Modeling and measurement of signaling outcomes affecting decision making in noisy intracellular networks using machine learning methods

Abstract

Characterization of decision-making in cells in response to received signals is of importance for understanding how cell fate is determined. The problem becomes multi-faceted and complex when we consider cellular heterogeneity and dynamics of biochemical processes. In this paper, we present a unified set of decision-theoretic, machine learning and statistical signal processing methods and metrics to model the precision of signaling decisions, in the presence of uncertainty, using single cell data. First, we introduce erroneous decisions that may result from signaling processes and identify false alarms and miss events associated with such decisions. Then, we present an optimal decision strategy which minimizes the total decision error probability. Additionally, we demonstrate how graphing receiver operating characteristic curves conveniently reveals the trade-off between false alarm and miss probabilities associated with different cell responses. Furthermore, we extend the introduced framework to incorporate the dynamics of biochemical processes and reactions in a cell, using multi-time point measurements and multi-dimensional outcome analysis and decision-making algorithms. The introduced multivariate signaling outcome modeling framework can be used to analyze several molecular species measured at the same or different time instants. We also show how the developed binary outcome analysis and decision-making approach can be extended to more than two possible outcomes. As an example and to show how the introduced methods can be used in practice, we apply them to single cell data of PTEN, an important intracellular regulatory molecule in a p53 system, in wild-type and abnormal cells. The unified signaling outcome modeling framework presented here can be applied to various organisms ranging from viruses, bacteria, yeast and lower metazoans to more complex organisms such as mammalian cells. Ultimately, this signaling outcome modeling approach can be utilized to better understand the transition from physiological to pathological conditions such as inflammation, various cancers and autoimmune diseases.

Exploring extreme signaling failures in intracellular molecular networks

“…Signals are typically transmitted from the cell membrane to the nucleus via intracellular signaling networks, to regulate some target molecules and alter different cellular functions. Intracellular signaling networks have been studied to address a variety of different questions [1][2][3][4][5][6][7][8].…”

“…They are particularly useful as they do not need detailed kinetic information and still provide certain biologically relevant insights and predictions. Compared to continuous differential equations models [7], discrete models do not require the knowledge of many mechanistic details and numerous kinetic parameters, and are more appropriate for our study in this paper.…”

Learning feedback molecular network models using integer linear programming

“…They can be portrayed as directed graphs in which nodes represent biological molecules, i.e., proteins, genes etc, and edges represent biochemical interactions between the molecules [1][2][3][4]. Research and development of such networks has application in target discovery and drugs development, and analyzing the role of the molecular component in disease pathogeneses [5,6], understanding cellular decision making processes [7,8], understanding cell development and cell differentiation [9], developing molecular fault diagnosis and signaling capacity analysis methods [10][11][12][13], identifying disease subtypes and their regulators [14], and many other applications for better understanding of human diseases. Hence, constructing and analyzing molecular network models have emerged particularly over the past decade as an important area of systems biology research.…”

Exploring Extreme Signaling Failures in Intracellular Molecular Networks

“…Signals are typically transmitted from the cell membrane to the nucleus via intracellular signaling networks, to regulate some target molecules and alter different cellular functions. Intracellular signaling networks have been studied to address a variety of different questions [1][2][3][4][5][6][7][8].…”