Is Entropy the driving force behind Life?

We all would be familiar with the second Law of Thermodynamics which simply put states that entropy of the system always increases with time. Entropy means the disorder in the system. Thus with the passage of time a system tends to loose its order and tends to go haywire. This is widely accepted and one of the laws of the universe and thus cannot be violated. Well one cannot violate the laws but can always bend it a bit to one's own advantage. Don't believe me, ask a lawyer. But I am not here to talk about lawyers. I talk about life.

One fine night, I walked on the roads of Bangalore, watching the traffic whiz by, in contemplative mood wondering how people were going about their lives in a hurry always wanting to go some place, always on the move and soon with passage of time they will die and fade away and new people will take their place and may be the hurry would be even more as technology would have advanced even more, we would be knowing more, would be living in better conditions and the pace of life would have become even faster. And as my thought process went on I realized life always ended, yet it always began and it always persisted. One word came to my mind, Entropy! How come Entropy is never able to catch up with life?

Consider an organism (be it unicellular or multi-cellular) as a unit of life. Now, organism as a whole be it unicellular or multi-cellular is a system having definite regulated boundary separating it from the environment. It exchanges energy and matter with the environment. As per the second law of thermodynamics, the system is suppose to disintegrate with time. That happens in case of an organism, the phenomenon we call aging (aging also has a relation with entropy as arrow of time which we will discuss later) and when the entropy is too high that it does not allow the normal life processes to function properly, the organism dies. So, the law of the universe prevails and everybody does not live happily ever after. But..

But before the organism dies it carries out an important process. It replicates (or reproduces in case of multi-cellular organisms). For maintaining simplicity of the explanation, we will consider only single cellular organism. So an organism divides into two. This division process happens after a certain period of time (ageing) and when the entropy of the cell has reached a certain level. The process of division reduces the entropy. Thus, one system with high entropy divides and gives rise to two systems with less entropy. So, the organism may "die" but the Life as a process goes on ad continuum. And this brings us to Evolution.

Evolution is nothing but a way or a method of Life to continue despite the changing environment. Remember, Life after all is nothing but a chemical system. And evolution happens by reproduction (or division of cells or whatever you want to call it that gives rise to new Life). So, to sum up Life continues by evolution and evolution happens by reproduction. So, we can safely assume that reproduction is a means of lowering entropy. But, at the same time reproduction is a means by which evolution happens. So, to put it together, evolution is a means of lowering entropy.

Come to think of it, evolution leads to a higher order in the life system (be it a cell or organism) i.e. it increases the efficiency of the system (cell or life). This in turn means lowering of randomness or entropy of the system. Hence, evolution leads to lowering of entropy, in other words evolution leads to survival. As discussed before, Life escapes from this second law of thermodynamics to 'SURVIVE', otherwise Life as any other chemical system will perish with time. Hence, for Life to survive means lowering of the entropy at a certain minimal level and reproducing is a means by which Life achieves this feat.

Now, entropy is also called the arrow of time because it is unidirectional in other words, irreversible. So, is Evolution as a recent study by University of Oregon researchers have found (appeared in Sep. 24, 2009 issue of Nature). Well, all of this shows that Entropy rather indirectly plays a very important role in Evolution and in other words continuation of Life. To put it spectacularly, Entropy is a force that drives Life although in an indirect way.

Well looks impossible isn't it? But all of this adds up. We need to take a look at the entropy level of a cell at various points thus establishing an entropy cycle of a cell. Well it is difficult at this point to calculate entropy of a whole cell but if found out can certainly give us key insight of functioning of Life.

Life is an Information Processing System

Before you read this article, I would like to tell you that the ideas and theories expressed here are my own views and still need to be scientifically established. So do not consider it as proof of anything. You are free to extrapolate them, comment on them or critique them. Hope, you will find it interesting.

When you see life (by life here I mean living organisms and not the philosophical meaning of it) in terms of collective steps of information processing then information in biological entities can be classified into three types:

1) Genetic information

2) Structural information

3) Non-genetic information (in case of higher organisms, particularly humans)

Genetic Information:

It is the latent information stored in genetic material that gives rise to the basic structure of an organism. It is stored in a coded form (hence condensed) and when decoded gives rise to the structural information. It is complete (in itself it is sufficient to give rise to whole biological organism) and is passed over from generation to generation.

Structural Information:

By structural information I mean the structure of the proteins they fold into and hence giving rise to their functionality. It is the most crucial information for any living organism because it makes them tick. No doubt, protein folding is holy grail for biology at this moment. Even though there is so much diversity in case of protein structure, the domains are very much conserved and the reactions are very specific. Structural information is derived from the genetic information and hence passes from generation to generation as genetic information which each organism has to decode into the structural information. And structural information is also responsible for expression of genetic information, its replication and storage.

Non-genetic Information:

I define non-genetic information as the information gathered from the environment. It is the information that an organism "remembers" or "reacts to". E.g. human learning, response to external stimuli, flight or fight response in animals, antigen information stored in immune system (memory B-cells) etc. It may be passed from generation to generation but not in genetic form. e.g. you teaching your child how to ride a bicycle, antibodies passed on from mother to child during birth etc.

If you consider various biological processes as systems, then, based on the above classification, they can be classified into systems processing one or more of the above information. This classification gives a whole new "view" of life and its processes. Also, there is a theory proposed for evolution which considers the interacting molecules as the basic entity of evolution instead of just molecules. This theory further highlights that reactions are a way of passing information (or signals) from one molecule to another and that information is in the structural form in molecules. This gives us a very interesting picture of how biochemical reactions and processes can be analysed as information processing and "passing". Consider a protein A with a unique structure (hence unique structure information) binds in a specific way to protein B which is a chemical reaction in biological terms but can be seen as information passing (from one structure to another). The information changes in terms of the content because the structure of protein B is different from protein A yet the ultimate sanctity of the information passing is conserved since we know that protein B with its unique structure will pass it only to protein C which is the intended receiver (notice again the information content changes since protein C structure i.e. structure information is different). Now, extrapolating the observation we just made, we can see that the structure of protein domains (since it is the domains that interact with each other) are very limited and conserved. Now, if we know the exact protein domain-domain interaction in a biological process, and we know the rate of the reaction (it would be the frequency of the information passing), we can construct a computer model that can not only represent the whole biological process but also replicate the same in silico.

If further analysed then we can see life processes as different information computing processes and hence theoretically can compute it and decide an outcome of the process. I would really like to try this out practically. And I believe Computational systems biology is the answer for that. Well, I know we need to fine tune this but it is a good start; at least for me! Let's see where it goes from here.

Cytoscape - A tool for biological networks

Cytoscape is an open-source software for creation, visualization and analysis of biological networks. Cytoscape is a systems biology software used for integrating biomolecular interaction networks with high-throughput expression data and other molecular state information. Cytoscape is an amazing tool when used with large databases of protein-protein, protein-DNA or genetic interactions. With Cytoscape its possible to have visual integration of the network with expression profiles, phenotypes and other molecular state information and link the network to databases of functional annotations.

Cytoscape basically consists of a "core" software that provides basic functionality and is extensible through plug-in architecture. There are various plug-ins available based on your need. And believe me, some of the plug-ins have great functionality and can do wonders with your data. The central organizing concept of Cytoscape is a network (graph), with nodes representing genes, proteins and molecules and edges or links between the nodes representing interactions between them.

Biology in a post-genomic era is an information science and converting the vast amount of biological information into useful knowledge is the key in today's world. Cytoscape allows you to do just that! It even has a functionality to do free text search across large number of databases! According to me Cytoscape is a very good tool for organizing and analyzing the ocean of biological information that is available today. Not only that, with the help of Cytoscape you can build your prediction models which can then be used to design your wet lab experiments. Well as they say it, Knowledge is Power, in that case Cytoscape is certainly a powerful computational systems biology tool.

Cytoscape is a collaborative project between the Institute for Systems Biology (Leroy Hood lab), the University of California San Diego (Trey Ideker lab), Memorial Sloan-Kettering Cancer Center (Chris Sander lab), the Institute Pasteur (Benno Schwikowski lab), Agilent Technologies (Annette Adler lab) and the University of California, San Francisco (Bruce Conklin lab).

For further information and download please visit http://www.cytoscape.org.

SBML

SBML stands for Systems Biology Markup Language. It is a model representation format for systems biology. SBML is built to descibe systems of biochemical reactions be it cell signaling pathways, metabolic pathways, gene regulatory networks and others. SBML is structured on XML (eXtensible Markup Language) and follows XML schema.

With rapidly evolving field of systems biology and various computational tools and techniques, a common format was required for "communication" between different tools and techniques. SBML tries to just that! Infact, SBML can be described as lingua franca of systems biology. A word of caution though SBML is not an attempt to define a universal language but to enable communication of most essential aspects of the models.

Structure of SBML:

A model is usually sum total of reactant and product species, reactions, reaction rates amd parameters of rate expression, compartments of the species and units. In SBML, each component is described using a specific type of data object that organizes the relevant information:

beginning of model definition
list of funtion definitions (optional)
list of unit definions (optional)
list of compartment types (optional)
list of species types (optional)
list of compartments (optional)
list of species (optional)
list of parameters (optional)
list of initial assignments (optional)
list of rules (optional)
list of constraints (optional)
list of reactions (optional)
list of events (optional)
end of model definition

The explanation of each component taken from SBML documentation is given below:

Function definition: A named mathematical function that may be used throughout the rest of a model.

Unit definition: A named definition of a new unit of measurement, or a redefinition of an SBML predefined unit. Named units can be used in the expression of quantities in a model.

Compartment Type: A type of location where reacting entities such as chemical substances may be located.

Species type: A type of entity that can participate in reactions. Typical examples of species types include ions such as Ca2+, molecules such as glucose or ATP, and more.

Compartment: A well-stirred container of a particular type and finite size where species may be located. A model may contain multiple compartments of the same compartment type. Every species in a model must be located in a compartment.

Species: A pool of entities of the same species type located in a specific compartment.

Parameter : A quantity with a symbolic name. In SBML, the term parameter is used in a generic sense to refer to named quantities regardless of whether they are constants or variables in a model. SBML Level 2 provides the ability to define parameters that are global to a model as well as parameters that are local to a single reaction.

Initial Assignment: A mathematical expression used to determine the initial conditions of a model. This type of object can only be used to define how the value of a variable can be calculated from other values and variables at the start of simulated time.

Rule: A mathematical expression added to the set of equations constructed based on the reactions defined in a model. Rules can be used to define how a variable’s value can be calculated from other variables, or used to define the rate of change of a variable. The set of rules in a model can be used with the reaction rate equations to determine the behavior of the model with respect to time. The set of rules constrains the model for the entire duration of simulated time.

Constraint: A means of detecting out-of-bounds conditions during a dynamical simulation and optionally issuing diagnostic messages. Constraints are defined by an arbitrary mathematical expression computing a true/false value from model variables, parameters and constants. An SBML constraint applies at all instants of simulated time; however, the set of constraints in model should not be used to determine the behavior of the model with respect to time.

Reaction: A statement describing some transformation, transport or binding process that can change the amount of one or more species. For example, a reaction may describe how certain entities (reactants) are transformed into certain other entities (products). Reactions have associated kinetic rate expressions describing how quickly they take place.

Event: A statement describing an instantaneous, discontinuous change in a set of variables of any type (species quantity, compartment size or parameter value) when a triggering condition is satisfied.

A software tool can read SBML and translate it into its own format for model analysis. The tool can then used for networking the model or smilulating the model by constructing differential equations representing the network and then perform numerical time integration on the equations to explore the model's dynamic behaviour.

Further infromation can be found at http://sbml.org/.

Computational Systems Biology

Computational Systems biology is a relatively new arm of systems biology. Computational systems biology aims to develop and use efficient algorithms, data structures and communication tools to orchestrate the integration of large quantities of biological data with the goal of modeling, and others. This field essentially looks at a cell as a system and hence it is understood that biological systems are results of interplay of the cause-and-effect among simpler, integrated parts. Computational systems biology allows you to compute large amount of biological data and derive knowledge out of it. Till now the approach to biology had been reductionist one i.e. look at the genes or proteins in isolation, but with the accumulation of large number of biological data and development of computational data mining techniques, it is imperative to come up with new approaches to look at biology through the prism of Computation and relate different types of biological data. In other words, the time has come to look at the big picture. Computational systems biology strives to do just that. It is a very new field and much needs to be done.

Enough of text book definitions ! To me, computational systems biology means computing life at systems level. Since, in this field we are studying at systems level, it allows for better implementation of the engineering principles to biology or at least that is my conclusion. This field has profound impacts on neighbouring fields such as synthetic biology and bioengineering.