arXiv subjects

Sandeep Krishna

Publications and source records attributed to Sandeep Krishna.

28 records · Page 2Linked to original sources

arXiv2005

Low Degree Metabolites Explain Essential Reactions and Enhance Modularity in Biological Networks

Recently there has been a lot of interest in identifying modules at the level of genetic and metabolic networks of organisms, as well as in identifying single genes and reactions that are essential for the organism. A goal of computational and systems biology is to go beyond identification towards an explanation of specific modules and essential genes and reactions in terms of specific structural or evolutionary constraints. In the metabolic networks of E. coli, S. cerevisiae and S. aureus, we identified metabolites with a low degree of connectivity, particularly those that are produced and/or consumed in just a single reaction. Using FBA we also determined reactions essential for growth in these metabolic networks. We find that most reactions identified as essential in these networks turn out to be those involving the production or consumption of low degree metabolites. Applying graph theoretic methods to these metabolic networks, we identified connected clusters of these low degree metabolites. The genes involved in several operons in E. coli are correctly predicted as those of enzymes catalyzing the reactions of these clusters. We independently identified clusters of reactions whose fluxes are perfectly correlated. We find that the composition of the latter `functional clusters' is also largely explained in terms of clusters of low degree metabolites in each of these organisms. Our findings mean that most metabolic reactions that are essential can be tagged by one or more low degree metabolites. Those reactions are essential because they are the only ways of producing or consuming their respective tagged metabolites. Furthermore, reactions whose fluxes are strongly correlated can be thought of as `glued together' by these low degree metabolites.

q-bio.MN↗

arXiv2005

Spiky oscillations in NF-kB signalling

The NF-kB signalling system is involved in a variety of cellular processes including immune response, inflammation, and apoptosis. Recent experiments have found oscillations in the nuclear-cytoplasmic translocation of the NF-kB transcription factor. How the cell uses the oscillations to differentiate input conditions and send specific signals to downstream genes is an open problem. We shed light on this issue by examining the small core network driving the oscillations, which, we show, is designed to produce periodic spikes in nuclear NF-kB concentration. The oscillations can be used to regulate downstream genes in a variety of ways. In particular, we show that genes to whose operator sites NF-kB binds and dissociates fast can respond very sensitively to changes in the input signal, with effective Hill coefficients in excess of 20.

q-bio.MN↗

arXiv2005

Evidence of a universal power law characterizing the evolution of metabolic networks

Metabolic networks are known to be scale free but the evolutionary origin of this structural property is not clearly understood. One way of studying the dynamical process is to compare the metabolic networks of species that have arisen at different points in evolution and hence are related to each other to varying extents. We have compared the reaction sets of each metabolite across and within 15 groups of species. For a given pair of species and a given metabolite, the number $Δk$ of reactions of the metabolite that appear in the metabolic network of only one species and not the other is a measure of the distance between the two networks. While $Δk$ is small within groups of related species and large across groups, we find its probability distribution to be $\sim (Δk)^{-γ'}$ where $γ'$ is a universal exponent that is the same within and across groups. This exponent equals, upto statistical uncertainties, the exponent $γ$ in the scale free degree distribution $\sim k^{-γ}$. We argue that this, as well as our finding that $Δk$ is approximately linearly correlated with the degree $k$ of the metabolite, is evidence of a `proportionate change' process in evolution. We also discuss some molecular mechanisms that might be responsible for such an evolutionary process.

q-bio.MN↗

arXiv2004

Formation and Destruction of Autocatalytic Sets in an Evolving Network Model

I analyse a model of an evolving network represented as a directed graph; each node corresponds to one molecular species and the links to catalytic interactions between species. Over short timescales the graph remains fixed while relative populations of the molecular species change according to a set of coupled differential equations. Over long timescales the system is subject to periodic perturbations, each of which adds one new node to the graph, with random links to other nodes, and removes one node with the least relative population. Starting from a sparse random graph, a small autocatalytic set (ACS) inevitably forms and then grows by accreting nodes until it spans the entire graph. The resultant fully autocatalytic graph, whose probability of forming by pure chance is very small, nevertheless forms in this model in an average time that grows only logarithmically with the size of the system. ACSs can also get destroyed, often accompanied by the sudden extinction of a large number of species. I show that the largest of the extinction events in this model are caused by one of three mechanisms, each of which produces a specific discontinuous change in the graph's topology. The model is analytically tractable: two theorems are proved which determine the set of nodes with least relative population in the attractor, for any given graph. This in turn can be used to analytically demonstrate the inevitability of the formation and growth of ACSs and calculate the associated timescales. Finally, I show that the formation and growth of ACSs is robust to the relaxation of many of the idealizations made to enhance the analytical tractability of the model.

nlin.AO↗

arXiv2002

Graph Theory and the Evolution of Autocatalytic Networks

We give a self-contained introduction to the theory of directed graphs, leading up to the relationship between the Perron-Frobenius eigenvectors of a graph and its autocatalytic sets. Then we discuss a particular dynamical system on a fixed but arbitrary graph, that describes the population dynamics of species whose interactions are determined by the graph. The attractors of this dynamical system are described as a function of graph topology. Finally we consider a dynamical system in which the graph of interactions of the species coevolves with the populations of the species. We show that this system exhibits complex dynamics including self-organization of the network by autocatalytic sets, growth of complexity and structure, and collapse of the network followed by recoveries. We argue that a graph theoretic classification of perturbations of the network is helpful in predicting the future impact of a perturbation over short and medium time scales.

nlin.AO↗

arXiv2001

Large extinctions in an evolutionary model: The role of innovation and keystone species

The causes of major and rapid transitions observed in biological macroevolution as well as in the evolution of social systems are a subject of much debate. Here we identify the proximate causes of crashes and recoveries that arise dynamically in a model system in which populations of (molecular) species co-evolve with their network of chemical interactions. Crashes are events that involve the rapid extinction of many species and recoveries the assimilation of new ones. These are analyzed and classified in terms of the structural properties of the network. We find that in the absence of large external perturbation, `innovation' is a major cause of large extinctions and the prime cause of recoveries. Another major cause of crashes is the extinction of a `keystone species'. Different classes of causes produce crashes of different characteristic sizes.

nlin.AO↗

arXiv2001

Crashes, Recoveries, and `Core-shifts' in a Model of Evolving Networks

A model of an evolving network of interacting molecular species is shown to exhibit repeated rounds of crashes in which several species get rapidly depopulated, followed by recoveries. The network inevitably self-organizes into an autocatalytic structure, which consists of an irreducible `core' surrounded by a parasitic `periphery'. Crashes typically occur when the existing autocatalytic set becomes fragile and suffers a `core-shift', defined graph theoretically. The nature of the recovery after a crash, in particular the time of recovery, depends upon the organizational structure that survives the crash. The largest eigenvalue of the adjacency matrix of the graph is an important signal of network fragility or robustness.

nlin.AO↗

arXiv2000

A model for the emergence of cooperation, interdependence and structure in evolving networks

Evolution produces complex and structured networks of interacting components in chemical, biological, and social systems. We describe a simple mathematical model for the evolution of an idealized chemical system to study how a network of cooperative molecular species arises and evolves to become more complex and structured. The network is modeled by a directed weighted graph whose positive and negative links represent `catalytic' and `inhibitory' interactions among the molecular species, and which evolves as the least populated species (typically those that go extinct) are replaced by new ones. A small autocatalytic set (ACS), appearing by chance, provides the seed for the spontaneous growth of connectivity and cooperation in the graph. A highly structured chemical organization arises inevitably as the ACS enlarges and percolates through the network in a short, analytically determined time scale. This self-organization does not require the presence of self-replicating species. The network also exhibits catastrophes over long time scales triggered by the chance elimination of `keystone' species, followed by recoveries.

nlin.AO↗

arXiv1998

Emergence and Growth of Complex Networks in Adaptive Systems

We consider the population dynamics of a set of species whose network of catalytic interactions is described by a directed graph. The relationship between the attractors of this dynamics and the underlying graph theoretic structures like cycles and autocatalytic sets is discussed. It is shown that when the population dynamics is suitably coupled to a slow dynamics of the graph itself, the network evolves towards increasing complexity driven by autocatalytic sets. Some quantitative measures of network complexity are described.

adap-org↗

arXiv1998

Autocatalytic Sets and the Growth of Complexity in an Evolutionary Model

A model of $s$ interacting species is considered with two types of dynamical variables. The fast variables are the populations of the species and slow variables the links of a directed graph that defines the catalytic interactions among them. The graph evolves via mutations of the least fit species. Starting from a sparse random graph, we find that an autocatalytic set (ACS) inevitably appears and triggers a cascade of exponentially increasing connectivity until it spans the whole graph. The connectivity subsequently saturates in a statistical steady state. The time scales for the appearance of an ACS in the graph and its growth have a power law dependence on $s$ and the catalytic probability. At the end of the growth period the network is highly non-random, being localized on an exponentially small region of graph space for large $s$.

adap-org↗