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How quickly do cells respond?

New algorithm uncovers timing of cell networks...


Biologists have long understood the various parts within the cell. But how these parts interact with and respond to each other is mostly unknown.
"We want to understand how cells make decisions, so we can control the decisions they make. A cell might decide to divide uncontrollably, which is the case with cancer. If we understand how cells make that decision, then we can design strategies to intervene."

Neda Bagheri PhD, Departments of Chemical and Biological Engineering, Chemistry of Life Processes, and Center for Synthetic Biology, Northwestern University, Evanston, Illinois.

To better understand the mysterious interactions that occur inside cells, Bagheri and her team designed a new machine learning algorithm to connect the dots between gene interactions inside cell networks. Called "Sliding Window Inference for Network Generation," or SWING, it uses time-series data to reveal the underlying structure of cell networks.

Supported by the National Science Foundation, National Institutes of Health, and Northwestern's Biotechnology Training Program, the research was published in the Proceedings of the National Academy of Sciences. Justin Finkle and Jia Wu, graduate students in Bagheri's laboratory, served as co-first authors of the paper.

In biological experiments, researchers often disturb the subject being studied by altering some part of its function to measure the subject's response to that alteration. For example, researchers might apply a drug that targets a gene's ability to function at a certain level — then observe how the gene and downstream components reacted after being 'disturbed'. But it is difficult to know whether the change in genetic landscape was a direct effect of the drug or the effect of other activities taking place inside the cell.

Justin Finkle: "While many algorithms obtain data from cued signal responses, we used time-series data more creatively to uncover connections among different genes and put them into a causal order."

SWING puts together a more exact picture of cause-and-effect interactions happening among genes by incorporating time delays and sliding windows. Rather than only looking at individual perturbations and responses to those perturbations, SWING uses time-resolved high-throughput, meaning the materials or chemical compounds being examined can be pierced by visible light and disperse that light through a prism by wavelength creating data inferring the time it takes for responses to occur.

Time algorithm

Jia Wu: "Other algorithms make the assumption that cell responses appear more-or-less uniformly in time. We incorporated a window that includes different temporal [time] ranges, so that it captures responses that have dynamic profiles or different delays in time."

 Q consecutive windows
"The dynamics are really important because it's not just if the cell responds to a certain input, but how. Is it slow? Is it fast? Is it a pulse-like or more dynamic? If I introduced a drug, for example, would the cell have an immediate response and then recover or become resistant to the drug? Understanding these dynamics can guide the design of new drugs."

Neda Bagheri PhD.

w ≤ T, and s is the step size between windows.

Both w and s are specified by the user. Each window Wq, where q ? {1, . . . , Q}, is a subset of rows from the time-series data T...

After designing the algorithm, Bagheri's team validated it in the laboratory in both computer simulations and in vitro in E. coli (bacteria found in foods; also in human, as well as animal, intestines) and S. cerevisiae (a species of yeast), both model systems used in laboratory experiments. The algorithm is open source and now available online. And although it was initially designed to probe the interior, mysterious life of cells, it can be applied to many subjects that display activity over time.

"The framework is not specific to cell signaling or even biological contexts," Bagheri adds. "It can be used in very broad contexts, such as in economics or finance. We expect that it could have a great impact."

Significance
Discovery of gene regulatory networks (GRNs) is crucial for gaining insights into biological processes involved in development or disease. Although time-resolved, high-throughput data are increasingly available, many algorithms do not account for temporal delays underlying regulatory systems—such as protein synthesis and posttranslational modifications—leading to inaccurate network inference. To overcome this challenge, we introduce Sliding Window Inference for Network Generation (SWING), which uniquely accounts for temporal information. We validate SWING in both in silico and in vitro experimental systems, highlighting improved performance in identifying time-delayed edges and illuminating network structure. SWING performance is robust to user-defined parameters, enabling identification of regulatory mechanisms from time-series gene expression data.

Abstract
Accurate inference of regulatory networks from experimental data facilitates the rapid characterization and understanding of biological systems. High-throughput technologies can provide a wealth of time-series data to better interrogate the complex regulatory dynamics inherent to organisms, but many network inference strategies do not effectively use temporal information. We address this limitation by introducing Sliding Window Inference for Network Generation (SWING), a generalized framework that incorporates multivariate Granger causality to infer network structure from time-series data. SWING moves beyond existing Granger methods by generating windowed models that simultaneously evaluate multiple upstream regulators at several potential time delays. We demonstrate that SWING elucidates network structure with greater accuracy in both in silico and experimentally validated in vitro systems. We estimate the apparent time delays present in each system and demonstrate that SWING infers time-delayed, gene–gene interactions that are distinct from baseline methods. By providing a temporal framework to infer the underlying directed network topology, SWING generates testable hypotheses for gene–gene influences.

Authors: Justin D. Finkle, Jia J. Wu and Neda Bagheri


Supporting data: Windowed Granger causal inference strategy improves discovery of gene regulatory networks Return to top of page

Feb 20, 2018   Fetal Timeline   Maternal Timeline   News   News Archive




Neda Bagheri and her team designed a machine learning algorithm to help connect the dots between gene interactions inside cell networks. Image credit: Neda Bagheri, Justin Finkle, and Jia Wu


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