Systems Biology and Ratio-Based, Real-Time Disease Surveillance

Abstract

Most infectious disease surveillance methods are not well fit for early detection. To address such limitation, here we evaluated a ratio- and Systems Biology-based method that does not require prior knowledge on the identity of an infective agent. Using a reference group of birds experimentally infected with West Nile virus (WNV) and a problem group of unknown health status (except that they were WNV-negative and displayed inflammation), both groups were followed over 22 days and tested with a system that analyses blood leucocyte ratios. To test the ability of the method to discriminate small data sets, both the reference group (n = 5) and the problem group (n = 4) were small. The questions of interest were as follows: (i) whether individuals presenting inflammation (disease-positive or D+) can be distinguished from non-inflamed (disease-negative or D-) birds, (ii) whether two or more D+ stages can be detected and (iii) whether sample size influences detection. Within the problem group, the ratio-based method distinguished the following: (i) three (one D- and two D+) data classes; (ii) two (early and late) inflammatory stages; (iii) fast versus regular or slow responders; and (iv) individuals that recovered from those that remained inflamed. Because ratios differed in larger magnitudes (up to 48 times larger) than percentages, it is suggested that data patterns are likely to be recognized when disease surveillance methods are designed to measure inflammation and utilize ratios.

Nature and Consequences of Biological Reductionism for the Immunological Study of Infectious Diseases

“…The postulates described in Figure 1 have been abundantly demonstrated (41, 44, 51, 52, 64). As shown in Figure 2C, data ambiguity may occur when structures of low complexity are used (41).…”

“…Both problems may be addressed with structures that reveal a single (one data point-wide) line of observations. Such structures eliminate variability from all dimensions—except along the line—and detect temporal changes that occur along the line, even when such changes are numerically small and/or the individuals being tested include “slow” and “fast” responders (41, 51). …”

“…Because, in 3D/4D space, the number of data combinations may approach infinity, some data structures may be “compressed”, i.e., unobservable (51, 52). Hence, hidden information is a common consequence of the combinatorial properties that characterize Biology.…”

“…Non-reductionist, combinatorial approaches can both detect false-negative and -positive errors and differentiate early from late immune stages (51). They also distinguish subsets of septic patients that differ in mortality rates and immunological profiles (44).…”

Expression profiles of the apoptosis signaling pathway mediated by death receptor and endoplasmic reticulum in rat liver regeneration

“…We identified 40 and 15 death-receptor and ER genes, respectively, related to liver regeneration. We also employed the gene synergy formula (Et) (Xu et al, 2012) to analyze the interactions between the genes identified above in an effort to determine the role of death receptor and ER apoptosis pathway-related genes in rat liver regeneration (Durmuş et al, 2015;Fair and Rivas, 2015).…”