In silico systems biology model of lipid-lowering therapies for cardiovascular disease

Abstract

Provided herein are methods and systems for making patient-specific therapy recommendations of a lipid-lowering therapy for patients with known or suspected cardiovascular disease, such as atherosclerosis.

What is claimed is: 
     
         1 . A computer-implemented method for generating an in silico systems biology model of atherosclerotic cardiovascular disease, the method comprising:
 obtaining multiple first inputs representing biological pathways associated with the atherosclerotic cardiovascular disease;   generating, based on the first inputs, a first set of networks, wherein each network comprises a plurality of nodes, each node representing a baseline level of a molecule, and a plurality of edges between pairs of nodes, each edge representing a molecule-molecule interaction;   obtaining second inputs indicative of calibration data from multiple test subjects who have been diagnosed with the atherosclerotic cardiovascular disease;   determining, from the second inputs, a disease-associated molecule level for nodes representing molecules in the first network; and   generating, based on the first network and the disease-associated molecule levels, a second set of networks, wherein the second set of networks, updated using the second inputs, represent a calibrated in silico systems biology model of the atherosclerotic cardiovascular disease and includes the disease-associated molecule levels for nodes representing proteins in the second set of networks.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein at least two of the nodes represent molecules whose levels are affected by the atherosclerotic cardiovascular disease. 
     
     
         3 . The computer-implemented method of  claim 1 , wherein at least one of the second set of networks includes a disease-associated molecule level for each of the nodes in the network. 
     
     
         4 . The computer-implemented method of  claim 1 , wherein the calibration data comprises non-invasively obtained imaging data. 
     
     
         5 . The computer-implemented method of  claim 4 , wherein the non-invasively obtained imaging data is obtained by computed tomography (CT), dual energy computed tomography (DECT), spectral computed tomography (spectral CT), computed tomography angiography (CTA), cardiac computed tomography angiography (CCTA), magnetic resonance imaging (MM), multi-contrast magnetic resonance imaging (multi-contrast MRI), ultrasound (US), positron emission tomography (PET), intra-vascular ultrasound (IVUS), optical coherence tomography (OCT), near-infrared radiation spectroscopy (NIRS), or single-photon emission tomography (SPECT) diagnostic images or any combination thereof. 
     
     
         6 . The computer-implemented method of  claim 1 , wherein the molecule refers to a protein, a gene, or a metabolite. 
     
     
         7 . The computer-implemented method of  claim 6 , wherein the first set of networks further comprises edges representing protein-protein interactions, gene-gene interactions, and/or protein-gene interactions. 
     
     
         8 . The computer-implemented method of  claim 7 , wherein interactions represent any one of activation, inhibition, indirect effect, state change, binding, dissociation, phosphorylation, dephosphorylation, glycosylation, ubiquitination, and methylation as a result of an interaction between two molecules. 
     
     
         9 . The computer-implemented method of  claim 1 , wherein the multiple first inputs are obtained by querying a pathway database. 
     
     
         10 . The computer-implemented method of  claim 9 , wherein the pathway database is Kyoto Encyclopedia of Genes and Genomes (KEGG). 
     
     
         11 . The computer-implemented method of  claim 1 , wherein each edge in the first network is directed with a weight, indicating a direction of the molecule-molecule interaction. 
     
     
         12 . The computer-implemented method of  claim 1 , wherein each network represents one or more cell types. 
     
     
         13 . The computer-implemented method of  claim 12 , wherein the one or more cell types comprise one or more of endothelial cells, vascular smooth muscle cells, macrophages, or lymphocytes. 
     
     
         14 . The computer-implemented method of  claim 12 , wherein the first network comprises:
 (i) a core network representing protein-protein interactions unique to each respective cell type;   (ii) a mid network representing protein-protein interactions that occur in multiple cell types, but not all cell types; and   (iii) a full network representing protein-protein interactions that occur in all cell types. [Does the first network really have the first two parts as well as a full network   
     
     
         15 . The computer-implemented method of  claim 1 , wherein the calibration data comprises proteomic data and/or transcriptomic data. 
     
     
         16 . The computer-implemented method of  claim 15 , wherein the transcriptomic data is obtained by microarray, RNA sequencing (RNA-seq), single cell RNA sequencing (scRNA-seq), reverse transcriptase PCR (RT-PCR), or any combination thereof.