymatic). The degree of hydrolysis and free amino acid concentrations were useful for the identification of the intensity of the hydrolytic process. The average molecular size was useful for the identification of polydispersity of the peptide size. In conclusion, the PHs can be adequately characterized only through a multi-analytical approach.


    Glioblastoma is the most common type of primary brain tumors in adults, with a dismal prognosis. The histological features include characteristic patterns called pseudopalisades, which exhibit garland-like structures made of aggregates of glioma cells stacked in rows at the periphery of regions with low pH and high necrosis surrounding the occlusion site(s) of one or several capillaries [39]. Such patterns are used to grade the tumor and are essential for diagnosis [6,23].

    The few continuous mathematical models proposed for the description of pseudopalisade patterns [1,26,27,30], involve systems of ODEs and PDEs which were set up in a heuristic manner or obtained from lower scale dynamics. They account for various aspects like phenotypic switch between proliferating and migrating glioma as a consequence of vasoocclusion and nutrient suppression associated therewith [1], interplay between normoxic/hypoxic glioma, necrotic matter, and oxygen supply [30], or tissue anisotropy and repellent pH-taxis, with [27] or without [26] vascularisation. The latter two works deduced effective equations for glioma dynamics from models set on the lower, microscopic and mesoscopic scales in the kinetic theory of active particles (KTAP) framework explained e.g., in [4]. Those deductions are in line with previous works concerning glioma invasion in anisotropic tissue [9,10,11,14,15,16,18,31], which use parabolic scaling to obtain the equations for glioma evolution on the macroscale from descriptions of subcellular and mesoscopic dynamics. Here we propose yet another approach, relying on the interpretation of the relevant components (glioma, normal tissue, necrotic matter, and acidity) as phases in a mixture - with the exception of acidity, which is characterised by proton concentration in the volume occupied by the phases.

    Multiphase models in the framework of mixture theory [2,12] have been considered in connection with cancer growth and invasion, (arguably) starting with [7,8] and followed by many others, involving different mathematical and biological aspects; see e.g., [17,19,32,33,34,36,37,40] and references therein. Particularly [34] provides a comprehensive discussion of the classes of multiphase models, along with their strengths and drawbacks. Such models employ a population level description with conservation laws analogous to balance equations for single cells, with supplementary terms accounting for interphase effects. Thereby, the living cells and tissues are most often seen as viscous fluids, while the interstitial fluid is inviscid. Few of the existing models (e.g., [17,34,40]) are explicitly handling necrosis, which is, however, an important component of advanced tumors. Likewise, there are relatively few models accounting for chemoattracting or chemorepellent agents (e.g., [17,37]), which are known to bias in a decisive way the expansion of the neoplasm and therewith associated dynamics of tumor cells and surrounding tissue. To our knowledge there are no multiphase models for glioma pseudopalisade development. In this context we propose such a model comprising necrotic matter as one of the phases in the mixture (although there are a few issues related to this approach, as mentioned, e.g., in [34]). As our main aim is to describe glioma behavior in an acidic environment fastly leading to extensive necrosis, we explicitly include these influences in our model. From the mass and momentum balance equations written for the phases composing the neoplasm and supplemented with appropriate constitutive relations, we then deduce, under some simplifying assumptions, a system of reaction-cross diffusion equations with repellent pH-taxis for the glioma cells and normal and necrotic tissues, also including a solenoidality constraint on the total flux. Our deduction has some similarities with that in [19], however is done in N dimensions instead of 1D, it accounts explicitly for the evolution of necrotic matter and the effects of acidity, and does not require the drag coefficients between the phases to be equal. The obtained equations are able to reproduce qualitatively the typical pseudopalisade patterns with all their aspects related to the glioma aggregates, acidity, necrotic inner region, normal tissue dynamics.

    The rest of this paper is organized as follows: Section 2 contains some notations and conventions. Section 3.1 contains the model setup with the considered phases (glioma cells, normal tissue, necrotic matter, and interstitial fluid) and the corresponding mass and momentum balance equations, along with their associated constitutive relations. That description is used to deduce the announced reaction-cross diffusion equations. Thereby, we investigate a model with an immovable component and observe that enforcing one of the phases to be fixed prevents the simultaneous validity of all basic conservation laws needed in the setting. Then we neglect the interstitial fluid, thus reducing the number of phases, and obtain the PDEs with reaction, nonlinear diffusion and taxis terms, while still ensuring all balance equations. The total flux needs to be given, satisfying a solenoidality constraint. Section 4 is dedicated to the existence of weak solutions to the obtained cross diffusion system coupled with the reaction-diffusion equation for acidity dynamics in the volume of interest. Finally, in Section 5 we provide numerical simulations for that system, studying several parameter scenarios in order to put in evidence the effect of acidity and of different drag coefficients on the obtained patterns exhibiting pseudopalisade formation.

    We will use the following notation throughout this paper:

    Notation 2.1. 1) By vector we always mean a column vector.

    2) We denote by e the vector of length 4 and all components equal to one. As usual, Il stands for the identity matrix of size l.

    3) For two vectors w(1) and w(2) of the same length we denote by w(1).w(2) the vector with elements w(1)iw(2)i. Similarly, for a vector w with nonzero elements we write w.1 meaning the vector with coordinates w1i.

    4) For a vector w we denote by diag(w) the diagonal matrix with (diag(w))ii=wi.

    5) As usual, denotes the scalar product.

    6) As usual, refers to the gradient with respect to the spatial variable x.

    Notation 2.2. Throughout the paper we often skip the arguments of coefficient functions.

    Model variables Motivated by the multiphase approach developed in [19] we view the tumour and its environment as a saturated mixture of several components. We assume these components to be: tumour cells, normal tissue (mainly the extracellular matrix, but also normal cells), necrotic tissue (was not included in [19]), and interstitial fluid. The latter, in turn, has several constituents, among which are protons. Depending on their concentration, the environment can be more or less acidic. pH levels drop due to enhanced glycolytic activity of neoplastic cells. We assume that neither cells nor living tissue are produced due to an already very acidic, and hence very unfavourable, environment. Thus we assume that the total volume of the mixture is preserved, the phases only transferring from one into another. The main variables in our models, all depending on time t0 and position in space xΩRN, Ω a domain, are:

    ● vector u:[0,)×Ω[0,1]4 of volume fractions of

    – tumour cells, uc,

    – normal tissue, um,

    – necrotic tissue, un,

    – interstitial fluid, uw.

    ● acidity (concentration of protons), h:[0,)×Ω[0,).

    Unlike [19] we do not require the space dimension, NN, to be one. The main goal of this section is to derive equations for the volume fractions based primarily on the conservation laws for mass and momentum as well as additional assumptions on some of the phases. To write down the physical laws, we introduce additional variables that are subsequently eliminated. These are:

    ● fluxes of the components, Ji:[0,)×ΩRN, i{c,m,n,w},

    ● common pressure, p:[0,)×ΩR.

    Model parameters The equations we develop below involve the following set of parameters:

    ● matrix of drag coefficients associated with each pair of components, KR4×4;

    ● additional, isotropic pressures by the components, τi:[0,1]4×RR, i{c,m,n,w};

    ● reaction terms, fi:[0,1]4×RR, i{c,m,n,w,h};

    ● diffusion coefficient of the protons, Dh>0.

    Assumptions on K. We assume throughout that

    Kij>0andKij=Kjifor i,j{c,m,n,w}, ij.

    Assumptions on fi's. In order to ensure that the sum of all volume fractions in the mixture is always one, we require

    i{c,m,n,w}fi0. (3.1)

    Since ui's and uh should be nonnegative, we require further that

    fi0for ui=0, i{c,m,n,w},fh0for h=0.

    Possible choices for the reaction terms are:

    Example 3.1. fc(uc,un,h)=c1ucun(hhmax), fm(um,h)=c2um(hhmax)+1+um+hc3um, fn=fcfm, fw=0. This accounts for the fact that the amount of both tissue and viable cancer cells (the latter being in interaction with the necrotic matter embedded in the acidic environment) decreases when the proton concentration h exceeds a certain maximum threshold, leading to acidosis and hypoxia. The tissue infers degradation due to causes other than direct influence of acidity or tumor cells, and, on the other hand, it experiences a certain amount of self-regeneration, which is limited by the already available tissue and low pH. Thereby, c1,c2,c3>0 are constant rates; they could, however, also include further dependencies. For the reaction term in the acidity equation we choose e.g., fh=a1uwh/(1+uwh/hmax)+a2uca3h, with ai0 (i=1,2,3) constants. This choice ensures proton uptake by the interstitial fluid (with saturation), production by glioma cells, and decay with rate a3.

    Assumptions on τi's. We assume that the non-living components (necrotic matter and extracellular fluid) acquire a tendency to reach equilibrium, so that only living matter exerts additional pressure, thus

    τnτw0. (3.2)

    The mechanical properties of living matter (tumour cells and normal tissue) are different; among others, they are able to generate both intra- and interphase forces in response to changes in the local environment. The influences include variations in the volume fraction of the cell phase and the presence of chemical cues, of which we account for the local acidity (via proton concentration). The corresponding forces manifest themselves as an additional intraphase pressure. It is assumed that the pressure in the tumour and normal tissue phases increases with their respective densities; moreover, glioma cells exert supplementary (isotropic) pressure on the normal tissue. Taking these effects into account, we choose for the additional pressure terms

    τc(uc,h):=αcuc+g(h),g(h):=χh1+hhmax, (3.3)
    τm(uc,um):=αm(1+θuc)um, (3.4)

    where αc,αm,θ,hmax,χ>0 are constants. In (3.3) we combine the influence of local cell mass and acidity, both adding to cancer cell pressure which, in turn, enhances glioma motility. Cell stress increases with growing cell mass, pushing the cells away from overcrowded regions. Stress due to acidity saturates for large acidity levels. Indeed, in highly acidic regions cell ion channels and pH-sensing receptors are mostly occupied, thus making cells insensitive to the presence of protons in their environment. As in [11,24,26,27], we call this a repellent pH-tactic behaviour. Similarly, the choice in (3.4) means that there is some intraspecific tissue stress (compression) further accentuated by the interaction between glioma cells and their fibrous environment. The forms proposed in (3.3), (3.4) are reminiscent of those chosen in [19].

    Main equations To derive our models we will rely on a set of equations describing physical laws. They are as follows.

    ● Since the the components of u are volume fractions, we have

    i{c,m,n,w}ui=1. (3.5)

    This is the so-called 'no void' condition.

    ● Mass conservation for ith component is given by

    tui=Ji+fi. (3.6)

    It would be reasonable to assume that this equation should hold for all phases. However, this may come into conflict with additional assumptions, see subsequent Subsection 3.2.

    ● Conservation of momentum for ith component is given by

    (uiσi)=Fi, (3.7)

    where

    σi=(p+τi)IN (3.8)

    is the stress tensor involving the common pressure p and the additional pressure τi. It is assumed that the response of the tumour to stress is elastic and isotropic, which implies that the deformation induced by the applied force on each of the considered cellular and tissue components is limited. An unlimited deformation would correspond to a viscoelastic material [20], which is not considered here. The forms of the stress tensors depend on the material properties of each phase and their response to mechanical and chemical cues in the environment. As in [19,28] the interstitial fluid is considered to be inert and isotropic and viscous effects within each phase are neglected.

    The right hand side in (3.7) represents the force acting on the ith phase and, neglecting any inertial effects and exterior body forces, it takes the following form:

    Fi=pui+j{c,m,n,w}{i}Kij(uiJjujJi). (3.9)

    The first term on the right hand side in (3.9) accounts as usual (see, e.g., [7,19,28]) for the pressure distribution at the interface between phases. The remaining term represents viscous drag between the phases, with drag coefficients Kij.

    Plugging (3.8) and (3.9) into (3.7) yields an equation for the variables we introduced above:

    (ui(p+τi)IN)=pui+j{c,m,n,w}{i}Kij(uiJjujJi). (3.10)

    Equation (3.10) is assumed to hold for all four phases.

    ● The proton concentration satisfies the reaction-diffusion equation

    th=DhΔh+fh. (3.11)

    In the remainder of this section we derive two models based on these laws. To begin with, we simplify Eq (3.10). Given that p and τi are scalar functions, we have for all i{c,m,n,w}

    j{c,m,n,w}{i}Kij(uiJjujJi)=(ui(p+τi)IN)pui=(ui(p+τi))pui=(uiτi)+uip. (3.12)

    Since K is symmetric, adding together the left-hand sides of (3.12) for all i{c,m,n,w}, we find that

    i{c,m,n,w}j{c,m,n,w}{i}Kij(uiJjujJi)=0. (3.13)

    Combining (3.12) with (3.5) and (3.13), we obtain

    0=i{c,m,n,w}(uiτi)uip=(uτ)+p,

    so that

    p=(uτ). (3.14)

    Plugging (3.14) into (3.12) we arrive at a system which no longer involves p:

    j{c,m,n,w}{i}Kij(uiJjujJi)=ui(uτ)+(uiτi),i{c,m,n,w}, (3.15)

    and it holds that

    i{c,m,n,w}(ui(uτ)+(uiτi))=0. (3.16)

    Next, we introduce a matrix function

    A:[0,1]4R4×4,Aim:={j{c,m,n,w}{i}Kijuj,i=m,Kimui,im,i,m{c,m,n,w}. (3.17)

    The symmetry of K once again implies that

    i{c,m,n,w}Aim0,m{c,m,n,w}. (3.18)

    System (3.15) can now be written in the form

    AJ(l)=uxl(uτ)+xl(τ.u),l{1,,N}, (3.19)

    where we denote by J(l) the vector made up of the lth coordinates of each Ji. Due to (3.16) and (3.18) for each l (at least) one equation is redundant. We exploit this in more detail in the next Subsections.

    Recall that um and un correspond to normal and necrotic tissues, respectively. A standard modelling assumption is that any tissue is completely immovable. This means that its flux is a zero function, turning the mass conservation law (3.6) into an ODE. However, as we show in this Section, already presupposing, e.g.,

    Jn0 (3.20)

    is problematic.

    Singling out the nth phase, we use the following convenient notation.

    Notation 3.2. For a vector y with components corresponding to the four phases we denote by ˜y the vector which is obtained by removing the component which corresponds to nth phase. Similarly, for a matrix A we denote by ˜A the submatrix which results from removing the row and column of A corresponding to that phase.

    Recall that fluxes Ji, i{c,m,n,w}, satisfy system (3.19) which is underdetermined. We notice that the submatrix ˜A is diagonal-dominant due to (3.18) and the nonnegativity of ui's and Kij's. Using the Gershgorin circle theorem, we infer that the real parts of the eigenvalues of ˜A do not exceed ρA(ui), where

    ρA:=unmini{c,m,w}Kni. (3.21)

    Therefore, for un>0 the submatrix ˜A of A is invertible. Consequently, system (3.19)–(3.20) is uniquely solvable.

    To simplify the calculations, let us now assume that the following technical assumption holds:

    K=kkTfor somek[0,1]4. (3.22)

    In other words, the symmetric matrix K has rank one. One readily verifies the following formulas:

    Lemma 3.3. Let assumption (3.22) hold. Then:

    ˜A=(ku)(I31ku(˜k.˜u)˜eT)diag(˜k) (3.23)

    and

    ˜A1=1kudiag1(˜k)(I3+1knun(˜k.˜u)˜eT), (3.24)

    so that

    ˜A1˜w=1ku(˜k.1.˜w+1knun˜u(˜e˜w)). (3.25)

    Using (3.25) we can resolve system (3.19)–(3.20) with respect to the components of the fluxes. For l{1,,N} and i{c,m,w} we obtain

    J(l)i=1ku(1ki(uixl(uτ)+xl(uiτi))+1knunui˜e(˜uxl(uτ)+xl(˜τ.˜u)))=1ku(1ki(uixl(uτ)+xl(uiτi))+1knunui((˜e˜u)xl(uτ)+xl(˜u˜τ)))=1ku(1ki(uixl(uτ)+xl(uiτi))+1knunui((1un)xl(uτ)+xl(˜u˜τ)))=1ku(1ki(uixl(˜u˜τ)+xl(uiτi))+1knuixl(˜u˜τ))=1ku(1kixl(uiτi)(1ki1kn)uixl(˜u˜τ)). (3.26)

    We used (3.5) and (3.2) in the third and fourth equalities, respectively. Plugging (3.26) into (3.6) and using (3.2), we obtain

    tui=(1ku(1ki(uiτi)(1ki1kn)ui(ucτc+umτm)))+fi,i{c,m}, (3.27a)
    tuw=(1ku((1kw1kn)uw(ucτc+umτm)))+fw. (3.27b)

    For variable un we have due to (3.6) and (3.20) that it satisfies an ODE:

    tun=fn. (3.28)

    However, the structure of the fluxes in (3.27) would ensure (3.5) only if

    i{c,m,w}Ji0.

    This condition fails to hold in general, unless for τc and τm such that

    i{c,m,w}(1kiu(uiτi)(1ki1kn)uiu(ucτc+umτm))=0  for all 0uc,um,un,i{c,m,w}ui1. (3.29)

    It is not fulfilled by the coefficients we have in mind, see Subsection 3.1.

    Let us assume that (3.27) holds only for i{c,w}. Assume further that

    uw0, (3.30)

    i.e., that the liquid phase is negligible. In this case we obtain a haptotaxis model:

    tuc=(1ku(1kc(ucτc(u,h))(1kc1kn)uc(ucτc(u,h)+(1(uc+un))τm(u,h))))+fc(u,h), (3.31a)
    tun=fn(u,h), (3.31b)
    um=1(uc+un), (3.31c)
    u=(uc,um,un). (3.31d)

    Unless τc is zero for uc+un=1, this model cannot ensure um0.

    Altogether we see that trying to explicitly enforce an immovable phase leads to a situation where not all basic conservation laws can hold at the same time.

    Let us now assume that the number of components in the mixture is three since

    uw0,

    thus, unlike previous multiphase models in a related context (see e.g., [19,37]), but compare also [36], we neglect the interstitial fluid and only take into account the more 'solid' components, namely cancer cells, necrotic matter, and normal tissue, all of which are assumed to be more or less heterogeneously interspersed within the volume of interest. In this Subsection we assume all physical laws from Subsection 3.1 to hold in full, so that, in particular, the mass conservation law (3.6) holds for each i{c,m,n}. Due to (3.1) and (3.5) we can replace (3.6) for i=n by

    Jsum=0, (3.32)

    where

    Jsum:=i{c,m,n}Ji.

    As we have seen previously, system (3.19) is underdetermined. However, if Jsum is given, then the system is equivalent to the following matrix equation:

    ˉA(Jc,Jm,Jn)T=(zc,zm,Jsum)T, (3.33)

    where

    ˉA=((Kcmum+Kcnun)KcmucKcnucKcmum(Kcmuc+Kmnun)Kmnum111) (3.34)

    and

    zi:=ui(uτ)+(uiτi).

    One can readily verify that

    ˉA1=1K2cmS1(KcmucKmn(um+un)uc(KcnKcm)ucK2cmS1um(KmnKcm)KcmumKcn(uc+un)umK2cmS1Kcm(uc+um)+KmnunKcm(uc+um)+KcnununK2cmS1), (3.35)

    where

    S1:=1K2cm(KcmKcnuc+KcmKmnum+KcnKmnun). (3.36)

    Using (3.5), we can rewrite (3.35) as

    ˉA1=1K2cmS1(uc(KmnKcm)Kmnuc(KcnKcm)ucK2cmS1um(KmnKcm)um(KcnKcm)KcnumK2cmS1un(KmnKcm)+Kcmun(KcnKcm)+KcmunK2cmS1)=1K2cmS1(u(B1,B2,K2cmS1)+(b1,b2,(0,0,0)T)), (3.37)

    where

    (B1,B2):=(KmnKcm,KcnKcm), (3.38)
    (b1,b2):=(Kmn00KcnKcmKcm). (3.39)

    Resolving (3.33) with respect to Ji's and using (3.37), we get

    (Jc,Jm,Jn)=1K2cmS1((u(B1,B2,K2cmS1)+(b1,b2,(0,0,0)T))(zc,zm,Jsum)T)T=1K2cmS1(zc(B1u+b1)T+zm(B2u+b2)T)+JsumuT. (3.40)

    Combining (3.38)–(3.40), we obtain

    Jc=1K2cmS1((Kcmuc+Kmn(1uc))zc+(KcmKcn)uczm)Jsumuc=1K2cmS1((Kcmuc+Kmn(1uc))(uc(uτ)+(ucτc))  +(KcmKcn)uc(um(uτ)+(umτm)))Jsumuc (3.41)

    and

    Jm=1K2cmS1((KcmKmn)umzc+(Kcmum+Kcn(1um))zm)Jsumum=1K2cmS1((KcmKmn)um(uc(uτ)+(ucτc))  +(Kcmum+Kcn(1um))(um(uτ)+(umτm)))Jsumum. (3.42)

    Recalling that τn=0 due to (3.2), we conclude from (3.41)–(3.42) that

    Jc+Jsumuc=1K2cmS1(Kcmuc+Kmn(1uc))((1uc)(ucτc)uc(umτm))+1K2cmS1(KcmKcn)uc(um(ucτc)+(1um)(umτm))=1K2cmS1((Kcmuc+Kmn(1uc))(1uc)(KcmKcn)ucum)(ucτc)+1K2cmS1((Kcmuc+Kmn(1uc))uc+(KcmKcn)uc(1um))(umτm). (3.43)

    and, similarly,

    Jm+Jsumum=1K2cmS1((Kcmum+Kcn(1um))um+(KcmKmn)um(1uc))(ucτc)+1K2cmS1((Kcmum+Kcn(1um))(1um)(KcmKmn)ucum)(umτm). (3.44)

    Set

    Dcc:=(Kcmuc+Kmn(1uc))(1uc)+(KcnKcm)ucum, (3.45)
    Dcm:=(Kcmuc+Kmn(1uc))uc(KcnKcm)uc(1um), (3.46)
    Dmc:=(Kcmum+Kcn(1um))um(KmnKcm)um(1uc), (3.47)
    Dmm:=(Kcmum+Kcn(1um))(1um)+(KmnKcm)ucum. (3.48)

    Then (3.43) and (3.44) take the form

    Jc=1K2cmS1Dcc(ucτc)+1K2cmS1Dcm(umτm)Jsumuc, (3.49)
    Jm=1K2cmS1Dmc(ucτc)+1K2cmS1Dmm(umτm)Jsumum. (3.50)

    Finally, combining (3.2)–(3.6), (3.32), (3.49), and (3.50) we arrive at the system

    tuc=(DccK2cmS1(uc,um)(ucτc(uc,h))+DcmK2cmS1(uc,um)(umτm(uc,um))Jsumuc) +fc(uc,um,h), (3.51a)
    tum=(DmcK2cmS1(uc,um)(ucτc(uc,h))+DmmK2cmS1(uc,um)(umτm(uc,um))Jsumum) +fm(uc,um,h), (3.51b)
    un:=1ucum, (3.51c)
    Jsum=0. (3.51d)

    Our construction guarantees that un satisfies an equation similar to (3.51a) and (3.51b):

    tun=(DwcK2cmS1(uc,um)(ucτc(uc,um,h))+DwmK2cmS1(uc,um)(umτm(uc,um,h))Jsumun)+fw(uc,um,h), (3.52)

    where

    Dwc:=(Dcc+Dmc),Dwm:=(Dcm+Dmm),

    and

    Dwc=Dwm=0for all u such that uc+um=1.

    This a priori ensures that un0 is satisfied if the solution components are smooth.

    Remark 3.4. Model (3.51) is a generalisation of the model derived in [19]. Unlike that work we require neither the constants Kij to be equal nor the space dimension N to be one.

    Remark 3.5. Our allowing the tissues to be displaced (thus giving up the hypothesis of their immovability) led to nonlinear diffusion and drift in (3.51d) (or, for that matter, in (3.52)). This might seem unusual, as most reaction-difusion-transport systems describing cell motility in tissues assume the latter fixed and use ODEs for their evolution. We emphasize that the obtained terms do not state a self-driven motion of these components, but rather the effect of population- and biochemical pressure exerted thereon.

    Remark 3.6. To close model (3.51), one needs to choose some divergence-free Jsum. In dimension one and for no-flux boundary conditions Jsum0 is the only option. In higher dimensions Jsum can be a curl field (in 3D) or, more generally in N dimensions (cf. [3]), an exterior product of gradients: Jsum=γ1γ2γN1. In the physically relevant 3D case this means that Jsum=γ1×γ2, thus there exist some scalar quantities γ1,γ2 (e.g., densities/volume fractions/concentrations) such that the total flux is orthogonal to each of their gradients, or, put in another way, Jsum is tangential to a curve which lies in the intersection of the surfaces described by γ1 and γ2. This would mean that the total flux is following a direction which is equally biased by those two species. The solenoidality of Jsum is a reasonable assumption in view of our previous requirement that there were no sources or sinks of material, either.

    Diffusion matrix Without loss of generality we assume that

    KmnKcnKcm>0. (3.53)

    One readily verifies that

    1K2cmS1(DccDcmDmcDmm)=S2KcmS1(1ucunS2εcucum1umunS2εm), (3.54)

    where

    εc:=KcnKcm1,εm:=KmnKcm1, (3.55)
    S1:=(1+εc)uc+(1+εm)um+(1+εc)(1+εm)(1ucum), (3.56)
    S2:=1+εc(1um)+εm(1uc). (3.57)

    Due to assumptions (3.53) we have for 0uc,um,uc+um1 that

    Dcc,Dmm0,Dcm,Dmc0,0εcεm,S1[1+εc,(1+εc)(1+εm)], (3.58)
    S2[1+εc,1+ε1+εm]. (3.59)

    In particular, for small εi matrix (3.54) can be regarded as a perturbation of matrix

    1Kcm(1ucucum1um)

    corresponding to

    εc=εm=0,

    i.e., to

    Kmn=Kcn=Kcm.

    This case was addressed in [22]. Further, we compute

    (uc(ucτc)um(ucτc)h(ucτc)uc(umτm)um(umτm)h(umτm))=(2αcuc0ucgθαmu2m2αm(1+θuc)um0). (3.60)

    Overall, the diffusion matrix of equations (3.51a) and (3.51b) takes the form

    S2KcmS1(1ucunS2εcucum1umunS2εm)(2αcuc0ucgθαmu2m2αm(1+θuc)um0). (3.61)

    The complete diffusion matrix of (3.51a), (3.51b), and (3.11) includes the third line

    (0,0,Dh). (3.62)

    In this section we use the method that was presented in [21] in order to establish an existence result for our cross diffusion system (3.51a)–(3.51c), (3.11). The key to applying the method is finding a suitable so-called entropy density. Motivated by the study in [22], where the case of equal Kij's and no acidity was treated, we consider the following entropy density:

    E:DR,  D:=Dcm×(0,),  Dcm:={(uc,um)(0,1)2:uc+um<1}, (4.1a)
    E(uc,um,h):=L(uc,um)+a2h2, (4.1b)
    L(uc,um):=uc(ln(uc)1)+um(ln(um)1)+un(ln(un)1), (4.1c)
    un:=1(uc+um). (4.1d)

    Here L is the well-known logarithmic entropy and a>0 is a sufficiently large constant yet to be fixed. For the subsequent computations we need the matrix of second-order partial derivatives of E:

    D2E(uc,um,h)=(1un1umuc1un01un1un1ucum000a). (4.2)

    {In order to be able to apply} the method from [21]{, we need to ensure positive (semi-)definiteness of} matrix (D2E)M in D where M is the diffusion matrix of (3.51a), (3.15b), and (3.11). In this Subsection we verify this property for the parameter values satisfying the following conditions:

    0<miny[0,1](4αm(1+εc(1y))(4αc(1+εm)2θαmy2εm)α2my2(θ(1+εc(1y))2εm)2) (4.3)

    and

    0<miny[0,1](4αm(1+θy)(1+εcy)(4αc(1+εm(1y))2θαm(1y)2εm) (2αcyεc+θαm(1y)(1+εcy)2αm(1y)(1+θy)εm)2), (4.4)

    where εc and εm are constants defined in (3.55).

    Remark 4.1. Since both functions which need to be minimised in (4.3) and (4.4) are fourth degree polynomials in y, these conditions can be readily checked numerically for a set of given parameters.

    Lemma 4.2 (Uniform ellipticity). Let (4.3) and (4.4) hold. Let E be as defined in (4.1). Then there exist some constants a>0 and 0<C1C2<, such that:

    C1|y|2yT((D2E)M)(uc,um,h)yC2|y|2for all(uc,um,h)D, yR3. (4.5)

    Proof. To begin with, we compute

    S2(2ucucE2ucumE2ucumE2umumE)(1ucunS2εcucum1umunS2εm)=(1ucβcεmεc1umβm), (4.6)

    where

    βc:=1+εm(1uc),βm:=1+εc(1um),

    so that

    βc[1,1+εm],βm[1,1+εc]. (4.7)

    Combining (3.61), (3.62), (4.2), and (4.6), we obtain (arguments of functions are omitted)

    (D2E)M=1KcmS1(2αcβcθαmu2mεm2αm(1+θuc)umεmβcg2αcucεc+θαmumβm2αm(1+θuc)βmucεcg00aDhS2). (4.8)

    We introduce the symmetric matrix

    P:=(D2E)M+((D2E)M)T=1KcmS1(˜Pβcgucεcgβcgucεcg2aDhS2),

    where

    ˜P:=(4αcβc2θαmu2mεm2αcucεc+θαmumβm2αm(1+θuc)umεm2αcucεc+θαmumβm2αm(1+θuc)umεm4αm(1+θuc)βm). (4.9)

    Next, we study det(˜P) in Dcm. We compute

    dP:=det(˜P)=4αm(1+θuc)βm(4αcβc2θαmu2mεm)(2αcucεc+θαmumβm2αm(1+θuc)umεm)2=4αm(1+θuc)(1+εc(1um))(4αc(1+εm(1uc))2θαmu2mεm)(2αcucεc+θαmum(1+εc(1um))2αm(1+θuc)umεm)2.

    Observe that dP is quadratic with respect to uc, and the coefficient of u2c is negative. Consequently, dP cannot attain its minimum inside Dcm((0,1)×{0}). It remains to ensure that dP is positive on the sets {0}×[0,1] and {(uc,um)(0,1]×[0,1): uc+um=1}. This the case if

    miny[0,1]dP(0,y)=miny[0,1](4αm(1+εc(1y))(4αc(1+εm)2θαmy2εm)α2my2(θ(1+εc(1y))2εm)2)>0 (4.10)

    and

    miny[0,1]dP(y,1y)=miny[0,1](4αm(1+θy)(1+εcy)(4αc(1+εm(1y))2θαm(1y)2εm) (2αcyεc+θαm(1y)(1+εcy)2αm(1y)(1+θy)εm)2)>0. (4.11)

    By Sylvester's criterion, P is positive definite if and only if ˜P is positive definite and det(P)>0. Due to (3.58)–(3.59), (4.7), and g Lipschitz all functions involved in (4.9) are bounded and functions S1,S2,βc,βm have positive lower bounds in D. In particular, ˜P222αm>0, so that ˜P is positive definite if and only if det(˜P)>0. Further, we have

    det(P)=aDhS2det(˜P)+φaDhdet(˜P)+φ,

    where φ:¯DR is a bounded function ({recall that g is Lipschitz}). Thus, for sufficiently large a, det(P)>0 holds provided that det(˜P)>0. Altogether, we conclude that conditions (4.10) and (4.11) imply that matrix P is positive for all triples {in} ¯D. Assuming these conditions to be satisfied, let λ(P) denote an eigenvalue of P. Then

    det(P)trace2(P)λ(P)trace(P).

    In ¯D, functions trace(P) and det(P) are bounded from above and from below, respectively, by some positive constants. Hence, we have positive lower and upper bounds for the eigenvalues of P.

    Now we are ready to state our existence result.

    Theorem 4.3. Let 0<KcmKcnKmn and αc,αm,θ,hmax,χ>0 be some constants which satisfy (3.55), (4.3), and (4.4). Let coefficients τc and τm be as defined in (3.3) and (3.4) and let Lipschitz functions fc,fm,fh:¯DR, with domain D as in (4.1a), be such that

    fi0forui=0,i{c,m}, (4.12a)
    fc+fn0foruc+um=1, (4.12b)
    fh0forh=0. (4.12c)

    Then for every given JsumL2loc([0,);(L2(Ω))n) and (uc0,um0,h)(L(Ω))2×L2(Ω) such that (uc0,um0,h)(x)¯D for a.a. xΩ there exists a weak solution (uc,um,h):[0,)×Ω¯D to system (3.51a)–(3.51c), (3.11) under no-flux boundary conditions. This means that:

    (uc,um,h)L2loc([0,);(H1(Ω))3),t(uc,um,h)L2loc([0,);((H1(Ω))3)), (4.13)
    tuc,φ=Ω(DccK2cmS1(uc,um)(ucτc(uc,h))+DcmK2cmS1(uc,um)(umτm(uc,um))Jsumuc)φdx+Ωφfc(uc,un,h)dx, (4.14a)
    tum,φ=Ω(DmcK2cmS1(uc,um)(ucτc(uc,h))+DmmK2cmS1(uc,um)(umτm(uc,um))Jsumum)φdx+Ωφfm(uc,um,h)dx, (4.14b)
    th,φ=ΩDhhφdx+Ωφfh(uc,um,h)dx (4.14c)

    a.e. in (0,) for all φH1(Ω)3, and the initial conditions for each variable are satisfied in L2(Ω)-sense.

    Remark 4.4. Using the assumptions on the coefficients of system (3.51a)–(3.51c), (3.11), one readily verifies that if (uc,um,h) satisfies (4.13) and (uc,um,h)D a.e., then the gradients (ucτc(uc,um)) and (umτm(uc,um)) as well as the fluxes in (4.14a) and (4.14b) belong to L2loc([0,);(L2(Ω))N).

    Proof of 4.3 (sketch). We rely on the theory of weak solvability for cross diffusion systems which was developed in [21].

    We recall that the main tool of the method in [21] is a suitable entropy density. Here we use function E:DR previously defined in (4.1). This function is the sum of the standard logarithmic entropy L for variables u1 and u2 and the quadratic ah2. Combining the corresponding properties of the logarithmic entropy and our previous calculations in this section, we obtain:

    H1: EC2(D) and is convex and bounded below by

    minDE=E(13,13,13,0).

    The derivative

    DE:DR3,DE=(ln(uc)ln(un),ln(um)ln(un),ah) (4.15)

    is invertible, the inverse being

    (DE)(1)(z1,z2,z3)=(ez11+ez1+ez2,ez21+ez1+ez2,1az3).

    H2: For sufficiently large a, left multiplying the diffusion matrix M by D2E yields a uniformly positive definite matrix (see Lemma 4.2).

    H2: The diffusion matrix M is uniformly bounded in D (compare (3.61) and (3.62), recall that g is Lipschitz).

    H3: M and fi, i{c,m,h}, are continuous mappings and there exists a constant C3 such that

    DE(fc,fm,fh)C3(1+EminDE)in D. (4.16)

    Estimate (4.16) is a standard consequence of the assumptions (4.12). Indeed, since zzlnz is negative and bounded below in (0,1), the part originating from the logarithmic entropy satisfies

    (ucL,umL)(fc,fm)=fcln(uc)+fmln(um)+(fcfm)ln(un)fcL(D)ucln(uc)fmL(D)umln(um)(fcL(D)+fmL(D))(1(uc+um))ln(1(uc+um))C4in D (4.17)

    for some constant C4>0. Further, since fh is Lipschitz, we also have

    hEfhC5(1+ah2)in D (4.18)

    for some constant C5>0. Adding (4.17) and (4.18) together, we obtain

    DE(fc,fm,fh)C6(1+ah2)C3(1+LminDL+ah2)=C3(1+EminDE)in D

    since obviously minE=minL.

    The main result of [21], Theorem 2 on existence of bounded weak solutions, cannot be directly applied in our case for two reasons: firstly, apart from diffusion and reaction, equations for uc and um also involve transport in the direction of a given vector-valued function Jsum; secondly, the domain D is not bounded. Were it not for these differences, the above properties H1–H3 would correspond to the hypotheses H1–H3 in [21]. Still, the proof of existence of weak solutions can be carried out very similar to the proofs presented in [21]. The latter go through the following steps: approximation of the time derivative using the implicit Euler scheme, regularisation of the diffusion operator by adding a higher order differential operator such as ϵ(I+(Δ)m) for m>N/2 and small ϵ, solving the linearised approximation problem using the Lax-Milgram lemma, solving the nonlinear approximation problem using the Leray-Schauder theorem, establishing uniform estimates, and, finally, using the compactness method in order to pass to the limit and solve the original problem. Our case can be handled in the very same way. Indeed, on the one hand the linear transport along Jsum is subordinate to diffusion, so that it, e.g., does not hinder the derivation of estimates. On the other hand even though D is not bounded, we actually know that for bounded h0 the h-component of a solution is a priori bounded on all finite time cylinders. This is a consequence of the standard theory of semilinear parabolic PDEs. General h0L2(Ω) can be regularised and a corresponding solution obtained by means of yet one more limit procedure.

    We omit further details and refer the interested reader to [21] where the complete proofs for very similar cases can be found.

    We perform numerical simulations of the system (3.51) supplemented with the PDE (3.11) for the evolution of proton concentration h, endowed with no-flux boundary conditions. For simplicity we choose Jsum=0. We also perform a nondimensionalisation of the model and use it in our simulations (for its concrete form and for the employed parameters refer to the Appendix). The initial conditions are as follows:

    uc(0,x)=0.05(e(x500)2(y500)22(25)2+e(x600)2(y500)22(20)2+e(x300)2(y400)22(10)2), (5.1a)
    h(0,x)=107e(x500)2(y500)22(15)2+107e(x600)2(y500)22(10)2+106.4e(x300)2(y400)22(7.5)2, (5.1b)
    un(0,x)=0.9(e(x500)2(y500)22(5)2+e(x600)2(y500)22(2)2+e(x300)2(y400)22(1)2), (5.1c)
    um(0,x)=1uc(0,x)un(0,x)withum(0,500,500)=um(0,600,500)=um(0,300,400)=0. (5.1d)

    These are illustrated in Figure 1. The problem is set in a square [0,1000]×[0,1000] (in μm), corresponding to the size of large pseudopalisades [5]. For the discretisation we use a method of lines approach. Thereby, the diffusion terms in all involved PDEs are computed by using a standard central difference scheme, while a first order upwind scheme is employed for the advection terms occurring in the equations for glioma and normal tissue. For the acidity equation we discretise time upon using an implicit-explicit (IMEX) method, with forward and backward Euler schemes for the diffusion and reaction terms, respectively. The glioma and normal tissue PDEs are discretised in time by an explicit Euler method.

    Figure 1.  Initial conditions (5.1).

    Figure 2 shows the computed volume fractions of glioma (first column), normal tissue (3rd column), necrotic matter (last column), and acidity concentration (second column) at several times, in a total time span which is relevant for pseudopalisade formation. The typical garland-like structure of glioma pseudopalisades is clearly visible. The tumor cells encircle a highly acidic and necrotic region, while outwards, beyond the glioma ring, the normal tissue remains non-depleted and the acidity decreases.

    Figure 2.  Computed uc, h, um, and un from (3.51) and (3.11) with initial conditions (5.1) and with no-flux boundary conditions.

    To investigate the effect of acidity we compare the previous results with those obtained for χ=0, i.e., in the absence of additional pressure on glioma cells due to acidosis, recall (3.3). The corresponding plots are shown in Figure 3. The effect of repellent pH-taxis can be seen in the difference between the solution components in two situations (with χ>0, with pH-taxis and acid-influenced motility, respectively with χ=0). The pseudopalisades develop faster and get thicker and more extensive in the former case, accompanied by enhanced acidity in the proximity of higher cancer cell densities and enhanced normal tissue depletion inside the ring-shaped structures and around the glioma aggregates, which also triggers a higher volume fraction for the necrotic matter. These observations are in line with those obtained in [26] by another modeling approach: the repellent pH-taxis is not the driving factor of pseudopalisade formation, but it leads to wider such structures.

    Figure 3.  Difference between solution components obtained with χ>0 and those computed with χ=0.

    Finally, we also study the effect of drag coefficients Kcm,Kmn,Kcn in (3.9). Recall that they represent the resistance inferred by the phases in contact when passing over each other. They are contained in the diffusion and drift coefficients of system (3.51). In the computations for Figure 2 we had considered them to be different, more precisely Kcm<Kmn<Kcn. This ensured a relatively easy movement of glioma through normal tissue when compared to the shift over necrotic matter of both normal tissue and cancer cells. Now let all these drag coefficients be equal (as was assumed in [19]) and take the previously lowermost value Kcm, thus reducing the drag between necrotic matter and the other two phases. The differences between the two cases are plotted in Figure 4 and show in the second case an enhanced outward migration of glioma cells, away from the highly acidic area at the core of the pseudopalisade and with a pronounced suppression of normal cells, and emergence of larger necrotic matter. An opposite effect is noticed when letting Kcm=Kmn=Kcn, all at the previously highest value Kcn; we do not show here those results (we refer for them to [25]), but rather illustrate in Figure 5 the situation when all drag coefficients equal the previously intermediate value Kmn. This means that the drag between cancer cells and normal tissue is increased, while they can easier shift over the necrotic matter. As a consequence, the extent of the pseudopalisades is reduced, but the glioma aggregates in the garland-like structures are larger. This is due not only to the higher drag between cancer cells and normal tissue, but also to the lower Kcn value, which ensures that the glioma cells can leave faster than previously the acidic area inside the 'ring'. Around the site where the cancer cells were initially more concentrated the necrosis is reduced and the normal tissue is better preserved.

    Figure 4.  Difference between solution components obtained with Kcm<Kmn<Kcn and those computed with Kcm=Kmn=Kcn, all at the previously lower value Kcm.
    Figure 5.  Difference between solution components obtained with Kcm<Kmn<Kcn and those computed with Kcm=Kmn=Kcn, all at the previously intermediate value Kmn.

    P. K. acknowledges funding by the German Academic Exchange Service (DAAD) in form of a PhD scholarship.

    The authors declare no conflict of interest.

    The following rescalings of the dimensional quantities involved in the obtain equations are considered:

    ˜h:=hhmax,˜t:=ta3,˜x:=xa3DH˜c1:=c1a3,˜c2:=c2a3,˜c3:=c3a3,˜Kmn:=KmnDHαc,˜Kcm:=KcmDHαc,˜Kcn:=KcnDHαc,˜χ:=χhmaxαc,˜αm:=αmαc,˜a2:=a2a3hmax,˜g(˜h):=˜χ˜h1+˜h,˜Dcc(uc,um,un):=(˜Kcmuc+˜Kmn(1uc))(1uc)(˜Kcm˜Kcn)ucum,˜Dcm(uc,um,un):=(˜Kcmuc+˜Kmn(1uc))uc+(˜Kcm˜Kcn)uc(1um),˜Dmc(uc,um,un):=(˜Kcmum+˜Kcn(1um))um+(˜Kcm˜Kmn)um(1uc),˜Dmm(uc,um,un):=(˜Kcmum+˜Kcn(1um))(1um)(˜Kcm˜Kmn)ucum,˜S(uc,um,un):=˜Kcm˜Kcnuc+˜Kcm˜Kmnum+˜Kcn˜Kmn(1ucum).

    Dropping the tildes for simplicity, system (3.51), (3.11) writes in the nondimensional form as

    tuc=(1S(uc,um,un)[Dcc(2uc+g(h))+Dcmαmu2mθ]uc)+(1S(uc,um,un)[Dccuc(g(h))+2Dcmαmum(1+θuc)um])c1ucun(h1), (5.2a)
    tum=(1S(uc,um,un)[2Dmmαmum(1+θuc)]um,)+(1S(uc,um,un)[Dmc(u2c+g(h)uc)+Dmmαmθu2muc])c2um(h1)+1+um+hc3um, (5.2b)
    th=Δh+a2uch, (5.2c)
    un=1ucum. (5.2d)

    The following table (see Table 1) summarises the parameters used in the simulations of Section 5.

    Table 1.  Parameters (dimensional quantities) involved in (3.51).
    Parameter Meaning Value Reference
    hmax acidity threshold for cancer cell death 106.4 mol/L [38]
    a2 proton production rate 109 mol /(Ls) [27,29]
    a3 proton removal rate 1011 /s [26]
    Dh acidity diffusion coefficient 510141011 m2/s [26,27]
    c1 glioma growth/decay rate 2.31106 /s [13,35]
    c2 normal cells decay rate 3.47106 /s this work
    c3 normal cells decay rate 3.47108 /s this work
    Kcm drag force coefficient between glioma and normal cells 1.621010 N m4s this work
    Kmn drag force coefficient between normal and necrotic cells 1.661010 N m4s this work
    Kcn drag force coefficient between glioma and normal cells 1.71010 N m4s this work
    χ coefficient in the additional pressure on glioma due to acidity interaction 2.98 N m2 L/mol this work
    θ dimensionless coefficient in the additional pressure on normal cells due to glioma 1 this work
    αm coefficient in the additional pressure on normal cells due to glioma 1.49107 N m2 this work
    αc coefficient in the additional pressure on glioma due to their crowding 1.49107 N m2 this work

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    [1] Calvo P, Nelson L, Kloepper JW (2014) Agricultural uses of plant biostimulants. Plant Soil 383: 3-41
    [2] European Biostimulants Industry Council, Economic over-view of the biostimulants sector in Europe. 2013. Available from: http://www.biostimulants.eu/wp-content/uploads/2013/04/ Biostimulant_economics_17April2013.pdf
    [3] Paradikovic N, Vinkovic T, Vrcek I V, et al. (2011) Effect of natural biostimulants on yield and nutritional quality: an example of sweet yellow pepper (Capsicum annum L.) plants. J Sci Food Agric 91: 2146-2152
    [4] Ertani A, Cavani L, Pizzeghello D, et al. (2009) Biostimulant activity of two protein hydrolysates on the growth and nitrogen metabolism in maize seedlings. J Plant Nutr Soil Sc 364: 145-158
    [5] Ertani A, Schiavon M, Muscolo A, et al. (2013) Alfalfa plant derived biostimulant stimulate short-term growth of salt stressed Zea mays L. plants. Plant Soil 364: 145-158 doi: 10.1007/s11104-012-1335-z
    [6] Colla G, Nardi S, Cardarelli M, et al. (2015) Protein hydrolysates as biostimulants in horticulture. Sci Hortic 196: 28-38 doi: 10.1016/j.scienta.2015.08.037
    [7] Friedman M (1999) Chemistry, nutrition, and microbiology of D-amino acids. J Agric Food Chem 47: 3457-3479 doi: 10.1021/jf990080u
    [8] Cunico R, Mayer AG, Wehr CT, et al. (1986) High sensitivity amino acids analysis using a novel automated pre-column derivatization system. Biochromatography 1: 6-14
    [9] Cavani L, Ciavatta C, Gessa C (2003) Determination of free L- and D-alanine in hydrolysed protein fertilisers by capillary electrophoresis. J Chromatogr A 985: 463-469 doi: 10.1016/S0021-9673(02)01733-8
    [10] Nielsen P M, Petersen D, Dambmann C (2001) Improved method for determining food protein degree of hydrolysis. J Food Sci 66: 642-646 doi: 10.1111/j.1365-2621.2001.tb04614.x
    [11] García Alvarez-Coque MC, Medina Hernández MJ, Villanueva Camañas RM (1989) Studies on the formation and stability of isoindoles derived from amino acids, o-phthalaldehyde and N-acetyl- L -cysteine. Anal Biochem 180: 172-176 doi: 10.1016/0003-2697(89)90107-3
    [12] Cavani L, Ter Halle A, Richard C et al. (2006) Photosensitizing properties of protein hydrolysate-based fertilizers. J Agric Food Chem 54: 9160-9167 doi: 10.1021/jf0624953
    [13] Yau WW, Kirkland JJ, Bly DD (1979) Modern size exclusion chromatography, Wiley-Interscience, New York.
    [14] R Core Team (2017) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available from: https://www.R-project.org/
    [15] Sosulski FW, Imafidon GI (1990) Amino acid composition and nitrogen-to-protein conversion factors for animal and plant foods. J Agric Food Chem 38: 1351-1356 doi: 10.1021/jf00096a011
    [16] Dawczynski C, Schubert R, Jahreis G (2007) Amino acids, fatty acids, and dietary fibre in edible seaweed products. Food Chem 103: 891-899 doi: 10.1016/j.foodchem.2006.09.041
    [17] Santi S, Zamboni A, Varanini Z, et al. (2017) Growth stimulatory effects and genome-wide transcriptional chages in protein hydrolysates in maize seedlings. Front Plant Sci 8: 433.
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