78 research outputs found
Bioengineering thermodynamics of biological cells
Background: Cells are open complex thermodynamic systems. They can be also regarded as complex engines that execute a series of chemical reactions. Energy transformations, thermo-electro-chemical processes and transports phenomena can occur across the cells membranes. Moreover, cells can also actively modify their behaviours in relation to changes in their environment.
Methods: Different thermo-electro-biochemical behaviours occur between health and disease states. But, all the living systems waste heat, which is no more than the result of their internal irreversibility. This heat is dissipated into the environment. But, this wasted heat represent also a sort of information, which outflows from the cell toward its environment, completely accessible to any observer.
Results: The analysis of irreversibility related to this wasted heat can represent a new approach to study the behaviour of the cells themselves and to control their behaviours. So, this approach allows us to consider the living systems as black boxes and analyze only the inflows and outflows and their changes in relation to the modification of the environment. Therefore, information on the systems can be obtained by analyzing the
changes in the cell heat wasted in relation to external perturbations.
Conclusions: The bioengineering thermodynamics bases are summarized and used to analyse possible controls of the calls behaviours based on the control of the ions fluxes across the cells membranes
How Turing parasites expand the computational landscape of digital life
Why are living systems complex? Why does the biosphere contain living beings
with complexity features beyond those of the simplest replicators? What kind of
evolutionary pressures result in more complex life forms? These are key
questions that pervade the problem of how complexity arises in evolution. One
particular way of tackling this is grounded in an algorithmic description of
life: living organisms can be seen as systems that extract and process
information from their surroundings in order to reduce uncertainty. Here we
take this computational approach using a simple bit string model of coevolving
agents and their parasites. While agents try to predict their worlds, parasites
do the same with their hosts. The result of this process is that, in order to
escape their parasites, the host agents expand their computational complexity
despite the cost of maintaining it. This, in turn, is followed by increasingly
complex parasitic counterparts. Such arms races display several qualitative
phases, from monotonous to punctuated evolution or even ecological collapse.
Our minimal model illustrates the relevance of parasites in providing an active
mechanism for expanding living complexity beyond simple replicators, suggesting
that parasitic agents are likely to be a major evolutionary driver for
biological complexity.Comment: 13 pages, 8 main figures, 1 appendix with 5 extra figure
- …