Abstract: The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.

Abstract: The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.

Abstract: Described herein is a system for inferring one or a population of biochemical interaction networks, including topology and chemical reaction rates and parameters, from dynamical or statical experimental data, with or without spatial localization information, and a database of possible interactions. Accordingly, the systems and methods described herein may be employed to infer the biochemical interaction networks that exist in a cell. To this end, the systems and methods described herein generate a plurality of possible candidate networks and then apply to these networks a forward simulation process to infer a network. Inferred networks may be analyzed via data fitting and other fitting criteria, to determine the likelihood that the network is correct. In this way, new and more complete models of cellular dynamics may be created.

Abstract: The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.

Abstract: Systems and methods are presented for cell simulation and cell state prediction. For example, a cellular biochemical network intrinsic to a phenotype of a cell can be simulated by specifying its components and their interrelationships. The various interrelationships can be represented with one or more mathematical equations which can be solved to simulate a first state of the cell. The simulated network can then be perturbed, and the equations representing the perturbed network can be solved to simulate a second state of the cell which can then be compared to the first state, identifying the effect of such perturbation on the network, and thereby identifying one or more components as targets. Alternatively, components of a cell can be identified as targets for interaction with therapeutic agents based upon an analytical approach, in which a stable phenotype of a cell is specified and correlated to the state of the cell and the role of that cellular state to its operation.

Abstract: Presently described is a formal language for describing the function of biochemical networks. Because it is a language, it includes a method of parsing, or understanding the language, which is a highly complex recursive algorithm. This formal language, the Cell Language, is described both informally, so that it may be written, and formally, so that it may be parsed. The Cell Language makes it possible to model all the interactions in a cell in a single diagram, with only a few representations of each molecule. The notation is compact and modular, in the sense that complex interactions composed of many subparts may be annotated with the same symbols as the simplest interactions composed of individual molecules or genes.

Abstract: Described herein is a system for inferring one or a population of biochemical interaction networks, including topology and chemical reaction rates and parameters, from dynamical or statical experimental data, with or without spatial localization information, and a database of possible interactions. Accordingly, the systems and methods described herein may be employed to infer the biochemical interaction networks that exist in a cell. To this end, the systems and methods described herein generate a plurality of possible candidate networks and then apply to these networks a forward simulation process to infer a network. Inferred networks may be analyzed via data fitting and other fitting criteria, to determine the likelihood that the network is correct. In this way, new and more complete models of cellular dynamics may be created.

Abstract: Presented herein are techniques and methodologies for creating large-scale data-driven models of biological systems and exemplary applications thereof including drug discovery and industrial applications. Exemplary embodiments include creating a core skeletal simulation (scaleable to any size) from known biological information, collecting quantitative and qualitative experimental data to constrain the simulation, creating a probable reactions database, integrating the core skeletal simulation, the database of probable reactions, and static and dynamical time course measurements to generate an ensemble of biological network structures and their corresponding molecular concentration profiles and phenotypic outcomes that approximate output of the original biological network used for prediction, and finally experimentally validating and iteratively refining the model.