Biomedical subjects

Jacques Ricard

Publications and source records attributed to Jacques Ricard.

3 recordsLinked to original sources

PubMed2005

[Emergence and biological complexity].

Biological networks possess an organization that expresses their potential information. A function, I(X:Y)N, called mutual information of integration, define, on a quantitative basis, three types of organization. If I(X:Y)N=0, the properties of the global system XY can be reduced to the properties of its component sub-systems X and Y. Hence, XY is not a real system displaying collective properties but the mere collection of X and Y. Its properties are the properties of the sub-systems X and Y. If I(X:Y)N>0, the system is integrated. Although it behaves as a coherent whole, it does not possess many collective properties. Last, if I(X:Y)N<0, the system possesses emergent collective properties and can be considered complex for it possesses many collective properties that cannot be predicted from the independent study of component sub-systems X and Y. In a biological system, the emergence of information usually means the emergence of a novel function. This is probably what is occurring with enzymes. If a protein binds two ligands able to interact, and if the condition above is fulfilled, then the protein behaves as an enzyme able to allow a catalytic reaction between the two reagents.

Catalysis↗

PubMed2004

Reduction, integration and emergence in biochemical networks.

Most studies of molecular cell biology are based upon a process of decomposition of complex biological systems into their components, followed by the study of these components. The aim of the present paper is to discuss, on a physical basis, the internal logic of this process of reduction. The analysis is performed on simple biological systems, namely protein and metabolic networks. A multi-sited protein that binds two ligands x and y can be considered the simplest possible biochemical network. The organization of this network can be described through a comparison of three systems, i.e. XY, X and Y. X and Y are component sub-systems that collect states x(i) and y(j), respectively, i.e. protein states that have bound either i molecules of x (whether or not these states have also bound y), or j molecules of y (whether or not these states have bound x). XY is a system made up of the specific association of X and Y that collects states x(i)y(j). One can define mean self-informations per node of the network, , and . Reduction of the system XY into its components is possible if, and only if, ,is equal to the sum of and . If is smaller than the sum of and , the system is integrated, for it has less self-information than the set of its components X and Y. It can also occur that , be larger than the sum of and . Hence, the system XY displays negative integration and emergence of self-information relative to its components X and Y. Such a system is defined as complex. Positive or negative integration of the system implies it cannot be reduced to its components. The degree of integration can be measured by a function , called mutual information of integration. In the case of enzyme networks, emergence of self-information is associated with emergence of catalytic activity. Moreover, if the enzyme reaction is part of a metabolic sequence, its mutual information of integration can be increased by an effect of context of this sequence.

Data Interpretation, Statistical↗

PubMed2003

What do we mean by biological complexity?

The purpose of the present paper is to offer a precise definition of the concepts of integration, emergence and complexity in biological networks through the use of the information theory. If two distinct properties of a network are expressed by two discrete variables, the classical subadditivity principle of Shannon's information theory applies when all the nodes of the network are associated with these properties. If not, the subadditivity principle may not apply. This situation is often to be encountered with enzyme and metabolic networks, for some nodes may well not be associated with these two properties. This is precisely what is occurring with an enzyme that binds randomly its two substrates. This situation implies that an enzyme, or a metabolic network, may display a joint entropy equal, smaller, or larger than the corresponding sum of individual entropies of component sub-systems. In the first case, the collective properties of the network can be reduced to the individual properties of its components. Moreover, the network is devoid of any information. In the second case, the system displays integration effects, behaves as a coherent whole, and has positive information. But if the joint entropy of the network is smaller than the sum of the individual entropies of its components, then the system has emergent collective properties and can be considered complex. Moreover, under these conditions, its information is negative. The extent of negative information is enhanced if the enzyme, or the metabolic network, is far away from equilibrium.

Entropy↗