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Identifiability of linear compartmental tree models and a general formula for input-output equations

Cashous Bortner, Elizabeth Gross, Nicolette Meshkat, Anne Shiu, Seth Sullivant

TL;DR

This work gives the first general formula for the coefficients of input-output equations (certain equations that can be used to determine identifiability) that allows for input and output to be in distinct compartments.

Abstract

A foundational question in the theory of linear compartmental models is how to assess whether a model is structurally identifiable -- that is, whether parameter values can be inferred from noiseless data -- directly from the combinatorics of the model. Our main result completely answers this question for models (with one input and one output) in which the underlying graph is a bidirectional tree; moreover, identifiability of such models can be verified visually}. Models of this structure include two families of models often appearing in biological applications: catenary and mammillary models. Our analysis of such models is enabled by two supporting results, which are significant in their own right. One result gives the first general formula for the coefficients of input-output equations (certain equations that can be used to determine identifiability) that allows for input and output to be in distinct compartments}. In another supporting result, we prove that identifiability is preserved when a model is enlarged and altered in specific ways involving adding a new compartment with a bidirected edge to an existing compartment.

Identifiability of linear compartmental tree models and a general formula for input-output equations

TL;DR

This work gives the first general formula for the coefficients of input-output equations (certain equations that can be used to determine identifiability) that allows for input and output to be in distinct compartments.

Abstract

A foundational question in the theory of linear compartmental models is how to assess whether a model is structurally identifiable -- that is, whether parameter values can be inferred from noiseless data -- directly from the combinatorics of the model. Our main result completely answers this question for models (with one input and one output) in which the underlying graph is a bidirectional tree; moreover, identifiability of such models can be verified visually}. Models of this structure include two families of models often appearing in biological applications: catenary and mammillary models. Our analysis of such models is enabled by two supporting results, which are significant in their own right. One result gives the first general formula for the coefficients of input-output equations (certain equations that can be used to determine identifiability) that allows for input and output to be in distinct compartments}. In another supporting result, we prove that identifiability is preserved when a model is enlarged and altered in specific ways involving adding a new compartment with a bidirected edge to an existing compartment.

Paper Structure

This paper contains 19 sections, 29 theorems, 62 equations, 5 figures, 1 table.

Key Result

Lemma 2.5

Every connected component of a spanning incoming forest contains exactly one sink node, i.e., exactly one node with no outgoing edges.

Figures (5)

  • Figure 1: A linear compartmental model with $In=Out=\{1\}$ and $Leak=\{2\}$.
  • Figure 2: Graphs arising from the linear compartmental model in Figure \ref{['fig:model-K3']}.
  • Figure 3: Depicted are three models, $\mathcal{M} = (G,\{1\},\{1\},\emptyset)$, $\mathcal{M}'=\{G',\{1\},\{4\},\emptyset\}$, and $\mathcal{M}"=\{G',\{1\},\{1\},\emptyset\}$, where $G'$ is the graph obtained from $G$ by adding a leaf edge at compartment $1$ (to a new compartment $4$). See Example \ref{['ex:add-leaf-move-output']}.
  • Figure 4: Two (catenary) models, $\mathcal{M} = (G,\{1\},\{1\}, \{1\})$ and $\mathcal{M}'=(G',\{4\},\{1\},\{1 \})$, where the graph $G'$ is obtained from $G$ by adding a leaf edge at compartment $1$.
  • Figure 5: Two bidirected graphs with $n$ compartments (cf. singularlocus). Left: Catenary (path), denoted by ${\rm Cat}_n$. Right: Mammillary (star), denoted by ${\rm Mam}_n$.

Theorems & Definitions (78)

  • Remark 2.1
  • Remark 2.2
  • Remark 2.3
  • Example 2.4
  • Lemma 2.5
  • proof
  • Lemma 2.6
  • proof
  • Lemma 2.7
  • proof
  • ...and 68 more