Parameter-free statistical model invalidation for...

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Parameter-free statistical model invalidation for biochemical reaction networks Kenneth L. Ho (Stanford) Joint work with Heather Harrington (Oxford) Theranos, Apr. 2015

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  • Parameter-free statistical model invalidation for biochemical reactionnetworks

    Kenneth L. Ho (Stanford)

    Joint work with Heather Harrington (Oxford)

    Theranos, Apr. 2015

  • Motivation

    I Model selection: observed data, multiple models; which model is ‘best’?

    I Example (sequential phosphorylation):

    S0κ01 //

    κ02

    77S1κ12 // S2

    I Two models: distributive (κ02 = 0), processive (κ02 > 0)

    I Closely related to model invalidation

    [Aoki/Yamada/Kunida/Yasuda/Matsuda]

  • Problem setting

    I Data x , model f = f (κ) with parameters κ

    I How to tell if model is incompatible with data?

    I Known parameters: compute x̂ = f (κ) and check ‖x − x̂‖I Unknown parameters: fit parameters and check best-case error

    Parameter problem: in biology, parameters are hardly ever known

    I Technical limitations, uncertainties, etc.

    I Partial data: experimentally inaccessible species

    I Nonlinear, high-dimensional optimization often required

    What can be done?

  • Problem setting

    I Data x , model f = f (κ) with parameters κ

    I How to tell if model is incompatible with data?

    I Known parameters: compute x̂ = f (κ) and check ‖x − x̂‖I Unknown parameters: fit parameters and check best-case error

    Parameter problem: in biology, parameters are hardly ever known

    I Technical limitations, uncertainties, etc.

    I Partial data: experimentally inaccessible species

    I Nonlinear, high-dimensional optimization often required

    What can be done?

  • Problem setting

    I Data x , model f = f (κ) with parameters κ

    I How to tell if model is incompatible with data?

    I Known parameters: compute x̂ = f (κ) and check ‖x − x̂‖I Unknown parameters: fit parameters and check best-case error

    Parameter problem: in biology, parameters are hardly ever known

    I Technical limitations, uncertainties, etc.

    I Partial data: experimentally inaccessible species

    I Nonlinear, high-dimensional optimization often required

    What can be done?

  • Previous work

    I Optimization ‘tricks’: random seeding, simulated annealing, etc.

    I Convex relaxation + SDP: lower bound for best-case error• Polynomial-time search through parameter space

    This talk: (quantitative) parameter-free methods

    I Like SDP but no dependence on parameters

    I Based only on model structure/topology

    I Not necessarily ‘better’ but new framework

    A((Bhh

    A �B�

    Philosophically related (qualitative):

    I Chemical reaction network theory

    I Stoichiometric network analysis

    [Clarke, Feinberg, Horn, Jackson, ...]

  • Previous work

    I Optimization ‘tricks’: random seeding, simulated annealing, etc.I Convex relaxation + SDP: lower bound for best-case error

    • Polynomial-time search through parameter space

    This talk: (quantitative) parameter-free methods

    I Like SDP but no dependence on parameters

    I Based only on model structure/topology

    I Not necessarily ‘better’ but new framework

    A((Bhh

    A �B�

    Philosophically related (qualitative):

    I Chemical reaction network theory

    I Stoichiometric network analysis

    [Clarke, Feinberg, Horn, Jackson, ...]

  • Previous work

    I Optimization ‘tricks’: random seeding, simulated annealing, etc.I Convex relaxation + SDP: lower bound for best-case error

    • Polynomial-time search through parameter space

    This talk: (quantitative) parameter-free methods

    I Like SDP but no dependence on parameters

    I Based only on model structure/topology

    I Not necessarily ‘better’ but new framework

    A((Bhh

    A �B�

    Philosophically related (qualitative):

    I Chemical reaction network theory

    I Stoichiometric network analysis

    [Clarke, Feinberg, Horn, Jackson, ...]

  • Previous work

    I Optimization ‘tricks’: random seeding, simulated annealing, etc.I Convex relaxation + SDP: lower bound for best-case error

    • Polynomial-time search through parameter space

    This talk: (quantitative) parameter-free methods

    I Like SDP but no dependence on parameters

    I Based only on model structure/topology

    I Not necessarily ‘better’ but new framework

    A((Bhh

    A �B�

    Philosophically related (qualitative):

    I Chemical reaction network theory

    I Stoichiometric network analysis

    [Clarke, Feinberg, Horn, Jackson, ...]

  • Chemical reaction networks

    I Reactions:

    N∑j=1

    rijXjκi−→

    N∑j=1

    pijXj , i = 1, . . . ,R

    I Mass-action dynamics: ẋj =R∑i=1

    κi (pij − rij)N∏

    k=1

    x rikk , j = 1, . . . ,N

    Example: X1 + X2κ1 // 2X1

    3X1κ2 // X2 + 2X3

    X1

    X3

    κ355

    κ4

    ))X2

    ẋ1 = κ1x1x2 − 3κ2x31 + κ3x3ẋ2 = −κ1x1x2 + κ2x31 + κ4x3ẋ3 = 2κ2x

    31 − (κ3 + κ4)x3

  • Chemical reaction networks

    I Reactions:

    N∑j=1

    rijXjκi−→

    N∑j=1

    pijXj , i = 1, . . . ,R

    I Mass-action dynamics: ẋj =R∑i=1

    κi (pij − rij)N∏

    k=1

    x rikk , j = 1, . . . ,N

    Example: X1 + X2κ1 // 2X1

    3X1κ2 // X2 + 2X3

    X1

    X3

    κ355

    κ4

    ))X2

    ẋ1 = κ1x1x2 − 3κ2x31 + κ3x3ẋ2 = −κ1x1x2 + κ2x31 + κ4x3ẋ3 = 2κ2x

    31 − (κ3 + κ4)x3

  • Chemical reaction networks

    I Reactions:

    N∑j=1

    rijXjκi−→

    N∑j=1

    pijXj , i = 1, . . . ,R

    I Mass-action dynamics: ẋj =R∑i=1

    κi (pij − rij)N∏

    k=1

    x rikk , j = 1, . . . ,N

    Example: X1 + X2κ1 // 2X1

    3X1κ2 // X2 + 2X3

    X1

    X3

    κ355

    κ4

    ))X2

    ẋ1 = κ1x1x2 − 3κ2x31 + κ3x3ẋ2 = −κ1x1x2 + κ2x31 + κ4x3ẋ3 = 2κ2x

    31 − (κ3 + κ4)x3

  • Chemical reaction networks

    I Reactions:

    N∑j=1

    rijXjκi−→

    N∑j=1

    pijXj , i = 1, . . . ,R

    I Mass-action dynamics: ẋj =R∑i=1

    κi (pij − rij)N∏

    k=1

    x rikk , j = 1, . . . ,N

    Example: X1 + X2κ1 // 2X1

    3X1κ2 // X2 + 2X3

    X1

    X3

    κ355

    κ4

    ))X2

    ẋ1 = κ1x1x2 − 3κ2x31 + κ3x3ẋ2 = −κ1x1x2 + κ2x31 + κ4x3ẋ3 = 2κ2x

    31 − (κ3 + κ4)x3

  • Chemical reaction networks

    I Reactions:

    N∑j=1

    rijXjκi−→

    N∑j=1

    pijXj , i = 1, . . . ,R

    I Mass-action dynamics: ẋj =R∑i=1

    κi (pij − rij)N∏

    k=1

    x rikk , j = 1, . . . ,N

    Example: X1 + X2κ1 // 2X1

    3X1κ2 // X2 + 2X3

    X1

    X3

    κ355

    κ4

    ))X2

    ẋ1 = κ1x1x2 − 3κ2x31 + κ3x3ẋ2 = −κ1x1x2 + κ2x31 + κ4x3ẋ3 = 2κ2x

    31 − (κ3 + κ4)x3

  • Chemical reaction networks

    I Reactions:

    N∑j=1

    rijXjκi−→

    N∑j=1

    pijXj , i = 1, . . . ,R

    I Mass-action dynamics: ẋj =R∑i=1

    κi (pij − rij)N∏

    k=1

    x rikk , j = 1, . . . ,N

    Example: X1 + X2κ1 // 2X1

    3X1κ2 // X2 + 2X3

    X1

    X3

    κ355

    κ4

    ))X2

    ẋ1 = κ1x1x2 − 3κ2x31 + κ3x3ẋ2 = −κ1x1x2 + κ2x31 + κ4x3ẋ3 = 2κ2x

    31 − (κ3 + κ4)x3

  • Model compatibility

    I ODE: quantitative test for model compatibility

    I Problem: partial data

    • Derivatives unreliable; assume steady state• Eliminate experimentally inaccessible species

    Example:

    0 =

    ẋ1 = κ1x1x2 − 3κ2x31 + κ3x3

    0 =

    ẋ2 = −κ1x1x2 + κ2x31 + κ4x3

    0 =

    ẋ3 = 2κ2x31 − (κ3 + κ4)x3

    I Observe x1, x2; eliminate x3 =2κ2x

    31

    κ3+κ4

    =⇒ steady-state invariants

    I In general, use computational algebraic geometry (Gröbner bases):

    0 =n∑

    i=1

    αi (κ)ϕi (x)

    [Manrai/Gunawardena]

  • Model compatibility

    I ODE: quantitative test for model compatibilityI Problem: partial data

    • Derivatives unreliable; assume steady state• Eliminate experimentally inaccessible species

    Example:

    0 =

    ẋ1 = κ1x1x2 − 3κ2x31 + κ3x3

    0 =

    ẋ2 = −κ1x1x2 + κ2x31 + κ4x3

    0 =

    ẋ3 = 2κ2x31 − (κ3 + κ4)x3

    I Observe x1, x2; eliminate x3 =2κ2x

    31

    κ3+κ4

    =⇒ steady-state invariants

    I In general, use computational algebraic geometry (Gröbner bases):

    0 =n∑

    i=1

    αi (κ)ϕi (x)

    [Manrai/Gunawardena]

  • Model compatibility

    I ODE: quantitative test for model compatibilityI Problem: partial data

    • Derivatives unreliable; assume steady state

    • Eliminate experimentally inaccessible species

    Example: 0 = ẋ1 = κ1x1x2 − 3κ2x31 + κ3x30 = ẋ2 = −κ1x1x2 + κ2x31 + κ4x30 = ẋ3 = 2κ2x

    31 − (κ3 + κ4)x3

    I Observe x1, x2; eliminate x3 =2κ2x

    31

    κ3+κ4

    =⇒ steady-state invariants

    I In general, use computational algebraic geometry (Gröbner bases):

    0 =n∑

    i=1

    αi (κ)ϕi (x)

    [Manrai/Gunawardena]

  • Model compatibility

    I ODE: quantitative test for model compatibilityI Problem: partial data

    • Derivatives unreliable; assume steady state• Eliminate experimentally inaccessible species

    Example: 0 = ẋ1 = κ1x1x2 − 3κ2x31 + κ3x30 = ẋ2 = −κ1x1x2 + κ2x31 + κ4x30 = ẋ3 = 2κ2x

    31 − (κ3 + κ4)x3

    I Observe x1, x2; eliminate x3 =2κ2x

    31

    κ3+κ4

    =⇒ steady-state invariants

    I In general, use computational algebraic geometry (Gröbner bases):

    0 =n∑

    i=1

    αi (κ)ϕi (x)

    [Manrai/Gunawardena]

  • Model compatibility

    I ODE: quantitative test for model compatibilityI Problem: partial data

    • Derivatives unreliable; assume steady state• Eliminate experimentally inaccessible species

    Example: 0 = ẋ1 = κ1x1x2 − 3κ2x31 + κ3x30 = ẋ2 = −κ1x1x2 + κ2x31 + κ4x30 = ẋ3 = 2κ2x

    31 − (κ3 + κ4)x3

    I Observe x1, x2; eliminate x3 =2κ2x

    31

    κ3+κ4

    =⇒ steady-state invariantsI In general, use computational algebraic geometry (Gröbner bases):

    0 =n∑

    i=1

    αi (κ)ϕi (x)

    [Manrai/Gunawardena]

  • Model compatibility

    I ODE: quantitative test for model compatibilityI Problem: partial data

    • Derivatives unreliable; assume steady state• Eliminate experimentally inaccessible species

    Example: 0 = κ1x1x2 +

    (2κ3

    κ3 + κ4− 3)κ2x

    31

    0 = −κ1x1x2 +(

    2κ4κ3 + κ4

    + 1

    )κ2x

    31

    I Observe x1, x2; eliminate x3 =2κ2x

    31

    κ3+κ4=⇒ steady-state invariants

    I In general, use computational algebraic geometry (Gröbner bases):

    0 =n∑

    i=1

    αi (κ)ϕi (x)

    [Manrai/Gunawardena]

  • Model compatibility

    I ODE: quantitative test for model compatibilityI Problem: partial data

    • Derivatives unreliable; assume steady state• Eliminate experimentally inaccessible species

    Example: 0 = κ1x1x2 +

    (2κ3

    κ3 + κ4− 3)κ2x

    31

    0 = −κ1x1x2 +(

    2κ4κ3 + κ4

    + 1

    )κ2x

    31

    I Observe x1, x2; eliminate x3 =2κ2x

    31

    κ3+κ4=⇒ steady-state invariants

    I In general, use computational algebraic geometry (Gröbner bases):

    0 =n∑

    i=1

    αi (κ)ϕi (x)

    [Manrai/Gunawardena]

  • Parameter-free invalidation

    n∑i=1

    αi (κ)ϕi (x(1)) = 0

    ...n∑

    i=1

    αi (κ)ϕi (x(m)) = 0

    I Let y (k) = (ϕ1(x(k)), . . . , ϕn(x

    (k))) ∈ Rn

    I Geometry: y (1), . . . , y (m) are coplanar

    I Depends only on data, hence parameter-free

    followed by cE00;11 and cF11;00 for the remaining cases. The

    specific values for the example in Fig. 3 are listed in theMathematica notebook. Fig. 3 also shows the plane definedby Eq. 6. As discussed above, this plane intersects the posi-tive quadrant and does not contain the origin. The disposi-tions of the four curves with respect to this plane follow thelimiting behavior summarized in Fig. 2. It can be seen thatprocessivity in either or both of the enzymes is clearly dis-tinguished by the geometry of the corresponding curve.The curves in Fig. 3 were generated from the rational pa-

    rameterization which, as mentioned previously, makes nodistinction between stable and unstable steady states. If the(y1, y2, y3) data were being obtained from an experiment, or ifthey were being generated from a numerical simulation of theequations, then only stable steady states would be found. Weundertook such numerical simulations using randomly se-lected sets of parameter values, as previously, along withrandomly chosen initial conditions. We found that for eachset of parameter values, the (y1, y2, y3) values of the stable

    states were distributed throughout the expected curves (datanot shown). In particular, the stable states were not confinedto any portion of the curve but were to be found everywherealong the curve. There was no difficulty in interpolating, byeye, the shape of the curve despite having a limited number ofpoints on it corresponding to only the stable steady states.

    Experimental tests

    The above results make clear predictions about existingkinase-phosphatase-substrate systems. For instance, in theMek-MKP3-Erk system, the substrate Erk is doubly phos-phorylated and both enzymes act distributively (10,11,13).We therefore predict that this system satisfies the planarityinvariant (Eq. 6). This can be tested in vitro using purifiedkinase, phosphatase, and substrate under conditions in whichATP is not limiting. It is remarkable that such ‘‘systemsbiochemistry’’ has rarely been attempted. Much has beenunderstood about individual kinases and phosphatasesthrough in vitro studies, but the two enzymes have rarelybeen brought together to study their systems properties. Al-though such experiments do not appear to be technicallychallenging, several issues need discussion.First, although the experimenter can control the total

    amounts of substrate and enzymes and, to a lesser extent, theinitial phosphorylation state of the substrate, the amounts offree enzymes at steady state are determined by the system’sdynamics. The parameter t ¼ [E]/[F] is not within the ex-perimenter’s direct control. However, it is not essential totrace the curve generated by t in Fig. 3 in any monotonicfashion. All that is required is to plot the (y1, y2, y3) pointsdefined by Eq. 7 as a set in R3. The t parameter can be ex-ercised by varying the total amount of substrate and enzymesover as broad a range as possible.Second, any method for detecting the substrate phospho-

    rylation state, whether antibodies or 2D gels or mass spec-trometry, will not preserve transient enzyme-substratecomplexes. To avoid misquantifying the amounts of phos-phoforms, it is necessary to maintain substrate in excess ofenzymes. In this regime, any error arising from breakdown ofenzyme-substrate complexes will be limited to no more thanthe total amount of enzyme.Third, it is necessary to distinguish and quantify each of

    the four phosphoforms. Although antibodies and 2D gelshave often been used to detect phosphorylation state, it can bedifficult to distinguish intermediate phosphoforms (S01 andS10) with these methods. For instance, although commercialantibodies are available against all four phosphoforms ofErk1/2, those against the intermediate phosphoforms showpoor specificity compared to the others (43). Mass spec-trometry (MS) is a better option and has become the methodof choice for detecting protein posttranslational modifica-tions (44). Mayya et al studied the cyclin-dependent kinasesCDK1/2, which are inhibited by double phosphorylation, andusedMS to track all four phosphoforms dynamically over the

    FIGURE 3 (y1, y2, y3) curves for each of the four combinations of enzymemechanisms, in the positive quadrant of R3. The paired labels indicate

    kinase/phosphatase, where D is distributive and P is processive. blue, D/Dcurve; cyan, D/P curve; red, P/D curve; purple, P/P curve. Each of the curvesis based on the same core set of parameter values as in the D/D case. These

    values were drawn randomly from the uniform distribution on [0.00, 5.00]

    and are listed in the Mathematica notebook. The plane defined by Eq. 6 isshown with the D/D curve lying on it. The D/P curve has, in addition to the

    already chosen parameter values, cF11;00 ¼ 2:57; whereas the P/D curve hascE00;11 ¼ 4:83: The D/P and P/D curves look similar but have differentbehaviors for small and large t, as described in Fig. 2. The P/P curve has bothcF11;00 ¼ 2:57 and cE00;11 ¼ 4:83: The value of t¼ [E]/[F] was varied in [0.01,100]. This example was representative of 100 similarly generated ones. The

    Mathematica notebook allows the vantage point of the plot to be varied,which reveals the shape of the curves more clearly.

    Geometry of Multisite Phosphorylation 5541

    Biophysical Journal 95(12) 5533–5543

    Linear algebra: Yα = 0

    I Compatible =⇒ ∃α ∈ null(Y ) =⇒ dim(null(Y )) > 0I Compute SVD, reject if σmin(Y ) > 0

    [Manrai/Gunawardena]

  • Parameter-free invalidation

    n∑i=1

    αi (κ)ϕi (x(1)) = 0

    ...n∑

    i=1

    αi (κ)ϕi (x(m)) = 0

    I Let y (k) = (ϕ1(x(k)), . . . , ϕn(x

    (k))) ∈ Rn

    I Geometry: y (1), . . . , y (m) are coplanar

    I Depends only on data, hence parameter-free

    followed by cE00;11 and cF11;00 for the remaining cases. The

    specific values for the example in Fig. 3 are listed in theMathematica notebook. Fig. 3 also shows the plane definedby Eq. 6. As discussed above, this plane intersects the posi-tive quadrant and does not contain the origin. The disposi-tions of the four curves with respect to this plane follow thelimiting behavior summarized in Fig. 2. It can be seen thatprocessivity in either or both of the enzymes is clearly dis-tinguished by the geometry of the corresponding curve.The curves in Fig. 3 were generated from the rational pa-

    rameterization which, as mentioned previously, makes nodistinction between stable and unstable steady states. If the(y1, y2, y3) data were being obtained from an experiment, or ifthey were being generated from a numerical simulation of theequations, then only stable steady states would be found. Weundertook such numerical simulations using randomly se-lected sets of parameter values, as previously, along withrandomly chosen initial conditions. We found that for eachset of parameter values, the (y1, y2, y3) values of the stable

    states were distributed throughout the expected curves (datanot shown). In particular, the stable states were not confinedto any portion of the curve but were to be found everywherealong the curve. There was no difficulty in interpolating, byeye, the shape of the curve despite having a limited number ofpoints on it corresponding to only the stable steady states.

    Experimental tests

    The above results make clear predictions about existingkinase-phosphatase-substrate systems. For instance, in theMek-MKP3-Erk system, the substrate Erk is doubly phos-phorylated and both enzymes act distributively (10,11,13).We therefore predict that this system satisfies the planarityinvariant (Eq. 6). This can be tested in vitro using purifiedkinase, phosphatase, and substrate under conditions in whichATP is not limiting. It is remarkable that such ‘‘systemsbiochemistry’’ has rarely been attempted. Much has beenunderstood about individual kinases and phosphatasesthrough in vitro studies, but the two enzymes have rarelybeen brought together to study their systems properties. Al-though such experiments do not appear to be technicallychallenging, several issues need discussion.First, although the experimenter can control the total

    amounts of substrate and enzymes and, to a lesser extent, theinitial phosphorylation state of the substrate, the amounts offree enzymes at steady state are determined by the system’sdynamics. The parameter t ¼ [E]/[F] is not within the ex-perimenter’s direct control. However, it is not essential totrace the curve generated by t in Fig. 3 in any monotonicfashion. All that is required is to plot the (y1, y2, y3) pointsdefined by Eq. 7 as a set in R3. The t parameter can be ex-ercised by varying the total amount of substrate and enzymesover as broad a range as possible.Second, any method for detecting the substrate phospho-

    rylation state, whether antibodies or 2D gels or mass spec-trometry, will not preserve transient enzyme-substratecomplexes. To avoid misquantifying the amounts of phos-phoforms, it is necessary to maintain substrate in excess ofenzymes. In this regime, any error arising from breakdown ofenzyme-substrate complexes will be limited to no more thanthe total amount of enzyme.Third, it is necessary to distinguish and quantify each of

    the four phosphoforms. Although antibodies and 2D gelshave often been used to detect phosphorylation state, it can bedifficult to distinguish intermediate phosphoforms (S01 andS10) with these methods. For instance, although commercialantibodies are available against all four phosphoforms ofErk1/2, those against the intermediate phosphoforms showpoor specificity compared to the others (43). Mass spec-trometry (MS) is a better option and has become the methodof choice for detecting protein posttranslational modifica-tions (44). Mayya et al studied the cyclin-dependent kinasesCDK1/2, which are inhibited by double phosphorylation, andusedMS to track all four phosphoforms dynamically over the

    FIGURE 3 (y1, y2, y3) curves for each of the four combinations of enzymemechanisms, in the positive quadrant of R3. The paired labels indicate

    kinase/phosphatase, where D is distributive and P is processive. blue, D/Dcurve; cyan, D/P curve; red, P/D curve; purple, P/P curve. Each of the curvesis based on the same core set of parameter values as in the D/D case. These

    values were drawn randomly from the uniform distribution on [0.00, 5.00]

    and are listed in the Mathematica notebook. The plane defined by Eq. 6 isshown with the D/D curve lying on it. The D/P curve has, in addition to the

    already chosen parameter values, cF11;00 ¼ 2:57; whereas the P/D curve hascE00;11 ¼ 4:83: The D/P and P/D curves look similar but have differentbehaviors for small and large t, as described in Fig. 2. The P/P curve has bothcF11;00 ¼ 2:57 and cE00;11 ¼ 4:83: The value of t¼ [E]/[F] was varied in [0.01,100]. This example was representative of 100 similarly generated ones. The

    Mathematica notebook allows the vantage point of the plot to be varied,which reveals the shape of the curves more clearly.

    Geometry of Multisite Phosphorylation 5541

    Biophysical Journal 95(12) 5533–5543

    Linear algebra: Yα = 0

    I Compatible =⇒ ∃α ∈ null(Y ) =⇒ dim(null(Y )) > 0I Compute SVD, reject if σmin(Y ) > 0

    [Manrai/Gunawardena]

  • Parameter-free invalidation

    n∑i=1

    αi (κ)ϕi (x(1)) = 0

    ...n∑

    i=1

    αi (κ)ϕi (x(m)) = 0

    I Let y (k) = (ϕ1(x(k)), . . . , ϕn(x

    (k))) ∈ Rn

    I Geometry: y (1), . . . , y (m) are coplanar

    I Depends only on data, hence parameter-free

    followed by cE00;11 and cF11;00 for the remaining cases. The

    specific values for the example in Fig. 3 are listed in theMathematica notebook. Fig. 3 also shows the plane definedby Eq. 6. As discussed above, this plane intersects the posi-tive quadrant and does not contain the origin. The disposi-tions of the four curves with respect to this plane follow thelimiting behavior summarized in Fig. 2. It can be seen thatprocessivity in either or both of the enzymes is clearly dis-tinguished by the geometry of the corresponding curve.The curves in Fig. 3 were generated from the rational pa-

    rameterization which, as mentioned previously, makes nodistinction between stable and unstable steady states. If the(y1, y2, y3) data were being obtained from an experiment, or ifthey were being generated from a numerical simulation of theequations, then only stable steady states would be found. Weundertook such numerical simulations using randomly se-lected sets of parameter values, as previously, along withrandomly chosen initial conditions. We found that for eachset of parameter values, the (y1, y2, y3) values of the stable

    states were distributed throughout the expected curves (datanot shown). In particular, the stable states were not confinedto any portion of the curve but were to be found everywherealong the curve. There was no difficulty in interpolating, byeye, the shape of the curve despite having a limited number ofpoints on it corresponding to only the stable steady states.

    Experimental tests

    The above results make clear predictions about existingkinase-phosphatase-substrate systems. For instance, in theMek-MKP3-Erk system, the substrate Erk is doubly phos-phorylated and both enzymes act distributively (10,11,13).We therefore predict that this system satisfies the planarityinvariant (Eq. 6). This can be tested in vitro using purifiedkinase, phosphatase, and substrate under conditions in whichATP is not limiting. It is remarkable that such ‘‘systemsbiochemistry’’ has rarely been attempted. Much has beenunderstood about individual kinases and phosphatasesthrough in vitro studies, but the two enzymes have rarelybeen brought together to study their systems properties. Al-though such experiments do not appear to be technicallychallenging, several issues need discussion.First, although the experimenter can control the total

    amounts of substrate and enzymes and, to a lesser extent, theinitial phosphorylation state of the substrate, the amounts offree enzymes at steady state are determined by the system’sdynamics. The parameter t ¼ [E]/[F] is not within the ex-perimenter’s direct control. However, it is not essential totrace the curve generated by t in Fig. 3 in any monotonicfashion. All that is required is to plot the (y1, y2, y3) pointsdefined by Eq. 7 as a set in R3. The t parameter can be ex-ercised by varying the total amount of substrate and enzymesover as broad a range as possible.Second, any method for detecting the substrate phospho-

    rylation state, whether antibodies or 2D gels or mass spec-trometry, will not preserve transient enzyme-substratecomplexes. To avoid misquantifying the amounts of phos-phoforms, it is necessary to maintain substrate in excess ofenzymes. In this regime, any error arising from breakdown ofenzyme-substrate complexes will be limited to no more thanthe total amount of enzyme.Third, it is necessary to distinguish and quantify each of

    the four phosphoforms. Although antibodies and 2D gelshave often been used to detect phosphorylation state, it can bedifficult to distinguish intermediate phosphoforms (S01 andS10) with these methods. For instance, although commercialantibodies are available against all four phosphoforms ofErk1/2, those against the intermediate phosphoforms showpoor specificity compared to the others (43). Mass spec-trometry (MS) is a better option and has become the methodof choice for detecting protein posttranslational modifica-tions (44). Mayya et al studied the cyclin-dependent kinasesCDK1/2, which are inhibited by double phosphorylation, andusedMS to track all four phosphoforms dynamically over the

    FIGURE 3 (y1, y2, y3) curves for each of the four combinations of enzymemechanisms, in the positive quadrant of R3. The paired labels indicate

    kinase/phosphatase, where D is distributive and P is processive. blue, D/Dcurve; cyan, D/P curve; red, P/D curve; purple, P/P curve. Each of the curvesis based on the same core set of parameter values as in the D/D case. These

    values were drawn randomly from the uniform distribution on [0.00, 5.00]

    and are listed in the Mathematica notebook. The plane defined by Eq. 6 isshown with the D/D curve lying on it. The D/P curve has, in addition to the

    already chosen parameter values, cF11;00 ¼ 2:57; whereas the P/D curve hascE00;11 ¼ 4:83: The D/P and P/D curves look similar but have differentbehaviors for small and large t, as described in Fig. 2. The P/P curve has bothcF11;00 ¼ 2:57 and cE00;11 ¼ 4:83: The value of t¼ [E]/[F] was varied in [0.01,100]. This example was representative of 100 similarly generated ones. The

    Mathematica notebook allows the vantage point of the plot to be varied,which reveals the shape of the curves more clearly.

    Geometry of Multisite Phosphorylation 5541

    Biophysical Journal 95(12) 5533–5543

    Linear algebra: Yα = 0

    I Compatible =⇒ ∃α ∈ null(Y ) =⇒ dim(null(Y )) > 0I Compute SVD, reject if σmin(Y ) > 0

    [Harrington/Ho/Thorne/Stumpf, Manrai/Gunawardena]

  • Statistical rejection

    I Null hypothesis: σmin(Y ) = min‖α‖=1 ‖Yα‖ = 0 =⇒ ∃α such that Yα = 0I Assume Gaussian noise in x (k), estimate noise in y (k) = ϕ(x (k))

    I To first-order, Gaussian noise in Y =⇒ z = Yα GaussianI Rescale rows: DY =⇒ (Dz)i ∼ N (0, σ2i ), σ2i ≤ 1I Tail bound: Pr(σmin(DY ) > t) ≤ Pr(‖Dz‖ > t) ≤ Pr(χm > t)I Other bounds possible: Weyl, Wielandt-Hoffman, concentration of measure, etc.

    [Harrington/Ho/Thorne/Stumpf]

  • Algorithm

    Data coplanar

    3

    Data not coplanar

    4

    Data coplanar

    2

    Data not coplanar

    3

    Models

    Model 1 Model 2 . . . . . .

    ẋ1 = . . . . . . . . . . . .

    ... . . . . . . . . . . . .

    ẋN = . . . . . . . . . . . .

    Observed Data

    (Steady state measurements)

    x̂1 . . .

    x̂2 . . .

    ... . . .

    x̂m . . .

    Steady state invariants

    1

    Models

    Model 1 Model 2 . . . . . .

    ẋ1 = . . . . . . . . . . . .

    ... . . . . . . . . . . . .

    ẋN = . . . . . . . . . . . .

    Observed Data

    (Steady state measurements)

    x̂1 . . .

    x̂2 . . .

    ... . . .

    x̂m . . .

    Steady state invariants

    1

    Models

    Model 1 Model 2 . . . . . .

    ẋ1 = . . . . . . . . . . . .

    ... . . . . . . . . . . . .

    ẋN = . . . . . . . . . . . .

    Observed Data

    (Steady state measurements)

    x̂1 . . .

    x̂2 . . .

    ... . . .

    x̂m . . .

    Steady state invariants

    1

    Models

    Model 1 Model 2 . . . . . .

    ẋ1 = . . . . . . . . . . . .

    ... . . . . . . . . . . . .

    ẋN = . . . . . . . . . . . .

    Observed Data

    (Steady state measurements)

    x̂1 . . .

    x̂2 . . .

    ... . . .

    x̂m . . .

    Steady state invariants

    1

    Models

    Model 1 Model 2 . . . . . .

    ẋ1 = . . . . . . . . . . . .

    ... . . . . . . . . . . . .

    ẋN = . . . . . . . . . . . .

    Observed Data

    (Steady state measurements)

    x̂1 . . .

    x̂2 . . .

    ... . . .

    x̂m . . .

    Steady state invariants

    1

    Models

    Model 1 Model 2 . . . . . .

    ẋ1 = . . . . . . . . . . . .

    ... . . . . . . . . . . . .

    ẋN = . . . . . . . . . . . .

    Steady state invariants

    Calculate elimination ideal

    Assess coplanarity

    1

    Calculate elimination ideal

    Assess coplanarity

    Reduce number of variables

    to include only observables

    Characterize steady states of models

    Transform model variables,

    parameters, and data

    2

    Calculate elimination ideal

    Assess coplanarity

    Reduce number of variables

    to include only observables

    Characterize steady states of models

    Transform model variables,

    parameters, and data

    2

    Calculate elimination ideal

    Assess coplanarity

    Reduce number of variables

    to include only observables

    Characterize steady states of models

    Transform model variables,

    parameters, and data

    2

    Calculate elimination ideal

    Assess coplanarity

    Reduce number of variables

    to include only observables

    Characterize steady states of models

    Transform model variables,

    parameters, and data

    2

    Models

    Model 1 Model 2 . . . . . .

    ẋ1 = . . . . . . . . . . . .

    ... . . . . . . . . . . . .

    ẋN = . . . . . . . . . . . .

    Steady state invariants

    Calculate elimination ideal

    Assess coplanarity

    1

    Calculate elimination ideal

    Assess coplanarity

    Reduce number of variables

    to include only observables

    Characterize steady states of models

    Transform model variables, parameters, and data

    parameters, and data

    2

    Data not coplanar

    Model compatible

    Model incompatible

    4

    Data not coplanar

    Model compatible

    Model incompatible

    4

    Models

    Model 1 . . . Model L

    ẋ1 = . . . . . . . . .

    ... . . . . . . . . .

    ẋN = . . . . . . . . .

    Observed Data

    (Steady state measurements)

    x̂1 . . .

    x̂2 . . .

    ... . . .

    x̂m . . .

    Steady state invariants

    1

    Models

    Model 1 . . . Model L

    ẋ1 = . . . . . . . . .

    ... . . . . . . . . .

    ẋN = . . . . . . . . .

    Observed Data

    (Steady state measurements)

    x̂1 . . .

    x̂2 . . .

    ... . . .

    x̂m . . .

    Steady state invariants

    1

    Models

    Model 1 . . . Model L

    ẋ1 = . . . . . . . . .

    ... . . . . . . . . .

    ẋN = . . . . . . . . .

    Observed Data

    (Steady state measurements)

    x̂1 . . .

    x̂2 . . .

    ... . . .

    x̂m . . .

    Steady state invariants

    1

    I Test coplanarity for each invariant

    I Reject model if any invariant fails

    I Main costs: elimination, σmin(Rm×n)

    [Harrington/Ho/Thorne/Stumpf]

  • Example: two-site phosphorylation

    S01 dd$$

    S00zz::

    dd$$

    oo // S11

    S10zz::

    I Kinase/phosphatase: distributive/processive

    I Four models: PP, PD, DP, DD

    I 12 species, 22 parameters

    I Variable ordering: (ks00, ks01, ks10, fs01, fs10, fs11, k, f ,

    obs︷ ︸︸ ︷s00, s01, s10, s11)

    I Kinase is discriminative, can reject DP/DD models on basis of PP data

    I Can discriminate instead on phosphatase by reversing variable ordering

    [Harrington/Ho/Thorne/Stumpf, Manrai/Gunawardena]

  • Example: cell death signaling

    I Extrinsic pathway

    I FasL/Fas interactions

    I Measure activated Fas

    I Crosslinking model: sequential Fas recruitment (8 species, 2 parameters)

    I Cluster model: scaffold for Fas clustering, bistable (6 species, 9 parameters)

    I Can reject crosslinking model from cluster data

    [Harrington/Ho/Thorne/Stumpf, Ho/Harrington, Lai/Jackson]

  • Summary

    I Parameter-free statistical model invalidation

    I Detection of non-parametric linear structure

    I Very efficient once invariants have been computed

    Broader perspective: parameter-independent model properties

    I Structure, topology, robustness, modularity

    I Algebraic systems biology

    Limitations: nonlinear elimination, steady-state data, necessary but not sufficient

  • Extension: complex-linear networks

    I CRNs: nonlinear ODEs =⇒ nonlinear eliminationI Fundamental insight of CRNT: hidden linearity

    I Complex-balanced networks: underlying Laplacian dynamics

    I Study properties of Laplacian matrices

    I Steady state: kernel = zero + constant + rank-one

    I No elimination: decomposition based on graph connectivity

    I Test σ1, σ2, etc.

    A((Bgg

    E''

    ��Fgg ∅}}

    C((

    FF

    Dhh G((Hhh 88 I

    ''Jee

    kk

    [Harrington/Ho]

  • Extension: complex-linear networks

    I CRNs: nonlinear ODEs =⇒ nonlinear eliminationI Fundamental insight of CRNT: hidden linearity

    I Complex-balanced networks: underlying Laplacian dynamics

    I Study properties of Laplacian matrices

    I Steady state: kernel = zero + constant + rank-one

    I No elimination: decomposition based on graph connectivity

    I Test σ1, σ2, etc.

    A((Bgg

    E''

    ��Fgg ∅}}

    C((

    FF

    Dhh G((Hhh 88 I

    ''Jee

    kk

    [Harrington/Ho]

  • Extension: complex-linear networks

    I CRNs: nonlinear ODEs =⇒ nonlinear eliminationI Fundamental insight of CRNT: hidden linearity

    I Complex-balanced networks: underlying Laplacian dynamics

    I Study properties of Laplacian matrices

    I Steady state: kernel = zero + constant + rank-one

    I No elimination: decomposition based on graph connectivity

    I Test σ1, σ2, etc.

    A((Bgg

    E''

    ��Fgg ∅}}

    C((

    FF

    Dhh G((Hhh 88 I

    ''Jee

    kk

    [Harrington/Ho]

  • Extension: complex-linear networks

    I CRNs: nonlinear ODEs =⇒ nonlinear eliminationI Fundamental insight of CRNT: hidden linearity

    I Complex-balanced networks: underlying Laplacian dynamics

    I Study properties of Laplacian matrices

    I Steady state: kernel = zero + constant + rank-one

    I No elimination: decomposition based on graph connectivity

    I Test σ1, σ2, etc.

    A((Bgg

    E''

    ��Fgg ∅}}

    C((

    FF

    Dhh G((Hhh 88 I

    ''Jee

    kk

    [Harrington/Ho]

  • Extension: time course data

    I Differential elimination: dynamical invariants involving derivatives

    I Example: Lotka-Volterra ẋ = ax − bxyẏ = −cy + dxy

    =⇒ acx2 + axẋ − bcx3 − bx2ẋ = −ẋ2 + xẍ

    I Estimate derivatives using Gaussian processes

    [Harrington/Ho/Meshkat]

  • Extension: numerical algebraic geometry

    I Goal: solve global nonlinear optimization

    I Exploit polynomial structure, numerical algebraic geometry

    I Find closest intersection between model and data varieties

    I Maximum-likelihood parameter estimation, model invalidation, model selection

    [Gross/Davis/Ho/Bates/Harrington]

  • Other work

    I Mathematical modeling of cell signaling networks

    I Automated all-atom protein crystal structure refinement

    I Fast multipole methods, direct solvers, matrix factorizations

    [Harrington/Ho/Ghosh/Tung, Ho/Harrington]

  • Other work

    I Mathematical modeling of cell signaling networks

    I Automated all-atom protein crystal structure refinement

    I Fast multipole methods, direct solvers, matrix factorizations

    [Bell/Ho/Farid]

  • Other work

    I Mathematical modeling of cell signaling networks

    I Automated all-atom protein crystal structure refinement

    I Fast multipole methods, direct solvers, matrix factorizations

    [Greengard/Ho/Lee, Ho/Greengard, Ho/Ying, Li/Yang/Martin/Ho/Ying, Minden/Damle/Ho/Ying]

  • Other work

    I Mathematical modeling of cell signaling networks

    I Automated all-atom protein crystal structure refinement

    I Fast multipole methods, direct solvers, matrix factorizations

    [Greengard/Ho/Lee, Ho/Greengard, Ho/Ying, Li/Yang/Martin/Ho/Ying, Minden/Damle/Ho/Ying]

  • References

    Parameter-free invalidation:I E. Gross, B. Davis, K.L. Ho, D.J. Bates, H.A. Harrington. Numerical algebraic geometry for model selection. In

    preparation.

    I H.A. Harrington, K.L. Ho. Parameter-free statistical model invalidation for weakly complex-balanced chemical reactionnetworks. In preparation.

    I H.A. Harrington, K.L. Ho, N. Meshkat. Model rejection using differential algebra techniques. In preparation.I H.A. Harrington, K.L. Ho, T. Thorne, M.P.H. Stumpf. Parameter-free model discrimination criterion based on steady-state

    coplanarity. Proc. Natl. Acad. Sci. U.S.A. 109 (39): 15746–15751, 2012.

    Other:I J.A. Bell, K.L. Ho, R. Farid. Significant reduction in errors associated with nonbonded contacts in protein crystal

    structures: automated all-atom refinement with PrimeX. Acta Cryst. D68: 935–952, 2012.

    I L. Greengard, K.L. Ho, J.-Y. Lee. A fast direct solver for scattering from periodic structures with multiple materialinterfaces in two dimensions. J. Comput. Phys. 258: 738–751, 2014.

    I H.A. Harrington, K.L. Ho, S. Ghosh, KC Tung. Construction and analysis of a modular model of caspase activation inapoptosis. Theor. Biol. Med. Model. 5: 26, 2008.

    I K.L. Ho. Fast direct methods for molecular electrostatics. Ph.D. thesis, New York University, 2012.I K.L. Ho, L. Greengard. A fast direct solver for structured linear systems by recursive skeletonization. SIAM J. Sci.

    Comput. 34 (5): A2507–A2532, 2012.

    I K.L. Ho, L. Greengard. A fast semidirect least squares algorithm for hierarchically block separable matrices. SIAM J.Matrix Anal. Appl. 35 (2): 725–748, 2014.

    I K.L. Ho, H.A. Harrington. Bistability in apoptosis by receptor clustering. PLoS Comput. Biol. 6 (10): e1000956, 2010.I K.L. Ho, L. Ying. Hierarchical interpolative factorization for elliptic operators: differential equations. Preprint,

    arXiv:1307.2895 [math.NA], 2013. To appear, Comm. Pure Appl. Math.

    I K.L. Ho, L. Ying. Hierarchical interpolative factorization for elliptic operators: integral equations. Preprint,arXiv:1307.2666 [math.NA], 2013. To appear, Comm. Pure Appl. Math.

    I Y. Li, H. Yang, E. Martin, K. Ho, L. Ying. Butterfly factorization. Preprint, arXiv:1502.01379 [math.NA], 2015.I V. Minden, A. Damle, K.L. Ho, L. Ying. A technique for updating hierarchical factorizations of integral operators.

    Preprint, arXiv:1411.5706 [math.NA], 2014.