Automated scientific discovery, a topic in artificial intelligence has mainly been used to generate scientific insight from data. Our work follows the knowledge-driven discovery approach and introduces the use of category theory as the foundation for modeling diverse engineering fields represented with combinatorial representation. We show how category theory provides support for all stages of the discovery process starting from modeling the engineering knowledge. We demonstrate the use of the approach to rediscover previous discoveries in mechanics and discover new devices, some of which need to be realized to be appreciated. Category theory allows expanding the process to disciplines not modeled with combinatorial representations. We intend to demonstrate this in future studies.

Throughout history, humanity cherished new scientific discoveries; many people devoted their lives to find them in laborious manual processes. Since the emergence of artificial intelligence, about 60 years ago, the possibility of automated scientific discovery has become conceivable and has attracted significant and growing interest. Many studies of automated scientific discovery have been presented over the years. This article presents a new automated discovery approach that crosses disciplines and transfers knowledge between them. The approach requires rich and formal background knowledge to find concepts, methods, or laws not known in one discipline by using their counterpart in other disciplines, specifically disciplinary knowledge that is represented in the Interdisciplinary Engineering Knowledge Genome. Three recent discoveries in mechanics were replicated automatically through software execution, demonstrating the validity of the approach. In future studies, the goal is to discover new knowledge in mechanics and/or electronics, as well as venture into other disciplines including outside engineering.

We introduce a simple, label-free cytometry technique, based on the spatio-temporal fluctuation analysis of pixel gray levels of a cell image utilizing the Gray Level Information Entropy (GLIE) function. In this study, the difference in GLIE random fluctuations and its biophysical etiology in a comparison cell model of leukemic Jurkat cells and human healthy donor lymphocytes was explored. A combination of common bright field microscopy and a unique imaging dish wherein cells are individually held untethered in a picoliter volume matrix of optical chambers was used. Random GLIE fluctuations were found to be greater in malignant Jurkat cells than in benign lymphocytes, while these fluctuations correlate with intracellular vesicle Mean Square Displacement (MSD) values and are inhibited by myosin-2 and adenosine triphosphate (ATP) inhibitors. These results suggest that the incoherent active forces acting on the cytoskeleton which cause mechanical dissipative fluctuation of the cytoskeletal and related intracellular content are the biophysical cellular mechanism behind the GLIE random fluctuation results. Analysis of the results in Jurkat cells and normal lymphocytes suggests the possible potential of this simple and automated label-free cytometry to identify malignancy, particularly in a diagnostic setup of multiple cell examination.

Cancer metastasis is known to cause 90% of cancer lethality. Metastasis is a multistage process which initiates with the penetration/invasion of tumor cells into neighboring tissue. Thus, invasion is a crucial step in metastasis, making the invasion process research and development of anti-metastatic drugs, highly significant. To address this demand, there is a need to develop 3D in vitro models which imitate the architecture of solid tumors and their microenvironment most closely to in vivo state on one hand, but at the same time be reproducible, robust and suitable for high yield and high content measurements. Currently, most invasion assays lean on sophisticated microfluidic technologies which are adequate for research but not for high volume drug screening. Other assays using plate-based devices with isolated individual spheroids in each well are material consuming and have low sample size per condition. The goal of the current protocol is to provide a simple and reproducible biomimetic 3D cell-based system for the analysis of invasion capacity in large populations of tumor spheroids. We developed a 3D model for invasion assay based on HMCA imaging plate for the research of tumor invasion and anti-metastatic drug discovery. This device enables the production of numerous uniform spheroids per well (high sample size per condition) surrounded by ECM components, while continuously and simultaneously observing and measuring the spheroids at single-element resolution for medium throughput screening of anti-metastatic drugs. This platform is presented here by the production of HeLa and MCF7 spheroids for exemplifying single cell and collective invasion. We compare the influence of the ECM component hyaluronic acid (HA) on the invasive capacity of collagen surrounding HeLa spheroids. Finally, we introduce Fisetin (invasion inhibitor) to HeLa spheroids and nitric oxide (NO) (invasion activator) to MCF7 spheroids. The results are analyzed by in-house software which enables semi-automatic, simple and fast analysis which facilitates multi-parameter examination.

Estrogen-induced apoptosis has become a successful treatment for postmenopausal metastatic, estrogen receptor-positive breast cancer. Nitric oxide involvement in the response to this endocrine treatment and its influence upon estrogen receptor-positive breast cancer progression is still unclear. Nitric oxide impact on the MCF7 breast cancer line, before and after estrogen-induced apoptosis, was investigated in 3D culture systems using unique live-cell imaging methodologies. Spheroids were established from MCF7 cells vulnerable to estrogen-induced apoptosis, before and after exposure to estrogen. Spheroids derived from estrogen-treated cells exhibited extensive apoptosis levels with downregulation of estrogen receptor expression, low proliferation rate and reduced metabolic activity, unlike spheroids derived from non-treated cells. In addition to basic phenotypic differences, these two cell cluster types are diverse in their reactions to exogenous nitric oxide. A dual effect of nitric oxide was observed in the breast cancer phenotype sensitive to estrogen-induced apoptosis. Nitric oxide, at the nanomolar level, induced cell proliferation, high metabolic activity, downregulation of estrogen receptor and enhanced collective invasion, contributing to a more aggressive phenotype. Following hormone supplementation, breast cancer 3D clusters were rescued from estrogen-induced apoptosis by these low nitric oxide-donor concentrations, since nitric oxide attenuates cell death levels, upregulates survivin expression and increases metabolic activity. Higher nitric oxide concentrations (100nM) inhibited cell growth, metabolism and promoted apoptosis. These results suggest that nitric oxide, in nanomolar concentrations, may inhibit estrogen-induced apoptosis, playing a major role in hormonal therapy. Inhibiting nitric oxide activity may benefit breast cancer patients and ultimately reduce tumor recurrence.

A simple, label-free cytometry technique is introduced. It is based on the analysis of the fluctuation of image Gray Level Information Entropy (GLIE) which is shown to reflect intracellular biophysical properties like generalized entropy. In this study, the analytical relations between cellular thermodynamic generalized entropy and diffusivity and GLIE fluctuation measures are explored for the first time. The standard deviation (SD) of GLIE is shown by experiments, simulation and theoretical analysis to be indifferent to microscope system “noise”. Then, the ability of GLIE fluctuation measures to reflect basic cellular entropy conditions of early death and malignancy is demonstrated in a cell model of human, healthy-donor lymphocytes, malignant Jurkat cells, as well as dead lymphocytes and Jurkat cells. Utilization of GLIE-based fluctuation measures seems to have the advantage of displaying biophysical characterization of the tested cells, like diffusivity and entropy, in a novel, unique, simple and illustrative way.

Characteristics of spatiotemporal fluctuation of gray values at a single-pixel level within bright field, label free image of a cell are explored in a variety of physiological and mitochondrial dysfunction states. From these fluctuations, the gray level information entropy (GLIE) is calculated and its derivative measures such as standard deviation, autocorrelation and periodic aspect are analyzed. This is realized by a user-friendly combination of common bright field microscopy and a unique imaging dish, wherein cells are individually held untethered, each within a picoliter volume optical chamber in an array, which allows repeatable spatiotemporal observation before, during and after bio-manipulation in situ, at a single-cell resolution, while in a population. GLIE fluctuation measures were exploited to demonstrate the gradual dying process of serum-deprived cells. Furthermore, these measures were realized in the evaluation of cell response to Phorbol Myristic Acetate (PMA), triiodothyronine (T3) thyroid hormone and mild hypothermia, and three different mitochondrial inhibitors as well: rotenone, carbonyl cyanide m-chlorophenyl hydrazine (CCCP) and oligomycin. The GLIE fluctuation-based measures demonstrated the ability to (a) significantly distinguish cellular response to all six mediators, and (b) identify subgroups of cells according to their response to mild hypothermia. On the whole, employment of high contrast microscopy approaches, i.e., Phase Contrast (PC) and Differential Interference Contrast (DIC), for tracing cellular events via the spatiotemporal fluctuation measures, did not show noticeable advantages over simple Bright Field (BF) microscopy.

Spatiotemporal fluctuation of homogeneity and randomness of gray values within an image was explored and utilized as a label-free means for cell examination. This was done by utilizing a user-friendly combination of simple bright field microscope and Cytocapture dish, wherein cells are individually held, each within a picoliter optical chamber, forming an array of cells to be repeatedly measured over time and biomanipulated in situ at single-cell resolution. First, the measured gray level information entropy (GLIE) was used and, based on the fact that living cells are not in a state of thermodynamic equilibrium but rather in a metastable state, two fluctuation-sensitive measures were proposed and examined: ASDE—the spatial average of temporal standard deviation (SD) of GLIE, and AA—the average time autocorrelation of GLIE. System performance was validated on cell-free solutions. This was followed by examining the performance of the measures AGLIE, ASDE, and AA to distinguish among individual live-still, dead and live cells from various cell lines, as well as between cells which were and were not induced to differentiate. Results, which were obtained on four types of cells, indicate advantages of the proposed measures which are believed to be significant additions to the microscope-based probe-free toolbox.