Institute of Fundamental Technological Research
Polish Academy of Sciences

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A. Abdi

New Jersey Institute of Technology (US)


Recent publications
1.  Ozen M., Lipniacki T., Levchenko A., Emamian E.S., Abdi A., Modeling and measurement of signaling outcomes affecting decision making in noisy intracellular networks using machine learning methods, Integrative Biology, ISSN: 1757-9708, DOI: 10.1093/intbio/zyaa009, Vol.12, No.5, pp.122-138, 2020

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.

Keywords:
Cell decision making, noise, decision theory, machine learning, signaling errors, p53 system

Affiliations:
Ozen M. - New Jersey Institute of Technology (US)
Lipniacki T. - IPPT PAN
Levchenko A. - Yale University (US)
Emamian E.S. - New Jersey Institute of Technology (US)
Abdi A. - New Jersey Institute of Technology (US)
2.  Habibi I., Cheong R., Lipniacki T., Levchenko A., Emamian E.S., Abdi A., Computation and measurement of cell decision making errors using single cell data, PLOS COMPUTATIONAL BIOLOGY, ISSN: 1553-7358, DOI: 10.1371/journal.pcbi.1005436, Vol.13, No.4, pp.e1005436-1-17, 2017

Abstract:
In this study a new computational method is developed to quantify decision making errors in cells, caused by noise and signaling failures. Analysis of tumor necrosis factor (TNF) signaling pathway which regulates the transcription factor Nuclear Factor κB (NF-κB) using this method identifies two types of incorrect cell decisions called false alarm and miss. These two events represent, respectively, declaring a signal which is not present and missing a signal that does exist. Using single cell experimental data and the developed method, we compute false alarm and miss error probabilities in wild-type cells and provide a formulation which shows how these metrics depend on the signal transduction noise level. We also show that in the presence of abnormalities in a cell, decision making processes can be significantly affected, compared to a wild-type cell, and the method is able to model and measure such effects. In the TNF—NF-κB pathway, the method computes and reveals changes in false alarm and miss probabilities in A20-deficient cells, caused by cell’s inability to inhibit TNF-induced NF-κB response. In biological terms, a higher false alarm metric in this abnormal TNF signaling system indicates perceiving more cytokine signals which in fact do not exist at the system input, whereas a higher miss metric indicates that it is highly likely to miss signals that actually exist. Overall, this study demonstrates the ability of the developed method for modeling cell decision making errors under normal and abnormal conditions, and in the presence of transduction noise uncertainty. Compared to the previously reported pathway capacity metric, our results suggest that the introduced decision error metrics characterize signaling failures more accurately. This is mainly because while capacity is a useful metric to study information transmission in signaling pathways, it does not capture the overlap between TNF-induced noisy response curves.

Keywords:
Decision making, Radar, Probability distribution, Transcription factors, Signal processing, Signal transduction, Signaling networks, Statistical signal processing

Affiliations:
Habibi I. - New Jersey Institute of Technology (US)
Cheong R. - Johns Hopkins University (US)
Lipniacki T. - IPPT PAN
Levchenko A. - Yale University (US)
Emamian E.S. - New Jersey Institute of Technology (US)
Abdi A. - New Jersey Institute of Technology (US)
3.  Tudelska K., Markiewicz J., Kochańczyk M., Czerkies M., Prus W., Korwek Z., Abdi A., Błoński S., Kaźmierczak B., Lipniacki T., Information processing in the NF-κB pathway, Scientific Reports, ISSN: 2045-2322, DOI: 10.1038/s41598-017-16166-y, Vol.7, pp.15926-1-14, 2017

Abstract:
The NF-κB pathway is known to transmit merely 1 bit of information about stimulus level. We combined experimentation with mathematical modeling to elucidate how information about TNF concentration is turned into a binary decision. Using Kolmogorov-Smirnov distance, we quantified the cell's ability to discern 8 TNF concentrations at each step of the NF-κB pathway, to find that input discernibility decreases as signal propagates along the pathway. Discernibility of low TNF concentrations is restricted by noise at the TNF receptor level, whereas discernibility of high TNF concentrations it is restricted by saturation/depletion of downstream signaling components. Consequently, signal discernibility is highest between 0.03 and 1 ng/ml TNF. Simultaneous exposure to TNF or LPS and a translation inhibitor, cycloheximide, leads to prolonged NF-κB activation and a marked increase of transcript levels of NF-κB inhibitors, IκBα and A20. The impact of cycloheximide becomes apparent after the first peak of nuclear NF-κB translocation, meaning that the NF-κB network not only relays 1 bit of information to coordinate the all-or-nothing expression of early genes, but also over a longer time course integrates information about other stimuli. The NF-κB system should be thus perceived as a feedback-controlled decision-making module rather than a simple information transmission channel.

Keywords:
cellular signaling networks, innate immunity, stress signaling

Affiliations:
Tudelska K. - IPPT PAN
Markiewicz J. - IPPT PAN
Kochańczyk M. - IPPT PAN
Czerkies M. - IPPT PAN
Prus W. - IPPT PAN
Korwek Z. - IPPT PAN
Abdi A. - New Jersey Institute of Technology (US)
Błoński S. - IPPT PAN
Kaźmierczak B. - IPPT PAN
Lipniacki T. - IPPT PAN

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