@article {Simula.simula.72,
	title = {Systems Approaches and Algorithms for Discovery of Combinatorial Therapies},
	journal = {Wiley Interdisciplinary Reviews: Systems Biology and Medicine},
	volume = {2},
	number = {2},
	year = {2010},
	pages = {93},
	abstract = {Effective therapy of complex diseases requires control of highly nonlinear complex networks that remain incompletely characterized. In particular, drug intervention can be seen as control of cellular network activity. Identification of control parameters presents an extreme challenge due to the combinatorial explosion of control possibilities in combination therapy and to the incomplete knowledge of the systems biology of cells. In this review paper, we describe the main current and proposed approaches to the design of combinatorial therapies, including the heuristic methods used now by clinicians and alternative approaches suggested recently by several authors. New approaches for designing combinations arising from systems biology are described. We discuss in special detail the design of algorithms that identify optimal control parameters in cellular networks based on a quantitative characterization of control landscapes, maximizing utilization of incomplete knowledge of the state and structure of intracellular networks. The use of new technology for high{-}throughput measurements is key to these new approaches to combination therapy and essential for the characterization of control landscapes and implementation of the algorithms. Combinatorial optimization in medical therapy is also compared with the combinatorial optimization of engineering and materials science and similarities and differences are delineated.},
	doi = {10.1002/wsbm.51},
	author = {Jacob D. {Feala} and Cortes, Jorge and Phillip M. {Duxbury} and Piermarocchi, Carlo and Andrew D. {McCulloch} and Paternostro, Giovanni}
}
@article {Simula.sc.1194,
	title = {Metabolism As Means for Hypoxia Adaptation: Metabolic Profiling and Flux Balance Analysis},
	journal = {BMC Systems Biology},
	volume = {3},
	number = {91},
	year = {2009},
	pages = {3-91},
	publisher = {BioMedCentral},
	doi = {10.1186/1752-0509-3-91},
	author = {Jacob D. {Feala} and Laurence C. {Coquin} and Zhou, Dan and Gabriel G. {Haddad} and Paternostro, Giovanni and Andrew D. {McCulloch}}
}
@article {Simula.sc.930,
	title = {Discovering Regulators of the Drosophila Cardiac Hypoxia Response Using Automated Phenotyping Technology},
	journal = {Annals of the New York Academy of Sciences},
	volume = {1123},
	year = {2008},
	month = {March},
	pages = {169-177},
	abstract = {Necrosis and apoptosis during acute myocardial infarction result in part from the inability of hy- poxic cardiac myocytes to match ATP supply and demand. In contrast, hypoxia-tolerant organisms, such as Drosophila, can rapidly regulate cellular metabolism to survive large oxygen fluctuations. A genetic screen of fly heart function during acute hypoxia can be an unbiased way to discover essential enzymes and novel signaling proteins involved in this response. We have developed a pro- totype to show proof of concept for a genome-scale screen, using computer automation to rapidly gather in vivo hypoxic heart data in adult Drosophila. Our system automatically anesthetizes flies, deposits them on a microscope slide, and locates the heart organ of each fly. The system then applies a hypoxia stimulus, acquires time-space (M-mode) images of the heart walls, and analyzes heart rate and rhythm. The prototype can produce highly controlled measurements of up to 55 flies per hour, which we demonstrated by characterizing the effect of temperature, oxygen con- tent, and genetic background on the hypoxia response. We discuss the possible applications of a genome-wide cardiac phenotype data set in systems biology analyses of hypoxic metabolism, using genome-scale interaction networks and constraint-based metabolic models.},
	doi = {10.1196/annals.1420.019},
	author = {Jacob D. {Feala} and Jeffrey H. {Omens} and Paternostro, Giovanni and Andrew D. {McCulloch}}
}
