Several authors have argued that causes differ in the degree to which they are ‘specific’ to their effects. Woodward has used this idea to enrich his influential interventionist theory of causal explanation. Here we propose a way to measure causal specificity using tools from information theory. We show that the specificity of a causal variable is not well defined without a probability distribution over the states of that variable.
The origins of life likely required the cooperation among a set of molecular species interacting in a network. If so, then the earliest modes of evolutionary change would have been governed by the manners and mechanisms by which networks change their compositions over time. For molecular events, especially those in a pre-biological setting, these mechanisms have rarely been considered. We are only recently learning to apply the results of mathematical analyses of network dynamics to prebiotic events.
We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 (doi:10.1371/journal.pone.0001672)) and that of the budding yeast Saccharomyces cerevisiae (Li et al. 2004 Proc. Natl Acad. Sci.
This review explores the incessant evolutionary interaction and co-development between immune system evolution and somatic evolution, to put it into context with the short, over 60-year, detailed human study of this extraordinary protective system. Over millions of years, the evolutionary development of the immune system in most species has been continuously shaped by environmental interactions between microbes, and aberrant somatic cells, including malignant cells.
We use a microfabricated ecology with a doxorubicin gradient and population fragmentation to produce a strong Darwinian selective pressure that drives forward the rapid emergence of doxorubicin resistance in multiple myeloma (MM) cancer cells. RNA sequencing of the resistant cells was used to examine (i) emergence of genes with high de novo substitution densities (i.e., hot genes) and (ii) genes never substituted (i.e., cold genes). The set of cold genes, which were 21% of the genes sequenced, were further winnowed down by examining excess expression levels.
We compare the informational architecture of biological and random networks to identify informational features that may distinguish biological networks from random. The study presented here focuses on the Boolean network model for regulation of the cell cycle of the fission yeast Schizosaccharomyces pombe. We compare calculated values of local and global information measures for the fission yeast cell cycle to the same measures as applied to two different classes of random networks: Erdös–Rényi and scale-free.
How can matter transition from the nonliving to the living state? The answer is essential for understanding the origin of life on Earth and for identifying promising targets in the search for life on other planets.
Astrobiology is the science that seeks to understand the story of life in our universe. Astrobiology includes investigation of the conditions that are necessary for life to emerge and flourish, the origin of life, the ways that life has evolved and adapted to the wide range of environmental conditions here on Earth, the search for life beyond Earth, the habitability of extraterrestrial environments, and consideration of the future of life here on Earth and elsewhere.
Life is so remarkable, and so unlike any other physical system, that it is tempting to attribute special factors to it. Physics is founded on the assumption that universal laws and principles underlie all natural phenomena, but is it far from clear that there are 'laws of life' with serious descriptive or predictive power analogous to the laws of physics. Nor is there (yet) a 'theoretical biology' in the same sense as theoretical physics. Part of the obstacle in developing a universal theory of biological organization concerns the daunting complexity of living organisms.
Cancer is the most intensively studied biological phenomenon, yet it remains poorly understood. Mortality and morbidity rates for many major cancer types have scarcely changed in decades. We posit that this lack of progress stems from a flawed conceptual model for the nature of cancer. A novel NCI physical science and cancer initiative encouraged us to re-consider the conceptual foundations of the current cancer paradigm, and we present an outline of an alternative view here.