Each cell of our body carries the same genome. Then what tells some cells to become kidney cells, and others to become liver cells, and still others to become neurons? The term ‘cell differentiation’ is used for this phenomenon. How does cell differentiation occur?
The answer has to do with 'genetic networks'. But first a word about networks in general. Use of graph theory and network theory in complexity science has paid rich dividends. The reasons are not far to see. A complex system comprises of a large number of individuals, and the individuals interact with one another. We can regard the individuals as the 'nodes' of a network, and an interaction (if any) between any pair of nodes can be represented by a line ('edge') joining those two nodes. Once we have mapped the complex system onto such a network of nodes and edges, the full power of the mathematics of graph theory and network theory can be brought to bear on the investigation of the problem. In a genetic network, the genes are the nodes of the network.
Networks can be of various types. A random network is one in which the presence or absence of any edge is a matter of random occurrence; the edges are distributed randomly. If there are N nodes, and if there is a probability p that any pair of nodes is connected, then it can be worked out that there are pN(N-1)/2 edges. Random networks are the simplest imaginable. We shall encounter other types as we go along.
French scientists François Jacob and Jacques Monod were awarded (along with Andre Lwoff) the Nobel Prize for physiology or medicine for 1965 for their work on 'circuits' in genetic networks. There are thousands of genes arrayed along a DNA molecule. And the genes may be either 'on' (i.e. active) or 'off' (not active). When a gene is on, it is said to be expressing itself; it is doing the transcription work, directing the synthesis of the protein it has the code for.
E. coli are a kind of bacteria. When exposed to lactose (a kind of sugar), they produce an enzyme (a protein) that can digest lactose. Take away the lactose, and the enzyme production stops. Jacob & Monod discovered the so-called 'lac operon' and the underlying gene regulation mechanisms responsible for the sensing of lactose and the production of the enzyme. They discovered that, adjacent to the gene encoding a protein called beta galactosidase, a small 'operator' DNA sequence (called 'O') bound a 'repressor protein' called 'R'. When R was bound to O, the adjacent gene for beta galactosidase could not be copied into its messenger RNA.
In more general terms, Jacob & Monod showed that a small fraction of the genes are 'regulatory genes' which can function as switches. Such activity is triggered by, say, the availability of a particular hormone in the surroundings of a cell (or the presence of lactose in the case of E. coli). This chemical may switch-on a particular gene. The newly activated gene sends out chemical signals to fellow genes, that can switch them on or off, depending on the states they are already in. The altered state of each of these genes then releases, or stops releasing, other chemical signals, which are received by the genetic switches in the network, altering their states in turn, in a cascading manner. This continues till the network of genetic switches settles down to a stable, self-consistent pattern.
The term 'gene regulation' covers all factors that control gene expression. A gene, or a set of genes, is said to be expressed if the protein expected to be synthesized by its expression is found in the cell. Gene-transcription and gene-translation are obviously the stages wherein this regulation can occur. Other factors which can regulate gene expression are temperature, pH value, and the presence of certain molecules (e.g. hormones).
The work of Jacob & Monod had several implications. For example, it established DNA as not just a repository of the blueprint for the cell, telling it how to synthesize the various proteins, but also as an engineer in charge of construction. The DNA was established to be a molecular-scale computer that computed how the cell was to build and repair itself, and how it was to interact with the surrounding world.
This work also solved the mystery of cell differentiation. It was concluded that each type of cell corresponds to a different pattern of the genetic network, influenced by the presence of specific hormones etc. Although there is only a single genome involved, the genome can have many stable patterns of activation or expression, each corresponding to a different cell type (liver, kidney, brain, etc.). Thus the genome was viewed as a complex network of interacting components, which control homeostasis and differentiation through very specific control circuits among the genes. [Homeostasis is the ability of higher animals to maintain an internal consistency.]
Stuart Kauffman carried the genetic-circuit idea still further. He had introduced in 1969 the notion of Random Boolean Networks (RBNs). We have seen above how genes in a genetic circuit may be on or off. He made the simplifying assumption that each node (gene) has two discrete (binary) states: 1 for on, 0 for off. Suppose there are N such nodes. An RBN is a random network of N binary-state nodes (representing genes in our case) with, say, K inputs to each node representing regulatory mechanisms.
As I shall explain in the next post, Kauffman went many steps further than Jacob & Monod, and demonstrated that even randomly constructed networks of high molecular specificity can undergo homeostasis and differentiation. This was a remarkable result because it meant that HIGHLY ORDERED DYNAMICAL BEHAVIOUR CAN ARISE EVEN FOR RANDOMLY CONSTRUCTED GENETIC NETWORKS, GETTING JUST A FEW INPUTS PER GENE.