Publications

Remarkable progress in bioengineering over the past two decades has enabled the formulation of fundamental design principles for a variety of medical and non-medical applications. These advancements have laid the foundation for building multicellular engineered living systems (M-CELS) from biological parts, forming functional modules integrated into living machines. These cognizant design principles for living systems encompass novel genetic circuit manipulation, self-assembly, cell–cell/matrix communication, and artificial tissues/organs enabled through systems biology, bioinformatics, computational biology, genetic engineering, and microfluidics. Here, we introduce design principles and a blueprint for forward production of robust and standardized M-CELS, which may undergo variable reiterations through the classic design-build-test-debug cycle. This Review provides practical and theoretical frameworks to forward-design, control, and optimize novel M-CELS. Potential applications include biopharmaceuticals, bioreactor factories, biofuels, environmental bioremediation, cellular computing, biohybrid digital technology, and experimental investigations into mechanisms of multicellular organisms normally hidden inside the “black box” of living cells.

JTD Keywords: cell-fate specification, endothelial-cells, escherichia-coli, extracellular-matrix, gene-expression noise, nuclear hormone-receptors, pluripotent stem-cells, primitive endoderm, transcription factors, Artificial tissues, Assembly cells, Biological parts, Biological systems, Bioremediation, Blood-brain-barrier, Cell engineering, Cell/matrix communication, Design principles, Environmental technology, Functional modules, Fundamental design, Genetic circuits, Genetic engineering, Living machines, Living systems, Medical applications, Molecular biology, Synthetic biology

Durable control of invasive solid tumors necessitates identifying therapeutic resistance mechanisms and effective drug combinations. In this work, we used a network-based mathematical model to identify sensitivity regulators and drug combinations for the PI3Ka inhibitor alpelisib in estrogen receptor positive (ER) PIK3CAmutant breast cancer. The model-predicted efficacious combination of alpelisib and BH3 mimetics, for example, MCL1 inhibitors, was experimentally validated in ER breast cancer cell lines. Consistent with the model, FOXO3 downregulation reduced sensitivity to alpelisib, revealing a novel potential resistance mechanism. Cell line-specific sensitivity to combinations of alpelisib and BH3 mimetics depended on which BCL2 family members were highly expressed. On the basis of these results, newly developed cell line-specific network models were able to recapitulate the observed differential response to alpelisib and BH3 mimetics. This approach illustrates how network-based mathematical models can contribute to overcoming the challenge of cancer drug resistance.

JTD Keywords: activation, akt, feedback, foxo, leads, p27(kip1), phosphorylation, reveals, transcription factors, Dependent kinase inhibitor

Ribonucleotide reductase (RNR) is a key enzyme that mediates the synthesis of deoxyribonucleotides, the DNA precursors, for DNA synthesis in every living cell. This enzyme converts ribonucleotides to deoxyribonucleotides, the building blocks for DNA replication, and repair. Clearly, RNR enzymes have contributed to the appearance of genetic material that exists today, being essential for the evolution of all organisms on Earth. The strict control of RNR activity and dNTP pool sizes is important, as pool imbalances increase mutation rates, replication anomalies, and genome instability. Thus, RNR activity should be finely regulated allosterically and at the transcriptional level. In this review we examine the distribution, the evolution, and the genetic regulation of bacterial RNRs. Moreover, this enzyme can be considered an ideal target for anti-proliferative compounds designed to inhibit cell replication in eukaryotic cells (cancer cells), parasites, viruses, and bacteria.

JTD Keywords: Anaerobiosis, Transcription Factors, Evolution, Gene regulation, Ribonucleotide reductase, DNA Synthesis, NrdR,nrd

Transcription Factor binding sites are short and degenerate sequences, located mostly at the promoter of the gene, where some proteins bind in order to regulate transcription. Locating these sequences is an important issue, and many experimental and computational methods have been developed. Algorithms to search binding sites are usually based on Position Specific Scoring Matrices (PSSM), where each position is treated independently. Mapping symbolical DNA to numerical sequences, a detector has been built with a Principal Component Analysis of the numerical sequences, taking into account covariances between positions. When a treatment of missing values is incorporated the Q-residuals detector, based on PCA, performs better than a PSSM algorithm. The performance on the detector depends on the estimation of missing values and the percentage of missing values considered in the model.

JTD Keywords: Binding sites, BPCA, Missing values, Numerical DNA, Principal components analysis, Transcription factors

Transcription starts when multiple proteins, known as transcription factors recognize and bind to transcription start site in DNA sequences. Since mutation in transcription factor binding sites are known to underlie diseases it remains a major challenge to identify these binding sites. Conversion from symbolic DNA to numerical sequences and genome data make it possible to construct a detector based on a numerical analysis of DNA binding sites. A subspace model for the TFBS is built. TFBS will show a very small distance to this particular subspace. Using this distance binding sites are distinguished from random sequences and from genome data.

JTD Keywords: Transcription factors, Binding sites, Principal components analysis