Homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostasis occurs frequently in biochemical networks and in other networks of interacting elements where mathematical models are based on differential equations associated to the network. These networks can be abstracted as digraphs G with a distinguished input node \iota, a different distinguished output node o, and a number of regulatory nodes \rho_1,\ldots,\rho_n. In these models the input-output map x_o(I) is defined by a stable equilibrium X_0 at I_0. Stability implies that there is a stable equilibrium X(I) for each I near I_0 and infinitesimal homeostasis occurs at I_0 when (dx_o/dI)(I_0) = 0. We show that there is an (n+1)\times(n+1) homeostasis matrix H(I) for which dx_o/dI = 0 if and only if \det(H) = 0. We  use combinatorial matrix theory to factor the polynomial \det(H) and thereby determine a menu of different types of possible homeostasis associated with each digraph G. Specifically, we prove that each factor corresponds to a subnetwork of G. The factors divide into two combinatorially defined classes: structural and appendage. Structural factors correspond to feedforward motifs and appendage factors correspond to feedback motifs. Finally, we discover an algorithm for determining the homeostatic subnetwork motif corresponding to each factor of \det(H) without performing numerical simulations on model equations. The algorithm allows us to classify low degree factors . There are two types of degree 1 homeostasis (negative feedback loops and kinetic or Haldane motifs) and there are two types of degree 2 homeostasis (feedforward loops and a degree two appendage motif ).
Homeostasis refers to a phenomenon whereby the output x_o of a system is approximately constant on variation of an input I. Homeostasis occurs frequently in biochemical networks and in other networks of interacting elements where mathematical models are based on differential equations associated to the network. These networks can be abstracted as digraphs G with a distinguished input node \iota, a different distinguished output node o, and a number of regulatory nodes \rho_1,\ldots,\rho_n. In these models the input-output map x_o(I) is defined by a stable equilibrium X_0 at I_0. Stability implies that there is a stable equilibrium X(I) for each I near I_0 and infinitesimal homeostasis occurs at I_0 when (dx_o/dI)(I_0) = 0. We show that there is an (n+1)\times(n+1) homeostasis matrix H(I) for which dx_o/dI = 0 if and only if \det(H) = 0. We note that the entries in H are linearized couplings and \det(H) is a homogeneous polynomial of degree n+1 in these entries. We use combinatorial matrix theory to factor the polynomial \det(H) and thereby determine a menu of different types of possible homeostasis associated with each digraph G. Specifically, we prove that each factor corresponds to a subnetwork of G. The factors divide into two combinatorially defined classes: structural and appendage. Structural factors correspond to feedforward motifs and appendage factors correspond to feedback motifs. Finally, we discover an algorithm for determining the homeostasis subnetwork motif corresponding to each factor of \det(H) without performing numerical simulations on model equations. The algorithm allows us to classify low degree factors of \det(H ).
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note that the entries in H are linearized couplings and \det(H) is a homogeneous polynomial of degree n+1 in these entries. We
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homeostatic
homeostasis
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. There are two types of degree 1 homeostasis (negative feedback loops and kinetic or Haldane motifs) and there are two types of degree 2 homeostasis (feedforward loops and a degree two appendage motif
of \det(H